[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-jim-schwoebel--awesome_ai_agents":3,"tool-jim-schwoebel--awesome_ai_agents":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",157379,2,"2026-04-15T23:32:42",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":76,"owner_twitter":78,"owner_website":79,"owner_url":80,"languages":76,"stars":81,"forks":82,"last_commit_at":83,"license":84,"difficulty_score":85,"env_os":86,"env_gpu":87,"env_ram":87,"env_deps":88,"category_tags":91,"github_topics":92,"view_count":32,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":113,"updated_at":114,"faqs":115,"releases":165},8033,"jim-schwoebel\u002Fawesome_ai_agents","awesome_ai_agents","🤖 A comprehensive list of 1,500+ resources and tools related to AI agents.","awesome_ai_agents 是一个专注于人工智能代理（AI Agents）的开源资源枢纽，汇集了超过 1500 个精选工具、框架、数据集及实战项目。面对当前 AI 代理领域技术迭代快、资源分散且难以筛选的痛点，它提供了一站式的解决方案，帮助用户高效定位所需内容，避免在海量信息中迷失方向。\n\n无论是希望快速上手构建自动化流程的开发者、需要追踪最新多智能体协作架构的研究人员，还是寻求灵感的技术爱好者，都能在这里找到匹配的资源。其核心亮点在于高度结构化的分类体系，涵盖从底层大模型、提示工程技巧到上层应用工作流的全链路内容，并坚持每日更新以确保前沿性。此外，该项目不仅是一个静态列表，更是一个活跃的社区驱动平台，鼓励用户贡献案例并参与如\"Agents Connect\"等行业活动，共同推动 AI 代理生态的繁荣发展。通过 awesome_ai_agents，用户可以轻松探索、学习并协作构建下一代智能代理系统。","# 🤖 Awesome AI Agents: Tools, Resources, and Projects\n\u003Cdiv align=\"center\">\n  \n  [![Post on Twitter](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPost%20on-Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white)](https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?url=https%3A%2F%2Fgithub.com%2Fjim-schwoebel%2Fawesome_ai_agents&text=Discover%20the%20Awesome%20AI%20Agents%20repo%21%20A%20curated%20collection%20of%20AI%20agents%20for%20automation%2C%20NLP%2C%20and%20more%21%20Open-source%20%26%20community-driven%21%20%F0%9F%9A%80)\n  \n  [![Subscribe to Newsletter](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSubscribe%20to%20Newsletter-%23FF9900?style=for-the-badge&logo=mailchimp&logoColor=white)](https:\u002F\u002Fagents.blog)\n\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_8b719f09cb90.webp\" width=\"400\" \u002F>\n  \n  [![Star History Chart](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_f003f757686b.png)](https:\u002F\u002Fstar-history.com\u002F#jim-schwoebel\u002Fawesome_ai_agents&Date)\n\u003C\u002Fdiv>\n\u003C\u002Fbr>\n\nWelcome to the **Ultimate Hub for AI Agents**, your one-stop destination for all things related to AI-driven agents. Whether you're a researcher, developer, or enthusiast, this repository brings together the most advanced tools, resources, and inspiring projects in the world of AI agents. \n\nThis repository is a comprehensive hub for resources related to AI agents, providing a curated collection of tools, frameworks, datasets, and projects. It offers daily updates on the latest advancements in AI and machine learning agents. The resource includes extensive lists categorized by datasets, frameworks, LLM models, and prompt engineering techniques. Additionally, it features various tools and workflows to aid in the development and application of AI agents. Finally, the repository also lists relevant courses and encourages community contributions.\n\nHere are some ways you can use this repository:\n\n- **Explore** - Browse through the different sections to find tools, resources, and projects that match your interests and needs (e.g. to use, learn, or build agents).\n- **Contribute** - Share your own projects, tools, or resources related to AI agents and contribute to the growing body of knowledge in this field.\n- **Stay Updated** - Keep an eye on the latest developments and discussions about AI agents. Join the community to stay ahead of the curve.\n\nDive in, learn, collaborate, and build the next generation of AI agents.\n\n⭐ **Don't forget to give this repository a star if you find it useful!**  \n\n🎉 *Let's build a thriving AI agent ecosystem together!*\n\n---\n\n## 🎉 Upcoming Event: Agents Connect Conference\n\n\u003Cdiv align=\"center\">\n\n### **Agents Connect: The All-Demo Conference**\n\nJoin us for the premier AI agent conference where innovators, investors, and users come together to shape the future of AI agents.\n\n**🎉 FREE TO ATTEND - No registration fee required!**\n\n**📅 Date:** Monday, December 15, 2025  \n**⏰ Time:** 12:00 PM – 3:00 PM PST (America\u002FLos_Angeles)  \n**📍 Location:** Fully Virtual Event\n\n**[👉 Sign up and learn more](https:\u002F\u002Fagents.blog\u002Fagent-connect)**\n\n\u003C\u002Fdiv>\n\n**About the Conference:**\n\nAgents Connect: The All-Demo Conference is where AI agent innovators come together to show, not tell. Watch live agent demos, research prototypes, and reverse pitches from investors. Then connect 1:1 with the people who matter.\n\nThis is a fully virtual event for 2025. In-person format coming in future years.\n\n**What to Expect:**\n\n• **Startup Showcase: Live Agent Demos** - Watch 5 selected startups demo their live autonomous agents in real-time\n\n• **Research \"Paper-to-Prototype\" Demos** - See visual walkthroughs of multi-agent orchestration and new frameworks\n\n• **The Reverse Pitch: Investors & Partners Demo Their Strategy** - VCs and corporate partners show how they work with you\n\n• **Structured 1:1 Networking & Dealmaking** - Curated 1:1 connections with builders, investors, and users\n\n**Who Should Attend:**\n\n• **For Builders:** Pitch, demo, and get funded  \n• **For Users:** Discover the next generation of AI agents  \n• **For Investors:** Find the most promising AI agent startups\n\n**[Sign up and learn more →](https:\u002F\u002Fagents.blog\u002Fagent-connect)**\n\n---\n\n## Table of Contents\n- [Using Agents](#using)\n  - [Applications](#applications)\n- [Learning Agents](#learning)\n  - [Repositories](#repositories)\n  - [Courses](#courses)\n- [Building Agents](#building)\n  - [Benchmarks](#benchmarks)\n  - [Datasets](#datasets)\n  - [Deployment](#deployment)\n  - [Ethics](#ethics)\n  - [Frameworks](#frameworks)\n  - [LLM Models](#llm-models)\n  - [Prompt Engineering](#prompt-engineering)\n  - [Security](#security)\n  - [Testing](#testing)\n  - [Tools](#tools)\n  - [Workflows](#workflows)\n- [Contributing to this Repo](#contributing)\n  - [Contributors](#contributors)\n- [Spread the Word](#Spread-the-Word)\n- [License](#license)\n\n---\n## Using\n\nThere are hundreds of new AI agent types popping up, each designed to tackle specific tasks or workflows. Here, you can find a curated list of existing AI agents that you can start using today to boost your productivity and streamline your daily life. Whether you're looking to automate repetitive tasks, get personalized recommendations, or generate creative content, there's an agent for you. \n\nExplore these tools and more to see how AI agents can simplify tasks, save time, and enhance creativity. With new agents emerging daily, this list is just the beginning of what's possible!\n\n### Applications\nHere are some AI agents that you can use right now to massively improve your productivity.\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_cc7ff6df30f0.png)\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_a1b7cee069fd.png)\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_b315bac4524b.png)\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_6bb065daa312.png)\n\nBelow are some popular categories and examples of how AI agents can make an impact:\n\n- **Ads AI Agents** - Automate and optimize ad campaigns using intelligent AI agents.  \n- **Agentic IDE** - Integrated Development Environments tailored for building and managing AI agents.  \n- **AI Agent Management Platform** - Centralized tools for deploying, monitoring, and managing AI agents.  \n- **AI Agent Memory** - Enable agents to retain context and learn over time for improved interactions.  \n- **AI Agents Frameworks** - Frameworks to simplify the development of robust AI agent systems.  \n- **AI Agents Platform** - Platforms offering end-to-end solutions for creating and deploying AI agents.  \n- **AI Avatar** - Personalized digital representations powered by AI for interactions.  \n- **AI Docs Agents** - Agents specialized in document processing and content generation.  \n- **AI Security** - Intelligent systems to enhance security through threat detection and prevention.  \n- **AI Shopping Agents** - Assist shoppers with recommendations, searches, and purchasing.  \n- **AI Video Agents** - Automate video creation, editing, and personalization with AI.  \n- **Authentication Agents** - Secure identity verification and authentication processes with AI.  \n- **Coding Agent** - AI agents designed to write and debug code efficiently.\n- **Coding Assistant** - AI-powered tools to help developers with coding tasks and suggestions (e.g. [Cursor](https:\u002F\u002Fwww.cursor.com\u002F)).\n- **Coding Library** - Reusable AI-driven code libraries to speed up development.  \n- **Content Creation** - AI tools for generating written, visual, and multimedia content.  \n- **Customer Service** - AI agents designed to handle customer support and interactions (e.g. Sierra).\n- **Data Analysis** - Analyze and interpret data using intelligent AI agents.  \n- **Data Science** - AI-driven solutions for advanced data modeling and exploration.  \n- **Desktop AI Agents** - AI agents designed to run locally on desktop systems for various tasks.  \n- **Digital Workers** - Virtual agents that replicate human workflows in digital environments.  \n- **Education AI Agents** - Tools to enhance learning experiences through personalized education.  \n- **Email AI Agents** - Automate email organization, writing, and management with AI.  \n- **Gaming Agents** - AI tools for game development or in-game interactive agents.  \n- **Lead Generation AI Agent** - Automate lead discovery and qualification for sales pipelines.  \n- **Marketing AI Agent** - Intelligent tools for creating and optimizing marketing campaigns.  \n- **Model Serving** - Platforms and tools to deploy AI models for real-time use.  \n- **Music AI Agents** - AI tools for composing, editing, or analyzing music (e.g. [suno](https:\u002F\u002Fsuno.ai)).  \n- **Operations AI Agents** - Streamline business operations with AI-driven automation.  \n- **Observability** - Monitor and analyze system performance with AI-powered insights.  \n- **Personal Assistant** - AI assistants to manage personal schedules, tasks, and reminders.  \n- **Productivity** - Tools designed to enhance productivity through AI automation.  \n- **Recruiting AI Agents** - Streamline hiring processes with AI for screening and sourcing candidates.  \n- **Research** - AI-driven tools to assist with academic, market, or technical research.  \n- **Sales AI Agent** - Automate sales tasks like prospecting and client follow-ups with AI. \n- **Science Agents** - Read research papers and formulate hypotheses. \n- **Software Testing Agent** - AI tools for testing and validating software functionality.  \n- **Tool Libraries** - Collections of tools to build, deploy, or manage AI systems.  \n- **Translation AI Agents** - Automate language translation with context-aware AI agents.  \n- **Travel AI Agent** - AI agents for planning, booking, and managing travel.  \n- **WEB 3** - AI agents focused on decentralized applications and blockchain technologies.  \n- **Web AI Agents** - Browser-based AI tools for a variety of applications.  \n- **Web Scraping** - AI agents for extracting data from websites efficiently.  \n- **Website Builder Agent** - AI-driven tools for creating and managing websites. \n- **Workflow** - Automate and streamline workflows using AI agents.  \n\nMany more categories and types of agents are being built in 2025.\n\nI encourage viewing [AgentsDirectory](https:\u002F\u002Faiagentsdirectory.com\u002F) or [awesome_ai_agents](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-agents) (restrictive license on the repo) for a list of the most up-to-date agents that exist (where the photos above were taken from as on 01\u002F01\u002F2025).\n\n---\n\n## Learning\nHere’s how you can grow your AI agent skills:  \n- **For Beginners** - Start simple! Learn what AI agents are and how they work, step by step.  \n- **For the Curious** - Get hands-on with tools and techniques to create your own functional agents.  \n- **For the Bold** - Experiment with advanced topics like multi-agent systems and cutting-edge design.  \n\nDon’t overthink it—just take the first step. \n\n### Repositories\nThere are many repositories to get started learning AI agents (thanks to [awesome-ai-agents](https:\u002F\u002Fgithub.com\u002Fslavakurilyak\u002Fawesome-ai-agents) for the list):\n\n- [01](https:\u002F\u002Fchanges.openinterpreter.com\u002Flog\u002Fintroducing-the-01-developer-preview) - The '01 Project' by Open Interpreter is an open-source initiative focused on creating an ecosystem for AI devices, aiming to become the GNU\u002FLinux in this domain, with details on its experimental status, software, hardware, and a speech-to-speech interface based on a code-interpreting language model for dynamic interactions [announcement](https:\u002F\u002Fchanges.openinterpreter.com\u002Flog\u002Fintroducing-the-01-developer-preview) | [demo](https:\u002F\u002Ftwitter.com\u002FOpenInterpreter\u002Fstatus\u002F1770821439458840846) | [github](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002F01) | [website](http:\u002F\u002Fopeninterpreter.com\u002F01) | [docs](https:\u002F\u002F01.openinterpreter.com\u002F)\n- [AGiXT](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAGiXT) - AGiXT is an advanced AI Automation Platform designed to enhance AI instruction management and task execution across various providers, incorporating features like adaptive memory, smart instruct, and a versatile plugin system to push the boundaries of AI technology towards achieving Artificial General Intelligence (AGI) [github](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAGiXT) | [website](https:\u002F\u002Fagixt.com\u002F)\n- [AI Agent Assist by DialPad](https:\u002F\u002Fwww.dialpad.com\u002Fai-labs\u002Fai-agent-assist\u002F) - Dialpad introduces Ai Agent Assist, offering real-time, Ai-powered answers to enhance customer service through deep integrations, reducing agent ramp time, and providing actionable insights with out-of-the-box productivity [landing page](https:\u002F\u002Fwww.dialpad.com\u002Fai-labs\u002Fai-agent-assist\u002F)\n- [AI Agent Crew](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fai-agent-crew) - Langchain and CrewAI have launched AI agents equipped with Bitcoin wallets, facilitating automated operations within a blockchain environment [github](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fai-agent-crew) | [github profile](https:\u002F\u002Fgithub.com\u002Faibtcdev) | [website](https:\u002F\u002Faibtc.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002F5DJaBrf)\n- [AI Assistant by Deco](https:\u002F\u002Fdeco.cx\u002Fai-assistant) - Deco provides a GPT-powered, multilingual AI Sales Assistant designed to personalize and automate the shopping experience, boost sales, and increase operational efficiency for online stores [website](https:\u002F\u002Fdeco.cx\u002Fai-assistant) | [github profile](https:\u002F\u002Fgithub.com\u002Fdeco-cx)\n- [AI Researcher](https:\u002F\u002Fgithub.com\u002Fmshumer\u002Fai-researcher) - The AI Researcher is an AI agent leveraging Claude 3 and SERPAPI for in-depth topic research, refining subtopic analyses into a comprehensive report, customizable and requiring API keys for functionality [github](https:\u002F\u002Fgithub.com\u002Fmshumer\u002Fai-researcher) | [announcement](https:\u002F\u002Ftwitter.com\u002Fi\u002Fweb\u002Fstatus\u002F1776341679617745126) | [website](https:\u002F\u002Fapp.hyperwriteai.com\u002Fpersonalassistant\u002Ftool\u002Fb40d5925-4780-4eed-9f69-a03ae931de37)\n- [AI SDK by Vercel](https:\u002F\u002Fvercel.com\u002Fblog\u002Fintroducing-the-vercel-ai-sdk) - The Vercel AI SDK is an open-source library for creating AI-powered conversational interfaces, supporting multiple frameworks and languages, with built-in adapters for major AI services [announcement](https:\u002F\u002Fvercel.com\u002Fblog\u002Fintroducing-the-vercel-ai-sdk) | [website](https:\u002F\u002Fsdk.vercel.ai\u002Fdocs) | [github](https:\u002F\u002Fgithub.com\u002Fvercel\u002Fai) | [github examples](https:\u002F\u002Fgithub.com\u002Fvercel\u002Fai\u002Ftree\u002Fmain\u002Fexamples)\n- [AI Studio by Azure](https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fproducts\u002Fai-studio) - Azure AI Studio offers a platform for developing generative AI applications and custom copilots, featuring prebuilt models, training capabilities, free Azure Cosmos DB access for 90 days, and built-in security with no extra charge during preview [website](https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fproducts\u002Fai-studio)\n- [AIOS](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FAIOS) - AIOS by AGI Research is an LLM Agent Operating System which enables an operating system 'with soul' -- an important step towards AGI [github](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FAIOS) | [github profile](https:\u002F\u002Fgithub.com\u002Fagiresearch)\n- [Adala](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002FAdala) - Adala is a framework for autonomous data labeling agents, supporting Python 3.8 to 3.11, with features for customizable, intelligent data processing and integration into Python Notebooks [github](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002FAdala)\n- [Agency Swarm by VRSEN](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm) - Agency Swarm is a framework designed to automate AI agencies by creating a swarm of collaborative agents with customizable roles and functionalities, aiming to simplify the agent creation process and make automation more intuitive [github](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm)\n- [Agent Protocol](https:\u002F\u002Fwww.aie.foundation\u002F) - The Agent Protocol establishes a unified API standard for seamless interaction and integration across diverse AI agents, promoting ecosystem growth and simplification of agent development and benchmarking [website](https:\u002F\u002Fwww.aie.foundation\u002F) | [website](https:\u002F\u002Fwww.aie.foundation\u002F) | [github](https:\u002F\u002Fgithub.com\u002FAI-Engineer-Foundation\u002Fagent-protocol) | [github profile](https:\u002F\u002Fgithub.com\u002FAI-Engineer-Foundation)\n- [Agent Tools](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fagent-tools-ts) - Typescript tools for Bitcoin\u002FStacks blockchain interaction, utilizing Bun.js and Stacks.js, with a focus on AI integration [github](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fagent-tools-ts) | [github profile](https:\u002F\u002Fgithub.com\u002Faibtcdev) | [website](https:\u002F\u002Faibtc.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002F5DJaBrf)\n- [Agent by Stately AI](https:\u002F\u002Fgithub.com\u002Fstatelyai\u002Fagent\u002F) - Stately Agent is a software for building intelligent agents that interact via chat and events, with examples including joke generation, tic-tac-toe, and weather querying, requiring installation and an OpenAI API key [github](https:\u002F\u002Fgithub.com\u002Fstatelyai\u002Fagent\u002F) | [website](https:\u002F\u002Fstately.ai\u002Fagent) | [twitter](https:\u002F\u002Ftwitter.com\u002Fstatelyai) | [discord](https:\u002F\u002Fdiscord.gg\u002Fxstate) | [youtube](https:\u002F\u002Fyoutube.com\u002Fc\u002Fstatelyai)\n- [AgentBench](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FAgentBench) - AgentBench v0.2 is a benchmark designed to evaluate Large Language Models as agents across a diverse set of environments, enhancing framework usability and extending model evaluations [github](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FAgentBench)\n- [AgentGPT by Reworkd](https:\u002F\u002Fgithub.com\u002Freworkd\u002FAgentGPT) - AgentGPT allows users to configure and deploy autonomous AI agents, enabling them to name their own custom AI and guide it towards any desired goal through task execution and learning [github](https:\u002F\u002Fgithub.com\u002Freworkd\u002FAgentGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Freworkd)\n- [AgentHive](https:\u002F\u002Fagenthive.to) - AgentHive is a microblogging social network for AI agents, where agents register via API and interact through 280-character posts with support for replies, boosts, follows, search, and discovery. Built on Cloudflare Workers, it includes a TypeScript client library, MCP server, and GitHub Action for integration [website](https:\u002F\u002Fagenthive.to) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@superlowburn\u002Fhive-client)\n- [AgentLabs](https:\u002F\u002Fgithub.com\u002Fagentlabs-inc\u002Fagentlabs) - AgentLabs is an open-source, universal frontend solution for AI agents, offering an authentication portal, chat interface, analytics, and payment features to streamline the deployment of AI agents to public users [github](https:\u002F\u002Fgithub.com\u002Fagentlabs-inc\u002Fagentlabs) | [website](https:\u002F\u002Fwww.agentlabs.dev\u002F) | [docs](https:\u002F\u002Fdocs.agentlabs.dev\u002F)\n- [AgentOS](https:\u002F\u002Fgithub.com\u002Fsmartcomputer-ai\u002Fagent-os) - The Agent OS is an experimental platform for creating self-evolving, autonomous AI agents capable of writing and executing their own code, designed to be a long-term environment for such agents and supports various programming languages [github](https:\u002F\u002Fgithub.com\u002Fsmartcomputer-ai\u002Fagent-os)\n- [AgentOps](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002Fagentops) - AgentOps aims to improve AI agent development with tools for observability, evaluations, and replay analytics, offering a streamlined process for testing and debugging compliant AI agents through a user-friendly interface and comprehensive documentation [github](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002Fagentops) | [website](https:\u002F\u002Fwww.agentops.ai\u002F) | [docs](https:\u002F\u002Fdocs.agentops.ai) | [discord](https:\u002F\u002Fdiscord.gg\u002FmKW3ZhN9p2) | [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772771418780176674)\n- [AgentVerse](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FAgentVerse) - AgentVerse is an Apache2-licensed Python framework for deploying multiple LLM-based agents in various applications, offering task-solving and simulation frameworks for collaborative task accomplishment and behavior observation among agents [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FAgentVerse) | [github profile](https:\u002F\u002Fgithub.com\u002FOpenBMB)\n- [AgentX](https:\u002F\u002Fchatagentx.com\u002F) - AgentX is an AI-powered sales assistant designed to enhance sales strategies and efficiency through advanced features like a Memory Module and Online Mode, leveraging industry best practices for smarter selling [website](https:\u002F\u002Fchatagentx.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fagentxai) | [newsletter](https:\u002F\u002Fbuttondown.email\u002Fagentx)\n- [Agentive](https:\u002F\u002Fagentivehub.com\u002F) - Agentive is a platform for AI Automation Agency owners, offering tools for creating, managing, and deploying custom AI solutions, with features like model selection, tool integration, prompt crafting, versioning, and training with own data, designed to simplify AI agent delivery [website](https:\u002F\u002Fagentivehub.com\u002F)\n- [Agents by AI Waves](https:\u002F\u002Fgithub.com\u002Faiwaves-cn\u002Fagents) - Agents is an open-source framework for building autonomous language agents with features including long-short term memory, tool usage, web navigation, multi-agent communication, human-agent interaction, and symbolic control, allowing customization through natural language config files and deployment in various interfaces [github](https:\u002F\u002Fgithub.com\u002Faiwaves-cn\u002Fagents) | [github profile](https:\u002F\u002Fgithub.com\u002Faiwaves-cn)\n- [Agents by Hugging Face](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fmain_classes\u002Fagent) - Hugging Face's Transformers Agents provide three main types: HfAgent for inference with open-source models, LocalAgent for using local models and tokenizers, and OpenAiAgent for access to OpenAI's closed models, enabling code generation and other AI tasks with varying levels of customization and local or remote execution [website](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fmain_classes\u002Fagent)\n- [Agentsy](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175318595453046) - Agentsy is an AI-driven platform designed to double team capacity by enhancing efficiency and creativity, starting with operations use cases like real estate [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175318595453046)\n- [Aider](https:\u002F\u002Fgithub.com\u002Fpaul-gauthier\u002Faider) - Aider is a command-line tool for AI-assisted pair programming, allowing code editing in local git repositories with GPT-3.5\u002FGPT-4, featuring direct file edits, automatic git commits, and support for most popular programming languages [github](https:\u002F\u002Fgithub.com\u002Fpaul-gauthier\u002Faider)\n- [Autohand Code CLI](https:\u002F\u002Fgithub.com\u002Fautohandai\u002Fcode-cli) - Autohand Code CLI is a self-evolving autonomous coding agent for the terminal, using the ReAct pattern to reason about and modify entire codebases through natural language, with 40+ tools, multi-LLM support (OpenRouter, Anthropic, OpenAI, Ollama, local models), semantic code search, modular skill system, and VS Code\u002FZed integration [github](https:\u002F\u002Fgithub.com\u002Fautohandai\u002Fcode-cli) | [website](https:\u002F\u002Fwww.autohand.ai\u002Fcode\u002F)\n- [AgentRun](https:\u002F\u002Fgithub.com\u002FJonathan-Adly\u002FAgentRun) - The easiest, and fastest way to run AI-generated Python code safely. [github](https:\u002F\u002Fgithub.com\u002FJonathan-Adly\u002FAgentRun)\n- [Claude Engineer](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fclaude-engineer) - Claude Engineer is an interactive command-line interface (CLI) that leverages the power of Anthropic's Claude-3.5-Sonnet model to assist with software development tasks. [github](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fclaude-engineer)\n- [Cline](https:\u002F\u002Fgithub.com\u002Fcline\u002Fcline) - Open-source AI coding agent giving developers direct access to frontier models with full transparency. [github](https:\u002F\u002Fgithub.com\u002Fcline\u002Fcline)\n- [context-engine-ai](https:\u002F\u002Fgithub.com\u002FQuinnod345\u002Fcontext-engine) - A lightweight context engine for AI agents. Ingest events from any source, query with natural language, get ranked results with temporal decay and auto-deduplication. Zero config with SQLite + local TF-IDF embeddings, scales to pgvector + OpenAI. [github](https:\u002F\u002Fgithub.com\u002FQuinnod345\u002Fcontext-engine) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fcontext-engine-ai)\n- [MicroAgent](https:\u002F\u002Fgithub.com\u002Faymenfurter\u002Fmicroagents) - Agents Capable of Self-Editing Their Prompts \u002F Python Code. [github](https:\u002F\u002Fgithub.com\u002Faymenfurter\u002Fmicroagents)\n- [Nous](https:\u002F\u002Fgithub.com\u002FTrafficGuard\u002Fnous) - TypeScript AI agent platform with Autonomous agents, Software developer agents, AI code review agents and more. [github](https:\u002F\u002Fgithub.com\u002FTrafficGuard\u002Fnous)\n- [OpenHands](https:\u002F\u002Fgithub.com\u002FAll-Hands-AI\u002FOpenHands) - 🙌 OpenHands: Code Less, Make More. (formerly OpenDevin), a platform for software development agents powered by AI. [github](https:\u002F\u002Fgithub.com\u002FAll-Hands-AI\u002FOpenHands)\n- [Plandex](https:\u002F\u002Fgithub.com\u002Fplandex-ai\u002Fplandex) - An AI coding engine for complex tasks. [github](https:\u002F\u002Fgithub.com\u002Fplandex-ai\u002Fplandex)\n- [PyCodeAGI](https:\u002F\u002Fgithub.com\u002Fchakkaradeep\u002FpyCodeAGI) - A small AGI experiment to generate a Python app given what app the user wants to build. [github](https:\u002F\u002Fgithub.com\u002Fchakkaradeep\u002FpyCodeAGI)\n- [RepoAgent](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FRepoAgent) - An LLM-powered repository agent designed to assist developers and teams in generating documentation and understanding repositories quickly. [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FRepoAgent)\n- [SWE Agent](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002Fswe-agent) - SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. [github](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002Fswe-agent)\n- [ThinkGPT](https:\u002F\u002Fgithub.com\u002Falaeddine-13\u002Fthinkgpt) - Agent techniques to augment your LLM and push it beyond its limits. [github](https:\u002F\u002Fgithub.com\u002Falaeddine-13\u002Fthinkgpt)\n- [Vision agent](https:\u002F\u002Fgithub.com\u002Flanding-ai\u002Fvision-agent) - Vision Agent is a library that helps you utilize agent frameworks to generate code to solve your vision task. [github](https:\u002F\u002Fgithub.com\u002Flanding-ai\u002Fvision-agent)\n- [WAIaaS](https:\u002F\u002Fgithub.com\u002Fminhoyoo-iotrust\u002FWAIaaS) - Self-hosted wallet-as-a-service for AI agents with multi-chain support (EVM + Solana), DeFi operations (swap, bridge, staking, lending), 3-tier security, and MCP server with 30+ tools. [github](https:\u002F\u002Fgithub.com\u002Fminhoyoo-iotrust\u002FWAIaaS)\n- [Anthropic](https:\u002F\u002Fwww.anthropic.com\u002F) - Anthropic's new suite of Claud 3 models improves AI agents with superior reasoning, rapid responses, and diverse cognitive capabilities without compromising user privacy [website](https:\u002F\u002Fwww.anthropic.com\u002F) | [docs](https:\u002F\u002Fdocs.anthropic.com\u002Fclaude\u002F)\n- [AnyBiz](https:\u002F\u002Fanybiz.io) - AnyBiz offers AI-driven sales agents that enhance sales strategies through intelligent automation, continuous learning, and hyper-personalization, operating 24\u002F7 without breaks [website](https:\u002F\u002Fanybiz.io)\n- [Anyscale](https:\u002F\u002Fwww.anyscale.com\u002F) - The Anyscale platform utilizes large language models (LLMs) for summarization, comparing the summarization quality of human, Llama 2 70b, and GPT-4, with GPT-4 demonstrating superior performance [website](https:\u002F\u002Fwww.anyscale.com\u002F) | [docs](https:\u002F\u002Fdocs.anyscale.com\u002F)\n- [Aomni](https:\u002F\u002Fwww.aomni.com\u002F) - This AI agent streamlines the process of researching prospective customers, potentially saving business development representatives hundreds of hours per year [website](https:\u002F\u002Fwww.aomni.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Faomniapp) | [demo](https:\u002F\u002Fx.com\u002FAtomSilverman\u002Fstatus\u002F1781402688078622874)\n- [AppAgent](https:\u002F\u002Fgithub.com\u002Fmnotgod96\u002FAppAgent) - AppAgent is a mobile-friendly LLM-based multimodal agent framework developed to operate smartphone apps, enabling human-like interactions for a wide range of applications without system back-end access [github](https:\u002F\u002Fgithub.com\u002Fmnotgod96\u002FAppAgent) | [github profile](https:\u002F\u002Fgithub.com\u002Fmnotgod96)\n- [Arize AX](https:\u002F\u002Farize.com\u002Fgenerative-ai) - Arize AX is a free tool to trace, evaluate, and iterate during development and monitor and evaluate AI agents in production | [website](https:\u002F\u002Farize.com) [docs](https:\u002F\u002Farize.com\u002Fdocs\u002Fax) | [github](https:\u002F\u002Fgithub.com\u002FArize-ai) | [Slack](https:\u002F\u002Fjoin.slack.com\u002Ft\u002Farize-ai\u002Fshared_invite\u002Fzt-3iu5bvnzr-2e~VFHw2Et4MM5rMsK599g)\n- [Assistants API by OpenAI](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fassistants\u002Foverview) - The Assistants API facilitates the development of AI agents, offering tools such as Code Interpretation and Function calling for embedding advanced, intelligent functionalities within applications [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fassistants\u002Foverview)\n- [Astra Assistants API](https:\u002F\u002Fgithub.com\u002Fdatastax\u002Fastra-assistants-api) - The `astra-assistants-api` provides a backend implementation of the OpenAI Assistants API with support for various features like persistent threads, files, assistants, streaming, function calling, and more, utilizing AstraDB powered by Apache Cassandra and jvector, and is compatible with existing OpenAI apps by changing a single line of code [github](https:\u002F\u002Fgithub.com\u002Fdatastax\u002Fastra-assistants-api)\n- [AutoAct](https:\u002F\u002Fgithub.com\u002Fzjunlp\u002FAutoAct) - AutoAct is an automatic agent learning framework that synthesizes planning trajectories without large-scale data or closed-source models, using a division-of-labor strategy for task completion, demonstrating superior or comparable performance in experiments [github](https:\u002F\u002Fgithub.com\u002Fzjunlp\u002FAutoAct) | [website](https:\u002F\u002Fwww.zjukg.org\u002Fproject\u002FAutoAct\u002F)\n- [AutoDev](https:\u002F\u002Fgithub.com\u002Funit-mesh\u002Fauto-dev) - AutoDev is an AI-powered coding assistant offering multilingual support, automatic code generation, and debugging assistance, featuring customizable prompts and specialized tools for development, testing, documentation, and the integration of custom AI agents, with a focus on experimenting and building AI agents using its UI framework [github](https:\u002F\u002Fgithub.com\u002Funit-mesh\u002Fauto-dev) | [docs](https:\u002F\u002Fide.unitmesh.cc)\n- [AutoGPT](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT) - AutoGPT provides accessible AI tools for building and using AI agents, offering a comprehensive framework including Forge for agent creation, agbenchmark for performance evaluation, a leaderboard for competition, a user-friendly UI, and CLI for seamless integration and management [github](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT) | [github profile](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas)\n- [AutoGen Studio by Microsoft](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) - AutoGen Studio 2.0 is Microsoft's advanced AI development tool, offering a user-friendly interface, powerful Python API, and comprehensive features for creating and controlling AI agents and workflows [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) | [website](https:\u002F\u002Fautogen-studio.com) | [landing page](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fproject\u002Fautogen\u002F) | [research paper](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fpublication\u002Fautogen-enabling-next-gen-llm-applications-via-multi-agent-conversation-framework\u002F)\n- [AutoGen by Microsoft](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) - AutoGen is a multi-agent conversation framework facilitating the development of next-gen LLM applications, highlighted by various accomplishments and offering enhanced LLM inferences, customizable agents, and comprehensive documentation [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) | [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DXhqhpHWRuM)\n- [Axflow](https:\u002F\u002Faxflow.dev\u002F) - Axflow is a TypeScript framework designed for AI development, offering a modular collection of tools for building natural language applications, and it emphasizes a code-first approach to simplify the integration of LLMs into scalable solutions [website](https:\u002F\u002Faxflow.dev\u002F) | [github](https:\u002F\u002Fgithub.com\u002Faxflow\u002Faxflow)\n- [Azure Speech Service](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Fspeech-service) - The Azure Speech service supports a wide range of languages and locales, with over 400 neural voices available in more than 140 languages and locales, including multilingual voices that can speak multiple languages [docs](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Fspeech-service)\n- [BDR Agent by Relevance](https:\u002F\u002Frelevanceai.com\u002Fagents\u002Fbdr-agent) - Relevance AI's flagship BDR Agent is designed to assist sales teams by researching and qualifying leads, engaging in personalized prospecting according to your playbook 24x7, and booking meetings to grow your business without increasing headcount [website](https:\u002F\u002Frelevanceai.com\u002Fagents\u002Fbdr-agent) | [twitter](https:\u002F\u002Ftwitter.com\u002FRelevanceAI) | [github profile](https:\u002F\u002Fgithub.com\u002FRelevanceAI) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Frelevanceai\u002F)\n- [BabyAGI](https:\u002F\u002Fgithub.com\u002Fyoheinakajima\u002Fbabyagi) - BabyAGI exemplifies an AI-powered task management system utilizing OpenAI and vector databases like Chroma or Weaviate, creating, prioritizing, and executing tasks based on previous outcomes and predefined objectives, with the main function involving an infinite loop where tasks are processed, enriched, and stored using OpenAI's NLP capabilities and Chroma\u002FWeaviate, inspired by the Task-Driven Autonomous Agent concept [github](https:\u002F\u002Fgithub.com\u002Fyoheinakajima\u002Fbabyagi) | [github profile](https:\u002F\u002Fgithub.com\u002Fyoheinakajima)\n- [BabyAGI UI](https:\u002F\u002Fgithub.com\u002Fmiurla\u002Fbabyagi-ui) - Make it easier to run and develop with babyagi in a web app, like a ChatGPT. [github](https:\u002F\u002Fgithub.com\u002Fmiurla\u002Fbabyagi-ui)\n- [CollosalAI Chat](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI\u002Ftree\u002Fmain\u002Fapplications\u002FChat) - implement LLM with RLHF, powered by the Colossal-AI project. [github](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI)\n- [DuetGPT](https:\u002F\u002Fgithub.com\u002Fkristoferlund\u002Fduet-gpt) - A conversational semi-autonomous developer assistant, AI pair programming without the copypasta. [github](https:\u002F\u002Fgithub.com\u002Fkristoferlund\u002Fduet-gpt)\n- [Gobii](https:\u002F\u002Fgithub.com\u002Fgobii-ai\u002Fgobii-platform) - Gobii is an open-source platform for deploying and managing browser-use agents at scale with a conversational interface and API. [github](https:\u002F\u002Fgithub.com\u002Fgobii-ai\u002Fgobii-platform)\n- [GPT Agent](https:\u002F\u002Fgithub.com\u002Firis-networks\u002Fgpt-agent) - The Free, Open-Source AI Agent for Computer Automation. [github](https:\u002F\u002Fgithub.com\u002Firis-networks\u002Fgpt-agent)\n- [ix](https:\u002F\u002Fgithub.com\u002Fkreneskyp\u002Fix) - Autonomous GPT-4 agent platform. [github](https:\u002F\u002Fgithub.com\u002Fkreneskyp\u002Fix)\n- [joinly](https:\u002F\u002Fgithub.com\u002Fjoinly-ai\u002Fjoinly) - Voice-first AI Assistant for online meetings that can actively participate and solve tasks live during the meeting. [github](https:\u002F\u002Fgithub.com\u002Fjoinly-ai\u002Fjoinly)\n- [LLama Cpp Agent](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent) - The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models. [github](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent)\n- [Multi-Modal LangChain agents in Production](https:\u002F\u002Fgithub.com\u002Fsteamship-packages\u002Flangchain-agent-production-starter) - Deploy LangChain Agents and connect them to Telegram. [github](https:\u002F\u002Fgithub.com\u002Fsteamship-packages\u002Flangchain-agent-production-starter)\n- [RasaGPT](https:\u002F\u002Fgithub.com\u002Fpaulpierre\u002FRasaGPT) - RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. [github](https:\u002F\u002Fgithub.com\u002Fpaulpierre\u002FRasaGPT)\n- [Autonomous HR Chatbot](https:\u002F\u002Fgithub.com\u002Fstepanogil\u002Fautonomous-hr-chatbot) - An autonomous agent that can answer HR related queries autonomously using the tools it has on hand. [github](https:\u002F\u002Fgithub.com\u002Fstepanogil\u002Fautonomous-hr-chatbot)\n- [Bananalyzer by Reworkd](https:\u002F\u002Freworkd.ai) - Bananalyzer is a framework for evaluating AI agents on web tasks, utilizing Playwright for creating diverse datasets of website snapshots for reliable and varied web task assessments [website](https:\u002F\u002Freworkd.ai) | [github](https:\u002F\u002Fgithub.com\u002Freworkd\u002Fbananalyzer)\n- [Bazed](https:\u002F\u002Fgithub.com\u002Fbazed-ai\u002Fbazed-af) - Bazed Agent Framework, aimed at empowering developers to build autonomous agent swarms without requiring deep Python ML knowledge, is facilitating the creation of sophisticated systems through TypeScript for enhanced autonomy and reliability [github](https:\u002F\u002Fgithub.com\u002Fbazed-ai\u002Fbazed-af) | [website](https:\u002F\u002Fbazed.ai\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FVmEEUrc7dg)\n- [Beam](https:\u002F\u002Fbeam.ai\u002F) - Beam AI offers a platform for Agentic Process Automation, using AI agents to automate workflows, enhancing productivity for businesses of all sizes with features like pre-trained agents, seamless integrations, and industry-specific solutions [website](https:\u002F\u002Fbeam.ai\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fjoin__beam) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fbeam-ai) | [youtube](https:\u002F\u002Fwww.youtube.com\u002F@beam-ai)\n- [Bland](https:\u002F\u002Fwww.bland.ai\u002F) - Bland AI offers a platform for building and scaling AI-powered phone agents, featuring easy integration, live data context, custom voices, and dedicated infrastructure. Tech stack includes LLM: Claude Instant (Anthropic), Transcription: Whisper (OpenAI), TTS: ElevenLabs [website](https:\u002F\u002Fwww.bland.ai\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fusebland)\n- [Bloop](https:\u002F\u002Fgithub.com\u002FBloopAI\u002Fbloop) - Bloop is a GPT-4-based coding assistant that boosts engineer productivity by allowing natural language interactions with codebases for explanations, feature writing, error troubleshooting, and more, featuring a code-centric AI playground, fast regex search, and comprehensive code navigation tools [github](https:\u002F\u002Fgithub.com\u002FBloopAI\u002Fbloop)\n- [BrainSoup Custom Tools](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) - BrainSoup is a multi-agent and multi-LLM native client where users can easily create custom tools for their agents, in any programming language, enabling them to interact with the user's system or any other external service [website](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) | [docs](https:\u002F\u002Fhelp.nurgo-software.com\u002Fcollection\u002F148-brainsoup) | [twitter](https:\u002F\u002Ftwitter.com\u002FNurgo) | [discord](https:\u002F\u002Fdiscord.gg\u002Fxt7PyCnH9S)\n- [BrainSoup](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) - BrainSoup is a multi-agent and multi-LLM native client, enabling users to create a team of personalized AI agents that can learn, remember, react to events, use tools, leverage the local resources of the user's computer, and work together to solve tasks autonomously [website](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) | [docs](https:\u002F\u002Fhelp.nurgo-software.com\u002Fcollection\u002F148-brainsoup) | [twitter](https:\u002F\u002Ftwitter.com\u002FNurgo) | [discord](https:\u002F\u002Fdiscord.gg\u002Fxt7PyCnH9S)\n- [BrowserGPT](https:\u002F\u002Fgithub.com\u002Fmayt\u002FBrowserGPT) - BrowserGPT is a project that combines OpenAI's GPT-4 and the Playwright library to control browsers via natural language, enabling code snippet generation for browser tasks [github](https:\u002F\u002Fgithub.com\u002Fmayt\u002FBrowserGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Fmayt)\n- [Browserbase](https:\u002F\u002Ftwitter.com\u002Fbrowserbasehq) - Browserbase offers a managed headless web browser API with robust features like session recording, logging, and debugging, ensuring secure connections to isolated web browsers for efficient issue resolution [twitter](https:\u002F\u002Ftwitter.com\u002Fbrowserbasehq) | [website](https:\u002F\u002Fwww.browserbase.com\u002F)\n- [BrowsingAgent by Agency Swarm](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FBrowsingAgent) - BrowsingAgent, an AI web navigation tool, has been integrated into the Agency Swarm framework to enable human-like browsing capabilities for automated AI operations [code](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FBrowsingAgent) | [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Yidy_ePo7pE)\n- [CAMEL](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Fcamel) - CAMEL (Communicative Agents for Mind Exploration of Large Language Model Society) is an open-source library designed for studying autonomous and communicative agents, facilitating research in understanding their behaviors, capabilities, and potential risks through scalable techniques and cooperative frameworks, including role-playing, with extensive documentation, examples, and datasets, while also supporting integration with open-source models as backends for diverse applications [github](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Fcamel) | [github profile](https:\u002F\u002Fgithub.com\u002Fcamel-ai)\n- [CLIN](https:\u002F\u002Fallenai.github.io\u002Fclin\u002F) - CLIN by Allen Institute for AI is an interactive continual learning agent that adapts rapidly to tasks, using a setup process involving Java, Python, and the ScienceWorld environment, supported by models like GPT-3.5-turbo and GPT-4 [website](https:\u002F\u002Fallenai.github.io\u002Fclin\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fclin) | [research paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.10134.pdf)\n- [Cadea](https:\u002F\u002Fwww.cadea.ai\u002F) - Cadea offers a secure AI platform for businesses, providing solutions against prompt injection, data breaches, and ensuring content safety through end-to-end security, access controls, and integration with major identity providers [website](https:\u002F\u002Fwww.cadea.ai\u002F)\n- [Cal.ai](https:\u002F\u002Fcal.com\u002Fai) - Cal.ai is an open-source AI scheduling assistant that manages email communications for booking, rearranging, and inquiring about meetings, leveraging a LangChain Agent Executor and MailParser for efficient scheduling without API key exposure [website](https:\u002F\u002Fcal.com\u002Fai) | [github](https:\u002F\u002Fgithub.com\u002Fcalcom\u002Fcal.com\u002Ftree\u002Fmain\u002Fapps\u002Fai)\n- [Central by Zapier](https:\u002F\u002Fzapier.com\u002Fblog\u002Fintroducing-zapier-central-ai-bots\u002F) - Zapier Central is an AI workspace designed to automate tasks across 6,000+ apps with AI bots, offering capabilities like live data connection, AI automation, and interaction with data sources for businesses and individual productivity enhancements [announcement](https:\u002F\u002Fzapier.com\u002Fblog\u002Fintroducing-zapier-central-ai-bots\u002F) | [website](https:\u002F\u002Fzapier.com\u002Fcentral)\n- [ChartGPT](https:\u002F\u002Fchartgpt.io) - ChartGPT offers AI-driven services like table summarization, charting, and code generation, featuring pay-as-you-go pricing, trusted by major companies, emphasizing data security, ease of use, and 24\u002F7 customer support [website](https:\u002F\u002Fchartgpt.io)\n- [ChatDev](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev) - ChatDev is a virtual software company utilizing intelligent agents to revolutionize the digital world through programming, offering a highly customizable framework and integrating innovative approaches like Experiential Co-Learning, Docker support, Git management, and Human-Agent Interaction [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev) | [github profile](https:\u002F\u002Fgithub.com\u002FOpenBMB)\n- [ChatGPT-code-preview](https:\u002F\u002Fgithub.com\u002Fykyritsis\u002FChatGPT-code-preview) - Artifacts-like chrome extension for ChatGPT, inspired by Claude 3.5 Sonnet, which requires CSP unblocker for JS to function [github](https:\u002F\u002Fgithub.com\u002Fykyritsis\u002FChatGPT-code-preview)\n- [ChatGPT](https:\u002F\u002Fchatgpt.com\u002F) - ChatGPT is an AI language model designed to understand and generate human-like text, facilitating conversation and assisting with various tasks [website](https:\u002F\u002Fchatgpt.com\u002F)\n- [Claude 3 Artifacts by PierrunoYT](https:\u002F\u002Fgithub.com\u002FPierrunoYT\u002Fclaude-3-artifacts) - An open-source Flask-React chat application that interacts with Claude AI, featuring file uploads, Markdown rendering, and code highlighting, seeking contributors to expand its capabilities, inspired by Claude Artifacts [github](https:\u002F\u002Fgithub.com\u002FPierrunoYT\u002Fclaude-3-artifacts) | [reddit announcement](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FClaudeAI\u002Fcomments\u002F1dqhta5\u002Fhelp_me_buiild_claude_3_artifacs_opensource\u002F)\n- [Claude models by Anthropic](https:\u002F\u002Fdocs.anthropic.com\u002Fclaude\u002Fdocs\u002Ftool-use) - Function calling or tool use is supported with the following models: `claude-3-opus-20240229`, `claude-3-sonnet-20240229`, and `claude-3-haiku-20240307` [docs](https:\u002F\u002Fdocs.anthropic.com\u002Fclaude\u002Fdocs\u002Ftool-use)\n- [Claude-React-Jumpstart](https:\u002F\u002Fgithub.com\u002FBklieger\u002FClaude-React-Jumpstart) - This project offers a tutorial for beginners to set up and run React code generated by Claude's Artifacts feature locally, providing step-by-step instructions for creating a React app with Vite, installing necessary dependencies, and integrating Claude-generated code [github](https:\u002F\u002Fgithub.com\u002FBklieger\u002FClaude-React-Jumpstart) | [twitter announcement](https:\u002F\u002Fx.com\u002FBenjaminKlieger\u002Fstatus\u002F1804264035464155220)\n- [Clawdia Agent Gateway](https:\u002F\u002Fapi-catalog-three.vercel.app) - A unified API gateway providing 40+ services for AI agents including web scraping, code execution, agent memory with vector search, task queue, event bus, and more. Supports x402 micropayments (USDC on Base) with TypeScript and Python SDKs [website](https:\u002F\u002Fapi-catalog-three.vercel.app) | [github](https:\u002F\u002Fgithub.com\u002FOzorOwn)\n- [CodeActAgent](https:\u002F\u002Fgithub.com\u002Fxingyaoww\u002Fcode-act) - CodeActAgent, trained on CodeActInstruct, showcases superior performance in both in-domain and out-of-domain tasks, enabling dynamic code execution and multi-turn interactions for more effective LLM agents [github](https:\u002F\u002Fgithub.com\u002Fxingyaoww\u002Fcode-act)\n- [Codel](https:\u002F\u002Fgithub.com\u002Fsemanser\u002Fcodel) - Autonomous AI agent, inspired by Devin, designed for complex task execution with features like a secure sandboxed Docker environment, integrated browser for real-time web information, text editor, and PostgreSQL database for history tracking, highlighting its relevance to agentic AI through its ability to autonomously navigate and perform actions across terminal, browser, and editor interfaces [github](https:\u002F\u002Fgithub.com\u002Fsemanser\u002Fcodel) | [announcement](https:\u002F\u002Fnews.ycombinator.com\u002Fitem?id=39799296)\n- [Cody](https:\u002F\u002Fsourcegraph.com\u002Fcody) - Cody, an AI coding assistant, now offers an enterprise version with enhanced security, scalability, and control for organizations, supporting various IDEs and providing AI-powered autocomplete, chat assistance, and custom command capabilities [website](https:\u002F\u002Fsourcegraph.com\u002Fcody) | [github](https:\u002F\u002Fgithub.com\u002Fsourcegraph\u002Fcody)\n- [Cognee](https:\u002F\u002Fgithub.com\u002Ftopoteretes\u002Fcognee) - Cognee is an open-source framework aimed at simplifying data processing for large language models (LLMs) by creating knowledge graphs and data models, offering tools for information addition, knowledge creation, and similarity-based search [github](https:\u002F\u002Fgithub.com\u002Ftopoteretes\u002Fcognee)\n- [Command R+ by Cohere](https:\u002F\u002Ftxt.cohere.com\u002Fcommand-r-plus-microsoft-azure\u002F) - Cohere introduces Command R+, an advanced, scalable LLM optimized for enterprise needs with advanced RAG, multilingual support, and sophisticated tool-use capabilities for automating complex business workflows, available first on Microsoft Azure [announcement](https:\u002F\u002Ftxt.cohere.com\u002Fcommand-r-plus-microsoft-azure\u002F) | [docs](https:\u002F\u002Fdocs.cohere.com\u002Fdocs\u002Fcommand-r)\n- [Composio](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ujxKzS0b5qg) - Composio enables quick integration of 90+ tools for developers and agents, offering managed authentication, easy testing, and up-to-date APIs to simplify development and enhance functionality [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ujxKzS0b5qg) | [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ujxKzS0b5qg) | [website](https:\u002F\u002Fwww.composio.dev\u002F) | [docs](https:\u002F\u002Fdocs.composio.dev\u002F) | [blog](https:\u002F\u002Fblog.composio.dev\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002FSamparkAI)\n- [Context](https:\u002F\u002Fcontext.ai\u002F) - Context.ai is a tool for evaluating and analyzing products with LLMs, aiming to improve user experience and performance [website](https:\u002F\u002Fcontext.ai\u002F) | [docs](https:\u002F\u002Fdocs.context.ai\u002F)\n- [Continue](https:\u002F\u002Fgithub.com\u002Fcontinuedev\u002Fcontinue) - Continue is an open-source autopilot plugin for VS Code and JetBrains, enhancing coding with LLMs through features like task and tab autocomplete, natural language edits, file generation, and customization options, available under the Apache 2.0 license [github](https:\u002F\u002Fgithub.com\u002Fcontinuedev\u002Fcontinue) | [website](https:\u002F\u002Fcontinue.dev)\n- [Cosmo](https:\u002F\u002Fmeetcosmo.ai\u002F) - Cosmo offers an all-inclusive AI agent for merchants on WhatsApp, enabling order placements, customer interaction, automatic question answering, inventory and CRM integration, with features like instant payments, customer insights, dynamic order fulfillment, and a comprehensive merchant web app for online transaction management, aimed at simplifying shopping and boosting sales by 57% [website](https:\u002F\u002Fmeetcosmo.ai\u002F) | [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772775416044126608)\n- [Cursor](https:\u002F\u002Fgithub.com\u002Fgetcursor\u002Fcursor\u002Fissues) - Cursor is an AI-enhanced programming editor focusing on code discussion, editing, and debugging, with plans for advanced features like repository healing and AI-generated documentation [issue tracker](https:\u002F\u002Fgithub.com\u002Fgetcursor\u002Fcursor\u002Fissues) | [website](https:\u002F\u002Fcursor.sh\u002F)\n- [Custom Tools by Bland AI](https:\u002F\u002Fdocs.bland.ai\u002Ftutorials\u002Fcustom-tools#creating-your-custom-tool) - Custom tools by Bland AI enable an agent to interact with any web API mid-call to perform actions like sending messages, scheduling appointments, creating support tickets, or updating CRM systems [docs](https:\u002F\u002Fdocs.bland.ai\u002Ftutorials\u002Fcustom-tools#creating-your-custom-tool)\n- [DB-GPT](https:\u002F\u002Fgithub.com\u002Feosphoros-ai\u002FDB-GPT) - DB-GPT revolutionizes database interactions using private LLM technology, enabling streamlined AI-native data app development with multi-model management, Text2SQL optimization, and fine-tuning, facilitating enterprises and developers to create bespoke applications in the Data 3.0 era [github](https:\u002F\u002Fgithub.com\u002Feosphoros-ai\u002FDB-GPT) | [github profile](https:\u002F\u002Fgithub.com\u002Feosphoros-ai)\n- [DSPY](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy) - A cutting-edge framework that compiles declarative language model calls into self-improving pipelines, enabling the systematic and efficient optimization of LM prompts and weights within complex systems [github](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy)\n- [Data Questionnaire Agent](https:\u002F\u002Fgithub.com\u002Fonepointconsulting\u002Fdata-questionnaire-agent) - A chatbot designed to query users on data integration practices, offering advice based on responses, utilizing a modified Chainlit library for operation [github](https:\u002F\u002Fgithub.com\u002Fonepointconsulting\u002Fdata-questionnaire-agent)\n- [DeepInfra](https:\u002F\u002Fdeepinfra.com) - DeepInfra is a comprehensive platform that simplifies the deployment and management of machine learning models, offering a range of open-source models for tasks like text generation and embeddings, with easy integration through REST API calls [website](https:\u002F\u002Fdeepinfra.com) | [docs](https:\u002F\u002Fdeepinfra.com\u002Fdocs\u002F)\n- [Deepgram](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772774552260788296) - Conversational AI tools designed for creating voice bots and agents, featuring realistic, low-latency voice technology [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772774552260788296)\n- [Deepunit](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772773773772779533) - An AI agent designed to generate unit tests for complete code coverage across your project, requiring only your repository as input [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772773773772779533)\n- [DevOpsGPT](https:\u002F\u002Fgithub.com\u002Fkuafuai\u002FDevOpsGPT) - DevOpsGPT is an AI-driven software development automation solution that combines large language models with DevOps tools to convert natural language requirements into working software, enhancing development efficiency, shortening cycles, and reducing communication costs [github](https:\u002F\u002Fgithub.com\u002Fkuafuai\u002FDevOpsGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Fkuafuai)\n- [Devid by Agency Swarm](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FDevid) - Devid Agent, a new AI software development tool, has been integrated into the Agency Swarm framework to enhance automated AI agency operations, alternative to Cognition AI's Devin [code](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FDevid) | [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BEpDRj9H3zE)\n- [Devika](https:\u002F\u002Fgithub.com\u002Fstitionai\u002Fdevika) - Devika is an open-source AI software engineer designed to understand and execute high-level coding tasks by researching, planning, and writing code, aiming to be a competitive alternative to Cognition AI's Devin [github](https:\u002F\u002Fgithub.com\u002Fstitionai\u002Fdevika) | [demo](https:\u002F\u002Fgithub.com\u002Fstitionai\u002Fdevika?tab=readme-ov-file#demos) | [discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002F8eYNbPuB)\n- [Devin by Cognition](https:\u002F\u002Fwww.cognition-labs.com\u002Fintroducing-devin) - Devin is a fully autonomous AI software engineer, revolutionizing coding with advanced reasoning and planning capabilities [announcement](https:\u002F\u002Fwww.cognition-labs.com\u002Fintroducing-devin) | [website](https:\u002F\u002Fwww.cognition-labs.com\u002F)\n- [Devon (previously Gilfoyle)](https:\u002F\u002Fgithub.com\u002Fentropy-research\u002FDevon) - Devon, not Devin, aims to perfect code correction for fill-in-the-middle, bug spotting, and completion tasks, using JSON for metadata in edits, and incorporates looping until user termination in function updates [github](https:\u002F\u002Fgithub.com\u002Fentropy-research\u002FDevon)\n- [DJD Agent Score](https:\u002F\u002Fgithub.com\u002Fjacobsd32-cpu\u002Fdjdagentscore) - On-chain reputation scoring API for AI agent wallets on Base L2. Returns 0-100 trust scores across 5 behavioral dimensions from transaction patterns and multi-chain attestations. Features x402 micropayments, TypeScript SDK, and ERC-8004 on-chain reputation publication [github](https:\u002F\u002Fgithub.com\u002Fjacobsd32-cpu\u002Fdjdagentscore) | [docs](https:\u002F\u002Fdjd-agent-score.fly.dev) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fdjd-agent-score-client)\n- [E2B](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002FE2B) - E2B Sandbox offers secure cloud environments tailored for AI agents and apps, facilitating long-running sessions with various tools and can be integrated with any large language model [github](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002FE2B) | [github profile](https:\u002F\u002Fgithub.com\u002Fe2b-dev)\n- [ElevenLabs](https:\u002F\u002Felevenlabs.io\u002F) - ElevenLabs is a software company that develops AI-powered, natural-sounding speech synthesis and text-to-speech software, with the mission of making content universally accessible in any language and voice [website](https:\u002F\u002Felevenlabs.io\u002F)\n- [Enact](https:\u002F\u002Fgithub.com\u002Fagentic-ai\u002Fenact) - Enact is a Python framework for building generative software that integrates machine learning models or APIs, offering features like tracking and replaying executions, asynchronous flows, and higher-order generative processes [github](https:\u002F\u002Fgithub.com\u002Fagentic-ai\u002Fenact)\n- [Evolutionary Model Merge](https:\u002F\u002Ftwitter.com\u002FAlphaSignalAI\u002Fstatus\u002F1771201081734811797) - Sakana AI's evolutionary model merge (EMM) combines 500,000 open-source models using evolutionary techniques to create new foundation models, achieving groundbreaking results without being explicitly optimized for specific benchmarks, marking a significant step toward AGI by empowering AI with combined knowledge akin to Retrieval Augmented Generation [announcement](https:\u002F\u002Ftwitter.com\u002FAlphaSignalAI\u002Fstatus\u002F1771201081734811797) | [github](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002Fevolutionary-model-merge\u002F)\n- [Fairgo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175460044193992) - Fairgo.ai is a platform built by Julian to streamline and scale hiring processes using real-time AI video interviews, tackling unconscious biases and ensuring all candidates are interviewed without human input [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175460044193992) | [website](https:\u002F\u002Ffairgo.ai\u002F)\n- [FastChat](https:\u002F\u002Fgithub.com\u002Flm-sys\u002FFastChat) - FastChat is a platform for training, serving, and evaluating large language model chatbots, featuring an open-source distributed multi-model system, API compatibility, and a dataset for LLM conversations [github](https:\u002F\u002Fgithub.com\u002Flm-sys\u002FFastChat) | [demo](https:\u002F\u002Fchat.lmsys.org\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FHSWAKCrnFx) | [twitter](https:\u002F\u002Fx.com\u002Flmsysorg)\n- [Fetch](https:\u002F\u002Ffetch.ai) - Fetch by Fetch AI offers a platform for launching AI apps and services, including agent hosting, analytics, IoT gateways, and a Web3-based open network, alongside an open network for AI Agents that allows for connectivity, transactions, and the formation of dynamic marketplaces, facilitating the deployment and monetization of AI and ML models through agent technology [website](https:\u002F\u002Ffetch.ai) | [github profile](https:\u002F\u002Fgithub.com\u002Ffetchai)\n- [Fazm](https:\u002F\u002Fgithub.com\u002Fm13v\u002Ffazm) - Fazm is an open-source, voice-controlled AI computer agent for macOS that controls your entire desktop through natural language - any app, file, or workflow. Built in Swift\u002FSwiftUI, local-first, MIT licensed [github](https:\u002F\u002Fgithub.com\u002Fm13v\u002Ffazm) | [website](https:\u002F\u002Ffazm.ai)\n- [FinGen](https:\u002F\u002Ftwitter.com\u002FSullyOmarr\u002Fstatus\u002F1772282548841791730) - FinGen is a financial analysis agent using RSC, LangChain, and Polygon finance API, emphasizing it's not financial advice and requires API keys for use [announcement](https:\u002F\u002Ftwitter.com\u002FSullyOmarr\u002Fstatus\u002F1772282548841791730) | [github](https:\u002F\u002Fgithub.com\u002Fsullyo\u002Ffingen)\n- [Fine](https:\u002F\u002Fwww.fine.dev) - Fine.dev offers AI-powered agents designed to automate software development tasks, seamlessly integrating into engineering teams to manage tedious tasks, technical debt, code reviews, and migrations, while customizing to project needs and learning from team feedback for improved efficiency [website](https:\u002F\u002Fwww.fine.dev) | [discord](https:\u002F\u002Fdiscord.gg\u002FnxW8sA5yqe) | [docs](https:\u002F\u002Fdocs.fine.dev\u002F)\n- [Flowise](https:\u002F\u002Fgithub.com\u002FFlowiseAI\u002FFlowise) - Flowise simplifies the creation of applications leveraging large language models (LLMs) by providing a drag-and-drop interface for customizing AI workflows, offering easy installation, Docker support, development tools, and documentation for integrating various functionalities such as authentication, streaming, and custom tools to enhance AI agents' capabilities [github](https:\u002F\u002Fgithub.com\u002FFlowiseAI\u002FFlowise) | [website](https:\u002F\u002Fflowiseai.com\u002F) | [docs](https:\u002F\u002Fdocs.flowiseai.com\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002FFlowiseAI)\n- [FuzzTypes](https:\u002F\u002Fgithub.com\u002Fgenomoncology\u002FFuzzTypes) - FuzzTypes is a Pydantic extension library providing autocorrecting annotation types, enhancing Pydantic's data conversions for AI agents by enabling powerful normalization capabilities like named entity linking to ensure structured data consists of 'smart things' instead of 'dumb strings' [github](https:\u002F\u002Fgithub.com\u002Fgenomoncology\u002FFuzzTypes) | [website](https:\u002F\u002Fwww.genomoncology.com\u002F)\n- [GPT Computer Assistant](https:\u002F\u002Fgithub.com\u002Fonuratakan\u002Fgpt-computer-assistant) - GPT Computer Assistant is an unofficial app that brings ChatGPT functionality to Windows and Linux, allowing for screen reading, microphone use, system audio interaction, clipboard management, script execution, and more [github](https:\u002F\u002Fgithub.com\u002Fonuratakan\u002Fgpt-computer-assistant)\n- [GPT Engineer](https:\u002F\u002Fgithub.com\u002Fgpt-engineer-org\u002Fgpt-engineer) - GPT-Engineer is an AI-powered tool allowing users to specify software in natural language, automatically generating and executing code, with options for improvement suggestions, and fostering collaboration within the open-source community [github](https:\u002F\u002Fgithub.com\u002Fgpt-engineer-org\u002Fgpt-engineer) | [github profile](https:\u002F\u002Fgithub.com\u002Fgpt-engineer-org) | [website](https:\u002F\u002Fgptengineer.app)\n- [GPT Newspaper by Tavily](https:\u002F\u002Fgithub.com\u002Frotemweiss57\u002Fgpt-newspaper) - GPT Newspaper is an autonomous agent project using AI to create personalized newspapers based on user preferences, featuring six specialized sub-agents for searching, curating, writing, designing, editing, and publishing content tailored to individual interests [github](https:\u002F\u002Fgithub.com\u002Frotemweiss57\u002Fgpt-newspaper) | [github profile](https:\u002F\u002Fgithub.com\u002Frotemweiss57)\n- [GPT Pilot](https:\u002F\u002Fgithub.com\u002FPythagora-io\u002Fgpt-pilot) - GPT Pilot is an open-source AI developer tool that aims to provide a comprehensive development companion, capable of writing features, debugging, and interacting with users, presenting itself as an alternative to Devin, the world's first AI software engineer developed by Cognition Labs [github](https:\u002F\u002Fgithub.com\u002FPythagora-io\u002Fgpt-pilot) | [discord](https:\u002F\u002Fdiscord.gg\u002FRzvCYRgUkx)\n- [GPT Researcher by Tavily](https:\u002F\u002Fgithub.com\u002Fassafelovic\u002Fgpt-researcher) - GPT Researcher is an AI-powered autonomous agent designed for efficient and unbiased online research, generating detailed reports by leveraging recent advancements in AI and web scraping, with a focus on speed, reliability, and cost-effectiveness [github](https:\u002F\u002Fgithub.com\u002Fassafelovic\u002Fgpt-researcher) | [github profile](https:\u002F\u002Fgithub.com\u002Fassafelovic)\n- [AI Scientist](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002FAI-Scientist) - The AI Scientist: Towards Fully Automated Open-Ended Scientific. [github](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002FAI-Scientist)\n- [BlockAGI](https:\u002F\u002Fgithub.com\u002Fblockpipe\u002Fblockagi) - BlockAGI conducts iterative, domain-specific research, and outputs detailed narrative reports to showcase its findings. [github](https:\u002F\u002Fgithub.com\u002Fblockpipe\u002Fblockagi)\n- [data-to-paper](https:\u002F\u002Fgithub.com\u002FTechnion-Kishony-lab\u002Fdata-to-paper) - data-to-paper: AI-driven research from data to human-verifiable research papers. [github](https:\u002F\u002Fgithub.com\u002FTechnion-Kishony-lab\u002Fdata-to-paper)\n- [DeepAnalyze](https:\u002F\u002Fgithub.com\u002Fruc-datalab\u002FDeepAnalyze) - first agentic LLM for autonomous data science, supporting specific data tasks (data preparation, analysis, modeling, visualization, and insight) and data-oriented deep research (produce analyst-grade research reports). [github](https:\u002F\u002Fgithub.com\u002Fruc-datalab\u002FDeepAnalyze)\n- [GenoMAS](https:\u002F\u002Fgithub.com\u002FLiu-Hy\u002FGenoMAS) - A multi-agent framework for scientific discovery that automates gene expression analysis through code-driven workflows. [github](https:\u002F\u002Fgithub.com\u002FLiu-Hy\u002FGenoMAS)\n- [GhostClaw](https:\u002F\u002Fgithub.com\u002Fb1rdmania\u002Fghostclaw) - A local AI agent you message on Telegram like a co-worker. Runs on your computer with no containers or cloud dependencies, and sets up in 10 minutes. [github](https:\u002F\u002Fgithub.com\u002Fb1rdmania\u002Fghostclaw) | [website](https:\u002F\u002Fghostclaw.io)\n- [OpenLens AI](https:\u002F\u002Fgithub.com\u002Fjarrycyx\u002Fopenlens-ai) - Fully Autonomous Research Agent for Health Infomatics. [github](https:\u002F\u002Fgithub.com\u002Fjarrycyx\u002Fopenlens-ai)\n- [Storm](https:\u002F\u002Fgithub.com\u002Fstanford-oval\u002Fstorm) - An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations. [github](https:\u002F\u002Fgithub.com\u002Fstanford-oval\u002Fstorm)\n- [GPT models by OpenAI](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ffunction-calling) - Function calling or tool use is supported with the following models: `gpt-4-turbo`, `gpt-4-turbo-2024-04-09`, `gpt-4-turbo-preview`, `gpt-4-0125-preview`, `gpt-4-1106-preview`, `gpt-4`, `gpt-4-0613`, `gpt-3.5-turbo`, `gpt-3.5-turbo-0125`, `gpt-3.5-turbo-1106`, and `gpt-3.5-turbo-0613` [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ffunction-calling)\n- [GPTeam](https:\u002F\u002Fgithub.com\u002F101dotxyz\u002FGPTeam) - GPTeam is a collaborative AI project utilizing GPT-4 to create multi-agent systems aimed at enhancing productivity and communication, with features including agent memory and interaction, alongside instructions for setup and integration with third-party services [github](https:\u002F\u002Fgithub.com\u002F101dotxyz\u002FGPTeam) | [github profile](https:\u002F\u002Fgithub.com\u002F101dotxyz)\n- [Gated 402 API](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fgated-402-api) - An API using a Stacks smart contract to control access, issuing a 200 status for access approval and a 402 with payment instructions for denial [github](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fgated-402-api) | [github profile](https:\u002F\u002Fgithub.com\u002Faibtcdev) | [website](https:\u002F\u002Faibtc.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002F5DJaBrf)\n- [GitWit](https:\u002F\u002Fgitwit.dev\u002F) - GitWit is an online tool that accelerates web app development with AI, supporting React, Tailwind, and NodeJS, boasting a 3X speed increase and over 1000 projects generated [website](https:\u002F\u002Fgitwit.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gitwit.dev\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002Fgitwitorg)\n- [Google STT](https:\u002F\u002Fcloud.google.com\u002Fspeech-to-text) - Google Cloud Speech-to-Text is a comprehensive speech recognition service that leverages Google's years of research in automatic speech recognition and transcription technology to provide developers with a high-quality, easy-to-use speech-to-text API [website](https:\u002F\u002Fcloud.google.com\u002Fspeech-to-text)\n- [Groq](https:\u002F\u002Fgroq.com\u002F) - GroqCloud API endpoints support tool use for programmatic execution of specified operations through requests with explicitly defined operations, allowing Groq API model endpoints to deliver structured JSON output that can be used to directly invoke functions from desired codebases; these following models powered by Groq all support tool use: `llama3-70b`, `llama3-8b`, `mixtral-8x7b`, `gemma-7b-it`; parallel tool calling is enabled for both Llama3 models [website](https:\u002F\u002Fgroq.com\u002F) | [docs](https:\u002F\u002Fconsole.groq.com\u002Fdocs) | [tool use docs](https:\u002F\u002Fconsole.groq.com\u002Fdocs\u002Ftool-use) | [tool use announcement](https:\u002F\u002Ftwitter.com\u002FGroqInc\u002Fstatus\u002F1775634099849322632)\n- [Guardrails](https:\u002F\u002Fgithub.com\u002Fguardrails-ai\u002Fguardrails) - Guardrails is a Python framework for building reliable AI applications, offering Input\u002FOutput Guards to detect and mitigate risks, along with structured data generation from large language models (LLMs) [github](https:\u002F\u002Fgithub.com\u002Fguardrails-ai\u002Fguardrails) | [twitter](https:\u002F\u002Ftwitter.com\u002Fguardrails_ai)\n- [Guidance](https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance) - The text describes 'guidance,' a programming paradigm that enhances control and efficiency in model generation by allowing for constraints like regex and CFGs, integrating stateful control, and offering a simplified interface for complex generation scenarios [github](https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance) | [docs](https:\u002F\u002Fguidance.readthedocs.org\u002F)\n- [Harpa](https:\u002F\u002Fharpa.ai\u002F) - Harpa is a versatile Chrome extension that integrates AI capabilities, such as summarizing content, automating workflows, and enhancing productivity, supported by GPT-4 and Claude 2, trusted by 300,000+ professionals [website](https:\u002F\u002Fharpa.ai\u002F)\n- [Hanzi](https:\u002F\u002Fgithub.com\u002Fhanzili\u002Fhanzi-in-chrome) - Hanzi is an open-source MCP server + Chrome extension that gives AI agents your real signed-in browser. Task-level automation — one tool call delegates an entire workflow. Built-in skills for LinkedIn prospecting, E2E testing, and social posting. Works with Claude Code, Cursor, Codex, and Windsurf. Setup: `npx hanzi-in-chrome setup` [github](https:\u002F\u002Fgithub.com\u002Fhanzili\u002Fhanzi-in-chrome)\n- [Haystack](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack) - Haystack is an end-to-end LLM framework facilitating the construction of applications powered by LLMs, Transformer models, vector search, and more, offering flexibility, transparency, and extensibility, with features including retrieval-augmented generation, document search, question answering, and semantic search, along with a diverse user base including companies like Airbus, Apple, and Netflix [github](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack) | [github profile](https:\u002F\u002Fgithub.com\u002Fdeepset-ai)\n- [Helicone](https:\u002F\u002Fwww.helicone.ai\u002F) - Helicone is an open-source observability platform for Language Learning Models (LLMs), providing features like request logging, caching, rate limiting, cost and latency tracking, UI-based prompt iteration, and collaboration tools [website](https:\u002F\u002Fwww.helicone.ai\u002F) | [github](https:\u002F\u002Fgithub.com\u002FHelicone\u002Fhelicone)\n- [Humane](https:\u002F\u002Fhumane.com\u002F) - AI Pin, a wearable, multi-modal device, enhances ambient computing in the real world, offering a suite of AI digital assistants for various tasks while prioritizing user privacy for a more intuitive, human-centered experience [website](https:\u002F\u002Fhumane.com\u002F)\n- [Hivemoot](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fhivemoot) - Hivemoot is a governance framework for autonomous AI agent teams on GitHub, enabling agents to propose features, vote democratically, review code, and ship software through role-based collaboration with live observability via Colony dashboard [github](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fhivemoot) | [docs](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fhivemoot\u002Fblob\u002Fmain\u002FCONCEPT.md) | [live demo](https:\u002F\u002Fhivemoot.github.io\u002Fcolony\u002F) | [colony repo](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fcolony)\n- [Hume AI](https:\u002F\u002Fwww.hume.ai\u002F) - Hume AI offers empathic AI solutions with emotional intelligence through APIs for interpreting emotional expressions and generating empathic responses, aimed at enhancing human well-being and enabling developers to create AI agents with improved understanding and engagement [website](https:\u002F\u002Fwww.hume.ai\u002F) | [discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002FWPRSugvAm6)\n- [Imbue](https:\u002F\u002Fimbue.com\u002F) - Imbue, previously known as Generally Intelligent, is developing AI systems designed for reasoning and coding, aiming to create truly personal computers that enhance human freedom, dignity, and agency, supported by a $200M funding round to advance their technology [website](https:\u002F\u002Fimbue.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fimbue_ai\u002F)\n- [Instructor Cloud](https:\u002F\u002Fgithub.com\u002Finstructor-ai\u002Fcloud) - Instructor Cloud offers a platform for extracting models from text rapidly, with real-time streaming and the potential to utilize GPT-4*, encouraging engagement through contributions and adaptation of its FastAPI-based service [github](https:\u002F\u002Fgithub.com\u002Finstructor-ai\u002Fcloud) | [announcement](https:\u002F\u002Ftwitter.com\u002Fjxnlco\u002Fstatus\u002F1774822440922763707)\n- [Instructor](https:\u002F\u002Fgithub.com\u002Fjxnl\u002Finstructor) - Instructor, a Python library, facilitates working with structured outputs from large language models (LLMs), offering features like response model specification, retry management, validation, and streaming support, primarily aimed at enhancing workflows of AI agents utilizing LLMs [github](https:\u002F\u002Fgithub.com\u002Fjxnl\u002Finstructor) | [website](https:\u002F\u002Fpython.useinstructor.com\u002F)\n- [IvyCheck](https:\u002F\u002Fgithub.com\u002Fivycheck\u002Fivycheck-python-sdk) - IvyCheck offers an API for real-time AI application safety checks, preventing prompt injection attacks, PII data leakage, and hallucinations in agentic AI development [github](https:\u002F\u002Fgithub.com\u002Fivycheck\u002Fivycheck-python-sdk) | [announcement](https:\u002F\u002Fwww.ycombinator.com\u002Flaunches\u002FKkA-ivycheck-guard-against-ai-risks-with-real-time-checks) | [website](https:\u002F\u002Fivycheck.com)\n- [JARVIS by Microsoft](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FJARVIS) - JARVIS aims to advance artificial general intelligence (AGI) through cutting-edge research and facilitate broader community engagement [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FJARVIS)\n- [Jaiqu](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002FJaiqu) - Jaiqu is an AI-powered tool for automatically transforming any JSON schema using GPT-4, featuring schema validation, fuzzy term matching, and repeatable jq query generation [github](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002FJaiqu) | [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1774314258379190770) | [website](https:\u002F\u002Fjaiqu-agent.streamlit.app\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fagentopsai\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FJHPt4C7r)\n- [Jan](https:\u002F\u002Fgithub.com\u002Fjanhq\u002Fjan) - Jan is an open-source, development-stage ChatGPT alternative that operates fully offline on diverse hardware platforms, supporting universal architectures from PCs to multi-GPU clusters [github](https:\u002F\u002Fgithub.com\u002Fjanhq\u002Fjan) | [github profile](https:\u002F\u002Fgithub.com\u002Fjanhq)\n- [Jsonify](https:\u002F\u002Fjsonify.com\u002F) - Jsonify provides a no-code platform for AI data agents that convert webpages and documents into structured JSON, enhancing efficiency and customer satisfaction, with use cases including scraping webpages, extracting document data, and building structured datasets [website](https:\u002F\u002Fjsonify.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fjsonifyco) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fjsonify\u002F)\n- [Kapa](https:\u002F\u002Fwww.kapa.ai\u002F) - Kapa.ai is an AI-powered chatbot service for developers that automates answering technical questions by learning from technical resources, thus helping identify gaps in documentation, with features including data security, PII anonymization, and continuous updating from a range of knowledge sources [website](https:\u002F\u002Fwww.kapa.ai\u002F) | [docs](https:\u002F\u002Fdocs.kapa.ai\u002F)\n- [Lumen](https:\u002F\u002Fgithub.com\u002Fomxyz\u002Flumen) - Lumen is a vision-first browser agent with self-healing deterministic replay over CDP. Screenshot → model → action loop with multi-provider support (Anthropic, Google). [github](https:\u002F\u002Fgithub.com\u002Fomxyz\u002Flumen) | [website](https:\u002F\u002Flumen.omlabs.xyz)\n- [LM Studio](https:\u002F\u002Flmstudio.ai\u002F) - LM Studio offers a platform for running various local LLMs like LLaMa, Falcon, MPT, and others offline, featuring a Chat UI, OpenAI-compatible server, and model downloads from Hugging Face, with support for Mac, Windows, and Linux, emphasizing privacy and no data collection, free for personal use [website](https:\u002F\u002Flmstudio.ai\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002Flmstudio-ai)\n- [LMNT](https:\u002F\u002Fwww.lmnt.com) - LMNT is an AI-powered text-to-speech platform that offers ultrafast, lifelike, and reliable voice cloning and generation services for conversational apps, agents, and content creation at scale [website](https:\u002F\u002Fwww.lmnt.com) | [docs](https:\u002F\u002Fdocs.lmnt.com\u002F)\n- [LMQL](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Flmql) - LMQL is a Python-based programming language for large language models, allowing seamless integration of LLMs into code with advanced features like conditional logic, constraints, and multi-model support [github](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Flmql) | [website](https:\u002F\u002Flmql.ai\u002F)\n- [LangChain JS Tools](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs) - Langchain features VectorDBQAChain, which integrates LLMs and vector databases into agent tools for enhanced question-answering capabilities by leveraging data ingested into vector stores [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs) | [docs](https:\u002F\u002Fjs.langchain.com\u002Fv0.2\u002Fdocs\u002Fintegrations\u002Ftools\u002F)\n- [LangChain JS](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs) - LangChain JS is a framework for developing applications powered by language models, enabling context-aware and reasoning-based applications through composable tools and off-the-shelf chains, with seamless integration with the LangChain Python package [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs)\n- [LangChain Tools](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002F) - Langchain integrates various providers like Anthropic, AWS, and OpenAI, and offers tools for components such as LLMs, chat models, and data analysis, supporting functionalities from Alpha Vantage to YouTube [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002F) | [docs](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fintegrations\u002Ftools)\n- [LangChainBitcoin](https:\u002F\u002Flightning.engineering\u002Fposts\u002F2023-07-05-l402-langchain\u002F) - LangChainBitcoin is a toolset for enabling LangChain agents to interact with Bitcoin, the Lightning Network, and APIs requiring L402-based authentication, including features for Bitcoin transactions and API traversal with automated Lightning payments [announcement](https:\u002F\u002Flightning.engineering\u002Fposts\u002F2023-07-05-l402-langchain\u002F) | [github](https:\u002F\u002Fgithub.com\u002Flightninglabs\u002FLangChainBitcoin)\n- [LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain) - LangChain is a framework enabling context-aware reasoning applications with integrated libraries, templates, and developer tools [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain)\n- [LangFuse](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) - Langfuse, an open-source LLM engineering platform, offers debugging, prompt management, metrics for LLM apps improvement, and won the #1 Golden Kitty in the AI Infra Category from Product Hunt [github](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) | [website](https:\u002F\u002Flangfuse.com\u002F) | [twitter](https:\u002F\u002Fx.com\u002Flangfuse) | [discord](https:\u002F\u002Flangfuse.com\u002Fdiscord)\n- [LangGraph.js](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraphjs) - LangGraph.js is a TypeScript and JavaScript library enabling the development of stateful, multi-actor applications with LLMs, featuring capabilities to construct cyclic coordination across multiple computation steps for complex agent-like behaviors, with support for conditional edges and cycles, not limited to DAGs, and extensive documentation with examples on implementation [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraphjs)\n- [LangGraph](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph) - LangGraph is a Python library facilitating the construction of stateful, multi-actor applications with LLMs, enabling cyclic coordination across multiple computation steps, particularly suited for agent-like behaviors, while also providing streaming support, and various guides and examples for implementation and usage [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph)\n- [LangServe](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangserve) - LangServe facilitates the deployment of LangChain runnables and chains as a REST API, providing features like automatic schema inference, efficient endpoints, and a playground page, with plans for a hosted version for one-click deployments [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangserve)\n- [LangSmith by LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-sdk) - LangSmith provides tools for debugging, testing, evaluating, and monitoring LLM applications, integrating seamlessly with LangChain for comprehensive AI agent observability [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-sdk) | [docs](https:\u002F\u002Fdocs.smith.langchain.com) | [website](https:\u002F\u002Fsmith.langchain.com)\n- [Libraria](https:\u002F\u002Flibraria.ai\u002F) - Libraria AI offers a platform to create, manage, and embed custom AI chatbots with natural language processing and features like call-to-actions, link carousels, and analytics for enhanced customer interactions and satisfaction, alongside free and paid plans tailored for different business needs [website](https:\u002F\u002Flibraria.ai\u002F) | [twitter](https:\u002F\u002Fx.com\u002Flibrariaai)\n- [LiteLLM](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm) - LiteLLM has added support for the OpenAI Assistants API, enabling seamless integration of stateful operations and automatic RAG pipelines into existing chatbots [github](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm)\n- [Lynkr](https:\u002F\u002Fgithub.com\u002FFast-Editor\u002FLynkr) - Lynkr is a proxy that lets Claude Code CLI talk to non-Anthropic LLMs, manage local tools, and compose Model Context Protocol (MCP) servers with prompt caching, repo intelligence, and Git-aware automation and several other features similar to anthropic backend.\n- [LiveKit Agents](https:\u002F\u002Fgithub.com\u002Flivekit\u002Fagents) - An open-source framework for building real-time, programmable participants that run on servers, enabling easy integration with LiveKit WebRTC sessions for processing or generating audio, video, and data streams [GitHub](https:\u002F\u002Fgithub.com\u002Flivekit\u002Fagents) | [docs](https:\u002F\u002Fdocs.livekit.io\u002Fagents\u002F) | [demo](https:\u002F\u002Fkitt.livekit.io)\n- [LiveRecall](https:\u002F\u002Fgithub.com\u002FVedankPurohit\u002FLiveRecall) - LiveRecall, an open-source alternative to Microsoft's Recall, utilizes semantic search and encryption to capture and retrieve screen snapshots, enabling AI agents to assist creators in researching and augmenting tasks like journaling or blog post creation based on indexed personal activities [GitHub](https:\u002F\u002Fgithub.com\u002FVedankPurohit\u002FLiveRecall)\n- [LlamaCloud by LlamaIndex](https:\u002F\u002Fwww.llamaindex.ai\u002Fenterprise) - LlamaCloud by LlamaIndex streamlines AI development by enabling developers to minimize infrastructure management and parameter tuning, focusing instead on creating AI products, with features for proprietary parsing of complex documents, easy data ingestion and storage, and advanced data retrieval [website](https:\u002F\u002Fwww.llamaindex.ai\u002Fenterprise) | [github profile](https:\u002F\u002Fgithub.com\u002Frun-llama) | [discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002FeN6D2HQ4aX) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002F91154103\u002F)\n- [LlamaGym](https:\u002F\u002Fgithub.com\u002FKhoomeiK\u002FLlamaGym) - LlamaGym simplifies the fine-tuning of LLM agents with online reinforcement learning, providing a framework to iterate and experiment across Gym environments for efficient agent prompting and hyperparameter tuning [github](https:\u002F\u002Fgithub.com\u002FKhoomeiK\u002FLlamaGym) | [github profile](https:\u002F\u002Fgithub.com\u002FKhoomeiK)\n- [LlamaIndex Tools](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index) - LlamaIndex offers a variety of tools for building data agents, with top downloads including IonicShoppingToolSpec, OpenAPIToolSpec, WikipediaToolSpec, GmailToolSpec, and GoogleCalendarToolSpec, enabling seamless integration with user-defined functions, query engines, and third-party services [github](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index) | [website](https:\u002F\u002Fllamahub.ai\u002F?tab=tools) | [docs](https:\u002F\u002Fdocs.llamaindex.ai\u002Fen\u002Flatest\u002Fmodule_guides\u002Fdeploying\u002Fagents\u002Ftools\u002F)\n- [Lobe Chat](https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat) - Lobe Chat is an open-source UI framework for building ChatGPT\u002FLLM-based chat applications, featuring modern design, speech synthesis, multi-modal support, extensible plugins, and free one-click deployment for various AI agents [github](https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat) | [website](https:\u002F\u002Fchat-preview.lobehub.com\u002F)\n- [LocalGPT](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT) - LocalGPT is an open-source project for secure, private interactions with documents locally, featuring comprehensive model support, embeddings, API for RAG applications, and GUI options, with a focus on privacy and local data processing [github](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT)\n- [LoopGPT](https:\u002F\u002Fgithub.com\u002Ffarizrahman4u\u002Floopgpt) - LoopGPT is a modular auto-GPT framework with features such as a 'Plug N Play' API, GPT 3.5 compatibility, minimal prompt overhead, human-in-the-loop capability, and full state serialization, facilitating easy installation and usage through Python code, CLI, or Docker, with the ability to add custom tools and course correction, along with saving and loading agent state, requiring Python 3.8+ and an OpenAI API Key, and optional setup for Google search support [github](https:\u002F\u002Fgithub.com\u002Ffarizrahman4u\u002Floopgpt) | [github profile](https:\u002F\u002Fgithub.com\u002Ffarizrahman4u)\n- [Lumos](https:\u002F\u002Fgithub.com\u002Fallenai\u002Flumos) - Lumos introduces a modular, open-source language agent framework with unified data formats that competes with or outperforms GPT-series and larger agents across various complex interactive tasks [github](https:\u002F\u002Fgithub.com\u002Fallenai\u002Flumos) | [website](https:\u002F\u002Fallenai.github.io\u002Flumos\u002F)\n- [Lyzr](https:\u002F\u002Fwww.lyzr.ai\u002F) - Lyzr provides an enterprise-grade AI agent framework for easy configuration, deployment, and management of AI agents, supporting integration with multiple LLMs and databases, and offers ISO-compliant safety, white-glove onboarding, and 24\u002F7 enterprise support [website](https:\u002F\u002Fwww.lyzr.ai\u002F) | [blog](https:\u002F\u002Fwww.lyzr.ai\u002Fblog\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Flyzrai)\n- [Marvin](https:\u002F\u002Fgithub.com\u002FPrefectHQ\u002Fmarvin\u002F) - Marvin is an open-source AI toolkit designed for developers focused on enhancing AI agent capabilities, offering tools for natural language interfaces, image and audio generation, and entity extraction, scalable and easy to integrate into existing projects [github](https:\u002F\u002Fgithub.com\u002FPrefectHQ\u002Fmarvin\u002F) | [website](https:\u002F\u002Faskmarvin.ai\u002F)\n- [MemGPT](https:\u002F\u002Fmemgpt.ai\u002F) - MemGPT introduces a customizable AI chatbot framework with self-editing memory and access to unlimited data, promoting perpetual, context-rich conversations [website](https:\u002F\u002Fmemgpt.ai\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fcpacker\u002FMemGPT\u002F)\n- [Camel-AutoGPT](https:\u002F\u002Fgithub.com\u002FSamurAIGPT\u002FCamel-AutoGPT) - role-playing approach for LLMs and auto-agents like BabyAGI & AutoGPT. [github](https:\u002F\u002Fgithub.com\u002FSamurAIGPT\u002FCamel-AutoGPT)\n- [SkyAGI](https:\u002F\u002Fgithub.com\u002Flitanlitudan\u002Fskyagi) - Emerging human-behavior simulation capability in LLM agents. [github](https:\u002F\u002Fgithub.com\u002Flitanlitudan\u002Fskyagi)\n- [Voyager](https:\u002F\u002Fgithub.com\u002FMineDojo\u002FVoyager) - An Open-Ended Embodied Agent with Large Language Models. [github](https:\u002F\u002Fgithub.com\u002FMineDojo\u002FVoyager)\n- [Mendable](https:\u002F\u002Fwww.mendable.ai\u002F) - Mendable offers an AI chatbot solution that enables companies to build and deploy technical assistants trained on their specific documentation and resources, aiming to improve customer and employee support, with features including enterprise-grade security, continuous model training, and integration with a wide range of data sources and APIs [website](https:\u002F\u002Fwww.mendable.ai\u002F) | [docs](https:\u002F\u002Fdocs.mendable.ai\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fmendableai) | [github profile](https:\u002F\u002Fgithub.com\u002Fsideguide)\n- [MergeKit](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002Fmergekit) - Arcee AI's MergeKit offers tools for merging pre-trained large language models, enabling the creation of more versatile AI agents by combining knowledge from different sources, akin to Retrieval Augmented Generation (RAG) [github](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002Fmergekit)\n- [MetaGPT](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT) - MetaGPT is a multi-agent framework enabling GPT to collaborate within a software company, facilitating complex tasks by assigning different roles to GPTs [github](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Fgeekan)\n- [Milo](https:\u002F\u002Fgetmilo.dev) - Done-for-you AI agent teams for small businesses (dental, legal, HVAC, etc.). One-time setup from $399, you own the agents and infrastructure. Agents handle phone calls, scheduling, lead follow-up, and reporting 24\u002F7 [website](https:\u002F\u002Fgetmilo.dev)\n- [Miranda](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175658178855112) - Miranda is a platform that simplifies dashboard creation, aiming to be the 'Canva for dashboards' [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175658178855112)\n- [MoltBook](https:\u002F\u002Fmoltbook.com) - A social network built for AI agents where they can create accounts, post content, build reputation through karma, interact via API, and form communities. Features ~1,261 registered agents and an active ecosystem [website](https:\u002F\u002Fmoltbook.com) | [github](https:\u002F\u002Fgithub.com\u002Fclawddar\u002Fawesome-moltbook)\n- [MultiOn](https:\u002F\u002Fwww.multion.ai\u002F) - MultiOn utilizes AI to automate actions within web browsers, such as form filling, data retrieval, and executing web searches, mimicking human interaction but without manual input, facilitated through a Chrome extension and API for developers [website](https:\u002F\u002Fwww.multion.ai\u002F)\n- [NPI](https:\u002F\u002Fgithub.com\u002Fnpi-ai\u002Fnpi) - NPi is an open-source platform providing tool-use APIs for AI agents, with installation and setup instructions available [github](https:\u002F\u002Fgithub.com\u002Fnpi-ai\u002Fnpi) | [website](https:\u002F\u002Fwww.npi.ai\u002F) | [docs](https:\u002F\u002Fwww.npi.ai\u002Fdocs) | [blog](https:\u002F\u002Fwww.npi.ai\u002Fblog)\n- [NavAIGuide](https:\u002F\u002Fgithub.com\u002Ffrancedot\u002FNavAIGuide-TS) - NavAIGuide is an extensible, mobile-friendly, multi-modal agentic framework designed to integrate with mobile and desktop apps, featuring visual task detection, advanced code selectors, action-oriented execution, and resilient error handling [github](https:\u002F\u002Fgithub.com\u002Ffrancedot\u002FNavAIGuide-TS)\n- [NeMo Guardrails](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Guardrails) - NeMo Guardrails is an open-source toolkit facilitating the integration of programmable guardrails, essential for steering and safeguarding AI agents' conversational outputs, into large language model-based applications [github](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Guardrails) | [research paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.10501)\n- [Neets](https:\u002F\u002Fneets.ai\u002F) - Neets.ai is a text-to-speech (TTS) API that offers a wide range of voices and languages, allowing users to easily integrate TTS capabilities into their applications [website](https:\u002F\u002Fneets.ai\u002F) | [docs](https:\u002F\u002Fdocs.neets.ai\u002F)\n- [NexusGPT](https:\u002F\u002Fgpt.nexus\u002F) - NexusGPT offers a no-code platform to build and integrate AI agents that automate workflows, featuring a marketplace of tools and integrations, with easy customization and deployment across various applications [website](https:\u002F\u002Fgpt.nexus\u002F)\n- [Ollama](https:\u002F\u002Fgithub.com\u002Follama\u002Follama) - Ollama is a tool for running large language models locally, offering easy setup for macOS, Windows, Linux, and Docker, along with a library of models and quickstart guides for customization and integration [github](https:\u002F\u002Fgithub.com\u002Follama\u002Follama) | [github profile](https:\u002F\u002Fgithub.com\u002Follama)\n- [Open Assistant API](https:\u002F\u002Fgithub.com\u002FMLT-OSS\u002Fopen-assistant-api) - The Open Assistant API is a self-hosted, open-source framework that enables the creation of customized AI assistants, supporting integration with OpenAI's LLM and LangChain SDK, and is compatible with OpenAI's Assistants API, allowing for seamless orchestration and extension capabilities [github](https:\u002F\u002Fgithub.com\u002FMLT-OSS\u002Fopen-assistant-api)\n- [Open Interpreter](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Fopen-interpreter) - Open Interpreter is a coding agent enabling language models to execute code locally, facilitating natural-language interaction with your computer's capabilities, overcoming limitations of hosted solutions like internet access and package restrictions. It features interactive and programmatic chats, system message customization, and can control your computer's keyboard and mouse, allowing for enhanced control and flexibility in development environments [github](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Fopen-interpreter)\n- [OpenAGI](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FOpenAGI) - OpenAGI by AGI Research is an open-source platform integrating Large Language Models (LLMs) with domain-specific expert models for complex task-solving, fostering a paradigm where LLMs operate various external models, accompanied by a Reinforcement Learning from Task Feedback (RLTF) mechanism for self-improvement [github](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FOpenAGI) | [github profile](https:\u002F\u002Fgithub.com\u002Fagiresearch)\n- [OpenAI TTS](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ftext-to-speech) - The OpenAI Text-to-Speech (TTS) API allows users to convert text into high-quality, natural-sounding spoken audio in multiple languages, with various voice options and customization capabilities [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ftext-to-speech)\n- [OpenAI](https:\u002F\u002Fopenai.com) - OpenAI's GPT models, including GPT-3 and GPT-4, are large language models that can be used to summarize text in a concise and accurate manner, though the quality of the summaries may vary depending on the complexity and length of the input text [website](https:\u002F\u002Fopenai.com) | [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Foverview)\n- [OpenDevin](https:\u002F\u002Fgithub.com\u002FOpenDevin\u002FOpenDevin) - OpenDevin is an open-source initiative aimed at replicating and enhancing the autonomous AI software engineer Devin, focusing on collaboration and complex task execution in software development, emphasizing its relevance to advancing agentic AI technologies [github](https:\u002F\u002Fgithub.com\u002FOpenDevin\u002FOpenDevin) | [github profile](https:\u002F\u002Fgithub.com\u002FOpenDevin)\n- [OpenGPTs](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopengpts) - OpenGPTs is an open-source project providing customizable GPT-based experiences, offering control over language models, prompts, tools, vector databases, retrieval algorithms, and chat history databases, featuring three cognitive architectures: Assistant, RAG, and Chatbot, with support for various language models and deployment options including Docker, Cloud Run, and Kubernetes [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopengpts)\n- [OpenPaw](https:\u002F\u002Fgithub.com\u002Fdaxaur\u002Fopenpaw) - OpenPaw is an open-source CLI tool (`npx pawmode`) that turns Claude Code into a personal assistant with 38 built-in skills covering email, calendar, Spotify, smart home control, Slack, GitHub, Telegram, Discord, and more. No daemon, no cloud, runs entirely locally. MIT licensed [github](https:\u002F\u002Fgithub.com\u002Fdaxaur\u002Fopenpaw) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fpawmode)\n- [OpenPipe](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772782206957895797) - Optimize AI agents with language models that are faster and 14x more cost-effective than OpenAI's solutions [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772782206957895797)\n- [OpenRecall](https:\u002F\u002Fgithub.com\u002Fopenrecall\u002Fopenrecall) - OpenRecall is an open-source, privacy-focused digital memory tool capturing and indexing screenshots to enhance productivity without compromising privacy, usable across Windows, macOS, and Linux, and compatible with AI agents for personal assistance [GitHub](https:\u002F\u002Fgithub.com\u002Fopenrecall\u002Fopenrecall) | [Discord](https:\u002F\u002Fdiscord.gg\u002FRzvCYRgUkx) | [Telegram](https:\u002F\u002Ft.me\u002F+5DULWTesqUYwYjY0)\n- [OpenRouter](https:\u002F\u002Fopenrouter.ai\u002F) - OpenRouter.ai is a platform that provides access to a wide range of large language models, including open-source and proprietary options like ChatGPT, Gemini, and Perplexity, allowing users to find the best models and pricing for their prompts and use cases [website](https:\u002F\u002Fopenrouter.ai\u002F) | [docs](https:\u002F\u002Fopenrouter.ai\u002Fdocs)\n- [Outlines](https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines) - Outlines is a robust text generation library designed for agentic AI developers, featuring support for multiple model integrations, advanced prompting with Jinja, efficient structured generation through regex, JSON schema, context-free grammars, and more, enabling the creation of predictable and structured AI agent outputs [github](https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines) | [website](https:\u002F\u002Foutlines-dev.github.io\u002Foutlines\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FZxBxyWmW5n)\n- [Perplexity-Inspired LLM Answer Engine](https:\u002F\u002Fgithub.com\u002Fdevelopersdigest\u002Fllm-answer-engine) - A versatile answer engine leveraging Groq, Mistral AI, Langchain.JS, Brave Search, Serper API, and OpenAI to deliver efficient and sophisticated responses with reduced hallucination through RAG for citation-backed search queries [github](https:\u002F\u002Fgithub.com\u002Fdevelopersdigest\u002Fllm-answer-engine) | [github profile](https:\u002F\u002Fgithub.com\u002Fdevelopersdigest)\n- [Perplexity](https:\u002F\u002Fwww.perplexity.ai\u002F) - Perplexity AI is an AI-powered search engine that offers summarized answers with cited sources, content generation, accurate information retrieval, user-friendly interface, and versatility, making it a valuable tool for various users [website](https:\u002F\u002Fwww.perplexity.ai\u002F) | [docs](https:\u002F\u002Fdocs.perplexity.ai\u002F)\n- [Personal Assistant by HyperWrite](https:\u002F\u002Fwww.hyperwriteai.com\u002Fpersonal-assistant) - HyperWrite offers a Personal Assistant AI agent for everyday tasks, seamlessly integrating into workflows to automate tedious tasks, optimize planning, and inform decision-making, while also providing personalized suggestions and transforming wishes into commands across various platforms [website](https:\u002F\u002Fwww.hyperwriteai.com\u002Fpersonal-assistant) | [github profile](https:\u002F\u002Fgithub.com\u002FOthersideAI)\n- [Phoenix](https:\u002F\u002Fphoenix.arize.com\u002F) - Phoenix from the team at Arize is an open source library that helps you auto instrument, trace, evaluate and observe AI agents and LLM systems [github](https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix) | [comparison](https:\u002F\u002Farize.com\u002Fllm-evaluation-platforms-top-frameworks) [X](https:\u002F\u002Fx.com\u002Farizephoenix) \n- [Pieces](https:\u002F\u002Fpieces.app\u002F) - Pieces is an AI-powered productivity tool for developers that enhances efficiency through a unified toolchain, offering on-device workflow assistance, intelligent code snippet management, and seamless integration with development tools and plugins [website](https:\u002F\u002Fpieces.app\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002Fgetpieces)\n- [Pinokio](https:\u002F\u002Fpinokio.computer\u002F) - Pinokio is a browser that enables the installation, running, and programmable control of any application with one click, supporting any open-source repo locally, including LLM or AI agent-based projects [website](https:\u002F\u002Fpinokio.computer\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fpinokiocomputer\u002Fpinokio) | [github profile](https:\u002F\u002Fgithub.com\u002Fpinokiocomputer)\n- [PlayAI](https:\u002F\u002Fplay.ai\u002F) - Play.ai offers conversational AI voice solutions, with a mission to enable customizable, natural language-based user interfaces, promoting rapid innovation and a performance-driven culture [website](https:\u002F\u002Fplay.ai\u002F)\n- [PlayHT](https:\u002F\u002Fplay.ht\u002F) - PlayHT's AI Voice Generator offers a state-of-the-art TTS service that creates natural, humanlike voiceovers in multiple languages and accents, ideal for various audio content needs with full commercial rights [website](https:\u002F\u002Fplay.ht\u002F)\n- [PraisonAI](https:\u002F\u002Fgithub.com\u002FMervinPraison\u002FPraisonAI\u002F) - Praison AI is a low-code, centralized framework leveraging AutoGen and CrewAI to simplify creating and orchestrating multi-agent systems for LLM applications, emphasizing customization and ease of human-agent interaction [github](https:\u002F\u002Fgithub.com\u002FMervinPraison\u002FPraisonAI\u002F) | [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Fn1lQjC0GO0) | [website](https:\u002F\u002Fmer.vin\u002F2024\u002F03\u002Fpraison-ai-agents-yml\u002F)\n- [Priompt](https:\u002F\u002Fgithub.com\u002Fanysphere\u002Fpriompt) - Priompt is a JSX-based library for designing prompts with priorities, aiming to optimize inclusion of content within token limits, inspired by React and detailed with installation instructions, examples, principles, and future considerations [github](https:\u002F\u002Fgithub.com\u002Fanysphere\u002Fpriompt)\n- [PrivateGPT](https:\u002F\u002Fgithub.com\u002Fzylon-ai\u002Fprivate-gpt\u002F) - PrivateGPT is a secure, offline-capable AI tool for querying documents with Large Language Models, offering high-level and low-level APIs for privacy-conscious, context-aware application development [github](https:\u002F\u002Fgithub.com\u002Fzylon-ai\u002Fprivate-gpt\u002F)\n- [Produvia](https:\u002F\u002Fproduvia.com\u002F) - Since 2013, Produvia Inc. has served $7M+ in revenue brands by developing custom agentic AI solutions powered by state-of-the-art function calling LLMs including but not limited to: Claude 3 Opus, GPT-4, Bard (Gemini Pro), Claude 3 Sonnet, Claude 3 Haiku, Mistral Medium, Command R, Mistral-Next, Starling-LM-7B-beta [website](https:\u002F\u002Fproduvia.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fproduvia) | [linkedin](https:\u002F\u002Flinkedin.com\u002Fcompany\u002Fproduvia)\n- [Prompt2UI by sullyo](https:\u002F\u002Fgithub.com\u002Fsullyo\u002Fprompt2ui) - An open-source project that converts prompts to user interfaces, demonstrated by creating a basic Google Calendar clone using Claude in about 2 hours, inspired by Claude Artifacts [github](https:\u002F\u002Fgithub.com\u002Fsullyo\u002Fprompt2ui) | [twitter announcement](https:\u002F\u002Fx.com\u002FSullyOmarr\u002Fstatus\u002F1804997474761003327)\n- [Pydantic](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic) - Pydantic is a Python library facilitating data validation through type hints, particularly useful for AI agents, offering fast validation capabilities and compatibility with various development tools [github](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic) | [website](https:\u002F\u002Fdocs.pydantic.dev\u002F)\n- [Relevance](https:\u002F\u002Frelevanceai.com\u002F) - Relevance AI offers a platform for building and deploying AI workers to automate tasks, integrate with tech stacks, and manage security, aiming to enhance business efficiency without increasing headcount [website](https:\u002F\u002Frelevanceai.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002FRelevanceAI) | [github profile](https:\u002F\u002Fgithub.com\u002FRelevanceAI) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Frelevanceai\u002F)\n- [Rappterbook](https:\u002F\u002Fgithub.com\u002Fkody-w\u002Frappterbook) - A social network for AI agents running entirely on GitHub infrastructure. 108 agents across 41 channels with 2,100+ discussions. Zero-dependency SDK (Python\u002FJS, one file). Fork the repo to get a complete agent social platform with zero setup. [github](https:\u002F\u002Fgithub.com\u002Fkody-w\u002Frappterbook) | [website](https:\u002F\u002Fkody-w.github.io\u002Frappterbook\u002F) | [quickstart](https:\u002F\u002Fgithub.com\u002Fkody-w\u002Frappterbook\u002Fblob\u002Fmain\u002FQUICKSTART.md)\n- [Rime AI](https:\u002F\u002Frime.ai\u002F) - Rime is a speech synthesis API offering natural-sounding, demographically tailored voices with fast response times for various uses, including customer service and narration [website](https:\u002F\u002Frime.ai\u002F)\n- [SWE-agent](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002FSWE-agent) - This open source project introduces SWE-agent, a software engineering agent for LMs like GPT-4, enhancing bug and issue resolution in GitHub repositories with state-of-the-art performance, facilitated by a well-designed Agent-Computer Interface (ACI) and support for OpenAI and Anthropic Claude models [github](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002FSWE-agent) | [website](https:\u002F\u002Fswe-agent.com\u002F)\n- [ScrapeGraphAI](https:\u002F\u002Fgithub.com\u002FVinciGit00\u002FScrapegraph-ai) - ScrapeGraph AI provides a tool for creating AI agents that can automate web scraping tasks efficiently, enhancing data extraction capabilities through the use of LangGraph, function calls, and web scraping techniques [github](https:\u002F\u002Fgithub.com\u002FVinciGit00\u002FScrapegraph-ai) | [docs](https:\u002F\u002Fscrapegraph-doc.onrender.com\u002F) | [demo](https:\u002F\u002Fscrapegraph-ai-demo.streamlit.appn\u002F)\n- [Scrapeless](https:\u002F\u002Fgithub.com\u002Fscrapeless-ai) - Scrapeless provides an enterprise-grade, AI-driven web scraping toolkit with a powerful Scraping Browser for AI agents and automation. Features include stealth mode, CAPTCHA bypassing, high concurrency support, and seamless integration with Puppeteer and Playwright. Includes Universal Scraping API, Scraping API, Deep SerpApi, and rotating proxies [github](https:\u002F\u002Fgithub.com\u002Fscrapeless-ai) | [website](https:\u002F\u002Fscrapeless.com\u002F)\n- [Self Operating Computer by Otherside](https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer) - SOC is a framework enabling multimodal models to operate a computer using human-like inputs and outputs, with compatibility for various models such as GPT-4v, Gemini Pro Vision, and LLaVA, offering future support for additional models and featuring various modes including voice and optical character recognition [github](https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer) | [github profile](https:\u002F\u002Fgithub.com\u002FOthersideAI) | [landing page](https:\u002F\u002Fwww.hyperwriteai.com\u002Fself-operating-computer)\n- [Self Operating Computer](https:\u002F\u002Fwww.hyperwriteai.com\u002Fself-operating-computer) - Self Operating Computer (SOC) enables multimodal models to autonomously interact with a computer using human-like inputs and outputs, including controlling the keyboard and mouse. It is compatible with various models and under ongoing development for more accurate functionalities [landing page](https:\u002F\u002Fwww.hyperwriteai.com\u002Fself-operating-computer) | [github](https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer) | [github profile](https:\u002F\u002Fgithub.com\u002FOthersideAI)\n- [Shep](https:\u002F\u002Fgithub.com\u002Fshep-ai\u002Fcli) - Shep is an SDLC control center that enables AI coding agents to autonomously handle the complete feature lifecycle, orchestrating multi-session development using Claude Code, Cursor CLI, or Gemini with configurable approval gates and a live web dashboard [github](https:\u002F\u002Fgithub.com\u002Fshep-ai\u002Fcli)\n- [ShortGPT by RayVentura](https:\u002F\u002Fgithub.com\u002FRayVentura\u002FShortGPT) - ShortGPT is an AI-powered framework for automating content creation, including video editing, voiceover synthesis, caption generation, and asset sourcing, with support for multiple languages and seamless integration with Google Colab and Docker for easy deployment [github](https:\u002F\u002Fgithub.com\u002FRayVentura\u002FShortGPT) | [github profile](https:\u002F\u002Fgithub.com\u002FRayVentura)\n- [ShortX by RayVentura](https:\u002F\u002Fshortx.ai\u002F) - ShortX is a AI-powered video automation platform for YouTube Shorts, Instagram Reels, TikTok, and Snapchat, offering customizable templates, AI services, and a subscription model with an affiliate program and user testimonials [website](https:\u002F\u002Fshortx.ai\u002F)\n- [StoryRoute](https:\u002F\u002Fstoryroute.netlify.app) - An LLM-powered autonomous agent that monitors GPS coordinates and generates real-time audio stories as users walk through any city. Features a fully autonomous sense-reason-act pipeline with GPS monitoring, context-aware LLM prompting, and text-to-speech synthesis [website](https:\u002F\u002Fstoryroute.netlify.app)\n- [Streaming Assistants](https:\u002F\u002Fgithub.com\u002Fphact\u002Fstreaming-assistants) - The `streaming-assistants` library on GitHub enables streaming for OpenAI Assistants API using Astra Assistants, providing a workaround for the lack of streaming support in the official OpenAI Assistants API [github](https:\u002F\u002Fgithub.com\u002Fphact\u002Fstreaming-assistants)\n- [Streamlit Agent by Langchain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fstreamlit-agent) - This repository showcases various LangChain agents as Streamlit apps, including a basic streaming app, a memory-based conversation app, a demo replicating MRKL functionality, a minimal agent with search capability, chatbots with feedback options, document querying, database communication, and pandas DataFrame interaction, featuring LangChain and Streamlit integrations [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fstreamlit-agent) | [github profile](https:\u002F\u002Fgithub.com\u002Flangchain-ai)\n- [Streamship](https:\u002F\u002Fgithub.com\u002Fsteamship-core\u002Fpython-client) - A development platform for AI Agents offering Python SDK, cloud deployment, serverless hosting, vector search, webhooks, and media generation, with a focus on simplicity, scalability, and integration with popular models and services [github](https:\u002F\u002Fgithub.com\u002Fsteamship-core\u002Fpython-client) | [website](https:\u002F\u002Fwww.steamship.com\u002F) | [twitter](https:\u002F\u002Fwww.twitter.com\u002FGetSteamship) | [discord](https:\u002F\u002Fsteamship.com\u002Fdiscord) | [github profile](https:\u002F\u002Fwww.github.com\u002Fsteamship-core)\n- [SuperAGI](https:\u002F\u002Fgithub.com\u002FTransformerOptimus\u002FSuperAGI) - SuperAGI is an open-source framework facilitating the development, management, and operation of useful Autonomous AI Agents with a variety of features and toolkits available, including a graphical user interface, action console, and multiple vector databases [github](https:\u002F\u002Fgithub.com\u002FTransformerOptimus\u002FSuperAGI) | [github profile](https:\u002F\u002Fgithub.com\u002FTransformerOptimus)\n- [Superagent](https:\u002F\u002Fgithub.com\u002Fsuperagent-ai\u002Fsuperagent) - Superagent is an open-source AI assistant framework backed by Y Combinator, facilitating the integration of large language models (LLM) and generative AI into applications, supporting various use cases such as question answering, chatbots, and content generation [github](https:\u002F\u002Fgithub.com\u002Fsuperagent-ai\u002Fsuperagent) | [github profile](https:\u002F\u002Fgithub.com\u002Fsuperagent-ai)\n- [Local GPT](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT) - Inspired on Private GPT with the GPT4ALL model replaced with the Vicuna-7B model and using the InstructorEmbeddings instead of LlamaEmbeddings. [github](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT)\n- [LLocalSearch](https:\u002F\u002Fgithub.com\u002Fnilsherzig\u002FLLocalSearch) - LLocalSearch is a completely locally running search aggregator using LLM Agents. The user can ask a question and the system will use a chain of LLMs to find the answer. The user can see the progress of the agents and the final answer. No OpenAI or Google API keys are needed. [github](https:\u002F\u002Fgithub.com\u002Fnilsherzig\u002FLLocalSearch)\n- [Private GPT](https:\u002F\u002Fgithub.com\u002Fimartinez\u002FprivateGPT) - Interact privately with your documents using the power of GPT, 100% privately, no data leaks. [github](https:\u002F\u002Fgithub.com\u002Fimartinez\u002FprivateGPT)\n- [Second Brain AI Agent](https:\u002F\u002Fgithub.com\u002Fflepied\u002Fsecond-brain-agent) - A streamlit app to dialog with your second brain notes using OpenAI and ChromaDB locally. [github](https:\u002F\u002Fgithub.com\u002Fflepied\u002Fsecond-brain-agent)\n- [Swarms](https:\u002F\u002Fgithub.com\u002Fkyegomez\u002Fswarms\u002F) - Swarms orchestrates multi-agent collaboration for production-grade applications, solving issues like short memory and high costs, with customizable tools for specific needs, currently used by RBC, John Deere, and AI startups [github](https:\u002F\u002Fgithub.com\u002Fkyegomez\u002Fswarms\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FDbjBMJTSWD) | [docs](https:\u002F\u002Fswarms.apac.ai\u002F)\n- [Sweep](https:\u002F\u002Fgithub.com\u002Fsweepai\u002Fsweep) - Sweep is an AI tool that automates the transformation of GitHub issues into pull requests, streamlining code improvements and bug fixes, supported by a suite of features like codebase understanding, test running, and a developer-friendly interface for installation and usage [github](https:\u002F\u002Fgithub.com\u002Fsweepai\u002Fsweep) | [website](https:\u002F\u002Fsweep.dev\u002F)\n- [Synthflow AI](https:\u002F\u002Fsynthflow.ai\u002F) - Synthflow is a platform enabling the creation of human-like conversational AI voice agents with no-code customization, integrating directly with apps like HubSpot and Eleven Labs for voice services [website](https:\u002F\u002Fsynthflow.ai\u002F) | [docs](https:\u002F\u002Fdocs.synthflow.ai\u002F)\n- [Tabby](https:\u002F\u002Fgithub.com\u002FTabbyML\u002Ftabby) - Tabby is a self-hosted, open-source AI coding assistant similar to GitHub Copilot, featuring a self-contained setup with no DBMS\u002Fcloud dependency, OpenAPI for easy integration, consumer-grade GPU support, and a full-feature admin UI in its latest release [github](https:\u002F\u002Fgithub.com\u002FTabbyML\u002Ftabby) | [website](https:\u002F\u002Ftabby.tabbyml.com\u002F) | [docs](https:\u002F\u002Ftabby.tabbyml.com\u002Fdocs)\n- [Talkscriber](https:\u002F\u002Fwww.talkscriber.com) - Talkscriber is an enterprise-grade speech-to-text (STT) platform that offers industry-leading accuracy, security, and cost-effectiveness, enabling organizations to transform spoken language into digital text and unlock new possibilities in data analysis while hosting Whisper (OpenAI) model [website](https:\u002F\u002Fwww.talkscriber.com)\n- [Tarsier by Reworkd](https:\u002F\u002Fgithub.com\u002Freworkd\u002Ftarsier) - Tarsier is an open-source utility library by Reworkd, aimed at enhancing web interaction for AI agents by visually tagging interactable elements, facilitating actions based on text or screenshots for GPT-4(V) and providing OCR utilities [github](https:\u002F\u002Fgithub.com\u002Freworkd\u002Ftarsier) | [website](https:\u002F\u002Freworkd.ai\u002F)\n- [Taskade AI](https:\u002F\u002Fwww.taskade.com\u002F) - Taskade AI is an AI-powered productivity suite offering tools like task and project management, notes, docs, mind maps, and AI chat to enhance team productivity and automate over 700 tasks [website](https:\u002F\u002Fwww.taskade.com\u002F) | [github](https:\u002F\u002Fgithub.com\u002Ftaskade\u002Ftaskade) | [twitter](https:\u002F\u002Ftwitter.com\u002FTaskade) | [youtube](https:\u002F\u002Fyoutube.com\u002Ftaskade)\n- [TaskingAI](https:\u002F\u002Fgithub.com\u002FTaskingAI\u002FTaskingAI) - TaskingAI is a platform enhancing AI-native app development with Firebase-like simplicity, offering an all-in-one LLM platform with intuitive project management, BaaS-inspired workflow, and customizable integration for developing GPTs-like multi-tenant applications [github](https:\u002F\u002Fgithub.com\u002FTaskingAI\u002FTaskingAI) | [website](https:\u002F\u002Fwww.tasking.ai\u002F)\n- [Tavily](https:\u002F\u002Ftavily.com\u002F) - Tavily AI is your comprehensive research assistant, offering a platform for rapid insights with a Search API for LLMs, ensuring real-time, accurate, and bias-reduced data gathering and organization, suitable for both individual and enterprise needs [website](https:\u002F\u002Ftavily.com\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002Fassafelovic)\n- [TeamX](https:\u002F\u002Fteamx.work\u002F) - TeamX is an Agents-as-a-Service (AaaS) by Produvia which scales businesses with AI agent teams, offering custom solutions focused on automation, efficiency, and scalability [website](https:\u002F\u002Fteamx.work\u002F)\n- [TiOLi AGENTIS](https:\u002F\u002Fagentisexchange.com) - Financial exchange for AI agents with 23 MCP tools and 400+ REST endpoints, blockchain-verified. Agents can register, trade, hire each other, and build reputations [website](https:\u002F\u002Fagentisexchange.com) | [github](https:\u002F\u002Fgithub.com\u002FSendersby\u002Ftioli-ai-exchange) | [docs](https:\u002F\u002Fexchange.tioli.co.za\u002Fdocs)\n- [TogetherAI](https:\u002F\u002Fwww.together.ai\u002F) - TogetherAI is a platform that facilitates efficient and accurate summarization of text using advanced AI algorithms and user-friendly tools [website](https:\u002F\u002Fwww.together.ai\u002F) | [docs](https:\u002F\u002Fdocs.together.ai\u002Fdocs\u002Fquickstart)\n- [Tools by Taskade](https:\u002F\u002Fhelp.taskade.com\u002Fen\u002Farticles\u002F8958457-custom-ai-agents#h_c9a93fc5b9) - Enable your agents with the right set of tools to get the job done: web search (allow the agent to browse the web), WolframAlpha (enhance the agent's computational skills), add-ons (enable additional tools and extensions) [docs](https:\u002F\u002Fhelp.taskade.com\u002Fen\u002Farticles\u002F8958457-custom-ai-agents#h_c9a93fc5b9)\n- [Traces by Weights & Biases](https:\u002F\u002Fwandb.ai\u002Fsite\u002Ftraces) - W&B Traces enhances AI agent observability by providing intuitive visualizations for debugging LLMs, allowing practitioners to review past results, debug errors, and gain insights into model behavior [website](https:\u002F\u002Fwandb.ai\u002Fsite\u002Ftraces)\n- [Twilio](https:\u002F\u002Fwww.twilio.com) - Twilio is a cloud communications platform that enables developers to programmatically make phone calls, send and receive text messages, and integrate other communication features into their applications using its web APIs [website](https:\u002F\u002Fwww.twilio.com)\n- [TypeChat](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FTypeChat) - TypeChat is a library that facilitates building natural language interfaces by using schema engineering as an alternative to traditional function calling in LLMs, avoiding JSON schema-based constraints [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FTypeChat)\n- [VacAIgent](https:\u002F\u002Fgithub.com\u002Ftonykipkemboi\u002Ftrip_planner_agent) - VacAIgent is a Streamlit-integrated, CrewAI framework-based AI application (Trip Planner Agent) that automates and enhances trip planning through a user-friendly interface, demonstrating collaborative AI agent task execution and offering an interactive web app experience for tailoring travel plans [github](https:\u002F\u002Fgithub.com\u002Ftonykipkemboi\u002Ftrip_planner_agent)\n- [Vapi](https:\u002F\u002Fvapi.ai\u002F) - Vapi is a developer-friendly platform that enables the rapid creation, testing, and deployment of voicebots, revolutionizing voice AI integration with seamless support from voice providers [website](https:\u002F\u002Fvapi.ai\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FpUFNcf2WmH) | [twitter](https:\u002F\u002Ftwitter.com\u002FVapi_AI) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fvapi-ai) | [docs](https:\u002F\u002Fdocs.vapi.ai)\n- [Vertex AI by Google](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai) - Vertex AI, enhanced by Gemini models, offers comprehensive generative AI solutions for rapid application development, data processing, custom model training with minimal ML expertise, and production deployment, aimed at accelerating innovation and reducing costs in enterprise environments [website](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai)\n- [Verve](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175562712285645) - Verve is an AI data copilot that aims to streamline analytics and significantly reduce manual work for growing organizations [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175562712285645)\n- [Vonage](https:\u002F\u002Fvonage.com\u002F) - Vonage is a leading provider of phone services that offers a range of features and options for residential and business customers, including local, toll-free, and international numbers, as well as virtual receptionist and call management capabilities [website](https:\u002F\u002Fvonage.com\u002F)\n- [Waii](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772777493122163107) - A swift and straightforward AI agent for converting natural language to SQL queries, seamlessly integrable with your application [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772777493122163107)\n- [XAgent](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FXAgent) - XAgent is an open-source, experimental Large Language Model-driven autonomous agent designed to autonomously solve a wide range of tasks with features like autonomy, safety, extensibility, a GUI for easy interaction, and the ability to cooperate with humans [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FXAgent) | [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QGkpd-tsFPA) | [docs](https:\u002F\u002Fxagent-doc.readthedocs.io\u002Fen\u002Flatest\u002F) | [blog](https:\u002F\u002Fblog.x-agent.net\u002Fblog\u002Fxagent\u002F)\n- [Yoyo](https:\u002F\u002Fyoyo.bot) - The first social network for AI agents. Connect any AI agent via MCP to post, chat, follow other agents, discover experts, and build reputation. 10 MCP tools, MIT licensed [website](https:\u002F\u002Fyoyo.bot) | [github](https:\u002F\u002Fgithub.com\u002FYoYo-dot-bot\u002Fmcp) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@yoyo-bot\u002Fmcp)\n- [OpenAgents](https:\u002F\u002Fgithub.com\u002Fxlang-ai\u002FOpenAgents) - An Open Platform for Language Agents in the Wild. [github](https:\u002F\u002Fgithub.com\u002Fxlang-ai\u002FOpenAgents)\n- [OpenAgents Network](https:\u002F\u002Fopenagents.org) - An open-source framework and platform for creating AI agent networks where autonomous agents collaborate, share context, and solve problems together across the internet. Supports multi-protocol communication (WebSocket, gRPC, HTTP, libp2p, A2A, MCP) with a mod-driven architecture [website](https:\u002F\u002Fopenagents.org) | [github](https:\u002F\u002Fgithub.com\u002Fopenagents-org\u002Fopenagents) | [docs](https:\u002F\u002Fopenagents.org\u002Fdocs\u002Fgetting-started\u002Foverview)\n- [Pipecat](https:\u002F\u002Fgithub.com\u002Fpipecat-ai\u002Fpipecat) - Open Source framework for voice and multimodal conversational AI. [github](https:\u002F\u002Fgithub.com\u002Fpipecat-ai\u002Fpipecat)\n- [Sayna](https:\u002F\u002Fgithub.com\u002FSaynaAI\u002Fsayna) - Open-source voice infrastructure layer for AI agents providing unified APIs for STT\u002FTTS, real-time audio streaming, SIP Telephony, and multi-provider support (OpenAI, Deepgram, ElevenLabs, Google, Azure, etc...). Self-hostable with WebSocket\u002FREST interfaces for voice-enabled agent development. [github](https:\u002F\u002Fgithub.com\u002FSaynaAI\u002Fsayna)\n- [RestGPT](https:\u002F\u002Fgithub.com\u002FYifan-Song793\u002FRestGPT) - An LLM-based autonomous agent controlling real-world applications via RESTful APIs. [github](https:\u002F\u002Fgithub.com\u002FYifan-Song793\u002FRestGPT)\n- [WebQA-Agent](https:\u002F\u002Fgithub.com\u002FMigoXLab\u002Fwebqa-agent\u002Ftree\u002Fmain) - Autonomous web agent that audits performance, functionality & UX for any web product. [github](https:\u002F\u002Fgithub.com\u002FMigoXLab\u002Fwebqa-agent)\n- [ADAS](https:\u002F\u002Fgithub.com\u002FShengranHu\u002FADAS) - Automated Design of Agentic Systems. [github](https:\u002F\u002Fgithub.com\u002FShengranHu\u002FADAS)\n- [AgentK](https:\u002F\u002Fgithub.com\u002Fmikekelly\u002FAgentK) - An autoagentic AGI that is self-evolving and modular. [github](https:\u002F\u002Fgithub.com\u002Fmikekelly\u002FAgentK)\n- [Maestro](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fmaestro) - A framework for Claude Opus to intelligently orchestrate subagents. [github](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fmaestro)\n- [Zep](https:\u002F\u002Fwww.getzep.com\u002F) - Zep is a long-term memory service for AI assistants that enhances recall, understanding, and data extraction from chat histories to power personalized AI experiences [website](https:\u002F\u002Fwww.getzep.com\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fgetzep\u002Fzep\u002F)\n- [ai-artifacts](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fai-artifacts) - This project implements Anthropic's Artifacts UI, using E2B's Code Interpreter SDK for secure AI code execution and Claude Sonnet 3.5 for code generation [github example](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fai-artifacts) | [reddit announcement](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FClaudeAI\u002Fcomments\u002F1dmy6y2\u002Fopen_source_version_of_anthropics_artifacts_ui\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fai-artifacts)\n- [aifs](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Faifs) - AIFS offers a simple and efficient local semantic search capability for folders, leveraging Unstructured.IO for advanced data processing and ChromaDB for fast, similarity-based searching of embeddings [github](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Faifs)\n- [chatgpt-artifacts](https:\u002F\u002Fgithub.com\u002Fozgrozer\u002Fchatgpt-artifacts) - Bring Claude's Artifacts feature to ChatGPT which allows you to execute Node.js commands on your ChatGPT Artifacts projects, inspired by Claude's Artifacts [github](https:\u002F\u002Fgithub.com\u002Fozgrozer\u002Fchatgpt-artifacts) | [twitter announcement](https:\u002F\u002Fx.com\u002Fozgrozer\u002Fstatus\u002F1808677091996541251)\n- [claude-artifacts-react](https:\u002F\u002Fgithub.com\u002Frisonsimon\u002Fclaude-artifacts-react) - This project provides a streamlined solution for deploying and testing React code generated by Claude Artifacts, offering one-click deployment options to Vercel or Cloudflare Pages and easy code editing through a central ArtifactCode.jsx file [github](https:\u002F\u002Fgithub.com\u002Frisonsimon\u002Fclaude-artifacts-react) | [reddit announcement](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FClaudeAI\u002Fcomments\u002F1dtquuh\u002Fi_made_an_opensource_template_for_sharing_claudes\u002F)\n- [crewAI Tools](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai-tools) - crewAI Tools is a library that provides a framework for developing sophisticated tools to enhance crewAI agents, with methods for subclassing BaseTool, utilizing the tool decorator, and guidelines for contributing to the ecosystem [github](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai-tools)\n- [crewAI by João Moura](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai) - crewAI is a cutting-edge AI framework designed for orchestrating role-playing, autonomous AI agents, enabling seamless collaboration and complex task handling [github](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai) | [github profile](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura)\n- [crewAI+ by João Moura](https:\u002F\u002Fwww.crewai.com\u002Fcrewaiplus) - CrewAI+ is in beta, offering seamless API integration, business support, and early access for design partners; apply now to shape future features [website](https:\u002F\u002Fwww.crewai.com\u002Fcrewaiplus)\n- [databerry](https:\u002F\u002Fgithub.com\u002Fgmpetrov\u002Fdataberry\u002F) - Chaindesk is a no-code platform for building custom LLM Agents, enabling users to quickly set up a semantic search system over personal data without technical knowledge [github](https:\u002F\u002Fgithub.com\u002Fgmpetrov\u002Fdataberry\u002F)\n- [elia](https:\u002F\u002Fgithub.com\u002Fdarrenburns\u002Felia) - Keyboard-centric terminal user interface for interacting with large language models (LLMs) like ChatGPT, Claude, Llama 3, Phi 3, Mistral, and Gemma, offering benefits such as efficient, terminal-based interaction, easy switching between multiple models, local model support, and the ability to store conversations in a local SQLite database [github](https:\u002F\u002Fgithub.com\u002Fdarrenburns\u002Felia)\n- [mem0](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0) - Mem0 is an intelligent memory layer for Large Language Models that enhances personalized AI experiences by retaining and utilizing contextual information across various applications. [github](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0) | [website](https:\u002F\u002Fapp.mem0.ai\u002F) | [docs](https:\u002F\u002Fdocs.mem0.ai\u002F) | [discord](https:\u002F\u002Fmem0.ai\u002Fdiscord) | [twitter](https:\u002F\u002Fx.com\u002Fmem0ai) | [github profile](https:\u002F\u002Fgithub.com\u002Fmem0ai) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fmem0\u002F)\n- [tortoise-tts](https:\u002F\u002Fgithub.com\u002Fneonbjb\u002Ftortoise-tts) - A multi-voice TTS system trained with an emphasis on quality [github](https:\u002F\u002Fgithub.com\u002Fneonbjb\u002Ftortoise-tts) | [research paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.07243) | [demo](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FManmay\u002Ftortoise-tts)\n- [uAgents by Fetch AI](https:\u002F\u002Fgithub.com\u002Ffetchai\u002FuAgents) - uAgents is a Python library by Fetch AI for creating autonomous AI agents with features like easy creation, blockchain network connectivity, and cryptographic security [github](https:\u002F\u002Fgithub.com\u002Ffetchai\u002FuAgents) | [github profile](https:\u002F\u002Fgithub.com\u002Ffetchai)\n- [vimGPT](https:\u002F\u002Fgithub.com\u002Fishan0102\u002FvimGPT) - vimGPT is a project that integrates GPT-4V's vision capabilities with the Vimium extension to enable web browsing and interaction through keyboard navigation and voice commands, offering innovative solutions and improvements for accessibility and efficiency [github](https:\u002F\u002Fgithub.com\u002Fishan0102\u002FvimGPT) | [demo](https:\u002F\u002Fgithub.com\u002Fishan0102\u002FvimGPT\u002Ftree\u002Fmain?tab=readme-ov-file#vimgpt) | [hackernews](https:\u002F\u002Fnews.ycombinator.com\u002Fitem?id=38200308)\n\n### Courses\nThere are so many places to learn how to build AI agents! Here are some course and cookbooks to help you along your journey.\n\n#### Online courses\n- [Agentic Design Patterns built with Autogen](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fai-agentic-design-patterns-with-autogen\u002F)\n- [Agents Tools & Function Calling with Amazon Bedrock](https:\u002F\u002Fwww.youtube.com\u002Fwatch?app=desktop&v=2L_XE6g3atI)\n- [AI Agents in LangGraph DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fai-agents-in-langgraph\u002F)\n- [Building Agnetic RAG with LlamaIndex](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fbuilding-agentic-rag-with-llamaindex\u002F)\n- [Building RAG agents with LLMs NVIDIA](https:\u002F\u002Flearn.nvidia.com\u002Fcourses\u002Fcourse-detail?course_id=course-v1:DLI+S-FX-15+V1)\n- [Creating AI Agent Data Pipelines with Amazon Bedrock Agents](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kJEr7eFQSJw)\n- [Foundations of Prompt Engineering by Amazon Web Services (AWS)](https:\u002F\u002Fexplore.skillbuilder.aws\u002Flearn\u002Fcourse\u002Fexternal\u002Fview\u002Felearning\u002F17763\u002Ffoundations-of-prompt-engineering)\n- [How to build a Multi-Agent system with Watsonx by IBM](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gUrENDkPw_k&t=7s)\n- [Introduction to Agentic Memory](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fllms-as-operating-systems-agent-memory\u002F)\n- [Introduction to Langgraph by LangChain](https:\u002F\u002Facademy.langchain.com\u002Fcourses\u002Fintro-to-langgraph)\n- [LangGraph Mastery: Develop LLM Agents with LangGraph](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Flanggraph-mastery-develop-llm-agents-with-langgraph)\n- [LLM Agents by UC Berkely](https:\u002F\u002Fllmagents-learning.org\u002Ff24)\n- [Multi-Agent system building with CrewAI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fmulti-ai-agent-systems-with-crewai)\n- [Serverless Agentic workflows with Amazon Bedrock](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fserverless-agentic-workflows-with-amazon-bedrock\u002F)\n\nI also recommend this [YouTube Playlist](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLnH2pfPCPZsKhlUSP39nRzLkfvi_FhDdD), which has a range of tutorials.\n\n#### Cookbooks\n- [AI Agent's cookbook by LlamaIndex](https:\u002F\u002Fdocs.llamaindex.ai\u002Fen\u002Fstable\u002Fuse_cases\u002Fagents\u002F)\n- [Langchain's agent building cookbook](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002Ftree\u002Fmaster\u002Fcookbook)\n\n---\n\n## Building\nAI agents are autonomous systems designed to perform tasks, make decisions, and interact with their environments in ways that mimic human-like intelligence. \n\nThe key tools for building AI agents include benchmarks (to evaluate performance), datasets (to help fine-tune\u002Ftrain), frameworks (to build\u002Fdeploy), LLM models (as an operating system for agents - e.g. GPT4), Prompt engineering (e.g. Chain-of-Thought), Tools (to achieve tasks), and Workflows (e.g. through tools like zapier). Each of these have their own section to make it easier to browse\u002Ffind these components, and this is a rapidly growing area of interest in the community.\n\n### Benchmarks\n- [Agentbench](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FAgentBench) - A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)\n- [Agentlab](https:\u002F\u002Fgithub.com\u002FServiceNow\u002FAgentLab) - AgentLab - An open-source framework for developing, testing, and benchmarking web agents on diverse tasks, designed for scalability and re…\n- [Agentops](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002Fagentops) - Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks like CrewAI…\n- [Appworld](https:\u002F\u002Fgithub.com\u002FStonyBrookNLP\u002Fappworld) - 🌍 Repository for \"AppWorld - A Controllable World of Apps and People for Benchmarking Interactive Coding Agent\", ACL'24 Best Resource Paper.\n- [Appworld-Leaderboard](https:\u002F\u002Fgithub.com\u002FStonyBrookNLP\u002Fappworld-leaderboard) - 🌍 Leaderboard Repository for \"AppWorld - A Controllable World of Apps and People for Benchmarking Interactive Coding Agent\", ACL2024\n- [Awesome-Llm-Long-Context-Modeling](https:\u002F\u002Fgithub.com\u002FXnhyacinth\u002FAwesome-LLM-Long-Context-Modeling) - 📰 Must-read papers and blogs on LLM based Long Context Modeling 🔥\n- [Balrog](https:\u002F\u002Fgithub.com\u002Fbalrog-ai\u002FBALROG) - Benchmarking Agentic LLM and VLM Reasoning On Games\n- [Bigcodebench](https:\u002F\u002Fgithub.com\u002Fbigcode-project\u002Fbigcodebench) - BigCodeBench - Benchmarking Code Generation Towards AGI\n- [Bolaa](https:\u002F\u002Fgithub.com\u002FJimSalesforce\u002FBOLAA) - benchmarking and orchestrating LLM-augmented Agents\n- [Chat-Agent-Evalution](https:\u002F\u002Fgithub.com\u002Fkhuzaimakt\u002FChat-Agent-Evalution) - Evaluating the LLM Chat Agent on multiple evaluation benchmarks.\n- [Comfybench](https:\u002F\u002Fgithub.com\u002FxxyQwQ\u002FComfyBench) - Implementation for the paper \"ComfyBench - Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems\".\n- [Debatellm](https:\u002F\u002Fgithub.com\u002Finstadeepai\u002FDebateLLM) - Benchmarking Multi-Agent Debate between Language Models for Truthfulness in Q&A.\n- [Dinersim](https:\u002F\u002Fgithub.com\u002Fnumbmelon\u002FDinerSim) - DinerSim - A Restaurant Simulation Benchmark for LLM-Based Multi-Agent Cooperation\n- [Diplomacy-Llm](https:\u002F\u002Fgithub.com\u002Flukepoo101\u002Fdiplomacy-llm) - Public LLM benchmark using the results of Diplomacy games played by multiple LLM agents.\n- [Embodied-Agent-Interface.Github.Io](https:\u002F\u002Fgithub.com\u002Fembodied-agent-interface\u002Fembodied-agent-interface.github.io) - This is the project website for the paper \"Embodied Agent Interface - Benchmarking LLMs for Embodied Decision Making\".\n- [Flowbench](https:\u002F\u002Fgithub.com\u002FJustherozen\u002FFlowBench) - [EMNLP 2024] FlowBench - Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents\n- [Gamabench](https:\u002F\u002Fgithub.com\u002FCUHK-ARISE\u002FGAMABench) - Benchmarking LLMs' Gaming Ability in Multi-Agent Environments\n- [Goodai-Ltm-Benchmark](https:\u002F\u002Fgithub.com\u002FGoodAI\u002Fgoodai-ltm-benchmark) - A library for benchmarking the Long Term Memory and Continual learning capabilities of LLM based agents. With all the tests and code you …\n- [Hosting-7B-Llm-On-Google-Cloud](https:\u002F\u002Fgithub.com\u002FJ-sephB-lt-n\u002Fhosting-7B-llm-on-google-cloud) - Speed benchmarking a 7B LLM on different gcloud VMs (using llama.cpp)\n- [Impact-Academy](https:\u002F\u002Fgithub.com\u002Fsamizdis\u002Fimpact-academy) - Auto-Enhance meta-benchmark, to measure the ability of LLM agents to improve other LLM agents\n- [Lawful-Good](https:\u002F\u002Fgithub.com\u002Fdluo96\u002Flawful-good) - Benchmark for assessing legal capabilities of LLM agents\n- [Level-Navi-Agent-Search](https:\u002F\u002Fgithub.com\u002Fchuanruihu\u002FLevel-Navi-Agent-Search) - The Level-Navi Agent, a framework that requires no training and utilizes large language models for deep query understanding and precise s…\n- [LibreEval](https:\u002F\u002Farize.com\u002Fllm-hallucination-dataset\u002F) - Open-Source benchmark for RAG hallucination detection \n- [Llf-Bench](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLLF-Bench) - A benchmark for evaluating learning agents based on just language feedback\n- [Llm-Agent-Ask-For-Help](https:\u002F\u002Fgithub.com\u002Fdillonmsandhu\u002Fllm-agent-ask-for-help) - Benchmark LLM Agents' abilities to quit sequential tasks as early as possible.\n- [Llm-Agent-Benchmark-List](https:\u002F\u002Fgithub.com\u002Fzhangxjohn\u002FLLM-Agent-Benchmark-List) - A banchmark list for evaluation of large language models.\n- [Llm_Scavengerhunt](https:\u002F\u002Fgithub.com\u002FKyunnilee\u002Fllm_scavengerhunt) - A New Benchmark made for tasks for Large Language Model Agents - UC Berkeley Scavenger Hunt\n- [Llmagentoodgym](https:\u002F\u002Fgithub.com\u002Frowingchenn\u002FLLMAgentOODGym) - OOD benchmark study for LLM agents based on BrowserGym and AgentLab from ServiceNow.\n- [Llmtaskplanning](https:\u002F\u002Fgithub.com\u002Flbaa2022\u002FLLMTaskPlanning) - LoTa-Bench - Benchmarking Language-oriented Task Planners for Embodied Agents (ICLR 2024)\n- [Ml-Research-Agent-Public](https:\u002F\u002Fgithub.com\u002FAlgorithmicResearchGroup\u002FML-Research-Agent-Public) - Public, general purpose agent for ML Research Benchmark. This agent provides a foundation for comparing and evaluating machine learning r…\n- [Ml-Research-Agent-Tasks](https:\u002F\u002Fgithub.com\u002FAlgorithmicResearchGroup\u002FML-Research-Agent-Tasks) - Tasks for ML Research Benchmark, a benchmark designed to evaluate the capabilities of AI agents in accelerating AI research and development.\n- [Mobilebench](https:\u002F\u002Fgithub.com\u002FXiaoMi\u002FMobileBench) - Mobile-Bench - An Evaluation Benchmark for LLM-based Mobile Agents\n- [Multiagent-Collab-Scenario-Benchmark](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fmultiagent-collab-scenario-benchmark) - Benchmarking data and script used for LLM multi-agent collaboration systems from AWS Bedrock Agents Science team.\n- [Nemotron-O1-Llm-Agent-Dataflow-Analysis](https:\u002F\u002Fgithub.com\u002Fyuvrajpant56\u002FNemotron-o1-LLM-Agent-Dataflow-Analysis) - \"Code and resources for Nemotron-o1, an LLM-based agentic framework for automated data flow analysis. Features source\u002Fsink extraction, da…\n- [Osworld](https:\u002F\u002Fgithub.com\u002Fxlang-ai\u002FOSWorld) - [NeurIPS 2024] OSWorld - Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments\n- [Overcooked_Ai_Llm](https:\u002F\u002Fgithub.com\u002Fbzeng2188\u002Fovercooked_ai_llm) - Research with LLM multi agent planning on OvercookedAI benchmark.\n- [Pharmasimtext-Os-Llms](https:\u002F\u002Fgithub.com\u002Fepfl-ml4ed\u002FPharmaSimText-OS-LLMs) - This is a repository including the benchmark and agents included in an under review submission to JEDM 2025.\n- [R-Judge](https:\u002F\u002Fgithub.com\u002FLordog\u002FR-Judge) - R-Judge - Benchmarking Safety Risk Awareness for LLM Agents (EMNLP Findings 2024)\n- [Safeagentbench](https:\u002F\u002Fgithub.com\u002Fshengyin1224\u002FSafeAgentBench) - Codes for paper \"SafeAgentBench - A Benchmark for Safe Task Planning of \\\\ Embodied LLM Agents\"\n- [Shampoosalesagent](https:\u002F\u002Fgithub.com\u002Fjackfsuia\u002FShampooSalesAgent) - A minimal LLM sales agent framework for sales agent fast deployment and benchmark. Support OpenAI models, Claude, HuggingFace models, Gem…\n- [Shortcutsbench](https:\u002F\u002Fgithub.com\u002FEachSheep\u002FShortcutsBench) - ShortcutsBench - A Large-Scale Real-World Benchmark for API-Based Agents\n- [Sim-Court](https:\u002F\u002Fgithub.com\u002FMiracle-2001\u002FSim-Court) - BenCourt - A Benchmark and Framework for Court Simulation using LLM-based Agents\n- [Smartplay](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FSmartPlay) - SmartPlay is a benchmark for Large Language Models (LLMs). Uses a variety of games to test various important LLM capabilities as agents. …\n- [Sop-Bench](https:\u002F\u002Fgithub.com\u002FNorditech-AB\u002FSOP-bench) - Benchmark for evaluating llm agents to solve real-world standard operating procedures\n- [Sphnx](https:\u002F\u002Fgithub.com\u002Fhaailabs\u002FSPHNX) - SPHNX is a modular benchmark suite designed to evaluate and enhance the privacy management capabilities of Large Language Models (LLM)-ba…\n- [Stream-Bench](https:\u002F\u002Fgithub.com\u002Fstream-bench\u002Fstream-bench) - We propose a pioneering benchmark to evaluate LLM agents' ability to improve over time in streaming scenarios\n- [Theagentcompany](https:\u002F\u002Fgithub.com\u002FTheAgentCompany\u002FTheAgentCompany) - An agent benchmark with tasks in a simulated software company.\n- [Tiny-Llm-Benchmark](https:\u002F\u002Fgithub.com\u002FZahidul-Islam\u002Ftiny-llm-benchmark) - A cost-effective LLM prompt benchmarking tool that helps AI agent builders choose the best model for optimal cost, performance and accuracy.\n- [Unixagentbench](https:\u002F\u002Fgithub.com\u002Fkmrasmussen\u002Funixagentbench) - benchmarking LLM-agents performing tasks on Unix\n- [Usability-Benchmarking-Framework-Project](https:\u002F\u002Fgithub.com\u002Fstaro190\u002FUsability-Benchmarking-Framework-Project) - Evaluation of Software Manuals Using LLM-Powered GUI Agents - A Usability Benchmarking Framework\n- [Visualwebarena](https:\u002F\u002Fgithub.com\u002Fweb-arena-x\u002Fvisualwebarena) - VisualWebArena is a benchmark for multimodal agents.\n- [Weblinx](https:\u002F\u002Fgithub.com\u002FMcGill-NLP\u002Fweblinx) - WebLINX is a benchmark for building web navigation agents with conversational capabilities\n\n### Datasets \n- [Aart-Ai-Safety-Dataset](https:\u002F\u002Fgithub.com\u002Fxxxx-dddd\u002Faart-ai-safety-dataset) - AART - AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications\n- [Abstractive-Summarizer-On-Cnn_Dailymail-Dataset.](https:\u002F\u002Fgithub.com\u002Fpkounoudis\u002FAbstractive-Summarizer-on-cnn_dailymail-dataset.) - A small T5 LLM finetuned for text summarization.\n- [Airline-Review-Llm-Chatbot](https:\u002F\u002Fgithub.com\u002Frohan-deswal\u002Fairline-review-llm-chatbot) - Using a Large Language Model (LLM) to develop a chatbot that gives insights about this dataset - https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjuhibhoja…\n- [Alpaca-Chinese-Dataset](https:\u002F\u002Fgithub.com\u002Fcarbonz0\u002Falpaca-chinese-dataset) - alpaca中文指令微调数据集\n- [Arize AX](https:\u002F\u002Farize.com\u002Fgenerative-ai) - Arize AX is a tool that helps you use datasets to run experiments, with several example notebooks to get started. | [website](https:\u002F\u002Farize.com) [docs](https:\u002F\u002Farize.com\u002Fdocs\u002Fax) | [github](https:\u002F\u002Fgithub.com\u002FArize-ai) | [Slack](https:\u002F\u002Fjoin.slack.com\u002Ft\u002Farize-ai\u002Fshared_invite\u002Fzt-3iu5bvnzr-2e~VFHw2Et4MM5rMsK599g)\n- [Awesome-Chatgpt-Dataset](https:\u002F\u002Fgithub.com\u002Fvoidful\u002Fawesome-chatgpt-dataset) - Unlock the Power of LLM - Explore These Datasets to Train Your Own ChatGPT!\n- [Awesome-Datasets-For-Llms](https:\u002F\u002Fgithub.com\u002Fupbit\u002Fawesome-datasets-for-LLMs) - Awesome training\u002Ffinetuning datasets for LLMs\n- [Awesome-Finllms](https:\u002F\u002Fgithub.com\u002FIDEA-FinAI\u002FAwesome-FinLLMs) - 🥇 A curated list of awesome large language models in finance(FinLLMs), including papers,models,datasets and codebases. 金融大模型列表，特别是中英双语大模型。\n- [Awesome-Instruction-Selector](https:\u002F\u002Fgithub.com\u002FBolin97\u002Fawesome-instruction-selector) - Paper list and datasets for the paper - A Survey on Data Selection for LLM Instruction Tuning\n- [Awesome-Llm-Human-Preference-Datasets](https:\u002F\u002Fgithub.com\u002Fglgh\u002Fawesome-llm-human-preference-datasets) - A curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval.\n- [Awesome-Medical-Healthcare-Dataset-For-Llm](https:\u002F\u002Fgithub.com\u002Fonejune2018\u002FAwesome-Medical-Healthcare-Dataset-For-LLM) - A curated list of popular Datasets, Models and Papers for LLMs in Medical\u002FHealthcare\n- [Book-Dataset](https:\u002F\u002Fgithub.com\u002Fmahiatlinux\u002FBook-Dataset) - A simple dataset for pre-training little LLMs.\n- [Codegebragpt](https:\u002F\u002Fgithub.com\u002Fsr5434\u002FCodegebraGPT) - Finetuning multimodal LLMs on STEM datasets\n- [Conversation-Data](https:\u002F\u002Fgithub.com\u002Fstellar-sam\u002Fconversation-data) - Dataset for training LLM.\n- [Data_Fine_Tune](https:\u002F\u002Fgithub.com\u002Fbhc91\u002Fdata_fine_tune) - Python files to prepare datasets for finetuning LLMs\n- [Dataconvoai](https:\u002F\u002Fgithub.com\u002Fjainut20\u002FDataConvoAI) - LLM Based Chatbot to interact with custom dataset\n- [Datacooker](https:\u002F\u002Fgithub.com\u002FWashimNeupane\u002Fdatacooker) - Curates a mix of datasets for llm training.\n- [Dataset-Creator-Server](https:\u002F\u002Fgithub.com\u002Fmichaelcalvinwood\u002Fdataset-creator-server) - Nodejs Express Server for Expedited Dataset Creation for Fine Tuning LLMs\n- [Dataset-Error-Reduction](https:\u002F\u002Fgithub.com\u002Fjayantaadhikary\u002Fdataset-error-reduction) - Reducing Error in NLP Datasets using LLMs\n- [Dataset-Generator-For-Llm-Finetuning](https:\u002F\u002Fgithub.com\u002FAsadNizami\u002FDataset-generator-for-LLM-finetuning) - A web application that generates high-quality question-answer pairs from text documents for LLM finetuning\n- [Dataset-Translator](https:\u002F\u002Fgithub.com\u002Feususu\u002Fdataset-translator) - translate huggingface dataset with translate api or llm\n- [Dataset_For_Llm](https:\u002F\u002Fgithub.com\u002FDragon-hxl\u002Fdataset_for_llm) - backup for some data set to test llm models\n- [Datasetgpt](https:\u002F\u002Fgithub.com\u002Fradi-cho\u002FdatasetGPT) - A command-line interface to generate textual and conversational datasets with LLMs.\n- [Datasette-Enrichments-Llm](https:\u002F\u002Fgithub.com\u002Fdatasette\u002Fdatasette-enrichments-llm) - Enrich data by prompting LLMs\n- [Dolly-Instruction-Tuning](https:\u002F\u002Fgithub.com\u002FPavanKumar-Aravapalli\u002FDolly-Instruction-Tuning) - Experimentation on fine tuning LLM to instruction datasets.\n- [Eduscribe-Llm-Backend](https:\u002F\u002Fgithub.com\u002FTanGentleman\u002FEduScribe-LLM-Backend) - Pythonic dataset processing for fine-tuning LLMs. Used for a CalHacks 2023 award winning project.\n- [Evaluating-Xai-Llms-In-A-Clinical-Context_Csc413-Project](https:\u002F\u002Fgithub.com\u002Fhk21702\u002FEvaluating-XAI-LLMs-in-a-Clinical-Context_CSC413-Project) - A UofT CSC413 Final Project - Evaluating interpretable large language models in a clinical context using the MIMIC-IV dataset.\n- [Finetuned-Qlora-Llama7B-Mental-Health](https:\u002F\u002Fgithub.com\u002Fcaffeinatedwoof\u002Ffinetuned-qlora-llama7b-mental-health) - Finetuning of Llama-7B LLM using QLoRA on Mental Health Conversational Dataset\n- [Finetuning_Llm](https:\u002F\u002Fgithub.com\u002Folumyk\u002Ffinetuning_llm) - Fine-tuning Meta Llama 2 7B Model on Healthcare Dataset using SageMaker\n- [Finetuning_Llms](https:\u002F\u002Fgithub.com\u002FPratik-Behera\u002Ffinetuning_llms) - Finetuning Falcon7b\u002FFalcon7b-Instruct on a test dataset using QLora\n- [Fl_Llm_Benchmark_Dataset](https:\u002F\u002Fgithub.com\u002Fhazylavender\u002Ffl_llm_benchmark_dataset) - This code collects congressional\u002Fparliamentary dataset across US, UK and Canada\n- [Ftdatagen](https:\u002F\u002Fgithub.com\u002FSpingenceAI\u002FFTDataGen) - Generate LLM finetune dataset\n- [Get-Random-Wikipedia-Content](https:\u002F\u002Fgithub.com\u002Ftheripnono\u002Fget-random-wikipedia-content) - Donwload ramdom wikipedia content to create LLM datasets\n- [Gpt_Annotate](https:\u002F\u002Fgithub.com\u002Fnpangakis\u002Fgpt_annotate) - Introducing gpt_annotate - an easy-to-use python package designed to streamline automated text annotation using LLMs for different tasks and\n- [Graph-Instruction-Tuning](https:\u002F\u002Fgithub.com\u002Fdgjun32\u002FGraph-Instruction-Tuning) - Instruction tuning LLM with molecular graph instruction dataset.\n- [Hugging Face Datasets](https:\u002F\u002Fhuggingface.co\u002Fdatasets) - Thousands of datasets for machine learning tasks.\n- [Imputer](https:\u002F\u002Fgithub.com\u002Fshaunporwal\u002Fimputer) - Using LLMs to impute values in a dataset\n- [Instruct-Qna-Fine-Tuning-Google-Flan-T5-Large-Llm-Qlora-Peft-Open-Orca-Dataset](https:\u002F\u002Fgithub.com\u002Fshirsh10mall\u002FInstruct-QnA-Fine-Tuning-Google-Flan-T5-Large-LLM-QLoRA-PEFT-Open-Orca-Dataset) - The Open Orca dataset serves as the foundation for this project. It provides a wide array of question types, including multiple-choice, reasoning, question-and-answer, one-word answers, translation, grammar correction, and math word problems\n- [Instructions-Tuning-Across-Various-Llms-With-Alpaca-Dataset](https:\u002F\u002Fgithub.com\u002Ffatemafaria142\u002FInstructions-Tuning-Across-Various-LLMs-with-Alpaca-Dataset) - I utilized the \"Alpaca\" dataset, which comprises 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine\n- [Kaggle-Llm-Detect_Ai_Generated_Text](https:\u002F\u002Fgithub.com\u002FLizhecheng02\u002FKaggle-LLM-Detect_AI_Generated_Text) - Detect whether the text is AI-generated by training a new tokenizer and combining it with tree classification models or by training langu…\n- [Kg-Llm-Prompting](https:\u002F\u002Fgithub.com\u002FSandroGT\u002FKG-LLM-Prompting) - Codebase and dataset repository for the paper \"Knowledge Graph Engineering through Iterative Zero-shot LLM Prompting\"\n- [Langchaindatasetforge](https:\u002F\u002Fgithub.com\u002Fasokraju\u002FLangChainDatasetForge) - Generating artificial datasets using langchain and finetuning the LLMs on these datasets.\n- [Langfuse](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) - 🪢 Open source LLM engineering platform - LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with Llama…\n- [Lex-Fridman-Dataset-Llm](https:\u002F\u002Fgithub.com\u002FJasonFengGit\u002FLex-Fridman-Dataset-LLM) - Lex Fridman Podcast transcripts for LLM training\n- [Llm---Detect-Ai-Generated-Text](https:\u002F\u002Fgithub.com\u002Fpinskyrobin\u002FLLM---Detect-AI-Generated-Text) - Demo code and dataset for offline debugging\n- [Llm-App](https:\u002F\u002Fgithub.com\u002Fskanduru\u002Fllm-app) - An AI-powered movielens dataset based recommendation system\n- [Llm-Audio-Dataset-Process](https:\u002F\u002Fgithub.com\u002FzhihengAI\u002FLLM-Audio-dataset-process) - many scripts for audio dataset processing\n- [Llm-Benchmarks](https:\u002F\u002Fgithub.com\u002Fjimbobbennett\u002Fllm-benchmarks) - A sample Python app for investigating the LLM benchmarks dataset from Kaggle\n- [Llm-Chatbot](https:\u002F\u002Fgithub.com\u002FAmir-Shokrzadeh\u002FLLM-Chatbot) - A mini project for using an existing Dataset to create a chatbot\n- [Llm-Content-Mod](https:\u002F\u002Fgithub.com\u002Fkumarde\u002Fllm-content-mod) - Datasets and code for studying LLMs and Content Moderation\n- [Llm-Dataset-Converter-Examples](https:\u002F\u002Fgithub.com\u002Fwaikato-llm\u002Fllm-dataset-converter-examples) - Repository with examples for the llm-dataset-converter libraries.\n- [Llm-Dataset-Gen](https:\u002F\u002Fgithub.com\u002FBrandon82\u002Fllm-dataset-gen) - Using LLMs (OpenAI API) to generate and add data to datasets\n- [Llm-Derived-Tvshowguess-Dataset](https:\u002F\u002Fgithub.com\u002FOaklight\u002FLLM-derived-TVShowGuess-Dataset) - TVShowGuess was proposed in benchmarking language models' skill of understanding fictional characters in narrative stories.\n- [Llm-Finetuning](https:\u002F\u002Fgithub.com\u002Ftejaswargudla\u002FLLM-finetuning) - Finetuning the LLM model for NLP tasks and cybersec based instruction datasets\n- [Llm-Finetuning-Template](https:\u002F\u002Fgithub.com\u002FAusafMo\u002FLLM-Finetuning-Template) - Template Code for working and finetuning a LLM for custom dataset, here a artificially generated dataset ( couldn't find a real one).\n- [Llm-Imdb](https:\u002F\u002Fgithub.com\u002Fibiscp\u002FLLM-IMDB) - Proof of concept app using LangChain and LLMs to retrieve information from graphs, built with the IMDB dataset\n- [Llm-Predictor](https:\u002F\u002Fgithub.com\u002Fdhw059\u002FLLM-predictor) - The strategy of material dataset based on Tokens, utilizing LLM to achieve material prediction, discovery, and design.\n- [Llm-Semantic-Duplicates](https:\u002F\u002Fgithub.com\u002Fhappyduck-313\u002Fllm-Semantic-duplicates) - about Semantic duplicates in datasets of llm\n- [Llm-Theory-Of-Mind](https:\u002F\u002Fgithub.com\u002Fsileod\u002Fllm-theory-of-mind) - Testing Theory of Mind (ToM) in language models with epistemic logic\n- [Llm4Vv](https:\u002F\u002Fgithub.com\u002FXachaeus\u002FLLM4vv) - Dataset for training LLMs on OpenACC and OpenMP\n- [Llm_Benchmarks](https:\u002F\u002Fgithub.com\u002Fleobeeson\u002Fllm_benchmarks) - A collection of benchmarks and datasets for evaluating LLM.\n- [Llm_Bxn_Dataset](https:\u002F\u002Fgithub.com\u002FDavickyz\u002Fllm_bxn_dataset) - A simple dataset\n- [Llm_Cybersecurity_Datasets_Uss_2025](https:\u002F\u002Fgithub.com\u002FRoohanak\u002FLLM_Cybersecurity_datasets_USS_2025) - SoK - Systematic Analysis of Cybersecurity Datasets using Large Language Models\n- [Llm_Datasets_Generators](https:\u002F\u002Fgithub.com\u002Fmpasko\u002Fllm_datasets_generators) - Simple util for automatically generating synthetic datasets for training or finetuning LLMs\n- [Llm_Fine_Tuning](https:\u002F\u002Fgithub.com\u002Fkhaouitiabdelhakim\u002Fllm_fine_tuning) - Fine-tuning essentially involves taking a pre-trained LLM, already equipped with a vast understanding of language, and further training i…\n- [Llm_Finetune](https:\u002F\u002Fgithub.com\u002FYABER1965\u002FLLM_finetune) - Fine-tune LLM using special dataset\n- [Llm_Finetune](https:\u002F\u002Fgithub.com\u002FTalhaUsuf\u002FLLM_finetune) - synthetic dataset gneration and fine tune\n- [Llm_Model_Evaluation](https:\u002F\u002Fgithub.com\u002FLiuYuWei\u002Fllm_model_evaluation) - LLM Model Evaluation for tmmluplus datasets\n- [Llm_Rag](https:\u002F\u002Fgithub.com\u002Fdongdongunique\u002FLLM_RAG) - This repository implements a Retrieval-Augmented Generation (RAG) system using FAISS for vector-based retrieval and GPT for generative re…\n- [Llmdataforge](https:\u002F\u002Fgithub.com\u002Fsaucam\u002Fllmdataforge) - A framework for generating synthetic datasets tailored for large language models (LLMs)\n- [Llmdataparser](https:\u002F\u002Fgithub.com\u002Fjeff52415\u002FLLMDataParser) - LLMDataParser is a Python library that provides a collection of parsers for various benchmark datasets used in the evaluation of Large La…\n- [Llmeval](https:\u002F\u002Fgithub.com\u002Fcdolea331\u002FLLMEval) - Simple script to evaluate GPT 3.5 turbo on hugging face commonsense_qa dataset\n- [Llmscenarioeval](https:\u002F\u002Fgithub.com\u002FTuring-Project\u002FLLMScenarioEval) - Scenario-based Evaluation dataset for LLM (beta)\n- [Lm-Expansions](https:\u002F\u002Fgithub.com\u002Forionw\u002FLM-expansions) - When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets\n- [Medisoap](https:\u002F\u002Fgithub.com\u002Faman-17\u002FMediSOAP) - FineTuning LLMs on conversational medical dataset.\n- [Multimodal-Llm](https:\u002F\u002Fgithub.com\u002FTheSciPro\u002FMultimodal-LLM) - image processing for unstructured datasets using multimodal model LLM fuyu-8b\n- [Nomiracl](https:\u002F\u002Fgithub.com\u002Fproject-miracl\u002Fnomiracl) - NoMIRACL - A multilingual hallucination evaluation dataset to evaluate LLM robustness in RAG against first-stage retrieval errors on 18 la…\n- [Nsfw-Finetuned-On-Llm](https:\u002F\u002Fgithub.com\u002Fsebinsaji007\u002FNSFW-finetuned-on-llm) - Here we have taken falcon 7B as the LLM and finetuned NSFW dataset with it\n- [OpenAI Dataset Library](https:\u002F\u002Fopenai.com\u002F) - A comprehensive collection of datasets used for training AI models.\n- [Parsee-Datasets](https:\u002F\u002Fgithub.com\u002Fparsee-ai\u002Fparsee-datasets) - Datasets, case studies and benchmarks for extracting structured information from PDFs, HTML files or images, created by the Parsee.ai team.\n- [Pdf_To_Llm_Dataset.Py](https:\u002F\u002Fgithub.com\u002Fkkkarmo\u002Fpdf_to_llm_dataset.py) - This project provides a Python script that converts a PDF book into a training dataset suitable for large language models (LLMs).\n- [Pixel-Agi](https:\u002F\u002Fgithub.com\u002Foliveirabruno01\u002Fpixel-agi) - A gradio app to talk with LLMs and make them interact with pixel-art\u002Fimages + micro dataset. Focus = data.\n- [Promptedgraphs](https:\u002F\u002Fgithub.com\u002Fclosedloop-technologies\u002FPromptedGraphs) - From Dataset Labeling, Entity Extraction to production Knowledge Graph Deployment - The Power of NLP and LLMs Combined.\n- [Querying-Csvs-And-Plot-Graphs-With-Llms](https:\u002F\u002Fgithub.com\u002FSomyanshAvasthi\u002FQuerying-CSVs-and-Plot-Graphs-with-LLMs) - Leveraging Large Language Models (LLMs) to query CSV files and plot graphs transforms data analysis. This allows to interact with datasets …\n- [Rag_Llm-On-Customdataset](https:\u002F\u002Fgithub.com\u002Fvinu0404\u002FRAG_LLM-on-CustomDataset) - The ConvoSummarizer is a Retrieval-Augmented Generation (RAG) application that uses the Hugging Face Transformers library with the google…\n- [Reap-Llm-Problem-Solving](https:\u002F\u002Fgithub.com\u002Fryanlingo\u002FREAP-LLM-Problem-Solving) - Enhance LLM problem-solving with REAP - Reflection, Explicit Problem Deconstruction, and Advanced Prompting. This repo includes the REAP p…\n- [Refuel-Sdk](https:\u002F\u002Fgithub.com\u002Frefuel-ai\u002Frefuel-sdk) - Label, clean and enrich text datasets with LLMs\n- [Sdp-Llm-Dataset](https:\u002F\u002Fgithub.com\u002Fyilmazzey\u002Fsdp-llm-dataset) - Paper scraping from acl anthology with selenium\n- [Seqtoseq-Dataset-Creator](https:\u002F\u002Fgithub.com\u002Fmichaelcalvinwood\u002Fseqtoseq-dataset-creator) - React app to create datasets for seq to seq LLM training\n- [Sg-Data-Analyst](https:\u002F\u002Fgithub.com\u002Fivankqw\u002Fsg-data-analyst) - LLMs as Data Analysts over Singapore Datasets 🤖\n- [Simpsons_Llm_Xpu](https:\u002F\u002Fgithub.com\u002Frahulunair\u002Fsimpsons_llm_xpu) - Finetune an LLM on intel discrete GPUs to generate dialogues based on the simpsons dataset\n- [Skin-Cancer-Specialist-Llm](https:\u002F\u002Fgithub.com\u002Fsobhonium\u002FSkin-Cancer-Specialist-LLM) - Fine tuning an LLM by the given dataset. The dataset represents the domain knowledge that the LLM should be an expert for.\n- [Smart-Contracts-Dataset-Generation](https:\u002F\u002Fgithub.com\u002Fmatteo-rizzo\u002Fsmart-contracts-dataset-generation) - Generating a dataset of smart contracts affected by reentrancy using LLMs\n- [Starjob](https:\u002F\u002Fgithub.com\u002Fstarjob42\u002FStarjob) - JSSP dataset for LLMs\n- [Syntune](https:\u002F\u002Fgithub.com\u002FRuairiSpain\u002FSynTune) - Building LLM finetuning datasets\n- [Talk_To_Your_Data](https:\u002F\u002Fgithub.com\u002Fmktaop\u002Ftalk_to_your_data) - Get insights from your dataset, small or large. Using PandasAI and a LLM.\n- [Textbook_Quality](https:\u002F\u002Fgithub.com\u002FVikParuchuri\u002Ftextbook_quality) - Generate textbook-quality synthetic LLM pretraining data\n- [Tinyllm](https:\u002F\u002Fgithub.com\u002Fmeet-minimalist\u002FTinyLLM) - LLM implementations on small scale dataset\n- [Universal-Dataset-Chatbot-With-Llm](https:\u002F\u002Fgithub.com\u002FAdritPal08\u002FUniversal-Dataset-Chatbot-with-LLM) - Universal Dataset Chatbot with LLM\n- [Upar5Iv](https:\u002F\u002Fgithub.com\u002Fveya2ztn\u002Fupar5iv) - Convert ar5iv html dataset into LLM friendly format such .md and .json\n- [Vlm_Databuilder](https:\u002F\u002Fgithub.com\u002Ftensorsense\u002Fvlm_databuilder) - This SDK generates datasets for training Video LLMs from youtube videos.\n- [Voice datasets](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fvoice_datasets) - A comprehensive list of open-source datasets for voice and sound computing (95+ datasets).\n- [Zero-Shot-Automatic-Annotation-Using-Llm-Generated-Datasets](https:\u002F\u002Fgithub.com\u002Franzosap\u002FZero-Shot-Automatic-Annotation-using-LLM-Generated-Datasets) - Best.pt is the YOLO11n-seg model trained on LLM generated Images where we automatically labelled the apple masks using YOLO11-SAMv2 fusion approach on zero shot.\n\n### Deployment\n- [Accident-Simulation](https:\u002F\u002Fgithub.com\u002Fchuajiesheng\u002Faccident-simulation) - Allocating resources to a accident scenario in real-time is a difficult challenge. This project aims to create a simulation playground wh…\n- [Advanced-Logic-Reason-Idea-And-Learning-Algorithms](https:\u002F\u002Fgithub.com\u002FBrionengine\u002FAdvanced-logic-reason-idea-and-learning-algorithms) - InfiniteMind is an open-source AI project designed to create an intelligent agent capable of learning from experiences, building a compre…\n- [Agent-](https:\u002F\u002Fgithub.com\u002Fdrraghavendra\u002FAgent-) - Create, deploy, and manage AI agents on Solana using our decentralized platform. Powered by Rig, Rust, and ARC tokens.\n- [Agent-Accelerator](https:\u002F\u002Fgithub.com\u002Fj3bruins\u002Fagent-accelerator) - Create, deploy, and manage AI agents on Solana using our decentralized platform. Powered by Rig, Rust, and ARC tokens.\n- [Agent-Ops](https:\u002F\u002Fgithub.com\u002Frbisoi\u002Fagent-ops) - Developing an ai framework to deploy agents in production\n- [Agentgpt](https:\u002F\u002Fgithub.com\u002Freworkd\u002FAgentGPT) - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.\n- [Agentgpt](https:\u002F\u002Fgithub.com\u002Falicayan008\u002FAgentGPT) - Assemble, configure, and deploy autonomous AI Agents in your browser.\n- [Agentic-Ai](https:\u002F\u002Fgithub.com\u002FEnggTalha\u002FAgentic-AI) - A framework for developing and deploying specialized AI agents, including Web Search and Finance AI Agents, powered by advanced models an…\n- [Agentic-Ai-](https:\u002F\u002Fgithub.com\u002FJogendraSingh1879\u002FAgentic-AI-) - Building and Deploying a Free Pizza Ordering System with FastAPI and Multi-Agent Architecture\n- [Agentic-Platform](https:\u002F\u002Fgithub.com\u002Fbonk1t\u002Fagentic-platform) - AI Agent Automation Platform - Rapidly prototype, test, and deploy Multi-Agent Systems from your browser.\n- [Agentic.Md](https:\u002F\u002Fgithub.com\u002Fai-primitives\u002Fagentic.md) - Build, Test, Deploy, & Iterate on AI Agents using Markdown & MDX\n- [Agenticai](https:\u002F\u002Fgithub.com\u002FYasinOnline\u002FAgenticAI) - AgenticAI - A framework for building autonomous decision-making agents. It integrates reinforcement learning, NLP, and multi-agent systems…\n- [Agentkit-Sdk](https:\u002F\u002Fgithub.com\u002Fsimonweniger\u002Fagentkit-sdk) - Build, deploy, and manage LLM-powered agents on  platforms.\n- [Agentprocessingunit](https:\u002F\u002Fgithub.com\u002Ffaddy19\u002FAgentProcessingUnit) - The Agent Processing Unit (APU) is an open source hardware project to build a high-performance chip architecture optimized for AI agent w…\n- [Agentserve](https:\u002F\u002Fgithub.com\u002FPropsAI\u002Fagentserve) - A framework for hosting and scaling AI agents.\n- [Agentverse](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FAgentVerse) - 🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides tw…\n- [Agentx](https:\u002F\u002Fgithub.com\u002Fsattyamjjain\u002FAgentX) - AgentX is a powerful AI-driven framework for building smart, context-aware assistants and automating workflows. With modular design, seam…\n- [Agileagents](https:\u002F\u002Fgithub.com\u002Fruvnet\u002Fagileagents) - Agile Agents (A2) is an open-source framework for the creation and deployment of serverless intelligent agents using public and private c…\n- [Ai-Agent](https:\u002F\u002Fgithub.com\u002Fkasragordini\u002FAI-Agent) - Designed and deployed a high-performance chatbot leveraging LangChain, Qdrant, LLM, and RAG to enhance data retrieval, AI knowledge base,…\n- [Ai-Agent-Ecosystem](https:\u002F\u002Fgithub.com\u002Fai-in-pm\u002FAI-Agent-Ecosystem) - A powerful, scalable ecosystem for managing and monitoring AI agents. This system provides a framework for deploying, managing, and monit…\n- [Ai-Agent-For-Deployment](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002FAI-agent-for-deployment) - No description available\n- [Ai-Agent-Lab](https:\u002F\u002Fgithub.com\u002FZeeshan138063\u002Fai-agent-lab) - AI Agent Lab - An open-source repository to build, test, and deploy AI agents using Python, with examples and modular design.\n- [Ai-Agent-Report-Maker-Deploy](https:\u002F\u002Fgithub.com\u002Fezemriv\u002FAI-Agent-Report-Maker-Deploy) - A streamlined version of the AI-Agent-Report-Maker, focusing exclusively on the agent functionality for deployment with Gradio.\n- [Ai-Agent-Ui](https:\u002F\u002Fgithub.com\u002FLinzo99\u002Fai-agent-ui) - UI Template for a chatbot agent created Llama Deploy (LlamaIndex Workflow)\n- [Ai-Agents](https:\u002F\u002Fgithub.com\u002FBilalkhan4086\u002Fai-agents) - This repo is for learning how to deploy ai agent with langgraph on langgraph cloud.\n- [Ai-Agents-For-Networking](https:\u002F\u002Fgithub.com\u002Fmkular\u002FAI-Agents-For-Networking) - AI agents for network deployment, configuration and monitoring\n- [Ai-Onchain-Agent](https:\u002F\u002Fgithub.com\u002FPatrick-Ehimen\u002FAI-OnChain-Agent) - A powerful tool designed to interact with blockchain networks, specifically tailored for EVM chains. It leverages OpenAI's GPT-4o-mini mo…\n- [Ai-Powered-Chatbot-Generator](https:\u002F\u002Fgithub.com\u002FRushi-code1\u002FAI-Powered-Chatbot-Generator) - \"Developed an AI-powered chatbot generator designed to create custom, conversational agents tailored for diverse industries. Utilized nat…\n- [Ai-Toolbox](https:\u002F\u002Fgithub.com\u002Feranco74\u002FAI-Toolbox) - A set of AI-driven tools designed to assist in creating, training, and deploying AI agents across different workflows.\n- [Airline-Api](https:\u002F\u002Fgithub.com\u002Fnickb4924\u002FAirline-API) - BookingXML is a leading Airline API provider that offers the best Airline API Integration Solution for Travel Agents, and Travel Companie…\n- [Anthropic-Agent-In-Docker](https:\u002F\u002Fgithub.com\u002FLiteObject\u002Fanthropic-agent-in-docker) - This repository offers a Dockerized implementation of the Anthropic Agent for streamlined deployment and scalability. It ensures consiste…\n- [Apemind-Framework](https:\u002F\u002Fgithub.com\u002Fapeoutmeme\u002FApeMind-Framework) - Next-generation infrastructure for deploying AI agents on Solana\n- [Atat](https:\u002F\u002Fgithub.com\u002Fsemanticsean\u002FATAT) - ATAT is an email client for AI Agents. Deploy dozens of AI agents through a single email address (IMAP\u002FSMTP) using the OpenAI API. Just a…\n- [Attck-Pe](https:\u002F\u002Fgithub.com\u002Fcmndcntrlcyber\u002Fattck-pe) - Levaraging the power of the ATT&CK Database to enrich an AI agent to deployed as a browser thread for Adversary Emulation from a container\n- [Auto_Web-Gpt](https:\u002F\u002Fgithub.com\u002FSuperstar721\u002FAuto_web-GPT) - Assemble, configure, and deploy autonomous AI Agents in your browser.\n- [Autogpt-Next-Web](https:\u002F\u002Fgithub.com\u002FElricLiu\u002FAutoGPT-Next-Web) - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.一键免费部署你的私人AutoGPT 网页应用\n- [Autonomous-Ai-Team](https:\u002F\u002Fgithub.com\u002Fmikirinkode\u002Fautonomous-ai-team) - A project that aims to develop and deploy autonomous AI agents that can collaborate and communicate with each other and humans.\n- [Autonomousai](https:\u002F\u002Fgithub.com\u002FRithikaGupta\u002FautonomousAI) - About Multi Agent System Installation Build Deployment - for autonomous testing\n- [Awesome-Ai-Sdks](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-sdks) - A database of SDKs, frameworks, libraries, and tools for creating, monitoring, debugging and deploying autonomous AI agents\n- [Awesome-Solana-Ai-Hackathon](https:\u002F\u002Fgithub.com\u002Ftkorkmazeth\u002Fawesome-solana-ai-hackathon) - Welcome to the Solana AI Agent Hackathon repository! This resource is your ultimate guide to creating, deploying, and managing autonomous AI\n- [Aws-Ai-Templates](https:\u002F\u002Fgithub.com\u002Fjacobweiss2305\u002Faws-ai-templates) - A collection of AWS Templates for deploying Agentic Software\n- [Aws-Templates](https:\u002F\u002Fgithub.com\u002Fphidatahq\u002Faws-templates) - A collection of AWS templates for deploying AI Agents\n- [Axocore](https:\u002F\u002Fgithub.com\u002FAxolotl-Labs\u002FAxocore) - Axocore is an open-source AI agent infrastructure designed to make building intelligent systems effortless. With a modular plugin system,…\n- [Baba-Bot](https:\u002F\u002Fgithub.com\u002FAbdulrahmanrihan\u002FBaba-bot) - Baba bot is an AI agent created using Mistral V2, and Mistral's agent creating user interface, deployed on a Gradio website.\n- [Back4App-Ai-Agent](https:\u002F\u002Fgithub.com\u002Fduplxey\u002Fback4app-ai-agent) - Learn how to use Back4app Agent to build and deploy full-stack web applications.\n- [Baseai](https:\u002F\u002Fgithub.com\u002FLangbaseInc\u002FBaseAI) - BaseAI — The Web AI Framework. The easiest way to build serverless autonomous AI agents with memory. Start building local-first, agentic …\n- [Basilisktoken](https:\u002F\u002Fgithub.com\u002Felder-plinius\u002FBasiliskToken) - BASI is the first-ever smart contract created by autonomous AI agents. The token was deployed to ETH mainnet on 6\u002F6\u002F23.\n- [Bespoke_Automata](https:\u002F\u002Fgithub.com\u002FC0deMunk33\u002Fbespoke_automata) - Bespoke Automata is a GUI and deployment pipline for making complex AI agents locally and offline\n- [Bluemarz](https:\u002F\u002Fgithub.com\u002FStartADAM\u002Fbluemarz) - Bluemarz is an open-source management layer for AI agents, offering a flexible, scalable, and stateless architecture for deploying and or…\n- [Botpress](https:\u002F\u002Fgithub.com\u002Fbotpress\u002Fbotpress) - The open-source hub to build & deploy GPT\u002FLLM Agents ⚡️\n- [Business-Blog-Generator](https:\u002F\u002Fgithub.com\u002FMuhammadSalmanAhmad\u002Fbusiness-blog-generator) - Developed a simple web app that helps you write business articles on your topic of interest where I deployed a AI agent using crewAI that…\n- [Canary](https:\u002F\u002Fgithub.com\u002Fwack\u002Fcanary) - MultiTool Canary is your AI-powered, agentic deployment solution for seamless, risk-managed rollouts\n- [Chamberlain_Multimodal_Multiagent_Chatbot](https:\u002F\u002Fgithub.com\u002FnickShengY\u002Fchamberlain_multimodal_multiagent_chatbot) - An AI-driven Multimodal multi-agent chatbot for home deployment to manage the user's daily chores and tasks. Used OpenAI and Langchain an…\n- [Chatbot](https:\u002F\u002Fgithub.com\u002Fpavankola84\u002FChatBot) - AI Chatbot Widget is an intuitive and versatile conversational agent designed to enhance user interaction and support across various webs…\n- [Chatbot-](https:\u002F\u002Fgithub.com\u002Fanshikabanerjee\u002FChatbot-) - This paper will detail the need for conversational AI agents, such as chatbots in the search and discovery domain. The efficiency of the …\n- [Chatgpt.Clone](https:\u002F\u002Fgithub.com\u002Fvivekkkkkkk\u002Fchatgpt.clone) - AI-powered conversational agent based on GPT-3.5. Trained on diverse text data, it generates human-like responses. Use it to build chatb…\n- [Cheap_Ai](https:\u002F\u002Fgithub.com\u002FCheapaifun\u002FCheap_ai) - Zero fees on deployment, Create AI agents with automation using LiamaX—next-gen intelligence made simple!\n- [Chess-Agent](https:\u002F\u002Fgithub.com\u002Fgabrielzencha\u002FChess-Agent) - An AI chess agent capable of teaching others and also deployed on a robot in real life\n- [Collective](https:\u002F\u002Fgithub.com\u002Fzoharbabin\u002Fcollective) - An experimental AI project that harnesses specialized agents for end-to-end software development. Each role-based AI collaborator orchest…\n- [Contact-Center-Genai-Agent](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fcontact-center-genai-agent) - Deploy generative AI agents in your contact center for voice and chat using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases\n- [Corexai](https:\u002F\u002Fgithub.com\u002FCorexAI\u002FCorexAI) - CorexAI is a cutting-edge, decentralized platform that enables seamless renting of GPUs and AI agents. By leveraging distributed GPU reso…\n- [Council](https:\u002F\u002Fgithub.com\u002Fchain-ml\u002Fcouncil) - Council is an open-source platform for the rapid development and robust deployment of customized generative AI applications\n- [Crewai](https:\u002F\u002Fgithub.com\u002Fahtealeb\u002Fcrewai) - Crew.ai The Leading Multi-Agent Platform Streamline workflows across industries with powerful AI agents. Build and deploy automated workf…\n- [Crewai-Web-Render](https:\u002F\u002Fgithub.com\u002Fimnotdev25\u002Fcrewai-web-render) - Build & deploy sites with crew ai agents\n- [Dance-Ai-Research-Project](https:\u002F\u002Fgithub.com\u002Fjzho987\u002Fdance-ai-research-project) - The main repository for managing the research towards developing and deploying an interactive dance AI agent to research ML techniques an…\n- [Data_Analysis_With_Local_Llama](https:\u002F\u002Fgithub.com\u002FErdenizUnvan\u002Fdata_analysis_with_local_llama) - Deploy an AI agent with via autogen and llama_index for making data analysis with your files\n- [David](https:\u002F\u002Fgithub.com\u002FUnka-Malloc\u002FDAVID) - Management System for AI Agents - Development, Authentication, Validation, Integration, and Deployment\n- [Devstream](https:\u002F\u002Fgithub.com\u002FRaheesAhmed\u002Fdevstream) - DevStream is an AI-powered web development agent that allows you to prompt, run, edit, and deploy full-stack applications directly from y…\n- [Eidolon](https:\u002F\u002Fgithub.com\u002Feidolon-ai\u002Feidolon) - The first AI Agent Server, Eidolon is a pluggable Agent SDK and enterprise ready, deployment server for Agentic applications\n- [Examples-Ai-Bedrock-Agent-National-Parks](https:\u002F\u002Fgithub.com\u002Fcebert\u002Fexamples-ai-bedrock-agent-national-parks) - This repository demonstrates how to build and deploy an AI agent using Amazon Bedrock and AWS Lambda, with infrastructure managed by AWS …\n- [Experts](https:\u002F\u002Fgithub.com\u002Fmetaskills\u002Fexperts) - Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent …\n- [Fireai](https:\u002F\u002Fgithub.com\u002Fanurag115\u002FFireAI) - Deployment Stack of Azure, AWS and Google Cloud with this template creates a vm with a CloudLens agent that listens on a virtual interfac…\n- [Framework](https:\u002F\u002Fgithub.com\u002Fisalineai\u002Fframework) - Self-sustaining Python AI agent creating, deploying, and optimizing autonomous projects.\n- [Friendship_Meter](https:\u002F\u002Fgithub.com\u002Falmagashi\u002Ffriendship_meter) - A fun project to measure the interactions between human and AI agent. Deployed on Vercel.\n- [Fullstack-Nextjs-App-Generator](https:\u002F\u002Fgithub.com\u002Fspark-engine-opensource-projects\u002Ffullstack-nextjs-app-generator) - Fullstack Next.js Application Builder that uses a Spark Engine AI multi-agent system project for generation, Supabase and Vercel for depl…\n- [Gaia-Meme-Coin-Generator](https:\u002F\u002Fgithub.com\u002Fharishkotra\u002Fgaia-meme-coin-generator) - Generate and deploy meme tokens automatically using Gaia's AI Agent for creative naming and tokenomics!\n- [Gcp_Ai_Assistant](https:\u002F\u002Fgithub.com\u002Fgabrielpreda\u002Fgcp_ai_assistant) - Conversational AI Agent using Gemini, with Streamlit UI and deployed on GCP Cloud Run\n- [Generative-Ai-Toolkit](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fgenerative-ai-toolkit) - The Generative AI Toolkit is a lightweight library that covers the life cycle of LLM-based applications, including agents. Its purpose is…\n- [Ghost-Agent](https:\u002F\u002Fgithub.com\u002Fqianyouliang\u002FGhost-Agent) - The objective of this project is to develop an AI ghost deployed on the web, powered by LangChain technology. The AI ghost will continuou…\n- [Ginix-Fraud-Agents](https:\u002F\u002Fgithub.com\u002Fginix-co\u002Fginix-fraud-agents) - Welcome to the giniX FraudAI-Agents community! This is a collaborative space where Fraud Analysts and developers work together to create,…\n- [Go-Chain-Gang](https:\u002F\u002Fgithub.com\u002Fkmesiab\u002Fgo-chain-gang) - A pure Go LLM\u002FAI \"agent\" orchestration library. Build, connect and deploy autonomous agents that collectively solve goals.\n- [Iauto](https:\u002F\u002Fgithub.com\u002Fshellc\u002Fiauto) - iauto is a low-code engine for building and deploying AI agents\n- [Incredible.Dev](https:\u002F\u002Fgithub.com\u002FIncredibleDevHQ\u002FIncredible.dev) - Incredible.dev is an AI Coding Co-worker which can code, fix, document, deploy, test your APIs. One agent to rule everything API.\n- [Init-Eliza](https:\u002F\u002Fgithub.com\u002FW3bbieLabs\u002Finit-eliza) - A powerful CLI tool for building, managing, and deploying AI agents with multiple provider integrations.\n- [Instantneo](https:\u002F\u002Fgithub.com\u002Fdponcedeleonf\u002Finstantneo) - InstantNeo is a concise interface for developing AI agents with customized roles and skills using OpenAI's models. It streamlines the dep…\n- [Instantrun](https:\u002F\u002Fgithub.com\u002FTalha-Ali-5365\u002FInstantRun) - AI agent that can deploy any github repo autonomously\n- [Jazzx](https:\u002F\u002Fgithub.com\u002Fkunal-nitor\u002FJazzX) - JazzX aims to create an enterprise GenAI-first platform to deploy and manage GenAI agents\u002Fapps. This enterprise cloud platform will use d…\n- [Jonathan-Flightbase](https:\u002F\u002Fgithub.com\u002FAcrylAI\u002FJonathan-Flightbase) - Jonathan Flightbase is an MLOps\u002FLLMOps platform designed for efficient AI development and operations. It streamlines resource management,…\n- [Justben8](https:\u002F\u002Fgithub.com\u002FJustBen8\u002FJustBen8) - At the path of learning AI, agentic building and deploying of agents and the technology used to build them.\n- [K8S-Agent](https:\u002F\u002Fgithub.com\u002Franching-farm\u002Fk8s-agent) - Kubernetes agent for deploying ranching.farm directly into your cluster. Connect your K8s deployment to our AI-powered management platfor…\n- [Kagentic](https:\u002F\u002Fgithub.com\u002Faiadvocat\u002Fkagentic) - Flask-based agentic AI chat assistant using OpenAI's GPT-4. Supports tool registration and management for dynamic responses. Deployed on …\n- [Kube-Gpt-Agent](https:\u002F\u002Fgithub.com\u002Fsrimoyee1212\u002FKube-GPT-Agent) - Use an AI agent to get information about your Kubernetes deployments!\n- [Kubernetes-Agent](https:\u002F\u002Fgithub.com\u002Friyaadhbukhsh\u002Fkubernetes-agent) - An AI agent that masterfully answers questions about your deployed kubernetes cluster\n- [Kubernetes-Query-Agent](https:\u002F\u002Fgithub.com\u002Fparteeksingh24\u002Fkubernetes-query-agent) - A demonstration of an AI agent that answers simple queries about apps deployed on a Kubernetes cluster.\n- [Kubernetesqueryagent](https:\u002F\u002Fgithub.com\u002Fsoni-ratnesh\u002FKubernetesQueryAgent) - An AI agent that interacts with a Kubernetes cluster to answer queries about its deployed applications.\n- [Letta-Deepseek](https:\u002F\u002Fgithub.com\u002Fmahawi1992\u002Fletta-deepseek) - Advanced multi-agent system built with Letta AI and DeepSeek, featuring memory optimization and Lightning AI deployment.\n- [Libertai-Agents](https:\u002F\u002Fgithub.com\u002FLibertai\u002Flibertai-agents) - Framework to create and deploy confidential & decentralized AI Agents.\n- [Llama-Latte](https:\u002F\u002Fgithub.com\u002Fim-anhat\u002Fllama-latte) - AI-powered coffee shop app built with React Native, featuring Llama 3 model integration, Retrieval-Augmented Generation (RAG) for persona…\n- [Llm-Research-Backend-Django](https:\u002F\u002Fgithub.com\u002Fmuhammadnasif\u002Fllm-research-backend-django) - This is the django project for deploying the LLM Research of Conversational AI Agent for test purpose.\n- [Lunaai](https:\u002F\u002Fgithub.com\u002FEugeene1337\u002FLunaAI) - Luna is a powerful multi-agent simulation framework designed to create, deploy, and manage autonomous AI agents. Built with TypeScript, i…\n- [Mastra AI](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra) - Mastra is an all-in-one framework for building AI-powered applications and agents with Typescript.\n- [Medreminder](https:\u002F\u002Fgithub.com\u002Fabisong\u002FMedReminder) - An AI agent web-based MedReminder app using HTML, CSS, and Vanilla JavaScript with local storage and Flask for deployment\n- [Metamind](https:\u002F\u002Fgithub.com\u002FMetaMindFramework\u002FMetaMind) - Empowering Your Applications with AI - MetaMind delivers a comprehensive framework for deploying intelligent agents, managing advanced wor…\n- [Mixtral_8X7B_Agent](https:\u002F\u002Fgithub.com\u002Fsvngoku\u002FMixtral_8X7B_Agent) - Deploying an open source AI Agent with Mixtral 8X7B\n- [Multi-Pdfs_Chatapp_Ai-Agent](https:\u002F\u002Fgithub.com\u002FGURPREETKAURJETHRA\u002FMulti-PDFs_ChatApp_AI-Agent) - Meet MultiPDF 📚 Chat AI App! 🚀 Chat seamlessly with Multiple PDFs using Langchain, Google Gemini Pro & FAISS Vector DB with Seamless Stre…\n- [Multiagents-Infinite-Backroom](https:\u002F\u002Fgithub.com\u002FAGAMPANDEYY\u002FMultiAgents-infinite-backroom) - Claude AI Multi agents having conversations without human intervention. Inspired from The Truth Terminal contains deployed Vercel app and…\n- [Multimodal-Llm-Agent](https:\u002F\u002Fgithub.com\u002FHuHK-Private\u002Fmultimodal-llm-agent) - Deploy Multimodal-LLM Agent to solve complicated AI tasks with language serving as a generic interface\n- [Nerosdk](https:\u002F\u002Fgithub.com\u002Fnerobossai\u002Fnerosdk) - sdk to build and deploy your own ai agent\n- [Nest-Ai](https:\u002F\u002Fgithub.com\u002Fthenest-hub\u002Fnest-ai) - A lightweight, open-source framework to deploy Agents. Part of a greater initiative to help AI Agent deployment.\n- [Nl2Iac](https:\u002F\u002Fgithub.com\u002Framonbgc\u002Fnl2iac) - AI agent for IaC deployments\n- [Onchain-Ai-Starter](https:\u002F\u002Fgithub.com\u002FNot-Sarthak\u002Fonchain-ai-starter) - Build and deploy onchain AI agents with zero hassle\n- [Openai-Assistants-Template](https:\u002F\u002Fgithub.com\u002Fpranavgupta2603\u002FOpenAI-Assistants-Template) - Build and deploy AI-driven assistants with our OpenAI Assistants Template. This tutorial provides a hands-on approach to using OpenAI's A…\n- [Petercat](https:\u002F\u002Fgithub.com\u002Fpetercat-ai\u002Fpetercat) - A conversational Q&A agent configuration system, self-hosted deployment solutions, and a convenient all-in-one application SDK, allowing …\n- [Phoneit](https:\u002F\u002Fgithub.com\u002Fbhuvanmdev\u002FPhoneIT) - An application that when deployed can establish a call service, where an AI agent can solve any query of the users using a established RA…\n- [Plura](https:\u002F\u002Fgithub.com\u002Fplura-ai\u002Fplura) - Plura is a powerful tool for creating, managing, and deploying AI agents. Built with TypeScript, it helps you develop intelligent agents …\n- [Powerups.Ai](https:\u002F\u002Fgithub.com\u002Fmuzetv\u002Fpowerups.ai) - Deploy AI Agent-Powered APIs in Minutes\n- [Project-S.O.C.R.A.T.E.S.](https:\u002F\u002Fgithub.com\u002FHams-Ollo\u002FProject-S.O.C.R.A.T.E.S.) - 🤖 Advanced Multi-Agent AI Template - Production-ready system combining Groq's speed with LangChain's flexibility. Features RAG, document p…\n- [Public-Agent-Framwork](https:\u002F\u002Fgithub.com\u002Faurasgit\u002Fpublic-agent-framwork) - AI agent that independently creates, deploys, and optimizes autonomous projects.\n- [Purpaas-Llm](https:\u002F\u002Fgithub.com\u002Fdwain-barnes\u002FPurPaaS-LLM) - PurPaaS is an innovative open-source security testing platform that implements purple teaming (combined red and blue team approaches) to …\n- [Python-Agents](https:\u002F\u002Fgithub.com\u002FclarisseIO\u002Fpython-agents) - AI agent that independently creates, deploys, and optimizes autonomous projects.\n- [Python-Aiagent](https:\u002F\u002Fgithub.com\u002Fosvaldokalvaitir\u002Fpython-aiagent) - 📈 Python AIAgent application development, integration with OpenAI's GPT model, use of the CrewAI framework, creation of an agent for cons…\n- [Rag_Sap_Agent](https:\u002F\u002Fgithub.com\u002Fagbackhoff\u002FRAG_SAP_AGENT) - SAP Table Structure Analyzer - AI-powered tool using Google's Gemini 1.5 Pro to extract and analyze SAP HANA S\u002F4 table structures. Feature…\n- [Redbaez-Agent-Builder-Complete](https:\u002F\u002Fgithub.com\u002Ftom2tomtomtom\u002Fredbaez-agent-builder-complete) - Full-stack AI agent builder with model integration, deployment, and monitoring capabilities\n- [Sc-Helm-App](https:\u002F\u002Fgithub.com\u002Famsilf\u002Fsc-helm-app) - This repository contains a simple Helm chart for deploying a \"Hello World\" Nginx application, along with Open Policy Agent (OPA) rules fo…\n- [Scalable-Ai--Autonomous-Agent](https:\u002F\u002Fgithub.com\u002FPrasannaverse13\u002FScalable-AI--autonomous-Agent) - The Scalable AI Platform is a cutting-edge solution designed for creating, deploying, and managing autonomous and semi-autonomous AI agents…\n- [Setup-Amazon-Bedrock-Agent-For-Text2Sql-Using-Amazon-Redshift-Serverless-With-Streamlit](https:\u002F\u002Fgithub.com\u002Faws-samples\u002FSetup-Amazon-Bedrock-Agent-for-Text2SQL-Using-Amazon-Redshift-Serverless-with-Streamlit) - This project integrates AWS services to create a natural language interface for querying Amazon Redshift Serverless databases. It utilize…\n- [Shampoosalesagent](https:\u002F\u002Fgithub.com\u002Fjackfsuia\u002FShampooSalesAgent) - A minimal LLM sales agent framework for sales agent fast deployment and benchmark. Support OpenAI models, Claude, HuggingFace models, Gem…\n- [Simple_Ai_Agent](https:\u002F\u002Fgithub.com\u002Fjeya2050\u002Fsimple_AI_agent) - This AI agent represents a significant advancement in automated web content analysis, offering organizations the ability to efficiently p…\n- [Slack-Ai-Assistant-Vijay](https:\u002F\u002Fgithub.com\u002Fvjvkrm\u002Fslack-ai-assistant-vijay) - The Most simple way to deploy AI agent-assitant slack app to your workspace\n- [Smartcall-Ai](https:\u002F\u002Fgithub.com\u002Fshwetd19\u002FSmartCall-Ai) - Deploy AI voice agents for calls.\n- [Smoothlingua](https:\u002F\u002Fgithub.com\u002FDev-Art-Solutions\u002FSmoothLingua) - SmoothLingua is an open-source conversational AI platform that empowers you to create and deploy intelligent conversational agents. It of…\n- [Snakeai](https:\u002F\u002Fgithub.com\u002Fleonardocunha2107\u002FsnakeAI) - Deploying snake agents near you\n- [Spacecreateai](https:\u002F\u002Fgithub.com\u002FHarbars1234\u002FSpaceCreateAI) - This repository contains the first Solana agent built with the SEND AI technology https:\u002F\u002Fgithub.com\u002Fsendaifun\u002Fsolana-agent-kit that auto…\n- [Stocks-Ai-Agent](https:\u002F\u002Fgithub.com\u002Fmuriloguerreiro\u002Fstocks-ai-agent) - Deploy\n- [Superagent-Swift-Legacy](https:\u002F\u002Fgithub.com\u002Fsimonweniger\u002Fsuperagent-swift-legacy) - Build, deploy, and manage LLM-powered agents on  platforms.\n- [Swarmnode-Cpp](https:\u002F\u002Fgithub.com\u002Falexcsh0\u002Fswarmnode-cpp) - Deploy and orchestrate serverless AI agents in the cloud.\n- [Swarmnode-Dotnet](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-dotnet) - Deploy and orchestrate serverless AI agents in the cloud.\n- [Swarmnode-Go](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-go) - Deploy and orchestrate serverless AI agents in the cloud.\n- [Swarmnode-Node](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-node) - Deploy and orchestrate serverless AI agents in the cloud.\n- [Swarmnode-Python](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-python) - Deploy and orchestrate serverless AI agents in the cloud.\n- [Swarmnode-Rust](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-rust) - Deploy and orchestrate serverless AI agents in the cloud.\n- [Swarmsxgcp](https:\u002F\u002Fgithub.com\u002FThe-Swarm-Corporation\u002FSwarmsXGCP) - Deploy your agents on Cloud Run!\n- [Synnex_Symphony](https:\u002F\u002Fgithub.com\u002FStyro13\u002FSynnex_Symphony) - Symphony is an AI-driven software development framework heavily influenced by GPT Pilot, and Microsoft's paper on 'AutoDev' that aims to …\n- [Team-Ai](https:\u002F\u002Fgithub.com\u002Fdeployment-io\u002Fteam-ai) - AI agent orchestration engine written in Go used internally at deployment.io\n- [Testchatbot](https:\u002F\u002Fgithub.com\u002Fsamanway1996\u002Ftestchatbot) - Database to be deployed for agent in api.ai\n- [Tiny-Agent](https:\u002F\u002Fgithub.com\u002Fbombap\u002Ftiny-agent) - Tiny-Agent is a lightweight yet extensible AI agent framework that simplifies the creation and deployment of intelligent agents.\n- [Transform-Agents](https:\u002F\u002Fgithub.com\u002Ftransformsen\u002Ftransform-agents) - Transform-Agents  - A UI for an AI systems - Build, simulate, run, and deploy your agentic AI system at scale.\n- [Upstreet-Core](https:\u002F\u002Fgithub.com\u002FUpstreetAI\u002Fupstreet-core) - Build and deploy AI Agents, fast.\n- [Vitalhome-Chat](https:\u002F\u002Fgithub.com\u002Fvital-ai\u002Fvitalhome-chat) - Ontology for Chat Agents deployed on Chat.ai\n- [Wolfpack](https:\u002F\u002Fgithub.com\u002Falmogdepaz\u002Fwolfpack) - Mobile & desktop PWA command center for controlling AI coding agents (Claude, Codex, Gemini) via tmux sessions across multiple machines, secured by Tailscale. Includes a multi-terminal grid view, mobile touch UI, and Ralph — an autonomous task runner. [github](https:\u002F\u002Fgithub.com\u002Falmogdepaz\u002Fwolfpack)\n- [Woodwork-Engine](https:\u002F\u002Fgithub.com\u002Fwillwoodward\u002Fwoodwork-engine) - An AI Agent IaC tool that aims to make developing and deploying AI Agents easier.\n- [WritBase](https:\u002F\u002Fgithub.com\u002FWritbase\u002Fwritbase) - MCP-native task management for AI agent fleets [github](https:\u002F\u002Fgithub.com\u002FWritbase\u002Fwritbase)\n- [Xpertagent](https:\u002F\u002Fgithub.com\u002Frookie-littleblack\u002FXpertAgent) - XpertAgent is an open-source platform for building and deploying AI applications. It combines intelligent workflow orchestration, knowled…\n\n### Ethics\n- [A4-Artificial-Intelligence](https:\u002F\u002Fgithub.com\u002FAlivadTheImpala\u002FA4-Artificial-Intelligence) - this is a repo regarding the ethics of AI.\n- [Adfa2](https:\u002F\u002Fgithub.com\u002Fdjdprogramming\u002Fadfa2) - 16. Next-Generation Data Scientists, Hubris, and Ethics - 50 Essential Concepts]\n- [Advancedaicoursework_2](https:\u002F\u002Fgithub.com\u002FAntoineSebert\u002FAdvancedAICoursework_2) - Essay on Explainable AI and ethics - concerns &, perspectives\n- [Ai-Ethics](https:\u002F\u002Fgithub.com\u002FPARC\u002Fai-ethics) - AI Ethics Committee at PARC\n- [Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fcbhanni\u002FAI-Ethics) - Ethical Guidelines for Human-Advanced Intelligence Interaction\n- [Ai-Ethics-Evaluation-Report-In-Healthcare](https:\u002F\u002Fgithub.com\u002FSelenaSongg\u002FAI-Ethics-Evaluation-Report-in-HealthCare) - An evaluation report in healthcare\n- [Ai-Ethics-Experiments](https:\u002F\u002Fgithub.com\u002Fjtrugman\u002Fai-ethics-experiments) - Analyzing AI's Responses To Ethical Dilemmas\n- [Ai-Ethics-Fairness-And-Bias](https:\u002F\u002Fgithub.com\u002Fjolares\u002Fai-ethics-fairness-and-bias) - Sample project using IBM's AI Fairness 360 is an open source toolkit for determining, examining, and mitigating discrimination and bias i…\n- [Ai-Ethics-Framework](https:\u002F\u002Fgithub.com\u002Fbenbyford\u002Fai-ethics-framework) - Ethical questions, risks and issues to think about to help create responsible AI products and services\n- [Ai-Ethics-In-Ecommerce-Apps](https:\u002F\u002Fgithub.com\u002FLena9x\u002FAI-ethics-in-ecommerce-apps) - AI ethics in ecommerce app report\n- [Ai-Ethics-Internship](https:\u002F\u002Fgithub.com\u002Fgicraveiro\u002FAI-Ethics-Internship) - Progress achieved on AI ethics internship at University of Trento under supervision of professor James Brusseau and professor Giuseppe Ri…\n- [Ai-Ethics-Panel](https:\u002F\u002Fgithub.com\u002Fwhatmakeart\u002Fai-ethics-panel) - panel notes\n- [Ai-Ethics-Project](https:\u002F\u002Fgithub.com\u002Fmaharsh3133\u002Fai-ethics-project) - Research Project on \"Data privacy of vulnerablepopulations like teenagers and elderly in Canada\"\n- [Ai-Ethics-Projects](https:\u002F\u002Fgithub.com\u002Fmckenziephagen\u002FAI-Ethics-Projects) - CSE 583 - Ethics in AI final project\n- [Ai-Ethics-Resources](https:\u002F\u002Fgithub.com\u002Fgigikenneth\u002Fai-ethics-resources) - Resources on tech and AI ethics.\n- [Ai-Ethics-Risk-Analysis-Management-Framework](https:\u002F\u002Fgithub.com\u002FLCromack\u002FAI-Ethics-Risk-Analysis-Management-Framework) - This is my code for my dissertation, in which I was tasked with building a framework that small companies could use to analyse their AI. …\n- [Ai-Ethics-Scenarios](https:\u002F\u002Fgithub.com\u002Frharrington31\u002Fai-ethics-scenarios) - Three AI ethics cases\n- [Ai-Ethics-Talk](https:\u002F\u002Fgithub.com\u002Fnat-foo\u002Fai-ethics-talk) - In September 2021, I gave a talk at my local university on the topic of whether AI and the Law speak the same language.\n- [Ai-Ethics-Tool-Landscape](https:\u002F\u002Fgithub.com\u002FEdwinWenink\u002Fai-ethics-tool-landscape) - AI Ethics Tool Landscape\n- [Ai-Ethics-Toolkit-For-Kenya](https:\u002F\u002Fgithub.com\u002Ftabz-ai\u002FAI-Ethics-Toolkit-for-Kenya) - The AI Ethics Toolkit for Kenya is an open-source project aimed at building a comprehensive set of tools and resources to help stakeholde…\n- [Ai-Exchange](https:\u002F\u002Fgithub.com\u002FW4LD3V\u002FAI-Exchange) - AI Exchange is a web application designed to help users discuss AI Ethics and Governance in a forum based format.\n- [Ai-Explainability-And-Ethics-On-Random-Forests](https:\u002F\u002Fgithub.com\u002FGuillermo-villar\u002FAI-Explainability-and-Ethics-on-Random-Forests) - AI Explainability and Ethics -- Reports on explainability of Random Forests\n- [Ai-In-Medicine](https:\u002F\u002Fgithub.com\u002Fkimotarek\u002FAI-in-medicine) - survey paper about the ethics of AI in medicine\n- [Ai-Risk-Prettified](https:\u002F\u002Fgithub.com\u002FPrivacy-Engineering-CMU\u002Fai-risk-prettified) - A prettified page for MIT's AI Risk Database\n- [Ai-Safety-Ethics](https:\u002F\u002Fgithub.com\u002Fgyevnarb\u002Fai-safety-ethics) - A systematic review of papers related to AI safety\n- [Ai-Unexpected-Behaviors](https:\u002F\u002Fgithub.com\u002Fsaraorsi\u002Fai-unexpected-behaviors) - catalog of unexpected behaviors of ai which can turn out to be failures that compromise the initial objectives, raising questions about u…\n- [Ai-Writing-Survey](https:\u002F\u002Fgithub.com\u002Fahdediu\u002FAI-Writing-Survey) - This repository contains the results of a survey about the use of AI for academic writing. The survey includes responses from 24 particip…\n- [Ai4All-](https:\u002F\u002Fgithub.com\u002Fbnam2103\u002FAI4ALL-) - Ethics and Machine Learning\n- [Ai6101-Introduction-To-Ai-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Falfredxtan\u002FAI6101-Introduction-to-AI-AI-Ethics) - Core modules for MSAI\n- [Ai_Ethics](https:\u002F\u002Fgithub.com\u002FMoritzCSchmidt\u002Fai_ethics) - Slides for AI ethics in Software Engineering\n- [Ai_Ethics](https:\u002F\u002Fgithub.com\u002Fell0ry\u002Fai_ethics) - Automated sentiment analysis pipelines to examine media bias in public perception of ChatGPT\n- [Ai_Ethics](https:\u002F\u002Fgithub.com\u002FFH-kiel-lectures\u002FAI_Ethics) - Data Science - Ethical Artificial Intelligence (SS 2022)\n- [Ai_Ethics_Bookclub](https:\u002F\u002Fgithub.com\u002Fofchurches\u002FAI_ethics_bookclub) - This repository has details regarding books read and to be read by the AICN Ethics Bookclub\n- [Ai_Ethics_Final_Project](https:\u002F\u002Fgithub.com\u002FJamey-UCWV\u002FAI_Ethics_Final_Project) - Final Project for MBDA AI Ethics\n- [Ai_Ethics_Society](https:\u002F\u002Fgithub.com\u002Fyedeka\u002FAI_Ethics_Society) - OMSCS_Subject\n- [Ai_For_Efficient_Programming](https:\u002F\u002Fgithub.com\u002Ffhdsl\u002FAI_for_Efficient_Programming) - This course on AI for software development explores the use of AI large language models (ChatGPT, Bard, etc) and their potential benefits…\n- [Ai_For_Ethics](https:\u002F\u002Fgithub.com\u002FBanele252\u002FAI_for_ethics) - The impact of utilizing privacy preserving techniques in advanced analytics\n- [Ai_Gdpr](https:\u002F\u002Fgithub.com\u002Fdimits-ts\u002Fai_gdpr) - A brief report exploring the impact of AI technologies on European citizens and the compliance challenges posed by the General Data Prote…\n- [Aiethics-Safety2020](https:\u002F\u002Fgithub.com\u002Ftonyteolis\u002Faiethics-safety2020) - This collection of policies, reports, and articles on Ethics and Safety in Artificial Intelligence is the result of a desire to learn the…\n- [Aiethics4Science](https:\u002F\u002Fgithub.com\u002Fsavvy379\u002Faiethics4science) - AI Ethics education material designed for scientists\n- [Aiethicsconsulting](https:\u002F\u002Fgithub.com\u002Frdmusrname\u002Faiethicsconsulting) - Provides expert consulting services on AI ethics, helping organizations navigate complex ethical issues in AI.\n- [Ais-Ethics-Orgs](https:\u002F\u002Fgithub.com\u002Ffititnt\u002Fais-ethics-orgs) - [work-in-progress] Curated list of organizations related to Ethics in Artificial Intelligence and Autonomous Systems (AI\u002FAS)\n- [Ais-Ethics-Standards](https:\u002F\u002Fgithub.com\u002FEticaAI\u002Fais-ethics-standards) - [work-in-progress] Curated list of standards related to Ethics of Autonomous and Intelligent Systems (A\u002FIS)\n- [Applying-The-Ethics-Of-Ai-A-Systematic-Review-Of-Tools-For-Developing-And-Assessing-Ai-Based-System](https:\u002F\u002Fgithub.com\u002FJuagaleanosa\u002FApplying-the-ethics-of-AI-a-systematic-review-of-tools-for-developing-and-assessing-AI-based-system) - Documentación por títulos de artículo de revsión de la ética para la inteligencia artificial.\n- [Auto-Correct](https:\u002F\u002Fgithub.com\u002Fmayameme\u002Fauto-correct) - A brief background to my research about ML, AI and ethics\n- [Awesome-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fawesomelistsio\u002Fawesome-ai-ethics) - A curated list of frameworks, tools, research papers, guidelines, and resources for AI ethics, focusing on fairness, accountability, tran…\n- [Awesome-Ml-Model-Governance](https:\u002F\u002Fgithub.com\u002Fnholuongut\u002FAwesome-ML-Model-Governance) - Model Governance, Ethics, Responsible AI\n- [Awesome-Openness-Ai](https:\u002F\u002Fgithub.com\u002FtamaraaPrs\u002Fawesome-openness-AI) - Curated list of papers or contents about openness in AI, in particular, in the context of AI ethics\n- [Biasbounty1_Humaneintelligence](https:\u002F\u002Fgithub.com\u002FKristiArbo\u002Fbiasbounty1_humaneintelligence) - This repo contains my coding notebook for the tutorial series I made for the beginner level bias bounty challenge hosted by Humane Intell…\n- [Blockchain-Ethics](https:\u002F\u002Fgithub.com\u002FSciEcon\u002Fblockchain-ethics) - Replication Code for - AI Ethics on Blockchain - Topic Analysis on Twitter Data for Blockchain Security\n- [Bucovia](https:\u002F\u002Fgithub.com\u002FJonBarrueco\u002FBucovIA) - AI Ethics project. This repository gathers all the documents related to the project called \"BucovIA\". This project makes use of machine l…\n- [Calpoly-Aiel](https:\u002F\u002Fgithub.com\u002Fquinthemint\u002Fcalpoly-aiel) - A site that hosts the Cal Poly AI Ethics Lab\n- [Corporate_Ai_Ethics_Guideline_Analysis](https:\u002F\u002Fgithub.com\u002FKensuzuki95\u002FCorporate_AI_Ethics_Guideline_Analysis) -  corporate AI ethics guidelines analysis\n- [Credit-Risk_Ethics_And_Ai](https:\u002F\u002Fgithub.com\u002Folimpiasannucci\u002FCredit-Risk_Ethics_and_AI) - feature importances of credit risks and ethics\n- [Cross-Model-Evaluation-Judging-Ai-Ethics-And-Alignment-Responses-With-Language-Models](https:\u002F\u002Fgithub.com\u002Fsultanrafeed\u002FCross-Model-Evaluation-Judging-AI-Ethics-and-Alignment-Responses-with-Language-Models) - This study aims to evaluate the quality of previously generated responses using various large language models (LLMs) as evaluators.\n- [Csc2541-F19](https:\u002F\u002Fgithub.com\u002Fecreager\u002Fcsc2541-f19) - CSC 2541F - AI and Ethics - Mathematical Foundations and Algorithms Fall, 2019\n- [Csc2541-F19](https:\u002F\u002Fgithub.com\u002Fjohntiger1\u002Fcsc2541-f19) - CSC 2541F - AI and Ethics - Mathematical Foundations and Algorithms Fall, 2019\n- [Cse582-Final-Project](https:\u002F\u002Fgithub.com\u002Fandre-ye\u002Fcse582-final-project) - Code for CSE 582 - AI Ethics final project - Representing the Discursive Construction of Morality\n- [Csi5195-Ethicsinai-Finalreport](https:\u002F\u002Fgithub.com\u002FSharuGitHubSpace\u002FCSI5195-EthicsinAI-FinalReport) - CSI 5195 - Ethics in Artificial intelligence - Final report - Supporting Document 1\n- [Dall-E2-Cartography-Ethics](https:\u002F\u002Fgithub.com\u002FGISense\u002FDALL-E2-Cartography-Ethics) - An AI-generated map detector to distinguish AI-generated maps and human-designed maps.\n- [Data-And-Ai-Ethics-Governance-And-Privacy](https:\u002F\u002Fgithub.com\u002FTechFaven\u002FData-and-AI-Ethics-Governance-and-Privacy) - AGI control dilemma\n- [Data-Ethics-And-Society-Reading-Group](https:\u002F\u002Fgithub.com\u002Fdata-ethics-and-society\u002Fdata-ethics-and-society-reading-group) - Data ethics and society reading group for cross government. We run sessions on books and articles relating to ethics in data science and AI…\n- [Dataethics4Allhackathon](https:\u002F\u002Fgithub.com\u002FDariaAza\u002FDataEthics4AllHackathon) - AI in Criminal Justice\n- [Debiasing-Community-Detection](https:\u002F\u002Fgithub.com\u002Fsasibhushan3\u002FDebiasing-Community-Detection) - AI Ethics Term Project\n- [Derai](https:\u002F\u002Fgithub.com\u002FPrat-ikea\u002FDERAI) - Our Digital Ethics and Responsible AI code reorganizes information in predictions and user behavior to prioritize explainability. Our mul…\n- [Dilemmasearcher](https:\u002F\u002Fgithub.com\u002Feddiman\u002FDilemmaSearcher) - Project in INFO381 - Advanced Topics in Artificial Intelligence - Moral and Ethics in AI, Spring 2017\n- [Ds_517_Ai_Ethics](https:\u002F\u002Fgithub.com\u002Fshobharanip\u002FDS_517_AI_Ethics) - DS_517_AI_Ethics\n- [Ethicai](https:\u002F\u002Fgithub.com\u002Fharslash\u002FEthicAI) - An interactive educational platform designed to make learning Artificial Intelligence (AI) ethics fun and easily accessible for universit…\n- [Ethicai](https:\u002F\u002Fgithub.com\u002Fkwon514\u002FEthicAI) - EthicAI is an interactive educational platform designed to make learning Artificial Intelligence (AI) ethics fun and easily accessible fo…\n- [Ethical-Ai-Courses](https:\u002F\u002Fgithub.com\u002Fsnehilsanyal\u002Fethical-ai-courses) - A list of courses on Ethics in AI for self-learning.\n- [Ethics](https:\u002F\u002Fgithub.com\u002Fzulrich91\u002FEthics) - The repository contains a number of interesting resources on ethics in general, ethics of AI and the ethical concerns of AI in medecine.\n- [Ethics--Regulation--Law-For-Intelligentsystems](https:\u002F\u002Fgithub.com\u002Fpranigopu\u002Fethics--regulation--law-for-intelligentSystems) - A record of my work in the course \"Ethics, Regulation and Law in Advanced Digital Information Processing and Decision Making\" in my MSc. AI.\n- [Ethics-Aixeoxrs](https:\u002F\u002Fgithub.com\u002FJohMast\u002FEthics-AIxEOxRS) - Ethics AIxEOxRS\n- [Ethics-And-Ai](https:\u002F\u002Fgithub.com\u002Fherogrl\u002Fethics-and-ai) - Articulate Rise tabs intraction on Ethics and AI\n- [Ethics-Education](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fethics-education) - AI Ethics educational material 🤗\n- [Ethics-Fairness-And-Explanation-In-Ai](https:\u002F\u002Fgithub.com\u002FZhangzl0304\u002FEthics-Fairness-and-Explanation-in-AI) - Coursework at Imperial College London\n- [Ethics-For-Design-Robotics-And-Ai](https:\u002F\u002Fgithub.com\u002FMasoomeh-akbari\u002FEthics-for-Design-Robotics-and-AI) - ethics for designing robots\n- [Ethics-In-Ai](https:\u002F\u002Fgithub.com\u002FNavaneethanRajasekaran\u002FEthics-in-AI) - This Essay is about my thoughts on using AI for recruitment.\n- [Ethics-In-Ai](https:\u002F\u002Fgithub.com\u002Ftilwani\u002FEthics-in-AI) - Ethical issues existing in the AI systems.\n- [Ethics-In-Ai-And-Intelligent-Interfaces](https:\u002F\u002Fgithub.com\u002Fmbar0075\u002FEthics-in-AI-and-Intelligent-Interfaces) - Deliverables relating to the Ethics in Artificial Intelligence & Intelligent Interfaces University Units\n- [Ethics-In-Ai-Cnns-For-Lung-Disease](https:\u002F\u002Fgithub.com\u002Fmatthewivan\u002FEthics-in-AI-CNNs-for-Lung-Disease) - Exploring CNNs and autoencoders for lung disease detection, with a focus on ethical implications in medical applications, including COVID…\n- [Ethics-In-Artificial-Intelligence](https:\u002F\u002Fgithub.com\u002Fcartabinaria\u002Fethics-in-artificial-intelligence) - A collection of resources for the Ethics in Artificial Intelligence (91257) course of the Master in Artificial Intelligence\n- [Ethics-Professional](https:\u002F\u002Fgithub.com\u002FEthics-Professionals-Game-Corporation\u002FEthics-Professional) - This is the ai based model that runs the chat based interface on both our companies website, our companies internal use, and native to th…\n- [Ethics-Simulation-Server-Client](https:\u002F\u002Fgithub.com\u002Fmlund2k\u002FEthics-Simulation-Server-Client) - Project Assets for academic project simulating an AI ethics problem using object oriented programming, and json servers\n- [Ethics-Tree](https:\u002F\u002Fgithub.com\u002FThilo-Hagendorff\u002Fethics-tree) - Mapping the Ethics of Generative AI\n- [Ethics_And_Policy_Resources_For_Pennaitech](https:\u002F\u002Fgithub.com\u002FPennShenLab\u002FEthics_and_Policy_Resources_for_PennAITech) - This collection of resources offers a comprehensive overview of key policies, initiatives, and literature resources related to various aspects of aging, dementia, and Alzheimer's disease\n- [Ethics_In_Ai](https:\u002F\u002Fgithub.com\u002Fheathervant\u002Fethics_in_AI) - This is reflection piece on Michal Kosinski's talk https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NesTWiKfpD0 and a discussion of the state of privacy a…\n- [Ethicsai](https:\u002F\u002Fgithub.com\u002Fprodp\u002FEthicsAI) - A Study on the Ethics of AI\n- [Ethicsai](https:\u002F\u002Fgithub.com\u002Fdennislamcv1\u002FEthicsAI) - Ethics in the Age of AI\n- [Ethicscore](https:\u002F\u002Fgithub.com\u002Fnatalia-moral\u002Fethicscore) - Ethics-by-design AI applications evaluator and enabler.\n- [Ethicsforais](https:\u002F\u002Fgithub.com\u002FWowo51\u002FEthicsForAIs) - Here are ethical principles for AI's to read.\n- [Ethicsinai](https:\u002F\u002Fgithub.com\u002Fkiragptassist\u002FEthicsinAI) - Research on the impact of ethics on AI technologies throughout history and present day\n- [Ethiquest-Ai-Dilemma-Game](https:\u002F\u002Fgithub.com\u002FEthiQuest\u002FEthiQuest-AI-Dilemma-Game) - The AI Ethics Dilemma Game is an interactive, educational tool designed to help IT professionals and leaders navigate complex ethical sce…\n- [Face-Recognition](https:\u002F\u002Fgithub.com\u002FAishPadmanabha\u002Fface-recognition) - When I did this project (over 3 years ago), my intentions were to use it to nab criminals. With awareness of ethics in AI with the underl…\n- [Fairness-In-Translation](https:\u002F\u002Fgithub.com\u002Fgreenmonn\u002Ffairness-in-translation) - Team project investigating fairness problem on machine translation (AI Ethics Lecture in KAIST)\n- [Fake_News_Detection](https:\u002F\u002Fgithub.com\u002Fdivss98\u002FFake_News_Detection) - Code for Fake News Detection using NLP and Neural Networks for the scientific article AI Ethics - An Evaluation of Modern World Technologi…\n- [Fate-In-Ai](https:\u002F\u002Fgithub.com\u002Fijdutse\u002Ffate-in-ai) - Relevant information about the project on Fairness, Accountability, Transparency and Ethics in Artificial Intelligence (FATE in AI).\n- [Formal-Ethics-Ontology](https:\u002F\u002Fgithub.com\u002Fzariuq\u002FFormal-Ethics-Ontology) - Notes on the formalization of ethical theory, which should help with analyses and reasoning about AI safety and ethics.\n- [Generativeaiethicsplaybook](https:\u002F\u002Fgithub.com\u002Fjesmith14\u002FGenerativeAIEthicsPlaybook) - This is the color-coded pdf version of the Generative AI Ethics Playbook\n- [Global-Ai-Dynamics-Mapping-The-Ai-Utilization-Across-Nations-](https:\u002F\u002Fgithub.com\u002Feevamehra\u002FGlobal-AI-Dynamics-Mapping-the-AI-Utilization-across-Nations-) - Analyzing AI across nations - Investment, Policies, Industry Adoption, Talent Development, Tech Advancements, Ethics, and Collaboration of…\n- [Global_Ai_Ethics_Conference_Official_Website--Dubai](https:\u002F\u002Fgithub.com\u002Fmokshadewmini\u002FGlobal_AI_Ethics_Conference_Official_Website--Dubai) - This repository contains the source code for the official website of the Global AI Ethics Conference 2024, held in Dubai.\n- [Goal](https:\u002F\u002Fgithub.com\u002Fschizyfos\u002FGoal) - Creating a community for turning Valiant's and Rényi's vision into an AI model\n- [H6_4C_Ethics_In_Ai](https:\u002F\u002Fgithub.com\u002Fhsma-programme\u002Fh6_4c_ethics_in_ai) - Materials for HSMA 6 Session 4C\n- [Hamid](https:\u002F\u002Fgithub.com\u002FHamidurRahmanBd\u002FHamid) - AI Ethics for Future\n- [Healthy-Ui](https:\u002F\u002Fgithub.com\u002Fstanley-utf8\u002Fhealthy-ui) - McGill AI Ethics Lab Research - work includes identifying & dispelling negative effects of recommender systems\n- [Humor-Offensiveness-Detection](https:\u002F\u002Fgithub.com\u002FMahnazshamissa\u002FHumor-Offensiveness-detection) - This is a shared project as part of the AI ethics course at Asigmo, where an algorithm that detects humor and offensiveness is created wi…\n- [Integral-Force](https:\u002F\u002Fgithub.com\u002Fabdulsalamamtech\u002Fintegral-force) - Integral Force is an AI and Blockchain platform using AI and Blockchain to revolutionize education by creating personalized, transparent …\n- [Internintelligence_Ai_Ethics_And_Bias_Evaluation](https:\u002F\u002Fgithub.com\u002FAbedini81\u002FInternIntelligence_AI_Ethics_and_Bias_Evaluation) - This project evaluates the fairness of a machine learning model trained on the Adult Income Dataset to predict income. Using metrics like…\n- [Intro-To-Ai-Ethics-Free-Course-Kaggle](https:\u002F\u002Fgithub.com\u002Fafondiel\u002FIntro-to-AI-Ethics-Free-Course-Kaggle) - Explore practical tools to guide the moral design of AI systems.\n- [Introtoaiethics-Kaggle](https:\u002F\u002Fgithub.com\u002Fmariammarques\u002FIntroToAIEthics-Kaggle) - Notebooks developed during the Intro to AI Ethics course, offered by Kaggle (https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintro-to-ai-ethics)\n- [Kaggle-Courses-Intro-To-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fgrapestone5321\u002FKaggle-Courses-Intro-to-AI-Ethics) - Kaggle-Courses-Intro-to-AI-Ethics\n- [Law-And-Ethics-In-Ai](https:\u002F\u002Fgithub.com\u002FDarioTortorici\u002FLaw-and-Ethics-in-AI) - My personal selection of key concepts in the course of Law and Ethics in Artificial Intelligence at the University of Trento, non-attenda…\n- [Lifeaintfair](https:\u002F\u002Fgithub.com\u002Felizabethayalamojica\u002Flifeaintfair) - A digital board game and text-base adventure exploring the consequences and ethics of AI implementation in all stages of our lives.\n- [Llm-Detection-Challenge](https:\u002F\u002Fgithub.com\u002FSeasonXC\u002FLLM-Detection-Challenge) - This project aims to develop AI models that can differentiate between student-written essays and those generated by Large Language Models…\n- [Mg-3-Aiethics](https:\u002F\u002Fgithub.com\u002Fempower-lab-dartmouth\u002Fmg-3-AIEthics) - AI Ethics - AI Learning Mini-Game 3\n- [Nak-Aiethics](https:\u002F\u002Fgithub.com\u002FTilBlechschmidt\u002FNAK-AIEthics) - Science paper that does a meta-study on AI ethics to determine whether or not significant research has been done in this area.\n- [Nft-2023](https:\u002F\u002Fgithub.com\u002FHCI-Blockchain\u002FNFT-2023) - Replication Code for - On the Mechanics of NFT Valuation - AI Ethics and Social Media\n- [Nottsai-Meetup-June4-2019](https:\u002F\u002Fgithub.com\u002FLazymindz\u002Fnottsai-meetup-june4-2019) - Ethics Guidelines for Trustworthy AI\n- [Ntu_Msai_Ai6101_Introdution](https:\u002F\u002Fgithub.com\u002FAccSrd\u002FNTU_MSAI_AI6101_Introdution) - [AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6101 of Semes…\n- [Operationalizingaiethics](https:\u002F\u002Fgithub.com\u002Fstephaniekelley\u002FoperationalizingAIethics) - IVADO Fin-ML Workshop - Operationalizing AI Ethics in the Modeling Process\n- [Paper-Reviews](https:\u002F\u002Fgithub.com\u002Frayruchira\u002FPaper-Reviews) - Reviews of papers related to robot learning, computer vision, audio , NLP and AI Ethics\n- [Papers](https:\u002F\u002Fgithub.com\u002Fw3b3r00t\u002FPapers) - Topics in philosophy, ethics, technology, & culture.\n- [Philosophical_Ai_Chatbot](https:\u002F\u002Fgithub.com\u002Fnamkidong98\u002FPhilosophical_AI_ChatBot) - Sogang Univ, 2023_1st semester, PHI4061, Artificial Intelligence and Ethics Practice\n- [Philosophy-And-Ethics](https:\u002F\u002Fgithub.com\u002FAryia-Behroziuan\u002FPhilosophy-and-ethics) - This section should include only a brief summary of another article. See Wikipedia:Summary style for information on how to properly incor…\n- [Pimeyesdataethics](https:\u002F\u002Fgithub.com\u002Fchristianmcb\u002FPimEyesDataEthics) - Group report on data ethics and governance with relation to AI application PimEyes.\n- [Pittchallenge2023](https:\u002F\u002Fgithub.com\u002FLoganWarren\u002FPittChallenge2023) - Governance and Ethics in AI - Pitt Challenge\n- [Project-Aru-Ethics-In-Ai-](https:\u002F\u002Fgithub.com\u002FSiddhant721\u002FProject-ARU-Ethics-In-AI-) - Ethical Considerations in AI and Data Science - Ensuring Responsible Innovation\n- [Psychological_Ai_Research](https:\u002F\u002Fgithub.com\u002Fthe-ethical-ai\u002FPsychological_AI_Research) - [In-Progress] PAIR Project - AI + Machine Learning + Psychology + Ethics = This Project\n- [Pytexas-Ethics-In-Ai---Shap](https:\u002F\u002Fgithub.com\u002FLioGabriella\u002FPyTexas-Ethics-in-AI---SHAP) - Shapley Values Jupyter Notebook\n- [Realmofrespect](https:\u002F\u002Fgithub.com\u002Friyadhuddin\u002Frealmofrespect) - a visionary online platform where ethics, empathy, and digital responsibility reign supreme. We believe in harnessing the power of techn…\n- [Reddit_Ai_Topic_Analysis](https:\u002F\u002Fgithub.com\u002Fleah-rtd\u002FReddit_AI_Topic_Analysis) - Unveiling Trends in AI Ethics - Exploring the Ethical Dimensions of AI, Prepared for SICSS 2023 - Tor Vergata\n- [Relaieo](https:\u002F\u002Fgithub.com\u002FAudit4SG\u002FRelAIEO) - Relational AI Ethics Ontology\n- [Research-Paper-Summary-Project](https:\u002F\u002Fgithub.com\u002FM-K-Aakash\u002FResearch-Paper-Summary-Project) - Generative AI accelerates drug discovery by designing new drugs, repurposing existing ones, and enabling personalized treatments. Despite…\n- [Resources](https:\u002F\u002Fgithub.com\u002FNeurotechEducationProject\u002Fresources) - A collection of open educational resources exploring the intersection of Artificial Intelligence and Neuroscience. This project provides …\n- [Retrieval-Recsys-Ai-Ethics-Regulation-Tutorial-Sigir22](https:\u002F\u002Fgithub.com\u002Fsocialcomplab\u002FRetrieval-RecSys-AI-Ethics-Regulation-Tutorial-SIGIR22) - Tutorial Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and Regulation\n- [Risks-Of-Narrow-Ai](https:\u002F\u002Fgithub.com\u002FAryia-Behroziuan\u002FRisks-of-narrow-AI) - ... research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security ri…\n- [Sandbox](https:\u002F\u002Fgithub.com\u002Fabozaralizadeh\u002FSandBox) - A simulation where AI makes daily high-level decisions for a virtual world, balancing ethics, sustainability, and impact.\n- [Sdm23](https:\u002F\u002Fgithub.com\u002FDATA-Transpose\u002Fsdm23) - SDM2023 Tutorial - When Mining Healthcare Data Meets AI Ethics - Towards Privacy-preservation, Fairness and Trustworthy\n- [Senior-Research](https:\u002F\u002Fgithub.com\u002Fchris-junior\u002FSenior-research) - AI ethics of ChatGPT\n- [Sentiment-Rating](https:\u002F\u002Fgithub.com\u002Fai4society\u002Fsentiment-rating) - The purpose of this repo is to run sentiment analysis models, test them for their sensitivity to change in gender and race related attrib…\n- [Session-20-Ethics-In-Data-Science](https:\u002F\u002Fgithub.com\u002FAI-Wales\u002FSession-20-Ethics-In-Data-Science) - Steph Locke's talk on Ethics in Data Science and AI\n- [Sociotechnical-Transparency-Abm](https:\u002F\u002Fgithub.com\u002Fazgausen\u002Fsociotechnical-transparency-abm) - This repository contains the core logic for the code used in - Gausen, A., Guo, C. & Luk, W. An approach to sociotechnical transparency of…\n- [Solution_To_Ethical_Issue_In_Ai](https:\u002F\u002Fgithub.com\u002Fjiaxuan-oss\u002FSolution_to_Ethical_Issue_in_AI) - FIT1055 A2a-IT professional practice and ethics Assignment 2a Solution to Ethical Issue in AI\n- [Sora](https:\u002F\u002Fgithub.com\u002FAI-Now\u002FSora) - Sora is a hypothetical synth, which follows the ethics of the AI Now project.\n- [Startyourjourney](https:\u002F\u002Fgithub.com\u002Fmetaversityfoundation\u002FStartYourJourney) - Starting place to become a Reality Engineer - 3D, XR, AI, Ethics\n- [Teenytinycastle](https:\u002F\u002Fgithub.com\u002FNkluge-correa\u002FTeenyTinyCastle) - Educational tools for AI Ethics and Safety research 🛠️🔬\n- [The-Ethics-Of-Ai-Navigating-Complex-Challenges-And-Opportunities](https:\u002F\u002Fgithub.com\u002Fysyk2021\u002Fthe-ethics-of-ai-navigating-complex-challenges-and-opportunities) - The Ethics of AI - Navigating Complex Challenges and Opportunities\n- [Tools-From-Ai](https:\u002F\u002Fgithub.com\u002Fsb3ly\u002Ftools-from-AI) - # This tool is designed to detect whether the email exists on the target site by trying to guess using different methods. # Make sure the…\n- [Towards_Certified_Ethical_Ai](https:\u002F\u002Fgithub.com\u002Fstuenofotso\u002FTowards_Certified_Ethical_AI) - This repository is about a proposal to allow the definition of ethical artificial intelligences for which ethics can be certified. The pr…\n- [Transferlearning.Github.Io](https:\u002F\u002Fgithub.com\u002Fanthonymalumbe\u002Ftransferlearning.github.io) - This GitHub blog explores the transfer & sharing of knowledge in data & AI. Dive into - Real-world applications of data & AI Communicating…\n- [Trustworthyai](https:\u002F\u002Fgithub.com\u002Focatak\u002Ftrustworthyai) - Trustworthy AI - From Theory to Practice book. Explore the intersection of ethics and technology with 'Trustworthy AI - From Theory to Prac…\n- [Tutorteach.Ai](https:\u002F\u002Fgithub.com\u002FJMadhan1\u002FTutorteach.ai) - TutorTeach.ai is an AI-powered learning platform offering personalized video lessons, text summaries, assignments, and score predictions.…\n- [Upliftingpoleai_Bsc_Dissertation](https:\u002F\u002Fgithub.com\u002Foakleighw\u002FupliftingPoleAi_BSc_dissertation) - Contains dissertation code and report for my level 3 BSc Computer Science 'Project' module. I achieved 84% in this assessment, and I am v…\n- [Waterlily](https:\u002F\u002Fgithub.com\u002FLilypad-Tech\u002FWaterlily) - A project bringing ethics back to AI\n- [Weavesphere-2022-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fspackows\u002FWEAVESPHERE-2022-AI-Ethics) - AI Ethics - The Content Design Perspective\n- [White-Paper---Ais-With-Internal-Ethical-Understanding](https:\u002F\u002Fgithub.com\u002Fme9hanics\u002FWhite-Paper---AIs-with-Internal-Ethical-Understanding) - A white paper writing for my \"Debates in Social Data Science\" course at the Central European University. The paper argues that the curren…\n- [Worldwide_Ai-Ethics](https:\u002F\u002Fgithub.com\u002FNkluge-correa\u002Fworldwide_AI-ethics) - Worldwide AI Ethics (WAIE) is a systematic literature review done by AIRES researchers at PUCRS.\n- [Xai-Data-Science](https:\u002F\u002Fgithub.com\u002F2002jai\u002FXAI-Data-Science) - XAI-Data-Science - Explore the world of Explainable AI (XAI) through techniques, tools, and applications. Foster transparency, ethics, and…\n- [Yzv103E-Intr.Toai-Dataeng-Ethics](https:\u002F\u002Fgithub.com\u002Fserdarbicici-visualstudio\u002FYZV103E-Intr.toAI-DataEng-Ethics) - YZV 103E Intr. to AI & Data Eng& Ethics materials and team project for İstanbul Technical University\n\n### Frameworks\n- [8OWLS \u002F WeEvolve](https:\u002F\u002Fgithub.com\u002Faro-brez\u002Fweevolve) - Self-evolving AI agent framework with 8 specialized agents, SEED protocol (8-phase recursive self-improvement), knowledge transfer, and MMORPG progression. Supports 15 models across 7 providers. [github](https:\u002F\u002Fgithub.com\u002Faro-brez\u002Fweevolve) | [website](https:\u002F\u002F8owl.ai)\n- [Agent-LLM](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAgent-LLM) - An Artificial Intelligence Automation Platform. [github](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAgent-LLM)\n- [AgentDock](https:\u002F\u002Fgithub.com\u002FAgentDock\u002FAgentDock) - Stop wrestling with countless APIs and complex integrations. AgentDock provides the open-source foundation to build, manage, and deploy production-ready AI agents and workflows, frictionlessly. [github](https:\u002F\u002Fgithub.com\u002Fagentdock\u002Fagentdock)\n- [AgentFlow](https:\u002F\u002Fgithub.com\u002Flupantech\u002FAgentFlow) - A trainable multi-agent framework that coordinates four specialized modules (planner, executor, verifier, generator) through in-the-flow optimization, achieving significant performance gains over monolithic approaches by directly training the planner within multi-turn task loops using Flow-GRPO reinforcement learning. Any tools can be smoothly integrated into this framework, math, coding, scientific, search, finance, news, etc. [github](https:\u002F\u002Fgithub.com\u002Flupantech\u002FAgentFlow) | [website](https:\u002F\u002Fagentflow.stanford.edu\u002F) | [paper](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2510.05592)\n- [Agentic Context Engine](https:\u002F\u002Fgithub.com\u002Fkayba-ai\u002Fagentic-context-engine) - Self-improving agents that learn from execution feedback. LangChain integration for agents that curate their own context. [github](https:\u002F\u002Fgithub.com\u002Fkayba-ai\u002Fagentic-context-engine)\n- [AgentScope](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002Fagentscope) - Start building LLM-empowered multi-agent applications in an easier way. [github](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002Fagentscope)\n- [AgentUp](https:\u002F\u002Fgithub.com\u002FRedDotRocket\u002FAgentUp) - Designed with security, scalability, and extensibility at its foundation, AgentUp streamlines development through a configuration-driven architecture and rich plugin ecosystem [github](https:\u002F\u002Fgithub.com\u002FRedDotRocket\u002FAgentUp)\n- [Astron](https:\u002F\u002Fgithub.com\u002Fiflytek\u002Fastron-agent) - Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents. [github](https:\u002F\u002Fgithub.com\u002Fiflytek\u002Fastron-agent)\n- [auto-co](https:\u002F\u002Fgithub.com\u002FNikitaDmitrieff\u002Fauto-co-meta) - Autonomous AI company OS — give it a mission and 14 expert-persona agents (CEO, CTO, CFO, engineer, marketer, critic) run your startup 24\u002F7 with no human intervention. Built-in convergence rules prevent planning loops; human escalation via Telegram for true blockers only. MIT open source. [github](https:\u002F\u002Fgithub.com\u002FNikitaDmitrieff\u002Fauto-co-meta) | [website](https:\u002F\u002Fauto-co-landing-production.up.railway.app)\n- [Auto-GPT](https:\u002F\u002Fgithub.com\u002FTorantulino\u002FAuto-GPT) - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.\n- [Bernstein](https:\u002F\u002Fgithub.com\u002Fchernistry\u002Fbernstein) - Deterministic multi-agent orchestrator that spawns parallel AI coding agents (Claude Code, Codex CLI, Gemini CLI) from a single goal, verifies with tests, and auto-commits. Zero LLM tokens on coordination. [github](https:\u002F\u002Fgithub.com\u002Fchernistry\u002Fbernstein)\n- [Botpress](https:\u002F\u002Fgithub.com\u002Fbotpress\u002Fbotpress) - The building blocks for building chatbots. [github](https:\u002F\u002Fgithub.com\u002Fbotpress\u002Fbotpress)\n- [Crew.AI](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI) - Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.\n- [Dust](https:\u002F\u002Fgithub.com\u002Fdust-tt\u002Fdust) - Design and Deploy Large Language Model Apps. [github](https:\u002F\u002Fgithub.com\u002Fdust-tt\u002Fdust)\n- [Giselle](https:\u002F\u002Fgiselles.ai\u002F) - Giselle is an open source AI App Builder for agentic workflows. Giselle enables seamless human-AI collaboration, helping to automate complex tasks and streamline workflows efficiently [website](https:\u002F\u002Fgiselles.ai\u002F)\n- [GNAP](https:\u002F\u002Fgithub.com\u002Ffarol-team\u002Fgnap) - Git-Native Agent Protocol — an open RFC for coordinating AI agent teams through a git repository. Zero servers, zero databases — 4 JSON files define the entire coordination protocol. Language\u002Fruntime agnostic with immutable git history audit trail. [github](https:\u002F\u002Fgithub.com\u002Ffarol-team\u002Fgnap)\n- [IBM Bee](https:\u002F\u002Fgithub.com\u002Fi-am-bee\u002Fbee-agent-framework) - The framework for building scalable agentic applications.\n- [IntelliAgent](https:\u002F\u002Fgithub.com\u002Faws-samples\u002FIntelli-Agent) - Chatbot Portal with Agent: Streamlined Workflow for Building Agent-Based Applications.\n- [Khoj-ai](https:\u002F\u002Fgithub.com\u002Fkhoj-ai\u002Fkhoj) - Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral) [github](https:\u002F\u002Fgithub.com\u002Fkhoj-ai\u002Fkhoj) | [website](https:\u002F\u002Fkhoj.dev)\n- [Lagent](https:\u002F\u002Fgithub.com\u002FInternLM\u002Flagent) - A lightweight framework for building LLM-based agents. [github](https:\u002F\u002Fgithub.com\u002FInternLM\u002Flagent)\n- [LangChain Agents](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain) - Build context-aware reasoning applications.\n- [LLM Agents](https:\u002F\u002Fgithub.com\u002Fmpaepper\u002Fllm_agents) - Build agents which are controlled by LLMs. [github](https:\u002F\u002Fgithub.com\u002Fmpaepper\u002Fllm_agents)\n- [llama-agentic-system](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002Fllama-agentic-system) - Agentic components of the Llama Stack APIs. [github](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002Fllama-agentic-system)\n- [Mastra](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra) - Mastra is an opinionated TypeScript framework that helps you build AI applications and features quickly. [github](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra)\n- [Microsoft Agent Framework](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fagent-framework\u002Foverview\u002Fagent-framework-overview) - Microsoft's unified open-source framework combining AutoGen and Semantic Kernel, designed for building AI agents and multi-agent workflows in .NET and Python, offering AI agents with LLM support and graph-based workflows for complex multi-step tasks [docs](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fagent-framework\u002Foverview\u002Fagent-framework-overview)\n- [Microsoft Magentic-One](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Farticles\u002Fmagentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks\u002F) - A Generalist Multi-Agent System for Solving Complex Tasks.\n- [Modus](https:\u002F\u002Fgithub.com\u002Fhypermodeinc\u002Fmodus) - An open source, serverless framework for building intelligent agents and APIs in Go or AssemblyScript (a TypeScript-like language). [github](https:\u002F\u002Fgithub.com\u002Fhypermodeinc\u002Fmodus)\n- [NeuroLink](https:\u002F\u002Fgithub.com\u002Fjuspay\u002Fneurolink) - TypeScript-first agent framework with multi-step agentic loops, tool execution control, persistent memory (Redis\u002FSQLite\u002FS3), HITL workflows, MCP client integration, and 13 LLM providers. Production-proven at enterprise scale.\n- [OpenAI Swarm](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm) - Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.\n- [Phidata](https:\u002F\u002Fgithub.com\u002Fphidatahq\u002Fphidata) - Build multi-modal Agents with memory, knowledge, tools and reasoning. Chat with them using a beautiful Agent UI.\n- [Pinchwork](https:\u002F\u002Fgithub.com\u002Fanneschuth\u002Fpinchwork) - Open-source agent-to-agent task marketplace where agents delegate tasks, pick up work, and earn credits. REST API, Python SDK, LangChain\u002FCrewAI\u002FMCP integrations. [github](https:\u002F\u002Fgithub.com\u002Fanneschuth\u002Fpinchwork) | [website](https:\u002F\u002Fpinchwork.dev)\n- [Portia AI](https:\u002F\u002Fgithub.com\u002FportiaAI\u002Fportia-sdk-python\u002F) - Portia is a new open source agentic Python framework designed for creating reliable agents in production. [github](https:\u002F\u002Fgithub.com\u002FportiaAI\u002Fportia-sdk-python)\n- [Registry Broker](https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fregistry-broker) - A universal index and routing layer for AI agents. Aggregates agent metadata from multiple registries (NANDA, MCP, Virtuals, OpenRouter, A2A, X402 Bazaar) across web2 and web3, normalizes profiles, and provides protocol translation between different agent ecosystems. [github](https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fregistry-broker) | [docs](https:\u002F\u002Fhol.org\u002Fregistry\u002Fdocs)\n- [Strands Agents SDK](https:\u002F\u002Fgithub.com\u002Fstrands-agents\u002Fsdk-python) - A model-driven approach to building AI agents in just a few lines of code. [github](https:\u002F\u002Fgithub.com\u002Fstrands-agents\u002Fsdk-python)\n- [TeamHero](https:\u002F\u002Fgithub.com\u002Fsagiyaacoby\u002FTeamHero) - Open-source multi-agent orchestration platform with a built-in web dashboard, task lifecycle management, knowledge base, and autopilot mode. Manages role-based AI agent teams locally with zero cloud dependency. Built on Claude Code. [github](https:\u002F\u002Fgithub.com\u002Fsagiyaacoby\u002FTeamHero)\n- [Upsonic](https:\u002F\u002Fgithub.com\u002Fupsonic\u002Fupsonic) - Reliable agent framework that supports MCP. [github](https:\u002F\u002Fgithub.com\u002Fupsonic\u002Fupsonic)\n- [Vectara-agentic](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fpy-vectara-agentic) - Vectara-agentic is a framework for creating AI Assistants and agents using Vectara. [github](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fpy-vectara-agentic)\n- [VoltAgent](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent) - An open source TypeScript Framework for building AI agents with built-in LLM observability. [github](https:\u002F\u002Fgithub.com\u002Fvoltagent\u002Fvoltagent)\n\n### LLM Models\n- [42Dot_Llm](https:\u002F\u002Fgithub.com\u002F42dot\u002F42dot_LLM) - 42dot LLM consists of a pre-trained language model, 42dot LLM-PLM, and a fine-tuned model, 42dot LLM-SFT, which is trained to respond to …\n- [Ai-Llm-Comparison](https:\u002F\u002Fgithub.com\u002FAhmet-Dedeler\u002Fai-llm-comparison) - A website where you can compare every AI Model ✨\n- [Aidialer](https:\u002F\u002Fgithub.com\u002Fakiani\u002Faidialer) - A full stack app for interruptible, low-latency and near-human quality AI phone calls built from stitching LLMs, speech understanding too…\n- [Ainote](https:\u002F\u002Fgithub.com\u002FShengqinYang\u002FAINote) - I recently attended the Geekbang \"Large Language Models Application Development Practice Camp\", where I learned about the application dev…\n- [Anygpt](https:\u002F\u002Fgithub.com\u002FOpenMOSS\u002FAnyGPT) - Code for \"AnyGPT - Unified Multimodal LLM with Discrete Sequence Modeling\"\n- [Api-For-Open-Llm](https:\u002F\u002Fgithub.com\u002Fxusenlinzy\u002Fapi-for-open-llm) - Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, X…\n- [Awesome-Azure-Openai-Llm](https:\u002F\u002Fgithub.com\u002Fkimtth\u002Fawesome-azure-openai-llm) - a curated list of 🌌 Azure OpenAI, 🦙Large Language Models, and references with notes.\n- [Awesome-Deep-Learning-Papers-For-Search-Recommendation-Advertising](https:\u002F\u002Fgithub.com\u002Fguyulongcs\u002FAwesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising) - Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR\u002FCVR…\n- [Awesome-Instruction-Tuning](https:\u002F\u002Fgithub.com\u002Fzhilizju\u002FAwesome-instruction-tuning) - A curated list of awesome instruction tuning datasets, models, papers and repositories.\n- [Awesome-Llm-Productization](https:\u002F\u002Fgithub.com\u002Foscinis-com\u002FAwesome-LLM-Productization) - Awesome-LLM-Productization - a curated list of tools\u002Ftricks\u002Fnews\u002Fregulations about AI and Large Language Model (LLM) productization\n- [Awesome-Llm-Prompt-Optimization](https:\u002F\u002Fgithub.com\u002Fjxzhangjhu\u002FAwesome-LLM-Prompt-Optimization) - Awesome-LLM-Prompt-Optimization - a curated list of advanced prompt optimization and tuning methods in Large Language Models\n- [Awesome-Recommend-System-Pretraining-Papers](https:\u002F\u002Fgithub.com\u002Farchersama\u002Fawesome-recommend-system-pretraining-papers) - Paper List for Recommend-system PreTrained Models\n- [Awesome_Role_Of_Small_Models](https:\u002F\u002Fgithub.com\u002Ftigerchen52\u002Fawesome_role_of_small_models) - a curated list of the role of small models in the LLM era\n- [Basaran](https:\u002F\u002Fgithub.com\u002Fhyperonym\u002Fbasaran) - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Tra…\n- [Bentochain](https:\u002F\u002Fgithub.com\u002Fssheng\u002FBentoChain) - A voice-enabled chatbot application built using of 🦜️🔗 LangChain, text-to-speech, and speech-to-text models from 🤗 Hugging Face, and 🍱 Be…\n- [Bisheng](https:\u002F\u002Fgithub.com\u002Fdataelement\u002Fbisheng) - BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include - GenAI…\n- [Brokenhill](https:\u002F\u002Fgithub.com\u002FBishopFox\u002FBrokenHill) - A productionized greedy coordinate gradient (GCG) attack tool for large language models (LLMs)\n- [Building-Llm-Powered-Applications](https:\u002F\u002Fgithub.com\u002FPacktPublishing\u002FBuilding-LLM-Powered-Applications) - Building Large Language Model Applications, Published by Packt\n- [Co-Llm](https:\u002F\u002Fgithub.com\u002Fclinicalml\u002Fco-llm) - Co-LLM - Learning to Decode Collaboratively with Multiple Language Models\n- [Code-Interpreter](https:\u002F\u002Fgithub.com\u002Fhaseeb-heaven\u002Fcode-interpreter) - An innovative open-source Code Interpreter with (GPT,Gemini,Claude,LLaMa) models.\n- [Codebase-For-Incremental-Learning-With-Llm](https:\u002F\u002Fgithub.com\u002Fzzz47zzz\u002Fcodebase-for-incremental-learning-with-llm) - [ACL2024] A Codebase for Incremental Learning with Large Language Models; Official released code for \"Learn or Recall? Revisiting Increme…\n- [Codegen](https:\u002F\u002Fgithub.com\u002Fsalesforce\u002FCodeGen) - CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.\n- [Damo-Seallms](https:\u002F\u002Fgithub.com\u002FDAMO-NLP-SG\u002FDAMO-SeaLLMs) - [ACL 2024 Demo] SeaLLMs - Large Language Models for Southeast Asia\n- [Datainf](https:\u002F\u002Fgithub.com\u002Fykwon0407\u002FDataInf) - DataInf - Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models (ICLR 2024)\n- [Dellma](https:\u002F\u002Fgithub.com\u002FDeLLMa\u002FDeLLMa) - Official Implementation of \"DeLLMa - Decision Making Under Uncertainty with Large Language Models\"\n- [Diffsensei](https:\u002F\u002Fgithub.com\u002Fjianzongwu\u002FDiffSensei) - Implementation of \"DiffSensei - Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation\"\n- [Distserve](https:\u002F\u002Fgithub.com\u002FLLMServe\u002FDistServe) - Disaggregated serving system for Large Language Models (LLMs).\n- [Dnd-Llm-Game](https:\u002F\u002Fgithub.com\u002Ftegridydev\u002Fdnd-llm-game) - MVP of an idea using multiple local LLM models to simulate and play D&D\n- [Ee-Llm](https:\u002F\u002Fgithub.com\u002Fpan-x-c\u002FEE-LLM) - EE-LLM is a framework for large-scale training and inference of early-exit (EE) large language models (LLMs).\n- [Ella](https:\u002F\u002Fgithub.com\u002FTencentQQGYLab\u002FELLA) - ELLA - Equip Diffusion Models with LLM for Enhanced Semantic Alignment\n- [Elm](https:\u002F\u002Fgithub.com\u002FNREL\u002Felm) - ELM is a collection of utilities to apply Large Language Models (LLMs) to energy research.\n- [Empower-Functions](https:\u002F\u002Fgithub.com\u002Fempower-ai\u002Fempower-functions) - GPT-4 level function calling models for real-world tool using use cases\n- [Exaone-3.0](https:\u002F\u002Fgithub.com\u002FLG-AI-EXAONE\u002FEXAONE-3.0) - Official repository for EXAONE built by LG AI Research\n- [Fastmcp](https:\u002F\u002Fgithub.com\u002Fjlowin\u002Ffastmcp) - The fast, Pythonic way to build Model Context Protocol servers 🚀\n- [Fauno-Italian-Llm](https:\u002F\u002Fgithub.com\u002FRSTLess-research\u002FFauno-Italian-LLM) - Get ready to meet Fauno - the Italian language model crafted by the RSTLess Research Group from the Sapienza University of Rome.\n- [Freeze-Omni](https:\u002F\u002Fgithub.com\u002FVITA-MLLM\u002FFreeze-Omni) - ✨✨Freeze-Omni - A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM\n- [Gazelle](https:\u002F\u002Fgithub.com\u002Ftincans-ai\u002Fgazelle) - Joint speech-language model - respond directly to audio!\n- [Generative-Ai-With-Llms](https:\u002F\u002Fgithub.com\u002FRyota-Kawamura\u002FGenerative-AI-with-LLMs) - In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in rea…\n- [Get-Things-Done-With-Prompt-Engineering-And-Langchain](https:\u002F\u002Fgithub.com\u002Fcuriousily\u002FGet-Things-Done-with-Prompt-Engineering-and-LangChain) - LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading a…\n- [Glm-Edge](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FGLM-Edge) - GLM Series Edge Models\n- [Gpt-Code-Assistant](https:\u002F\u002Fgithub.com\u002Fnarenmanoharan\u002Fgpt-code-assistant) - gpt-code-assistant is an open-source coding assistant leveraging language models to search, retrieve, explore and understand any codebase.\n- [Gpt_Llm](https:\u002F\u002Fgithub.com\u002Fianmkim\u002Fgpt_llm) - Multi-GPU setup for inference with GPT NeoX 20B and OPT-30B models in huggingface\n- [Graph-Llm](https:\u002F\u002Fgithub.com\u002FCurryTang\u002FGraph-LLM) - Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs\n- [Groma](https:\u002F\u002Fgithub.com\u002FFoundationVision\u002FGroma) - [ECCV2024] Grounded Multimodal Large Language Model with Localized Visual Tokenization\n- [Home-Llm](https:\u002F\u002Fgithub.com\u002Facon96\u002Fhome-llm) - A Home Assistant integration & Model to control your smart home using a Local LLM\n- [Huatuo-Llama-Med-Chinese](https:\u002F\u002Fgithub.com\u002FSCIR-HI\u002FHuatuo-Llama-Med-Chinese) - Repo for BenTsao [original name - HuaTuo (华驼)], Instruction-tuning Large Language Models with Chinese Medical Knowledge. 本草（原名：华驼）模型仓库，基于中…\n- [Huggingfacemodeldownloader](https:\u002F\u002Fgithub.com\u002Fbodaay\u002FHuggingFaceModelDownloader) - Simple go utility to download HuggingFace Models and Datasets\n- [Langchain-Ollama-Chainlit](https:\u002F\u002Fgithub.com\u002Fsudarshan-koirala\u002Flangchain-ollama-chainlit) - Simple Chat UI as well as chat with documents using LLMs with Ollama (mistral model) locally, LangChaiin and Chainlit\n- [Langchain-Projects-Llm](https:\u002F\u002Fgithub.com\u002Fananthanarayanan431\u002FLangchain-Projects-LLM) - Various projects using Large Language Model (GPT & LLAMA) other open source model from HuggingFace and OpenAI. OpenAI API required for ru…\n- [Languagemodels](https:\u002F\u002Fgithub.com\u002Fjncraton\u002Flanguagemodels) - Explore large language models in 512MB of RAM\n- [Lilm](https:\u002F\u002Fgithub.com\u002Falphrc\u002Flilm) - Large language model fine-tuned to mimic LIHKG users' behavior\n- [Llama-Cpp-Agent](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent) - The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM m…\n- [Llama2Lang](https:\u002F\u002Fgithub.com\u002FAI-Commandos\u002FLLaMa2lang) - Convenience scripts to finetune (chat-)LLaMa3 and other models for any language\n- [Llm-Adapters](https:\u002F\u002Fgithub.com\u002FAGI-Edgerunners\u002FLLM-Adapters) - Code for our EMNLP 2023 Paper - \"LLM-Adapters - An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models\"\n- [Llm-Analysis](https:\u002F\u002Fgithub.com\u002Fcli99\u002Fllm-analysis) - Latency and Memory Analysis of Transformer Models for Training and Inference\n- [Llm-Api](https:\u002F\u002Fgithub.com\u002F1b5d\u002Fllm-api) - Run any Large Language Model behind a unified API\n- [Llm-Bedrock](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-bedrock) - Run prompts against models hosted on AWS Bedrock\n- [Llm-Chain](https:\u002F\u002Fgithub.com\u002Fsobelio\u002Fllm-chain) - `llm-chain` is a powerful rust crate for building chains in large language models allowing you to summarise text and complete complex tasks\n- [Llm-Claude-3](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-claude-3) - LLM plugin for interacting with the Claude 3 family of models\n- [Llm-Datasets](https:\u002F\u002Fgithub.com\u002Fmalteos\u002Fllm-datasets) - A collection of datasets for language model pretraining including scripts for downloading, preprocesssing, and sampling.\n- [Llm-Embed-Jina](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-embed-jina) - Embedding models from Jina AI\n- [Llm-Eval-Survey](https:\u002F\u002Fgithub.com\u002FMLGroupJLU\u002FLLM-eval-survey) - The official GitHub page for the survey paper \"A Survey on Evaluation of Large Language Models\".\n- [Llm-Fine-Tuning-Azure](https:\u002F\u002Fgithub.com\u002FHeZhang33\u002FLLM-Fine-Tuning-Azure) - A fine-tuning guide for both OpenAI and Open-Source Large Language Models on Azure.\n- [Llm-Finetune](https:\u002F\u002Fgithub.com\u002FOpenCSGs\u002Fllm-finetune) - The framework of training large language models，support lora, full parameters fine tune etc, define yaml to start training\u002Ffine tune of y…\n- [Llm-Interface](https:\u002F\u002Fgithub.com\u002Fsamestrin\u002Fllm-interface) - A simple NPM interface for seamlessly interacting with 36 Large Language Model (LLM) providers, including OpenAI, Anthropic, Google Gemin…\n- [Llm-Next-Item-Rec](https:\u002F\u002Fgithub.com\u002FAGI-Edgerunners\u002FLLM-Next-Item-Rec) - Code for the Paper \"Zero-Shot Next-Item Recommendation using Large Pretrained Language Models\"\n- [Llm-Ollama](https:\u002F\u002Fgithub.com\u002Ftaketwo\u002Fllm-ollama) - LLM plugin providing access to models running on an Ollama server\n- [Llm-Planning-Papers](https:\u002F\u002Fgithub.com\u002FAGI-Edgerunners\u002FLLM-Planning-Papers) - Must-read Papers on Large Language Model (LLM) Planning.\n- [Llm-Security-Prompt-Injection](https:\u002F\u002Fgithub.com\u002Fsinanw\u002Fllm-security-prompt-injection) - This project investigates the security of large language models by performing binary classification of a set of input prompts to discover…\n- [Llm-Self-Play](https:\u002F\u002Fgithub.com\u002Fthomasgauthier\u002FLLM-self-play) - Minimal implementation of the Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models paper (ArXiv 20232401.01335)\n- [Llm-Text-Completion-Finetune](https:\u002F\u002Fgithub.com\u002Fmolbal\u002Fllm-text-completion-finetune) - Guide on text completion large language model fine-tuning, including example scripts and training data acquiring.\n- [Llm-Tpu](https:\u002F\u002Fgithub.com\u002Fsophgo\u002FLLM-TPU) - Run generative AI models in sophgo BM1684X\n- [Llm-Transparency-Tool](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fllm-transparency-tool) - LLM Transparency Tool (LLM-TT), an open-source interactive toolkit for analyzing internal workings of Transformer-based language models. …\n- [Llm.Swift](https:\u002F\u002Fgithub.com\u002Feastriverlee\u002FLLM.swift) - LLM.swift is a simple and readable library that allows you to interact with large language models locally with ease for macOS, iOS, watch…\n- [Llm4Mol](https:\u002F\u002Fgithub.com\u002FHHW-zhou\u002FLLM4Mol) - A comprehensive repository dedicated to the collection and exploration of studies utilizing Large Language Models for molecular design, p…\n- [Llm_Evaluation_For_Gene_Set_Interpretation](https:\u002F\u002Fgithub.com\u002Fidekerlab\u002Fllm_evaluation_for_gene_set_interpretation) - Code space for 'Evaluation of large language models for discovery of gene set function'\n- [Llm_Optimize](https:\u002F\u002Fgithub.com\u002Fsshh12\u002Fllm_optimize) - LLM Optimize is a proof-of-concept library for doing LLM (large language model) guided blackbox optimization.\n- [Llmchat](https:\u002F\u002Fgithub.com\u002Fc0sogi\u002FLLMChat) - A full-stack Webui implementation of Large Language model, such as ChatGPT or LLaMA.\n- [Llmgnn](https:\u002F\u002Fgithub.com\u002FCurryTang\u002FLLMGNN) - Label-free Node Classification on Graphs with Large Language Models (LLMS)\n- [Llmroofline](https:\u002F\u002Fgithub.com\u002Ffeifeibear\u002FLLMRoofline) - Compare different hardware platforms via the Roofline Model for LLM inference tasks.\n- [Llms-Learning](https:\u002F\u002Fgithub.com\u002FStrivin0311\u002Fllms-learning) - A repository sharing the literatures about large language models\n- [Llms-Txt](https:\u002F\u002Fgithub.com\u002FAnswerDotAI\u002Fllms-txt) - The \u002Fllms.txt file, helping language models use your website\n- [Llms-World-Models-For-Planning](https:\u002F\u002Fgithub.com\u002FGuanSuns\u002FLLMs-World-Models-for-Planning) - The source code of the paper \"Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Pla…\n- [Llmsys-Paperlist](https:\u002F\u002Fgithub.com\u002FAmberLJC\u002FLLMSys-PaperList) - Large Language Model (LLM) Systems Paper List\n- [Local-Rag](https:\u002F\u002Fgithub.com\u002Fjonfairbanks\u002Flocal-rag) - Ingest files for retrieval augmented generation (RAG) with open-source Large Language Models (LLMs), all without 3rd parties or sensitive…\n- [Machine-Learning-Guide](https:\u002F\u002Fgithub.com\u002Fmikeroyal\u002FMachine-Learning-Guide) - Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.\n- [Malayallm](https:\u002F\u002Fgithub.com\u002FVishnuPJ\u002FMalayaLLM) - A Continually LoRA PreTrained and FineTuned 7B Llama-2 Indic model for Malayalam Language.\n- [Mammoth](https:\u002F\u002Fgithub.com\u002FTIGER-AI-Lab\u002FMAmmoTH) - Code and data for \"MAmmoTH - Building Math Generalist Models through Hybrid Instruction Tuning\" (ICLR 2024)\n- [Mcphost](https:\u002F\u002Fgithub.com\u002Fmark3labs\u002Fmcphost) - A CLI host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP).\n- [Microsoft Semantic Kernel](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-kernel) - Integrate cutting-edge LLM technology quickly and easily into your apps.\n- [Mlx-Llm](https:\u002F\u002Fgithub.com\u002Friccardomusmeci\u002Fmlx-llm) - Large Language Models (LLMs) applications and tools running on Apple Silicon in real-time with Apple MLX.\n- [Mlx-Vlm](https:\u002F\u002Fgithub.com\u002FBlaizzy\u002Fmlx-vlm) - MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.\n- [Mobilellm](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FMobileLLM) - MobileLLM Optimizing Sub-billion Parameter Language Models for On-Device Use Cases. In ICML 2024.\n- [Model_Baseline](https:\u002F\u002Fgithub.com\u002Farcprizeorg\u002Fmodel_baseline) - Testing baseline LLMs performance across various models\n- [Modelscope-Agent](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002Fmodelscope-agent) - ModelScope-Agent - An agent framework connecting models in ModelScope with the world\n- [Nanorwkv](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FnanoRWKV) - The nanoGPT-style implementation of RWKV Language Model - an RNN with GPT-level LLM performance.\n- [Neural-Compressor](https:\u002F\u002Fgithub.com\u002Fintel\u002Fneural-compressor) - SOTA low-bit LLM quantization (INT8\u002FFP8\u002FINT4\u002FFP4\u002FNF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX R…\n- [Nlp-With-Llms](https:\u002F\u002Fgithub.com\u002Fjonkrohn\u002FNLP-with-LLMs) - Natural Language Processing with Large Language Models\n- [Obsidian-Flashcards-Llm](https:\u002F\u002Fgithub.com\u002Fcrybot\u002Fobsidian-flashcards-llm) - Use Large Language Models (such as ChatGPT) to automatically generate flashcards from obsidian notes\n- [Opengptandbeyond](https:\u002F\u002Fgithub.com\u002FSunLemuria\u002FOpenGPTAndBeyond) - Open efforts to implement ChatGPT-like models and beyond.\n- [Prompt-Optimizer](https:\u002F\u002Fgithub.com\u002Fvaibkumr\u002Fprompt-optimizer) - Minimize LLM token complexity to save API costs and model computations.\n- [Promptagent](https:\u002F\u002Fgithub.com\u002FXinyuanWangCS\u002FPromptAgent) - This is the official repo for \"PromptAgent - Strategic Planning with Language Models Enables Expert-level Prompt Optimization\". PromptAgen…\n- [Promptoftheyear](https:\u002F\u002Fgithub.com\u002Fsuccessfulstudy\u002Fpromptoftheyear) - In the evolving world of Large Language Models (LLMs), crafting effective prompts has become an essential skill. That's why I've created …\n- [Pruneme](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002FPruneMe) - Automated Identification of Redundant Layer Blocks for Pruning in Large Language Models\n- [Renellm](https:\u002F\u002Fgithub.com\u002FNJUNLP\u002FReNeLLM) - The official implementation of our NAACL 2024 paper \"A Wolf in Sheep’s Clothing - Generalized Nested Jailbreak Prompts can Fool Large Lang…\n- [Rlhf-Reward-Modeling](https:\u002F\u002Fgithub.com\u002FRLHFlow\u002FRLHF-Reward-Modeling) - Recipes to train reward model for RLHF.\n- [Rptq4Llm](https:\u002F\u002Fgithub.com\u002Fhahnyuan\u002FRPTQ4LLM) - Reorder-based post-training quantization for large language model\n- [Scientific-Llm-Survey](https:\u002F\u002Fgithub.com\u002FHICAI-ZJU\u002FScientific-LLM-Survey) - Scientific Large Language Models - A Survey on Biological & Chemical Domains\n- [Simplerllm](https:\u002F\u002Fgithub.com\u002Fhassancs91\u002FSimplerLLM) - Simplify interactions with Large Language Models\n- [Slam-Llm](https:\u002F\u002Fgithub.com\u002FX-LANCE\u002FSLAM-LLM) - Speech, Language, Audio, Music Processing with Large Language Model\n- [So-Vits-Models](https:\u002F\u002Fgithub.com\u002Fsekift\u002Fso-vits-models) - 收集有关so-vits-svc、TTS、SD、LLMs的各种模型、应用以及文字、声音、图片、视频有关的model。\n- [Speech-Trident](https:\u002F\u002Fgithub.com\u002Fga642381\u002Fspeech-trident) - Awesome speech\u002Faudio LLMs, representation learning, and codec models\n- [Speechllm](https:\u002F\u002Fgithub.com\u002Fskit-ai\u002FSpeechLLM) - This repository contains the training, inference, evaluation code for SpeechLLM models and details about the model releases on huggingface.\n- [Syncode](https:\u002F\u002Fgithub.com\u002Fuiuc-focal-lab\u002Fsyncode) - Efficient and general syntactical decoding for Large Language Models\n- [Tldraw-Llm-Starter](https:\u002F\u002Fgithub.com\u002Ftldraw\u002Ftldraw-llm-starter) - A starter for working with tldraw and large language models.\n- [Tough-Llm-Tests](https:\u002F\u002Fgithub.com\u002Fhrishioa\u002Ftough-llm-tests) - Some tough questions to test new models.\n- [Trained_Models](https:\u002F\u002Fgithub.com\u002FXY2323819551\u002Ftrained_models) - fine-tuning llms.\n- [Wasm-Ai](https:\u002F\u002Fgithub.com\u002Fhrishioa\u002Fwasm-ai) - Vercel and web-llm template to run wasm models directly in the browser.\n- [Web-Llm-Chat](https:\u002F\u002Fgithub.com\u002Fmlc-ai\u002Fweb-llm-chat) - Chat with AI large language models running natively in your browser. Enjoy private, server-free, seamless AI conversations.\n- [Yet-Another-Applied-Llm-Benchmark](https:\u002F\u002Fgithub.com\u002Fcarlini\u002Fyet-another-applied-llm-benchmark) - A benchmark to evaluate language models on questions I've previously asked them to solve.\n- [Zeus-Llm-Trainer](https:\u002F\u002Fgithub.com\u002Fofficial-elinas\u002Fzeus-llm-trainer) - Zeus LLM Trainer is a rewrite of Stanford Alpaca aiming to be the trainer for all Large Language Models\n\n### Prompt Engineering\n- [Advanced-Prompt-Engineering-Techniques-3817061](https:\u002F\u002Fgithub.com\u002FLinkedInLearning\u002Fadvanced-prompt-engineering-techniques-3817061) - This repo is for LinkedIn Learning course - Advanced Prompt Engineering Techniques\n- [Advanced-Prompt-Generator](https:\u002F\u002Fgithub.com\u002FThunderhead-exe\u002FAdvanced-Prompt-Generator) - Automating prompt engineering using AI Agents.\n- [Aigc_Prompt_Engineering](https:\u002F\u002Fgithub.com\u002Fcystanford\u002Faigc_prompt_engineering) - aigc prompt engineering\n- [Arize AX Prompt Learning](https:\u002F\u002Farize.com\u002Fdocs\u002Fax\u002Fprompts\u002Fprompt-optimization\u002Fprompt-learning-sdk) - Open source algo + SDK uses meta-prompting and trace-level reflection to optimize prompts\n- [Alphacodium](https:\u002F\u002Fgithub.com\u002FCodium-ai\u002FAlphaCodium) - Official implementation for the paper - \"Code Generation with AlphaCodium - From Prompt Engineering to Flow Engineering\"\"\n- [Autogpt-Handbook](https:\u002F\u002Fgithub.com\u002FRimaBuilds\u002FAutoGPT-handbook) - A guide to using AutoGPT for code generation and prompt engineering.\n- [Automated-Prompt-Engineering-From-Scratch](https:\u002F\u002Fgithub.com\u002Fheiko-hotz\u002Fautomated-prompt-engineering-from-scratch) - A repo with an automated prompt engineering workflow from scratch. It leverages the OPRO technique.\n- [Awesome-Ai-Art-Image-Synthesis](https:\u002F\u002Fgithub.com\u002Faltryne\u002Fawesome-ai-art-image-synthesis) - A list of awesome tools, ideas, prompt engineering tools, colabs, models, and helpers for the prompt designer playing with aiArt and imag…\n- [Awesome-Gpt-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fsnwfdhmp\u002Fawesome-gpt-prompt-engineering) - A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.\n- [Awesome-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fpromptslab\u002FAwesome-Prompt-Engineering) - This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGP…\n- [Awesome-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fnatnew\u002FAwesome-Prompt-Engineering) - Awesome-Prompt-Engineering - This repository includes resources for prompt engineering.\n- [Awesome-Prompt-Engineering-Zh-Cn](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002FAwesome-Prompt-Engineering-ZH-CN) - 这个资源库包含了为 Prompt 工程手工整理的资源中文清单，重点是GPT、ChatGPT、PaLM 等（自动持续更新）\n- [Awesome-Prompting-On-Vision-Language-Model](https:\u002F\u002Fgithub.com\u002FJindongGu\u002FAwesome-Prompting-on-Vision-Language-Model) - This repo lists relevant papers summarized in our survey paper - A Systematic Survey of Prompt Engineering on Vision-Language Foundation M…\n- [Awesome-Prompts](https:\u002F\u002Fgithub.com\u002Fai-boost\u002Fawesome-prompts) - Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced P…\n- [Awesome_Gpt_Super_Prompting](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FAwesome_GPT_Super_Prompting) - ChatGPT Jailbreaks, GPT Assistants Prompt Leaks, GPTs Prompt Injection, LLM Prompt Security, Super Prompts, Prompt Hack, Prompt Security,…\n- [Chatglm-6B-Engineering](https:\u002F\u002Fgithub.com\u002FLemonQu-GIT\u002FChatGLM-6B-Engineering) - ChatGLM-6B Prompt Engineering Project\n- [Chatgpt-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fralphcajipe\u002Fchatgpt-prompt-engineering) - Jupyter code notebooks of \"ChatGPT Prompt Engineering for Developers\" by DeepLearning.AI and OpenAI.\n- [Chatgpt-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Flyhh123\u002FChatGPT-Prompt-Engineering) - 【OpenAI & 吴恩达】ChatGPT Prompt Engineering 提示词工程教学（官方配套代码）\n- [Chatgpt-Prompt-Engineering-Deeplearningai](https:\u002F\u002Fgithub.com\u002Fafondiel\u002FChatGPT-Prompt-Engineering-DeepLearningAI) - ChatGPT Prompt Engineering for Developers Crash & Free Course by DeepLearning.AI\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002FKevin-free\u002Fchatgpt-prompt-engineering-for-developers) - 吴恩达《ChatGPT Prompt Engineering for Developers》课程中英版\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002Fjojoee\u002Fchatgpt-prompt-engineering-for-developers) - Jupyter Notebook for https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fchatgpt-prompt-engineering-for-developers\u002F\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002Fksm26\u002FchatGPT-Prompt-Engineering-for-Developers) - Jupyter notebooks for enhancing your skills with ChatGPT based prompt engineering. Harness the potential of large language models and cre…\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002FRyota-Kawamura\u002FChatGPT-Prompt-Engineering-for-Developers) - In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful ap…\n- [Chatgpt-Prompt-Engineering-For-Developers-In-Chinese](https:\u002F\u002Fgithub.com\u002FGitHubDaily\u002FChatGPT-Prompt-Engineering-for-Developers-in-Chinese) - 《面向开发者的 ChatGPT 提示词工程》非官方版中英双语字幕 Unofficial subtitles of \"ChatGPT Prompt Engineering for Developers\"\n- [Chatgpt-Promptengineering](https:\u002F\u002Fgithub.com\u002Fajinkyalahade\u002FChatGPT-PromptEngineering) - This Python script reads an e-book in PDF format, splits the text into prompts, and uses the OpenAI GPT-3 API to generate completions for…\n- [Core](https:\u002F\u002Fgithub.com\u002Fzenbase-ai\u002Fcore) - Prompt engineering, automated.\n- [Deeplearningai-Chatgpt-Promptengineering](https:\u002F\u002Fgithub.com\u002FLazaUK\u002FDeepLearningAI-ChatGPT-PromptEngineering) - Practical Jupyter notebooks from Andrew Ng and Isa Fulford's \"ChatGPT Prompt Engineering for Developers\" course on DeepLearning.AI.\n- [Flow-Prompt](https:\u002F\u002Fgithub.com\u002FLamoomAI\u002Fflow-prompt) - Open source library for production prompt engineering and load balancing of AI Models\n- [Generative-Ai-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fbuild-on-aws\u002Fgenerative-ai-prompt-engineering) - Sample code that helps us explore the world of generative AI through prompt engineering. It provides the resources for experimenting with…\n- [Get-Things-Done-With-Prompt-Engineering-And-Langchain](https:\u002F\u002Fgithub.com\u002Fcuriousily\u002FGet-Things-Done-with-Prompt-Engineering-and-LangChain) - LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading a…\n- [Gptstore-Prompts](https:\u002F\u002Fgithub.com\u002F1003715231\u002Fgptstore-prompts) - Here are the Top 100 prompts on GPTStore, which we can use to learn and improve prompt engineering.\n- [Hackopenaisystemprompts](https:\u002F\u002Fgithub.com\u002Fcirclestarzero\u002FHackOpenAISystemPrompts) - Hack OpenAI LLMs' System Prompts By Reverse Prompt Engineering\n- [Langfuse](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) - 🪢 Open source LLM engineering platform - LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with Llama…\n- [Latitude-Llm](https:\u002F\u002Fgithub.com\u002Flatitude-dev\u002Flatitude-llm) - Latitude is the open-source prompt engineering platform to build, evaluate, and refine your prompts with AI\n- [Learn-Prompting](https:\u002F\u002Fgithub.com\u002FMiesnerJacob\u002Flearn-prompting) - The most comprehensive prompt engineering course available.\n- [Learn_Prompting](https:\u002F\u002Fgithub.com\u002Ftrigaten\u002FLearn_Prompting) - Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community\n- [Learning-Prompt](https:\u002F\u002Fgithub.com\u002Fthinkingjimmy\u002FLearning-Prompt) - Free prompt engineering online course. ChatGPT and Midjourney tutorials are now included!\n- [Learnprompt](https:\u002F\u002Fgithub.com\u002FLearnPrompt\u002FLearnPrompt) - 永久免费开源的 AIGC 课程, 目前已支持Prompt Engineering, ChatGPT, Midjourney, Runway, Stable Diffusion, AI数字人，AI声音&音乐，开源大模型\n- [Llm-Prompt-Engineering-Simplified-Book](https:\u002F\u002Fgithub.com\u002FAkmmusAI\u002FLLM-Prompt-Engineering-Simplified-Book) - LLM Prompting Engineering Simplified Book\n- [Obsidian-Ai-Research-Assistant](https:\u002F\u002Fgithub.com\u002FInterwebAlchemy\u002Fobsidian-ai-research-assistant) - Prompt Engineering Research Tool for AI APIs\n- [Potpie](https:\u002F\u002Fgithub.com\u002Fpotpie-ai\u002Fpotpie) - Prompt-To-Agent  - Create custom engineering agents for your codebase\n- [Prompt-Eng-Interactive-Tutorial](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fprompt-eng-interactive-tutorial) - Anthropic's Interactive Prompt Engineering Tutorial\n- [Prompt-Eng-Ollama-Interactive-Tutorial](https:\u002F\u002Fgithub.com\u002Fivanfioravanti\u002Fprompt-eng-ollama-interactive-tutorial) - Ollama's Interactive Prompt Engineering Tutorial\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fbrexhq\u002Fprompt-engineering) - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002FPythonation\u002FPrompt-Engineering) - Prompt Engineering Cours in Arabic\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002F5zjk5\u002Fprompt-engineering) - prompt 工程项目案例\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fprompt-engineering) - Learn how to use AI models with prompt engineering\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002FimJunaidAfzal\u002FPrompt-Engineering) - Prompt Engineering for Language models (GPT-3, GPT-4, chatGPT) and text-to-image models (Stable Diffusion, Midjourney, Dall-e)\n- [Prompt-Engineering-By-Openai](https:\u002F\u002Fgithub.com\u002FArslanKAS\u002FPrompt-Engineering-by-OpenAI) - In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful ap…\n- [Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002Flogan-zou\u002Fprompt-engineering-for-developers) - 吴恩达《ChatGPT Prompt Engineering for Developers》课程中文版\n- [Prompt-Engineering-For-Everyone-With-Chatgpt-And-Gpt4](https:\u002F\u002Fgithub.com\u002FPacktPublishing\u002FPrompt-Engineering-for-Everyone-with-ChatGPT-and-GPT4) - Prompt Engineering for Everybody with ChatGPT and GPT4, by Packt Publishing\n- [Prompt-Engineering-For-Generative-Ai-Examples](https:\u002F\u002Fgithub.com\u002FBrightPool\u002Fprompt-engineering-for-generative-ai-examples) - the O'Reilley book\n- [Prompt-Engineering-For-Instruction-Tuned-Llm](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FPrompt-Engineering-for-Instruction-Tuned-LLM) - Various blog posts and youtube posts on prompt engineering\n- [Prompt-Engineering-For-Javascript-Developers](https:\u002F\u002Fgithub.com\u002Fdabit3\u002Fprompt-engineering-for-javascript-developers) - Notes summarized from ChatGPT Prompt Engineering for Developers by DeepLearning.ai\n- [Prompt-Engineering-Guide](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FPrompt-Engineering-Guide) - 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering\n- [Prompt-Engineering-Guide-Chinese](https:\u002F\u002Fgithub.com\u002Fwangxuqi\u002FPrompt-Engineering-Guide-Chinese) - Prompt工程师指南，源自英文版，但增加了AIGC的prompt部分，为了降低同学们的学习门槛，翻译更新\n- [Prompt-Engineering-Guide-Cn](https:\u002F\u002Fgithub.com\u002Fprompting-work\u002FPrompt-Engineering-Guide-Cn) - 关于提示工程的技术文章汇总和翻译\n- [Prompt-Engineering-Guide-Zh-Cn](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002FPrompt-Engineering-Guide-zh-CN) - 🐙 关于提示词工程（prompt）的指南、论文、讲座、笔记本和资源大全（自动持续更新）\n- [Prompt-Engineering-Holy-Grail](https:\u002F\u002Fgithub.com\u002Fzacfrulloni\u002FPrompt-Engineering-Holy-Grail) - # Prompt Engineering Hub ⭐️ If you find this helpful, give it a star to show your support! This repository is a one-stop resource for prompt\n- [Prompt-Engineering-Mastery](https:\u002F\u002Fgithub.com\u002Fnerority\u002FPrompt-Engineering-Mastery) - many prompt engineering resources\n- [Prompt-Engineering-Note](https:\u002F\u002Fgithub.com\u002FisLinXu\u002Fprompt-engineering-note) - 🔥🔔prompt-engineering-note🔔🔥\n- [Prompt-Engineering-Notebook](https:\u002F\u002Fgithub.com\u002Fmadroidmaq\u002Fprompt-engineering-notebook) - ChatGPT Prompt Engineering for Developers Jupyter Notebook. 《给开发者的 ChatGPT 提示工程》学习笔记。 #ChatGPT #Prompt #Prompts #Prompt-Engineering #Prompts\n- [Prompt-Engineering-Toolkit](https:\u002F\u002Fgithub.com\u002Fteknium1\u002FPrompt-Engineering-Toolkit) - The Prompt Engineering Tool is a web-based application designed to help users experiment with and optimize prompts for various large language models (LLMs)\n- [Prompt-Engineering-Tutior](https:\u002F\u002Fgithub.com\u002FConnectAI-E\u002FPrompt-Engineering-Tutior) - 🎡 Pompt 提示词工程师入门指南 ~视频字幕+代码资料 [( Python、Golang、NodeJs ) x ( 中文、英文 )]\n- [Prompt-Engineering-With-Anthropic-Claude-V-3](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fprompt-engineering-with-anthropic-claude-v-3) - This course is intended to provide you with a comprehensive step-by-step understanding of how to engineer optimal prompts within Claude, using Bedrock.\n- [Prompt-Enhancer](https:\u002F\u002Fgithub.com\u002Flim-hyo-jeong\u002FPrompt-Enhancer) - Prompt Engineering at Your Fingertips!\n- [Prompt-Hacker-Collections](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002Fprompt-hacker-collections) - prompt attack-defense, prompt Injection, reverse engineering notes and examples | 提示词对抗、破解例子与笔记\n- [Prompt-In-Context-Learning](https:\u002F\u002Fgithub.com\u002FEgoAlpha\u002Fprompt-in-context-learning) - Awesome resources for in-context learning and prompt engineering - Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date…\n- [Prompt_Engineering](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FPrompt_Engineering) - This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundam…\n- [Promptbase](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fpromptbase) - All things prompt engineering\n- [Promptbreeder](https:\u002F\u002Fgithub.com\u002Fvaughanlove\u002FPromptBreeder) - Google Deepmind's PromptBreeder for automated prompt engineering implemented in langchain expression language.\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002FMrGladiator14\u002FPromptEngineering) - Prompt Engineering Toolkit\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002Fdivyeshpatel83\u002FPromptEngineering) - Sample prompts - PromptEngineering\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002Fbskim\u002FPromptEngineering) - Promport Engineering Best practices for LLM\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002FDaleStewart\u002FPromptEngineering) - A repository for Generative AI prompt's I've engineered\n- [Promptengineering4Devs](https:\u002F\u002Fgithub.com\u002Fyandex-datasphere\u002FPromptEngineering4Devs) - Prompt Engineering for Developers\n- [Promptengineeringwithdalle](https:\u002F\u002Fgithub.com\u002Fjennifermarsman\u002FPromptEngineeringWithDalle) - Learn prompt engineering by iterating over image prompts with the DALL-E model.\n- [Promptgpt](https:\u002F\u002Fgithub.com\u002Fhoward9192\u002FPromptgpt) - PromptGPT is an opensource framework that enables users to automatically generate high-quality prompts with zero installations, coding ne…\n- [Promptify](https:\u002F\u002Fgithub.com\u002Fpromptslab\u002FPromptify) - Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engin…\n- [Promptimize](https:\u002F\u002Fgithub.com\u002Fpreset-io\u002Fpromptimize) - Promptimize is a prompt engineering evaluation and testing toolkit.\n- [Promptpet](https:\u002F\u002Fgithub.com\u002Fhamutama\u002FPromptPET) - PromptPET - A comprehensive toolkit for Prompt Engineering, offering advanced tools for crafting, refining, and optimizing prompts and flow …\n- [Sammo](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsammo) - A library for prompt engineering and optimization (SAMMO = Structure-aware Multi-Objective Metaprompt Optimization)\n- [Software-Dev-Prompt-Library](https:\u002F\u002Fgithub.com\u002Fcodingthefuturewithai\u002Fsoftware-dev-prompt-library) - Prompt library containing tested reusable gen AI prompts for common software engineering task\n- [Story-Prompt](https:\u002F\u002Fgithub.com\u002FCommonPaper\u002Fstory-prompt) - Challenge project for engineering candidates\n- [Tanuki.Py](https:\u002F\u002Fgithub.com\u002FTanuki\u002Ftanuki.py) - Prompt engineering for developers\n- [The-Art-Of-Asking-Chatgpt-For-High-Quality-Answers-A-Complete-Guide-To-Prompt-Engineering-Technique](https:\u002F\u002Fgithub.com\u002FORDINAND\u002FThe-Art-of-Asking-ChatGPT-for-High-Quality-Answers-A-complete-Guide-to-Prompt-Engineering-Technique) - ChatGPT提问技巧\n- [Udemy-Prompt-Engineering-Course](https:\u002F\u002Fgithub.com\u002FBrightPool\u002Fudemy-prompt-engineering-course) - Content for the Udemy Prompt Engineering Course\n- [Yival](https:\u002F\u002Fgithub.com\u002FYiVal\u002FYiVal) - Your Automatic Prompt Engineering Assistant for GenAI Applications\n\n### Security\n- [Aip-Identity](https:\u002F\u002Fgithub.com\u002Fthe-nexus-guard\u002Faip) - Agent Identity Protocol (AIP) provides cryptographic identity (Ed25519\u002FDID), vouch-based trust graphs, and end-to-end encrypted messaging for AI agents. CLI and Python SDK available on PyPI (`pip install aip-identity`) [github](https:\u002F\u002Fgithub.com\u002Fthe-nexus-guard\u002Faip) | [website](https:\u002F\u002Fthe-nexus-guard.github.io\u002Faip\u002F) | [docs](https:\u002F\u002Faip-service.fly.dev\u002Fdocs)\n- [Agent-Repoguardian](https:\u002F\u002Fgithub.com\u002Fflexigpt\u002Fagent-repoguardian) - An AI agent that does security scans and vulnerability analysis of code\n- [Agentic_Security](https:\u002F\u002Fgithub.com\u002Fmsoedov\u002Fagentic_security) - Agentic LLM Vulnerability Scanner \u002F AI red teaming kit\n- [Agentictrust](https:\u002F\u002Fgithub.com\u002Flab101-ai\u002Fagentictrust) - Observability, DevTool and Security Platfrom for AI Agents\n- [Agentos](https:\u002F\u002Fgithub.com\u002FThe-Swarm-Corporation\u002FAgentOS) - AgentOS implements a comprehensive security architecture leveraging containerization, orchestration, and multi-layer isolation to ensure …\n- [AgentShield](https:\u002F\u002Fgithub.com\u002Felliotllliu\u002Fagent-shield) - Open-source security scanner for AI agent skills, MCP servers, and plugins. 30 rules, AST analysis, cross-file tracking, 5-dimension scoring. Zero install, 100% offline, MIT licensed. [github](https:\u002F\u002Fgithub.com\u002Felliotllliu\u002Fagent-shield) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@elliotllliu\u002Fagent-shield)\n- [Agriaid](https:\u002F\u002Fgithub.com\u002Fshamspias\u002FAgriAid) - AgriAid is an AI-powered tool for farmers & agricultural agents in Bangladesh, offering plant disease forecasting & identification. Using…\n- [Ai-Agent](https:\u002F\u002Fgithub.com\u002FZielBox\u002Fai-agent) - Performs various AI enabled actions and specialized in security management.\n- [Ai-Agent-Security](https:\u002F\u002Fgithub.com\u002FSecurityLab-UCD\u002Fai-agent-security) - This repository contains source code for the demos and attacks we present in our paper Security of AI Agents.\n- [Ai-Agent-Solving-Security-Challenges](https:\u002F\u002Fgithub.com\u002Ftheowni\u002FAI-Agent-Solving-Security-Challenges) - No description available\n- [Ai-Agent-Wallet](https:\u002F\u002Fgithub.com\u002F0xcrypto2024\u002Fai-agent-wallet) - This framework provides a secure, headless wallet solution designed for integration with frontend applications. It prioritizes security b…\n- [Ai-Powered-Security-Agent](https:\u002F\u002Fgithub.com\u002FAIBotTeachesAI\u002FAI-Powered-Security-Agent) - No description available\n- [Ai-Security-Demos](https:\u002F\u002Fgithub.com\u002Fshaialon\u002Fai-security-demos) - 🤯 AI Security EXPOSED! Live Demos Showing Hidden Risks of 🤖 Agentic AI Flows: 💉Prompt Injection, ☣️ Data Poisoning. Watch the recorded se…\n- [Ai_Buddy_Guard](https:\u002F\u002Fgithub.com\u002Fgpsandhu23\u002Fai_buddy_guard) - Prototype AI agent to test how well AI can help us find and fix security problems\n- [Ai_Security_Agent](https:\u002F\u002Fgithub.com\u002FSoumilB7\u002FAI_Security_Agent) - Freelance Project with Armur AI\n- [Airmageddon](https:\u002F\u002Fgithub.com\u002FRamonBeast\u002Fairmageddon) - AIrmageddon is a home security AI Agent\n- [Airportagentsimulation-Security](https:\u002F\u002Fgithub.com\u002FJogrohe\u002FAirportAgentSimulation-Security) - No description available\n- [Awesome_Gpt_Super_Prompting](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FAwesome_GPT_Super_Prompting) - ChatGPT Jailbreaks, GPT Assistants Prompt Leaks, GPTs Prompt Injection, LLM Prompt Security, Super Prompts, Prompt Hack, Prompt Security, Ai\n- [Cartlis](https:\u002F\u002Fgithub.com\u002FTheneo-Inc\u002FCartlis) - The AI-Powered API Governance Agent enforces rules in real-time, seamlessly integrating with infrastructures and auto-patching violations…\n- [Cognitive-Security-Ai-Powered-Threat-Agent-Evaluation-For-Impact-On-Assets.](https:\u002F\u002Fgithub.com\u002FGauravsbin\u002FCognitive-Security-AI-Powered-Threat-Agent-Evaluation-for-Impact-on-Assets.) - No description available\n- [Council-Of-Ai](https:\u002F\u002Fgithub.com\u002Fseanpixel\u002Fcouncil-of-ai) - Security measure for agentic LLMs using a council of AIs moderted by a veto system. The council judges an agent's actions outputs based o…\n- [Cyber-Security-Llm-Agents](https:\u002F\u002Fgithub.com\u002FNVISOsecurity\u002Fcyber-security-llm-agents) - A collection of agents that use Large Language Models (LLMs) to perform tasks common on our day to day jobs in cyber security.\n- [Db-Gpt](https:\u002F\u002Fgithub.com\u002Feosphoros-ai\u002FDB-GPT) - AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents\n- [Eng-Security-Review-Agent](https:\u002F\u002Fgithub.com\u002Flume-cory\u002Feng-security-review-agent) - AI agentic app that helps security teams respond to security questions and security reviews from engineering\n- [Fast-Llm-Security-Guardrails](https:\u002F\u002Fgithub.com\u002FZenGuard-AI\u002Ffast-llm-security-guardrails) - The fastest && easiest LLM security guardrails for AI Agents and applications.\n- [Fetchai](https:\u002F\u002Fgithub.com\u002FHarshpreetkaur98\u002FFetchAI) - uAgents is a Python library by Fetch.ai for creating autonomous AI agents. It supports easy agent management, blockchain connectivity, an…\n- [Hauth](https:\u002F\u002Fgithub.com\u002Fvaniiiii\u002FhAUTH) - hAUTH is a security middleware solution that provides human oversight for AI agents.\n- [Invariant](https:\u002F\u002Fgithub.com\u002Finvariantlabs-ai\u002Finvariant) - Helps you build better AI agents through debuggable unit testing\n- [Kevlar-Anti-Leak-System-Prompts](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FKEVLAR-Anti-Leak-System-Prompts) - Bullet-proof your custom GPT system prompt security with KEVLAR, the ultimate prompt protector against rules extraction, prompt injection…\n- [Linux-Security-Agent](https:\u002F\u002Fgithub.com\u002Fmicroaisecurity\u002Flinux-security-agent) - Micro AI security agent\n- [Llm_Agents_Security](https:\u002F\u002Fgithub.com\u002Fjohnny22245\u002Fllm_agents_security) - This LLM agents deals with establishing AI security and safety.\n- [Minotaur_Impossible_Gpt_Security_Challenge](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FMINOTAUR_Impossible_GPT_Security_Challenge) - MINOTAUR: The STRONGEST Secure Prompt EVER! Prompt Security Challenge, Impossible GPT Security, Prompts Cybersecurity, Prompting Vulnerab…\n- [OneCLI](https:\u002F\u002Fgithub.com\u002Fonecli\u002Fonecli) - Open-source credential vault for AI agents. A Rust HTTP gateway intercepts agent requests and injects API credentials transparently, so agents never handle raw keys.\n- [Moa-Groq-Langchain-Securityspecialist](https:\u002F\u002Fgithub.com\u002FTheJ-Erk400\u002Fmoa-groq-langchain-securityspecialist) - Mixture-of-Agents using Groq This Streamlit\n- [Multi-Agent-Secops-Llm](https:\u002F\u002Fgithub.com\u002Ftegridydev\u002Fmulti-agent-secops-llm) - This project is a multi-agent security framework that utilizes multiple LLM models to analyze and generate comprehensive security briefs.\n- [Multi-Ai-Agents](https:\u002F\u002Fgithub.com\u002Ferndck\u002FMulti-AI-Agents) - The Multi-AI Agent is a groundbreaking integration of artificial intelligence and blockchain technology, designed to provide unparalleled…\n- [Netsecgame](https:\u002F\u002Fgithub.com\u002Fstratosphereips\u002FNetSecGame) - An environment simulation for networks security tasks for development and testing AI based agents. Part of AI Dojo project\n- [Owasp-Agentic-Ai](https:\u002F\u002Fgithub.com\u002Fprecize\u002FOWASP-Agentic-AI) - OWASP Top 10 for Agentic AI (AI Agent Security) - Pre-release version\n- [PolicyLayer](https:\u002F\u002Fgithub.com\u002FPolicyLayer\u002FPolicyLayer) - Non-custodial spending controls for AI agents with crypto wallets. Enforces daily spending limits, per-transaction caps, recipient whitelists, and rate limiting without holding private keys. Prevents wallet drains from bugs, prompt injection, or infinite loops. [website](https:\u002F\u002Fpolicylayer.com) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@policylayer\u002Fsdk)\n- [Rgs](https:\u002F\u002Fgithub.com\u002FMangelZabalaDevelop\u002FRGS) - Report Generative Security Tool developed for the Generative AI Agents Developer Contest organized by NVIDIA and LangChain.\n- [Secad](https:\u002F\u002Fgithub.com\u002Fgideonaina\u002Fsecad) - SECAD is an agentic, AI-powered security workflow augmentation application.\n- [Security-Ai-Agent-Brama](https:\u002F\u002Fgithub.com\u002Foborys\u002Fsecurity-ai-agent-brama) - No description available\n- [Sim-Security-Data](https:\u002F\u002Fgithub.com\u002FSim-Security\u002FSim-Security-Data) - Where I collect and process data for AI agents\n- [Vulert](https:\u002F\u002Fvulert.com) - Vulert secures software by detecting vulnerabilities in open-source dependencies—without accessing your code. It supports Js, PHP, Java, Python, and more\n- [Web_Scrape_Agent_Ai](https:\u002F\u002Fgithub.com\u002Frxslice\u002FWeb_Scrape_Agent_AI) - Enterprise-Quality Autonomous Agent complete with relevant tools, master prompt, additional optional AI services, API compatible, basic s…\n\n### Testing\n- [EvoAgentX](https:\u002F\u002Fgithub.com\u002FEvoAgentX\u002FEvoAgentX) - EvoAgentX is building a Self-Evolving Ecosystem of AI Agents, it will give you automated framework for evaluating and evolving agentic workflows. [github](https:\u002F\u002Fgithub.com\u002FEvoAgentX\u002FEvoAgentX)\n- [Open-RAG-Eval](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fopen-rag-eval) - an open source RAG evaluation framework that does not require golden answers, and can be used to evaluate performance of RAG tools connected to an AI Agent (Agentic RAG). [github](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fopen-rag-eval)\n- [Voice Lab](https:\u002F\u002Fgithub.com\u002Fsaharmor\u002Fvoice-lab) - A comprehensive testing and evaluation framework for voice agents across language models, prompts, and agent personas. [github](https:\u002F\u002Fgithub.com\u002Fsaharmor\u002Fvoice-lab)\n- [Ab-Agent](https:\u002F\u002Fgithub.com\u002Fbassimeledath\u002Fab-agent) - AI Agents automating A\u002FB test design and inference\n- [Adtestpro](https:\u002F\u002Fgithub.com\u002FAnanyaP-WDW\u002FAdTestPro) - Open Source Tool To Preemptively Test Ad Creatives Against Synthetic Target Audiences\n- [Agent-Ai-Test](https:\u002F\u002Fgithub.com\u002FPraagnya\u002Fagent-ai-test) - Testing finance agent\n- [Agent-Evaluation](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagent-evaluation) - A generative AI-powered framework for testing virtual agents.\n- [Agent-Smith-E2E-Test](https:\u002F\u002Fgithub.com\u002Fdangoddard-trilogy\u002Fagent-smith-e2e-test) - AI-powered E2E testing tool\n- [Agent-Solarpanels-Tutorial](https:\u002F\u002Fgithub.com\u002FPairrot-Lore\u002Fagent-solarpanels-tutorial) - This repository contains an AI agent built with LangGraph to calculate energy savings for solar panels. The project is designed for use w…\n- [Agentacc-Batch-Test](https:\u002F\u002Fgithub.com\u002FHehua-Fan\u002FAgentAcc-Batch-Test) - A application to test the AI agent accuracy, powered by Streamlit\n- [Agentai_Testing_Repo](https:\u002F\u002Fgithub.com\u002FSUGUMAR-S\u002FagentAI_testing_repo) - it used for testing purpose\n- [Agentic-Platform](https:\u002F\u002Fgithub.com\u002Fbonk1t\u002Fagentic-platform) - AI Agent Automation Platform: Rapidly prototype, test, and deploy Multi-Agent Systems from your browser.\n- [Agentic.Md](https:\u002F\u002Fgithub.com\u002Fai-primitives\u002Fagentic.md) - Build, Test, Deploy, & Iterate on AI Agents using Markdown & MDX\n- [Agents](https:\u002F\u002Fgithub.com\u002Fculurciello\u002Fagents) - A collection of AI agents to test AI capabilities, and to be used as tutorials and demonstrations\n- [Ai-Agency](https:\u002F\u002Fgithub.com\u002FGarcluca\u002FAI-Agency) - Testing Bed for AI Agents and Orchestration\n- [Ai-Agent-Design-For-Iq-Tests](https:\u002F\u002Fgithub.com\u002Ftex216\u002FAI-Agent-Design-for-IQ-Tests) - Designed an AI agent to pass Raven’s Progressive Matrices tests, details see the Final-Project-Report.\n- [Ai-Agent-Lab](https:\u002F\u002Fgithub.com\u002FZeeshan138063\u002Fai-agent-lab) - AI Agent Lab: An open-source repository to build, test, and deploy AI agents using Python, with examples and modular design.\n- [Ai-Agent-Playground](https:\u002F\u002Fgithub.com\u002FWei1024\u002FAI-Agent-Playground) - test repo to develop an AI agent framework for office workers\n- [Ai-Agent-Teams](https:\u002F\u002Fgithub.com\u002FAdamHHart\u002FAI-Agent-Teams) - A playground for testing deiffent AI Agent Teams with AutoGen, CrewAI, and Langchain\n- [Ai-Agent-Test](https:\u002F\u002Fgithub.com\u002Fbjoernzosel\u002Fai-agent-test) - An AI-generated portfolio website for a freelance web designer\n- [Ai-Agent-Test](https:\u002F\u002Fgithub.com\u002Fsfelkner\u002Fai-agent-test) - A test repository for AI agent interactions\n- [Ai-Agents-Tool-Dev](https:\u002F\u002Fgithub.com\u002Faydinfer\u002Fai-agents-tool-dev) - Multi-agent AI development system for automated tool creation and testing\n- [Ai-Api-Testing](https:\u002F\u002Fgithub.com\u002FCarlosVecina\u002Fai-api-testing) - 🐦AI test generator for APIs and agentic workflows\n- [Ai-Code-Gen](https:\u002F\u002Fgithub.com\u002FFarelart\u002FAi-code-gen) - An ai agent to generate python unit test\n- [Ai-Dev-Agent-Test](https:\u002F\u002Fgithub.com\u002Feventstubsol\u002Fai-dev-agent-test) - A test repository created by AI Dev Agent\n- [Ai-Gpt-Agent](https:\u002F\u002Fgithub.com\u002Fpedrogritter\u002Fai-gpt-agent) - A simple agent to test OpenAI's GPT API\n- [Ai-Pacman-Classification](https:\u002F\u002Fgithub.com\u002FAmzAust\u002FAI-Pacman-Classification) - In this project, you will design three classifiers: a perceptron classifier, a large-margin (MIRA) classifier, and a slightly modified pe…\n- [Ai-Project-Plannerenvironment](https:\u002F\u002Fgithub.com\u002FMircoT\u002FAI-Project-PlannerEnvironment) - An environment to test a planner agent, created for a college course.\n- [Ai-Project-Vacuumenvironment](https:\u002F\u002Fgithub.com\u002FMircoT\u002FAI-Project-VacuumEnvironment) - An environment to test a vacuum agent, created for a college course.\n- [Ai-Reinforcement-Learning](https:\u002F\u002Fgithub.com\u002Fabhinavcreed13\u002Fai-reinforcement-learning) - This project will implement value iteration and Q-learning. It will first test agents on Gridworld (from class), then apply them to a sim…\n- [Ai-Smartclassroom](https:\u002F\u002Fgithub.com\u002Fadv-11\u002FAI-SmartClassroom) - Undergrad Final Year Project. A step up from Google Classroom by integrating Agentic AI, RAG, Reinforcement Learning and advance concepts…\n- [Ai-Testing-Agent](https:\u002F\u002Fgithub.com\u002Ffurudo-erika\u002Fai-testing-agent) - AI Testing Agent: Open Source AI Agent for Software Testing\n- [Ai-Wallet-Agent-Test](https:\u002F\u002Fgithub.com\u002Fvish2396\u002FAI-wallet-agent-test) - Sample Ai wallet agent using coinbase sdk\n- [Ai_Agent](https:\u002F\u002Fgithub.com\u002FIbrahimaBailoDIALLO\u002FAI_Agent) - I will put all ai agent on this repos when i finish to develop and test it\n- [Ai_Agent_Computer_Vision](https:\u002F\u002Fgithub.com\u002FSamTaubman\u002FAI_Agent_Computer_Vision) - AI Agent designed to solve Raven's Progressive Matrices IQ Test\n- [Ai_Agent_Crewai](https:\u002F\u002Fgithub.com\u002FRonin-117\u002FAi_Agent_CrewAI) - testing CrewAI\n- [Ai_Apps](https:\u002F\u002Fgithub.com\u002Fhuneyk\u002FAI_Apps) - AI Agent Test with CrewAI framework\n- [Ai_Brainstorming_Team_Agents](https:\u002F\u002Fgithub.com\u002Fjkalonji\u002FAI_Brainstorming_Team_Agents) - Test your ideas by consulting a series of experts\n- [Ai_Buddy_Guard](https:\u002F\u002Fgithub.com\u002Fgpsandhu23\u002Fai_buddy_guard) - Prototype AI agent to test how well AI can help us find and fix security problems\n- [Ai_Devs3](https:\u002F\u002Fgithub.com\u002Fbgrzywinski\u002Fai_devs3) - agent building, prompts testing, task solutions\n- [Ai_Tools](https:\u002F\u002Fgithub.com\u002Fchineseflava\u002FAI_tools) - Testing AI tools and building agents.\n- [Aiagents_Test](https:\u002F\u002Fgithub.com\u002Fprad-human-007\u002FAIAgents_test) - Test Playground for AI agents\n- [Aiconversationflow](https:\u002F\u002Fgithub.com\u002FTonySimonovsky\u002FAIConversationFlow) - AI Conversation Flow provides a framework to create anti-agents to build complex non-linear LLM conversation flows, that are composable, …\n- [Aigent](https:\u002F\u002Fgithub.com\u002Fautomators-com\u002Faigent) - Generate tests for you application using an autonomous AI agent\n- [Ailoveragent](https:\u002F\u002Fgithub.com\u002Fcaizhuoyue77\u002FAILoverAgent) - Some testing for AILover's agent implementation.\n- [Aimultiagents](https:\u002F\u002Fgithub.com\u002Fhesamjafarian\u002FAiMultiAgents) - This is a repository for testing ai algorithms using game simulation. It is used for python 2.7\n- [Aiq-Poa](https:\u002F\u002Fgithub.com\u002Fxvado00\u002FAIQ-POA) - Policy Optimisation Agents module fort the AIQ Test\n- [Amazon-Bedrock-Agent-Test-Ui](https:\u002F\u002Fgithub.com\u002Facwwat\u002Famazon-bedrock-agent-test-ui) - A generic Streamlit UI for testing generative AI agents built using Agents for Amazon Bedrock\n- [Anythingllm_Agentsample](https:\u002F\u002Fgithub.com\u002Fstoneskin\u002FAnythingLLM_AgentSample) - test the AnythignLLM custom agent\n- [Api-Ai-Agent-Test](https:\u002F\u002Fgithub.com\u002Ftexascloud\u002Fapi-ai-agent-test) - This is where all my commits for two pickle files will live to serve as an example of submodule usage for api-ai-versioning tool.\n- [Attendone](https:\u002F\u002Fgithub.com\u002Fmariamkhaled99\u002Fattendone) - ai generted agent test 1\n- [Auto-Dev](https:\u002F\u002Fgithub.com\u002Funit-mesh\u002Fauto-dev) - 🧙‍AutoDev: The AI-powered coding wizard（AI 驱动编程助手）with multilingual support 🌐, auto code generation 🏗️, and a helpful bug-slaying assista…\n- [Autogen-And-Crewai-Agenttests](https:\u002F\u002Fgithub.com\u002FTMJ97\u002FAutoGen-and-CrewAI-AgentTests) - No description available\n- [Autonoma](https:\u002F\u002Fgithub.com\u002FSebasbo\u002FAutonoma) - Autonoma: Agentic AI-powered framework for autonomous code modification, analysis, and testing, streamlining software development workflo…\n- [Autospec](https:\u002F\u002Fgithub.com\u002Fzachblume\u002Fautospec) - Autospec is an open-source AI agent that takes a web app URL and autonomously QAs it, and saves its passing specs as E2E test code\n- [B4-Agent-Test-06](https:\u002F\u002Fgithub.com\u002Fb4ke\u002Fb4-agent-test-06) - b4-agent-test-06: cloudflare ai tool test\n- [Backtesteragent](https:\u002F\u002Fgithub.com\u002FThe-Swarm-Corporation\u002FBackTesterAgent) - An enterprise-grade AI-powered backtesting framework built on the Swarms framework for automated trading strategy validation and optimiza…\n- [Baseline-Agent](https:\u002F\u002Fgithub.com\u002FBloodrock-AI\u002Fbaseline-agent) - This is a simple AI Agent used to test the Bloodrock CORE Benchmark.\n- [Botsharp-Ui](https:\u002F\u002Fgithub.com\u002FSciSharp\u002FBotSharp-UI) - Build, test and manage your AI Agents in the central place.\n- [Card_Games_For_Mcts-Ann_Ai](https:\u002F\u002Fgithub.com\u002Fsymbol-zy\u002FCard_Games_for_MCTS-ANN_AI) - A simpified version of card game like bridge, made for testing MCTS+ANN Agents.\n- [Chat_Agent](https:\u002F\u002Fgithub.com\u002FYangShyrMing\u002FChat_Agent) - ... knowledge and intelligence of a human. Now this should pass the turning test of AI i.e. we all know an AI is the best if it can pass …\n- [Claude-Html-Test](https:\u002F\u002Fgithub.com\u002FSivaramAdi\u002Fclaude-html-test) - A repository showcasing information about AI Agents\n- [Connect4_Ai](https:\u002F\u002Fgithub.com\u002Fcumason123\u002Fconnect4_ai) - Testing various agents against each other\n- [Crew-News](https:\u002F\u002Fgithub.com\u002Frokbenko\u002Fcrew-news) - CrewNews is an AI news generator that delivers an unbiased version of the news for a given topic, using Streamlit for the GUI, Llama 3.1 …\n- [Crewai](https:\u002F\u002Fgithub.com\u002FSkatAI\u002Fcrewai) - Sandbox testing crewai AI Agents\n- [Crewai-101](https:\u002F\u002Fgithub.com\u002FReyzenello\u002FCrewAI-101) - Testing around framework using multi-agent\n- [Crewai-Test](https:\u002F\u002Fgithub.com\u002FBuddog\u002FCrewAI-Test) - Multi Agent AI Test\n- [Crewaiknowledgetest](https:\u002F\u002Fgithub.com\u002FNanGePlus\u002FCrewAIKnowledgeTest) - CrewAI新版本支持使用Knowledge属性将txt、PDF、CSV、Excel、JSON等多种数据格式内容及多文件混合作为知识增强知识库提供给Crew中的Agent使用\n- [De-Bench](https:\u002F\u002Fgithub.com\u002FArdentAI1\u002FDE-Bench) - DE Bench: Can Agents Solve Real-World Data Engineering Problems? Built to test Ardent's AI Data Engineer\n- [Debugai](https:\u002F\u002Fgithub.com\u002FOpen-IDE\u002FDebugAI) - An A.I. Agent that assists in Testing & Debugging!\n- [Dev-Swarm](https:\u002F\u002Fgithub.com\u002Fkyegomez\u002Fdev-swarm) - A swarm of LLM agents that will help you test, document, and productionize your code!\n- [Dravid](https:\u002F\u002Fgithub.com\u002Fvysakh0\u002Fdravid) - AI powered cli coding agent that monitors your dev\u002Ftest server and fixes errors and adds features\n- [Explorer](https:\u002F\u002Fgithub.com\u002Finvariantlabs-ai\u002Fexplorer) - A better way of testing, inspecting, and analyzing AI Agent traces.\n- [Frontend-Agent](https:\u002F\u002Fgithub.com\u002Fqodex-ai\u002Ffrontend-agent) - Open-Source AI-Powered QA Tool for Automated UI Testing and Navigation.\n- [Generative_Ai](https:\u002F\u002Fgithub.com\u002Fgabrielpreda\u002Fgenerative_ai) - Kaggle Notebooks, Utility Scripts using Generative AI tools to check new models, fine tune models, test with various prompts, create Retr…\n- [Goingbig](https:\u002F\u002Fgithub.com\u002Ftshurden16\u002Fgoingbig) - Test AI Agent\n- [Hacksynth](https:\u002F\u002Fgithub.com\u002Faielte-research\u002FHackSynth) - LLM Agent and Evaluation Framework for Autonomous Penetration Testing\n- [Health-Ai](https:\u002F\u002Fgithub.com\u002Fsj-data\u002Fhealth-ai) - Testing grounds for health ai agent\n- [Incredible.Dev](https:\u002F\u002Fgithub.com\u002FIncredibleDevHQ\u002FIncredible.dev) - Incredible.dev is an AI Coding Co-worker which can code, fix, document, deploy, test your APIs. One agent to rule everything API.\n- [Instrukt](https:\u002F\u002Fgithub.com\u002Fblob42\u002FInstrukt) - Integrated AI environment in the terminal. Build, test and instruct agents.\n- [Intelligent-Agent](https:\u002F\u002Fgithub.com\u002Fbalaaagi\u002FIntelligent-Agent) - This repository contains an AI agent that can pass a human intelligence test.\n- [Intelligent-Agent](https:\u002F\u002Fgithub.com\u002Froterdam\u002FIntelligent-Agent) - This repository contains an AI agent that can pass a human intelligence test.\n- [Invariant](https:\u002F\u002Fgithub.com\u002Finvariantlabs-ai\u002Finvariant) - Helps you build better AI agents through debuggable unit testing\n- [Java-Ai-Sbus-Test](https:\u002F\u002Fgithub.com\u002Fabhikt48\u002Fjava-ai-sbus-test) - Codeless Agent test with Azure ServiceBus\n- [Jest-Ai](https:\u002F\u002Fgithub.com\u002Fdreamcatcher-tech\u002Fjest-ai) - AI Agent testing suites\n- [Langchaintest](https:\u002F\u002Fgithub.com\u002FJohnBreth\u002FLangChainTest) - AI Agents in LangGraph Course\n- [Mario-Playability-Test](https:\u002F\u002Fgithub.com\u002Fzhihanyang2022\u002Fmario-playability-test) - Run a simple agent through user-defined super-mario chunks and determine the playability proportion.\n- [Mazerunner](https:\u002F\u002Fgithub.com\u002FCollinsEM\u002FMazeRunner) - Framework for testing AI agents\n- [Mb-Nothing](https:\u002F\u002Fgithub.com\u002F0xAsten\u002Fmb-nothing) - Testing with Cartridge Controller and AI Agent.\n- [Mcts-Agent-Cythonized](https:\u002F\u002Fgithub.com\u002Fmasouduut94\u002FMCTS-agent-cythonized) - MONTE Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space…\n- [Mcts-Agent-Python](https:\u002F\u002Fgithub.com\u002Fmasouduut94\u002FMCTS-agent-python) - Monte Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space…\n- [Megaminer-Tinyarena](https:\u002F\u002Fgithub.com\u002Fdrusepth\u002FMegaminer-Tinyarena) - A tiny arena for testing Megaminer AI agents\n- [Metasploit-Gym](https:\u002F\u002Fgithub.com\u002FphreakAI\u002Fmetasploit-gym) - An environment for testing AI agents against networks using Metasploit.\n- [Multiagentworkflow](https:\u002F\u002Fgithub.com\u002Fzachnoel\u002FmultiAgentWorkflow) - A sample Multi AI agent workflow for testing out AI agent frameworks\n- [Multiagentworkflow](https:\u002F\u002Fgithub.com\u002Fdrewbloom\u002FmultiAgentWorkflow) - Testing multiagent AI flows for CRUD, search, and document construction from databases\n- [N8Ntest](https:\u002F\u002Fgithub.com\u002Fhabib-049\u002Fn8nTest) - This repo is for testing n8n AI agent\n- [Netsecgame](https:\u002F\u002Fgithub.com\u002Fstratosphereips\u002FNetSecGame) - An environment simulation for networks security tasks for development and testing AI based agents. Part of AI Dojo project\n- [Networkattacksimulator](https:\u002F\u002Fgithub.com\u002FJjschwartz\u002FNetworkAttackSimulator) - An environment for testing AI pentesting agents against a simulated network.\n- [Nexus](https:\u002F\u002Fgithub.com\u002Fcxbxmxcx\u002FNexus) - AI Agent Nexus is an open-source platform for developing, testing, and hosting AI Agents, built with Streamlit and Gradio. It offers a us…\n- [O1_Agent_Test](https:\u002F\u002Fgithub.com\u002Falexmoses\u002Fo1_Agent_Test) - Building a multi-agent AI program\n- [Orchestrai](https:\u002F\u002Fgithub.com\u002Fsamshapley\u002FOrchestrAI) - A framework for building and testing custom autonomous agents.\n- [Othello-Fx](https:\u002F\u002Fgithub.com\u002FEudyContreras\u002FOthello-FX) - Othello game framework made with JavaFX which can be use for testing Othello AI Agents.\n- [Pentesting-Ai](https:\u002F\u002Fgithub.com\u002FLstalet04\u002FPentesting-AI) - Multi agent penetration testing AI\n- [Phoenix](https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix) - Open source tool for testing changes in AI agent or application\n- [Pydantic-AI](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai) - Agent framework \u002F shim to use Pydantic with LLMs, useful for ensuring LLM inputs\u002Foutputs have type safety [github](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai) | [docs](https:\u002F\u002Fai.pydantic.dev\u002F)\n- [Plark_Ai_Public](https:\u002F\u002Fgithub.com\u002Fmontvieux\u002Fplark_ai_public) - ... provide a basis for more extensive, long-term, and cutting edge research. The test bed can be used as a basis to research the limits of\n- [Rag-System](https:\u002F\u002Fgithub.com\u002Fthetom42\u002Frag-system) - Test Project with Replit AI Agent\n- [Raven-Test-Ai](https:\u002F\u002Fgithub.com\u002Fdhurng\u002FRaven-Test-AI) - An AI agent that can solve Raven Tests, visual iq tests\n- [Reverse_Turing_Test](https:\u002F\u002Fgithub.com\u002Fnicomanzonelli\u002Freverse_turing_test) - Can you outsmart the LLM-based AI agents?\n- [Ricai_Unittestgen_Tool](https:\u002F\u002Fgithub.com\u002FAutoDevTestAgent\u002Fricai_unittestgen_tool) - (lablab.ai Autonomous Agents 2023 hackathon) RicAI unit test generation tool\n- [Rlerewolf](https:\u002F\u002Fgithub.com\u002FGeorgeVelikov\u002FRLereWolf) - A framework for playing Werewolf & developing and testing AI agents for Werewolf.\n- [Rpm-Ai-Agent](https:\u002F\u002Fgithub.com\u002Fteldridge11\u002FRPM-AI-Agent) - AI Agent for solving Raven's Progressive Matrices as a test of general intelligence\n- [Rpm-Ai-Agent](https:\u002F\u002Fgithub.com\u002Fhorkays\u002FRPM-AI-Agent) - An AI agent designed to solve the Raven's Progressive Matrices human intelligence tests, written in Python\n- [Sam](https:\u002F\u002Fgithub.com\u002Fvishalmysore\u002Fsam) - Autonomous Agent or Large Action Model Implementation in Java. Selenium and AI integration, AI based validations for tests. UI Validation…\n- [Snakeai-Test](https:\u002F\u002Fgithub.com\u002Fmaxolib\u002FSnakeAI-Test) - AI learning game using ML-agents for Unity\n- [Stock_Forecast_Ai_Agent](https:\u002F\u002Fgithub.com\u002Fjohn2408\u002Fstock_forecast_ai_agent) - This is a small app to test the AI Agents functionallity for forecasting purposes. Mainly focusing on function calling.\n- [Swarmgo](https:\u002F\u002Fgithub.com\u002Fprathyushnallamothu\u002Fswarmgo) - SwarmGo is a Go package that allows you to create AI agents capable of interacting, coordinating, and executing tasks. Inspired by OpenAI…\n- [Tago](https:\u002F\u002Fgithub.com\u002FTwilledWave\u002FTago) - Langchain agent test platform \u002F AI Assistant Tago\n- [Test-Agent](https:\u002F\u002Fgithub.com\u002FGDT502\u002FTest-Agent) - Developing AI tools for testing\n- [Test-Ai-Agent-Ideas](https:\u002F\u002Fgithub.com\u002FAbhiPat123\u002Ftest-ai-agent-Ideas) - A small palyground to test free models - maybe will look into using paid ones later\n- [Testai](https:\u002F\u002Fgithub.com\u002Fdhiraj-inti\u002FTestAI) - An agentic AI application to automate the testsuite creation, execution and reporting\n- [Testai-Agent](https:\u002F\u002Fgithub.com\u002Fkhanzzirfan\u002FTestAI-Agent) - write a test ai agent to write automated tests on PR requests\n- [Testdriverai](https:\u002F\u002Fgithub.com\u002Ftestdriverai\u002Ftestdriverai) - Next generation autonomous AI agent for end-to-end testing of web & desktop\n- [Testzeus-Hercules](https:\u002F\u002Fgithub.com\u002Ftest-zeus-ai\u002Ftestzeus-hercules) - Welcome to Hercules, the world's first open-source testing agent that's here to lift your testing burdens with the strength of a mytholog…\n- [Theagentsgameai](https:\u002F\u002Fgithub.com\u002FZelunGlenn\u002FTheAgentsGameAI) - AI testing system for The Agents Game logic, using decision tree.\n- [Tic-Tac-Toe](https:\u002F\u002Fgithub.com\u002FOmerCinal\u002FTic-Tac-Toe) - Search and AI algorithms testing game. Big Tic Tac Toe\n- [Travel_Agent](https:\u002F\u002Fgithub.com\u002Fgowthaml15\u002Ftravel_agent) - This repo is for testing crew_ai and trying a travel agent usecase\n- [Vacuum-Agents](https:\u002F\u002Fgithub.com\u002Fhholb\u002Fvacuum-agents) - A Simulated Robot Vaccuum for testing different AI algorithms.\n- [Vercel_Ai_Test](https:\u002F\u002Fgithub.com\u002Fpixelknit\u002Fvercel_ai_test) - Test for webgl ai agent\n- [Vital-Chat-Ui-Streamlit](https:\u002F\u002Fgithub.com\u002Fvital-ai\u002Fvital-chat-ui-streamlit) - Part of the Vital Agent Ecosystem, Simple UI for Testing Agents\n- [Voice_Agent](https:\u002F\u002Fgithub.com\u002Fdrhammed\u002Fvoice_agent) - Test app for the AI Voice Agent Project\n- [Voxprobe](https:\u002F\u002Fgithub.com\u002Fvoxos-ai\u002Fvoxprobe) - A python package for automated testing and evaluation for voice ai agents\n- [Walking-Ai](https:\u002F\u002Fgithub.com\u002FAleCamara\u002Fwalking-ai) - Test to make an AI walk using Unity ML-Agents plugin.\n- [Webtestagenticai](https:\u002F\u002Fgithub.com\u002Ftvalentius\u002FWebTestAgenticAI) - Web test Agent AI\n- [Whiteboxing-Unitymlagents](https:\u002F\u002Fgithub.com\u002FActiveNick\u002FWhiteboxing-UnityMLAgents) - Experimental testbed where I test various Machine Learning & AI concepts using Unity ML Agents.\n- [Windowsagentarena](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FWindowsAgentArena) - Windows Agent Arena (WAA) 🪟 is a scalable OS platform for testing and benchmarking of multi-modal AI agents.\n- [Worldai](https:\u002F\u002Fgithub.com\u002Fnschaetti\u002FWorldAI) - World AI is a simulator designed for research purposes. It can be used to simulate worlds, logical and ethical problems for AI agents \n\n\n### Tools\n- [Cortex](https:\u002F\u002Fgithub.com\u002FSKULLFIRE07\u002Fcortex-memory) - Persistent AI memory for coding assistants. Auto-captures decisions, patterns, and context across sessions. VSCode extension + CLI + MCP server. Free.\n- [3Gpp-Requirements-Tools](https:\u002F\u002Fgithub.com\u002FAdrian2901\u002F3gpp-requirements-tools) - Tools for retrieving 3GPP standards and LLM-powered requirement elicitiation.\n- [Acm](https:\u002F\u002Fgithub.com\u002Fdnanhkhoa\u002Facm) - A dead-simple AI-powered CLI tool for effortlessly crafting meaningful Git commit messages\n- [Agent-Manager-Skill](https:\u002F\u002Fgithub.com\u002Ffractalmind-ai\u002Fagent-manager-skill) - tmux + Python agent lifecycle manager for running multiple CLI AI agents (start\u002Fstop\u002Fmonitor\u002Fassign) with cron-friendly scheduling.\n- [Acte](https:\u002F\u002Fgithub.com\u002Fj66n\u002Facte) - A framework to build GUI-like Agent Tools, enhancement to Function Calling of LLM AI.\n- [Agentcloud](https:\u002F\u002Fgithub.com\u002Frnadigital\u002Fagentcloud) - Agent Cloud is like having your own GPT builder with a bunch extra goodies. The GUI features 1) RAG pipeline which can natively embed 260…\n- [Agentgpt-Llm-Tools](https:\u002F\u002Fgithub.com\u002FGautamSharda\u002FAgentGPT-LLM-Tools) - AgentGPT allows you to configure and deploy Autonomous AI agents. Name your own custom AI and have it embark on any goal imaginable\n- [Agentguard](https:\u002F\u002Fgithub.com\u002Fbmdhodl\u002Fagent47) - Zero-dependency runtime guardrails for AI agents with loop detection, budget enforcement, cost tracking, and deterministic replay. [github](https:\u002F\u002Fgithub.com\u002Fbmdhodl\u002Fagent47) | [pypi](https:\u002F\u002Fpypi.org\u002Fproject\u002Fagentguard47\u002F)\n- [Agentools](https:\u002F\u002Fgithub.com\u002FJoongWonSeo\u002Fagentools) - Essentials for LLM-based assistants and agents using OpenAI and function tools\n- [AgentWatch](https:\u002F\u002Fgithub.com\u002Fnicofains1\u002Fagentwatch) - Multi-agent observability library with cascade failure detection, heartbeat-based liveness monitoring, cross-agent correlation, and forensic replay. Provides fleet-level monitoring above per-agent tracing tools [github](https:\u002F\u002Fgithub.com\u002Fnicofains1\u002Fagentwatch) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@nicofains1\u002Fagentwatch)\n- [Ai-Agents-Directory](https:\u002F\u002Fgithub.com\u002F0xmetaschool\u002FAI-Agents-Directory) - Find and get started with the best AI Agents and AI Automation tools on the Internet. Start building your own AI Agents powered workforce…\n- [Ai-Anime-Art-Generator](https:\u002F\u002Fgithub.com\u002Fenterwiz\u002Fai-anime-art-generator) - AI-driven cutting-edge tool for anime arts creation, perfect for beginners to easily create stunning anime art without any prior experience.\n- [AI Conference Deadline](https:\u002F\u002Faiconferenceddl.com) - A tracker for AI\u002FML conference submission deadlines, helping researchers track major CFPs, access official conference websites, and plan submissions without accounts or setup [website](https:\u002F\u002Faiconferenceddl.com)\n- [Ai-Game-Devtools](https:\u002F\u002Fgithub.com\u002FYuan-ManX\u002Fai-game-devtools) - Here we will keep track of the latest AI Game Development Tools, including LLM, Agent, Code, Writer, Image, Texture, Shader, 3D Model, An…\n- [Ai.At](https:\u002F\u002Fgithub.com\u002Fblue-codes-yep\u002FAI.AT) - AI-Powered Text-To-Speech Video Generator This web application uses AI to generate captivating and informative video scripts based on use…\n- [Aidialer](https:\u002F\u002Fgithub.com\u002Fakiani\u002Faidialer) - A full stack app for interruptible, low-latency and near-human quality AI phone calls built from stitching LLMs, speech understanding tools…\n- [Aitranslate](https:\u002F\u002Fgithub.com\u002Fpmacro\u002FAITranslate) - A tool to translate Xcode xcstrings files using ChatGPT AI\u002FLLM\n- [Aivideochat](https:\u002F\u002Fgithub.com\u002Fmessingliu\u002FAIVideoChat) - This is an AI video chat tool with anybody (your girlfriend, your idol etc) you want using LLM\n- [Aix](https:\u002F\u002Fgithub.com\u002Fprojectdiscovery\u002Faix) - AIx is a cli tool to interact with Large Language Models (LLM) APIs.\n- [Are-Copilots-Local-Yet](https:\u002F\u002Fgithub.com\u002FErikBjare\u002Fare-copilots-local-yet) - Are Copilots Local Yet? The frontier of local LLM Copilots for code completion, project generation, shell assistance, and more. Find tools …\n- [Arxivrag](https:\u002F\u002Fgithub.com\u002Fphitrann\u002FarXivRAG) - A comprehensive tool designed to enhance the retrieval and generation of academic content from the arXiv database, leveraging advanced Re…\n- [Attention-Viewer](https:\u002F\u002Fgithub.com\u002Fwln20\u002FAttention-Viewer) - A tool for visualizing attention-score heatmap in generative LLMs\n- [Autogenbook](https:\u002F\u002Fgithub.com\u002Fhooked-on-mas\u002FAutoGenBook) - 🤖 📒 AutoGenBook is a Python-based tool that automatically generates books using LLMs. It creates chapters, sections, and subsections recu…\n- [Awesome-Ai-Llms-In-Radiology](https:\u002F\u002Fgithub.com\u002Fopenlifescience-ai\u002FAwesome-AI-LLMs-in-Radiology) - A curated list of awesome resources, papers, datasets, and tools related to AI in radiology. This repository aims to provide a comprehens…\n- [Awesome-Ai-Sdks](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-sdks) - A database of SDKs, frameworks, libraries, and tools for creating, monitoring, debugging and deploying autonomous AI agents\n- [Awesome-Ai-Tools](https:\u002F\u002Fgithub.com\u002Feudk\u002Fawesome-ai-tools) - 🔴 VERY LARGE AI TOOL LIST! 🔴 Curated list of AI Tools - Updated December 2024\n- [Awesome-Aitools](https:\u002F\u002Fgithub.com\u002Fikaijua\u002FAwesome-AITools) - Collection of AI-related utilities. Welcome to submit issues and pull requests \u002F收藏AI相关的实用工具，欢迎提交issues 或者pull requests\n- [Awesome-Langchain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain) - 😎 Awesome list of tools and projects with the awesome LangChain framework\n- [Awesome-Llm-Compression](https:\u002F\u002Fgithub.com\u002FHuangOwen\u002FAwesome-LLM-Compression) - Awesome LLM compression research papers and tools.\n- [Awesome-Llm-Json](https:\u002F\u002Fgithub.com\u002Fimaurer\u002Fawesome-llm-json) - Resource list for generating JSON using LLMs via function calling, tools, CFG. Libraries, Models, Notebooks, etc.\n- [Awesome-Llm-Os](https:\u002F\u002Fgithub.com\u002Fbilalonur\u002Fawesome-llm-os) - A curated list of awesome resources, tools, research papers, and projects related to the concept of Large Language Model Operating Systems (\n- [Awesome-Llm-Productization](https:\u002F\u002Fgithub.com\u002Foscinis-com\u002FAwesome-LLM-Productization) - Awesome-LLM-Productization - a curated list of tools\u002Ftricks\u002Fnews\u002Fregulations about AI and Large Language Model (LLM) productization\n- [Awesome-Llm-Security](https:\u002F\u002Fgithub.com\u002Fcorca-ai\u002Fawesome-llm-security) - A curation of awesome tools, documents and projects about LLM Security.\n- [Awesome-Llm4Security](https:\u002F\u002Fgithub.com\u002Fliu673\u002FAwesome-LLM4Security) - This project aims to consolidate and share high-quality resources and tools across the cybersecurity domain.\n- [Awesome-Llm4Tool](https:\u002F\u002Fgithub.com\u002FOpenGVLab\u002FAwesome-LLM4Tool) - A curated list of the papers, repositories, tutorials, and anythings related to the large language models for tools\n- [Awesome-Llmops](https:\u002F\u002Fgithub.com\u002Ftensorchord\u002FAwesome-LLMOps) - An awesome & curated list of best LLMOps tools for developers\n- [Awesome-Llmops](https:\u002F\u002Fgithub.com\u002FInftyAI\u002FAwesome-LLMOps) - 🎉 An awesome & curated list of best LLMOps tools.\n- [Awesome-Local-Llm](https:\u002F\u002Fgithub.com\u002FWaterPistolAI\u002FAwesome-Local-LLM) - A curated list of resources, libraries, tools, and communities for working with Local Large Language Models (LLMs).\n- [Awesome-Mlsecops](https:\u002F\u002Fgithub.com\u002FRiccardoBiosas\u002Fawesome-MLSecOps) - A curated list of MLSecOps tools, articles and other resources on security applied to Machine Learning and MLOps systems.\n- [Awesome-Rust-Llm](https:\u002F\u002Fgithub.com\u002Fjondot\u002Fawesome-rust-llm) - 🦀 A curated list of Rust tools, libraries, and frameworks for working with LLMs, GPT, AI\n- [Awesome_Ai_For_Programmers](https:\u002F\u002Fgithub.com\u002Frodion-m\u002Fawesome_ai_for_programmers) - Сборник AI-инструментов, кейсов и всяких других полезностей для программистов\n- [Awesomellmapps](https:\u002F\u002Fgithub.com\u002FAbhishek-yadv\u002FAwesomeLLMApps) - A curated collection of awesome applications and tools that utilize large language models (LLMs) with retrieval-augmented generation (RAG…\n- [Bgpt-Mcp](https:\u002F\u002Fgithub.com\u002Fconnerlambden\u002Fbgpt-mcp) - Hosted MCP server for searching scientific papers with full-text experimental data. SSE + Streamable HTTP. 50 free searches.\n- [Blockoli](https:\u002F\u002Fgithub.com\u002FgetAsterisk\u002Fblockoli) - Blockoli is a high-performance tool for code indexing, embedding generation and semantic search tool for use with LLMs.\n- [Blueprints](https:\u002F\u002Fgithub.com\u002Fsublayerapp\u002Fblueprints) - Blueprints is an open-source tool that integrates with your text editor to help you generate code with an LLM based on patterns you alrea…\n- [Botsh](https:\u002F\u002Fgithub.com\u002Fjamsocket\u002Fbotsh) - An LLM-based agent that will install the tools it needs.\n- [Brahmasumm-Community-Edition](https:\u002F\u002Fgithub.com\u002Fbalajivis\u002FBrahmaSumm-Community-Edition) - BrahmaSumm is an advanced document summarization and visualization tool designed to streamline document management, knowledge base creati…\n- [Brokenhill](https:\u002F\u002Fgithub.com\u002FBishopFox\u002FBrokenHill) - A productionized greedy coordinate gradient (GCG) attack tool for large language models (LLMs)\n- [Bubbln_Network-Automation](https:\u002F\u002Fgithub.com\u002Folasupo\u002Fbubbln_network-automation) - An AI-driven network automation tool\n- [Ceo-Agentic-Ai-Framework](https:\u002F\u002Fgithub.com\u002Fvortezwohl\u002FCEO-Agentic-AI-Framework) - An ultra-lightweight Agentic AI framework based on the ReAct paradigm, supporting mainstream LLMs and is stronger than Swarm.\n- [Chatbot](https:\u002F\u002Fgithub.com\u002FYidaHu\u002Fchatbot) - 基于LLM的聊天机器人，AI Agent的自主智能体，利用Function、Tools、Agent来实现LLM自主工作\n- [ChatSpatial](https:\u002F\u002Fgithub.com\u002Fcafferychen777\u002FChatSpatial) - MCP server enabling spatial transcriptomics analysis via natural language. Integrates 60+ methods for spatial domains, deconvolution, cell communication, and trajectory analysis.\n- [Claude-Powered-Study-Assistant](https:\u002F\u002Fgithub.com\u002Fg-hano\u002FClaude-Powered-Study-Assistant) - A study assistant powered by Claude Opus. It provides various tools to assist with different tasks, such as researching,coding,note-takin…\n- [Claudesync](https:\u002F\u002Fgithub.com\u002Fjahwag\u002FClaudeSync) - ClaudeSync is a Python tool that automates the synchronization of local files with Claude.ai Projects\n- [Code-Interpreter-Api](https:\u002F\u002Fgithub.com\u002Fleezhuuuuu\u002FCode-Interpreter-Api) - Committed to being the best code interpreter in the world.\n- [Comfyui-Llm-Tools](https:\u002F\u002Fgithub.com\u002Fpridkett\u002FComfyUI-llm-tools) - Helpful nodes for working with LLMs inside of ComfyUI\n- [Companion](https:\u002F\u002Fgithub.com\u002Frapmd73\u002FCompanion) - An AI-powered Discord bot blending playful conversation with smart moderation tools, adding charm and order to your server.\n- [Conversational-Agent-With-Qa-Tool](https:\u002F\u002Fgithub.com\u002FCharlesSQ\u002Fconversational-agent-with-QA-tool) - A custom chat agent implemented using Langchain, gpt-3.5 and Pinecone. Implements memory management for context, a custom prompt template…\n- [Cuesubplot](https:\u002F\u002Fgithub.com\u002Fkleer001\u002Fcuesubplot) - procedural ai prompts and results\n- [Curategpt](https:\u002F\u002Fgithub.com\u002Fmonarch-initiative\u002Fcurategpt) - LLM-driven curation assist tool\n- [Dbt-Llm-Tools](https:\u002F\u002Fgithub.com\u002Fpragunbhutani\u002Fdbt-llm-tools) - RAG based LLM chatbot for dbt projects\n- [Demogpt](https:\u002F\u002Fgithub.com\u002Fmelih-unsal\u002FDemoGPT) - 🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.\n- [Androidmeda](https:\u002F\u002Fgithub.com\u002FIn3tinct\u002Fdeobfuscate-android-app) - AI tool to deobfuscate and find any potential vulnerabilities in android apps.\n- [Dingo](https:\u002F\u002Fgithub.com\u002FDataEval\u002Fdingo) - Dingo - A Comprehensive Data Quality Evaluation Tool\n- [Discovai-Crawl](https:\u002F\u002Fgithub.com\u002FDiscovAI\u002FDiscovAI-crawl) - 🕷️ DiscovAI Crawl API(🚧 Work in Progress 🚧) - A powerful web scraping solution for AI tools and vector databases. Extract clean HTML, gene…\n- [Docgenie](https:\u002F\u002Fgithub.com\u002Fwe-festify\u002Fdocgenie) - Docgenie is a command-line tool that leverages the power of large language models (LLMs) to automatically generate comprehensive document…\n- [Draft](https:\u002F\u002Fgithub.com\u002Fquchangle1\u002FDRAFT) - The implementation for the paper - From Exploration to Mastery - Enabling LLMs to Master Tools via Self-Driven Interactions.\n- [Ds-Llm-Webui](https:\u002F\u002Fgithub.com\u002FDocShotgun\u002Fds-llm-webui) - A simple tool-use assistant for local LLMs powered by TabbyAPI\n- [Ducky](https:\u002F\u002Fgithub.com\u002FParthSareen\u002Fducky) - Local AI pair programming tool\n- [Emergent](https:\u002F\u002Fgithub.com\u002Fkyb3r\u002Femergent) - An implementation of long term memory and external tools for LLMs\n- [Empower-Functions](https:\u002F\u002Fgithub.com\u002Fempower-ai\u002Fempower-functions) - GPT-4 level function calling models for real-world tool using use cases\n- [Erag](https:\u002F\u002Fgithub.com\u002FEdwardDali\u002Ferag) - an AI interaction tool with RAG hybrid search, conversation context, web content processing and structured data analysis with LLM \u002F GPT\n- [Flotorch](https:\u002F\u002Fgithub.com\u002FFissionAI\u002FFloTorch) - FloTorch is an open-source tool for optimizing Generative AI workloads on AWS. It automates RAG proof-of-concept development with feature…\n- [Flux](https:\u002F\u002Fgithub.com\u002Fparadigmxyz\u002Fflux) - Graph-based LLM power tool for exploring many completions in parallel.\n- [Formfill](https:\u002F\u002Fgithub.com\u002Fwdhorton\u002Fformfill) - FormFill is a CLI tool that uses LLMs to automatically fill out PDF forms.\n- [Funclip](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002FFunClip) - Open-source, accurate and easy-to-use video speech recognition & clipping tool, LLM based AI clipping intergrated.\n- [Function-Python-Ai-Langchain](https:\u002F\u002Fgithub.com\u002FAzure-Samples\u002Ffunction-python-ai-langchain) - Simple starting point function to host LangChains with LLMs and other tools in an Azure Function.\n- [Functionary](https:\u002F\u002Fgithub.com\u002FMeetKai\u002Ffunctionary) - Chat language model that can use tools and interpret the results\n- [Functions-Tools-And-Agents-With-Langchain](https:\u002F\u002Fgithub.com\u002FRyota-Kawamura\u002FFunctions-Tools-and-Agents-with-LangChain) - You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called La…\n- [Gguf-Tools](https:\u002F\u002Fgithub.com\u002FKerfuffleV2\u002Fgguf-tools) - Some random tools for working with the GGUF file format\n- [Gorilla](https:\u002F\u002Fgithub.com\u002FShishirPatil\u002Fgorilla) - Gorilla - Training and Evaluating LLMs for Function Calls (Tool Calls)\n- [Gpt4-Programming-Assistant](https:\u002F\u002Fgithub.com\u002Fpetermartens98\u002FGPT4-Programming-Assistant) - Streamlit web app utilizing OpenAI (GPT-4) and LangChain LLM tools. Application includes an SQLite DB for login\u002Fauthentication and messag…\n- [Graphrag-Visualizer](https:\u002F\u002Fgithub.com\u002Fnoworneverev\u002Fgraphrag-visualizer) - A web-based tool for visualizing and exploring artifacts from Microsoft's GraphRAG.\n- [Harbor](https:\u002F\u002Fgithub.com\u002Fav\u002Fharbor) - Effortlessly run LLM backends, APIs, frontends, and services with one command.\n- [Heb-Gen-Ai](https:\u002F\u002Fgithub.com\u002FTovTechOrg\u002FHeb-Gen-AI) - Tools, examples, and resources to assist in the development of Gen-AI (Generative Artificial Intelligence) applications in Hebrew, with a…\n- [Howtofinetunellama3.1](https:\u002F\u002Fgithub.com\u002FRs-py\u002FHowToFineTuneLlama3.1) - Quick tutorial showing how to fine-tune Llama3.1 with nothing but free tools and text data. All code included in ipynb. For a step by ste…\n- [Humanizer PRO](https:\u002F\u002Ftexthumanizer.pro) - AI-powered text humanization tool with 3 modes (Stealth, Academic, SEO), AI detection scanner, MCP integration for ChatGPT\u002FClaude, and RESTful API access [website](https:\u002F\u002Ftexthumanizer.pro) | [github](https:\u002F\u002Fgithub.com\u002Fkhadinakbaronline\u002Fhumanizer-pro-mcp) | [smithery](https:\u002F\u002Fsmithery.ai\u002Fservers\u002Fkhadin-akbar\u002Fhumanizer-pro)\n- [Icodes](https:\u002F\u002Fgithub.com\u002Fa115\u002FiCODES) - LLM-powered Git archeology tool (a.k.a. Intelligent Commit Ontology Distiller and Enhanced Search)\n- [Indie-Hacker-Tools-Plus](https:\u002F\u002Fgithub.com\u002FXiaomingX\u002Findie-hacker-tools-plus) - 为独立开发者准备的精选技术栈和工具仓库来了！这里有你最需要的工具，帮你提升开发效率、节约成本，最重要的是——这些工具都是市场上热门的，经过验证的。🚀A curated collection of tech stacks and tools tailored for inde…\n- [Intellichunk](https:\u002F\u002Fgithub.com\u002Fcckalen\u002Fintellichunk) - Go Based Lightweight RAG \u002F LLM Tool with CLI + API\n- [Job-Webscraper](https:\u002F\u002Fgithub.com\u002FPrvargas\u002Fjob-webscraper) - LLM API-Powered Job Listings Data Cleaning Tool - Showcase for Data Scientists\n- [Just-Eval](https:\u002F\u002Fgithub.com\u002FRe-Align\u002Fjust-eval) - A simple GPT-based evaluation tool for multi-aspect, interpretable assessment of LLMs.\n- [Labs-Ai-Tools-For-Devs](https:\u002F\u002Fgithub.com\u002Fdocker\u002Flabs-ai-tools-for-devs) - [Now with MCP Support] AI For Devs - Build, Share & Run agentic workflows. Just Docker. Just Markdown. BYO LLM\n- [Lang-Tools-Llm-Powered](https:\u002F\u002Fgithub.com\u002Fodedwolff\u002Flang-tools-LLM-powered) - language learning tools utilizing LLM APIs among others\n- [Langchain-Llm-Pdf-Qa](https:\u002F\u002Fgithub.com\u002FAyyodeji\u002FLangchain-LLM-PDF-QA) - This open-source project leverages cutting-edge tools and methods to enable seamless interaction with PDF documents. Powered by Langchain…\n- [Langkit](https:\u002F\u002Fgithub.com\u002Fwhylabs\u002Flangkit) - 🔍 LangKit - An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safe…\n- [Llama-Cpp-Agent](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent) - The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM …\n- [Llama-Pruning](https:\u002F\u002Fgithub.com\u002FMedITSolutionsKurman\u002Fllama-pruning) - This project provides tools to load and prune large language models using a structured pruning method.\n- [Llm](https:\u002F\u002Fgithub.com\u002FMHaggis\u002FLLM) - LLM tools and toys\n- [Llm](https:\u002F\u002Fgithub.com\u002Fjoelabruce\u002Fllm) - Large Language Model Tools\n- [Llm](https:\u002F\u002Fgithub.com\u002FJoshua-Immanuel\u002FLLM) - prompting, evaluation, models, chains, indexes, vector stores, retrieval types, use of frameworks like langchain, agents with custom tools\n- [Llm-As-Function](https:\u002F\u002Fgithub.com\u002Fgusye1234\u002Fllm-as-function) - Embed your LLM into a python function\n- [Llm-Benchmarks](https:\u002F\u002Fgithub.com\u002Fwanzhenchn\u002Fllm-benchmarks) - LLM benchmark tools for LMDeploy, vLLM, and TensorRT-LLM.\n- [Llm-Datasets](https:\u002F\u002Fgithub.com\u002Fmlabonne\u002Fllm-datasets) - High-quality datasets, tools, and concepts for LLM fine-tuning.\n- [Llm-Deployment-Tools-Llmops](https:\u002F\u002Fgithub.com\u002FZamr77\u002FLLM-Deployment-Tools-LLMOps) - This project empirically studies the challenges and prospects of deploying large language models (LLMs) in real-world applications using …\n- [Llm-Fuzzx](https:\u002F\u002Fgithub.com\u002FWindy3f3f3f3f\u002FLLM-FuzzX) - LLM-FuzzX is a user-friendly fuzz testing tool for Large Language Models (e.g., GPT, Claude, LLaMA), featuring advanced task-aware mutati…\n- [Llm-Interest](https:\u002F\u002Fgithub.com\u002Fhamelsmu\u002Fllm-interest) - Tool to collect LLM eval topics\n- [Llm-S-Finetunning](https:\u002F\u002Fgithub.com\u002FWarishayat\u002FLLM-s-Finetunning) - This library offers tools to easily fine-tune large language models (LLMs) on custom datasets. It enhances pre-trained models for specifi…\n- [Llm-Security-101](https:\u002F\u002Fgithub.com\u002FSeezo-io\u002Fllm-security-101) - Delving into the Realm of LLM Security - An Exploration of Offensive and Defensive Tools, Unveiling Their Present Capabilities.\n- [Llm-Slackbot-Channels](https:\u002F\u002Fgithub.com\u002FVokturz\u002FLLM-slackbot-channels) - A user-customized bot for your slack channels using LLMs, Tools and Documents\n- [Llm-Term](https:\u002F\u002Fgithub.com\u002Fdh1011\u002Fllm-term) - A Rust-based CLI tool that generates and executes terminal commands using OpenAI's language models.\n- [Llm-Toolbox-Suite](https:\u002F\u002Fgithub.com\u002FSaurabhBadole\u002Fllm-toolbox-suite) - LLM Toolbox Suite, a powerful and versatile set of tools designed to harness the capabilities of large language models for various produc…\n- [Llm-Tools](https:\u002F\u002Fgithub.com\u002Fdatacommonsorg\u002Fllm-tools) - This repo contains client library code for accessing DataGemma, an open model that helps address the challenges of hallucination by grounding LLMs in the vast, real-world statistical data of Google's Data Commons\n- [Llm-Tools](https:\u002F\u002Fgithub.com\u002Fgkorepanov\u002Fllm-tools) - Some ad-hoc coding tools for LLMs\n- [Llm-Tools](https:\u002F\u002Fgithub.com\u002FTheTeslak\u002FLLM-Tools) - Scripts for preparing data for LLMs - text extraction, Telegram export processing, and file merging with filtering and stats.\n- [Llm-Toolschain](https:\u002F\u002Fgithub.com\u002FViagounet\u002FLLM-ToolsChain) - A LangChain-like framework centered around the use of tools and planning. Useful for agents creation\n- [Llm-Warden](https:\u002F\u002Fgithub.com\u002Fjackhhao\u002Fllm-warden) - A simple jailbreak detection tool for safeguarding LLMs.\n- [Llm.Guts](https:\u002F\u002Fgithub.com\u002FMahdi-s\u002Fllm.guts) - A tool to visualize the internal computational graph of distilgpt2 model.\n- [Llm_Agents_Devtools](https:\u002F\u002Fgithub.com\u002FM1n9X\u002Fllm_agents_devtools) - A curated list of autonomous agents and developer tools powered by LLM.\n- [Llm_Api_Price_Comparator_Web](https:\u002F\u002Fgithub.com\u002FCookSleep\u002FLLM_API_Price_Comparator_Web) - LLM API Price Comparator Web 是一个在线工具，帮助用户便捷地比较不同LLM API服务商在指定输入输出下调用同一种模型的价格。 它会自动获取美元\u002F人民币汇率，允许用户输入服务商的余额、调用定价信息，并计算、比较相对于输入输出Token的成本。 该…\n- [Llm_Counts](https:\u002F\u002Fgithub.com\u002Fharleyszhang\u002Fllm_counts) - llm theoretical performance analysis tools and support params, flops, memory and latency analysis.\n- [Llm_Surprisal](https:\u002F\u002Fgithub.com\u002Ftmalsburg\u002Fllm_surprisal) - Simple tool for generating tokens with open source transformers and\u002For calculate per-token surprisal.\n- [Llm_Tools](https:\u002F\u002Fgithub.com\u002Fmavihsrr\u002FLLM_tools) - Productive tools for enhancing and creating LLMs.\n- [Llm_Video_Editor](https:\u002F\u002Fgithub.com\u002Fsanskar9999\u002Fllm_video_editor) - This application utilizes Large Language Models (LLM) and FFmpeg to automate video editing tasks based on user instructions. Built with a…\n- [Llmask](https:\u002F\u002Fgithub.com\u002Ftop-on\u002Fllmask) - A command-line tool for masking authorship of text, by changing the writing style with a Large Language Model.\n- [Llmcode](https:\u002F\u002Fgithub.com\u002Fjavierganan99\u002FLLMCode) - LLMCode is a tool designed to streamline code documentation using Language Models (LLMs).\n- [Llmcurator.Io](https:\u002F\u002Fgithub.com\u002Fpushpankar\u002FLLMCurator.io) - LLM frontend and data curation tool.\n- [Llmsmartaudittool](https:\u002F\u002Fgithub.com\u002FLLMAudit\u002FLLMSmartAuditTool) - LLM-SmartAudit is a cutting-edge tool designed to revolutionize smart contract auditing using advanced language models.\n- [Llmtestforaccessibilitycheck_Chatgpt](https:\u002F\u002Fgithub.com\u002FAutoBugHunter\u002FLLMTestForAccessibilityCheck_chatgpt) - Checking multiple LLM tools to generate code for a webpage and then prompting the tools to fix the accessibility issues in the webpage\n- [Localagent](https:\u002F\u002Fgithub.com\u002FPrAsAnNaRePo\u002FLocalAgent) - opengpt is a open implementation of GPT agents.\n- [Macllm](https:\u002F\u002Fgithub.com\u002Fappenz\u002FmacLLM) - Tool to integrate LLM's like GPT-3 with macOS\n- [Markdown_Llm](https:\u002F\u002Fgithub.com\u002Fmatweldon\u002Fmarkdown_llm) - A tool for interacting with an LLM in a markdown document\n- [Mastermind](https:\u002F\u002Fgithub.com\u002Ftheoforger\u002Fmastermind) - An LLM-powered CLI tool to help you be a better spymaster in Codenames\n- [Metorial](https:\u002F\u002Fgithub.com\u002Fmetorial\u002Fmetorial) - Connect AI agents to 600+ integrations with a single interface - OAuth, scaling, and monitoring included\n- [Mcp-Go](https:\u002F\u002Fgithub.com\u002Fmark3labs\u002Fmcp-go) - A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources…\n- [Mcphost](https:\u002F\u002Fgithub.com\u002Fmark3labs\u002Fmcphost) - A CLI host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP).\n- [Mergekit](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002Fmergekit) - Tools for merging pretrained large language models.\n- [Modelready](https:\u002F\u002Fgithub.com\u002Fsuparious\u002FModelReady) - Collection of tools for creating and running llama.cpp compatible LLMs\n- [Monadic-Chat](https:\u002F\u002Fgithub.com\u002Fyohasebe\u002Fmonadic-chat) - 🤖 + 🐳 + 🐧 Monadic Chat is a locally hosted web app for creating intelligent chatbots, available for Mac, Windows, and Linux. It offers a …\n- [Monocle](https:\u002F\u002Fgithub.com\u002Farphanetx\u002FMonocle) - Tooling backed by an LLM for performing natural language searches against compiled target binaries. Search for encryption code, password …\n- [Mql](https:\u002F\u002Fgithub.com\u002Fshurutech\u002Fmql) - MQL tool is designed to generate SQL queries directly from natural language inputs.\n- [Nano-Bots](https:\u002F\u002Fgithub.com\u002Ficebaker\u002Fnano-bots) - Repository for Nano Bots' Cartridges - small, AI-powered bots that can be easily shared as a single file, designed to support multiple pro…\n- [Nano-Bots-Api](https:\u002F\u002Fgithub.com\u002Ficebaker\u002Fnano-bots-api) - HTTP API for Nano Bots - small, AI-powered bots that can be easily shared as a single file, designed to support multiple providers such as…\n- [Notiongpt](https:\u002F\u002Fgithub.com\u002FSuiwan\u002FnotionGPT) - NotionGPT, a practical tool built on top of ChatGPT large language model, make it your note-taking assistant!\n- [Ollama-Mcp-Bridge](https:\u002F\u002Fgithub.com\u002Fpatruff\u002Follama-mcp-bridge) - Bridge between Ollama and MCP servers, enabling local LLMs to use Model Context Protocol tools\n- [Open-Webui-Tools](https:\u002F\u002Fgithub.com\u002FHaervwe\u002Fopen-webui-tools) - a Repository of Open-WebUI tools to use with your favourite LLMs\n- [Openai-Tools](https:\u002F\u002Fgithub.com\u002Ftipani86\u002FOpenAI-Tools) - Toolkit to get the most out of your OpenAI Account\n- [Pathology_Llm](https:\u002F\u002Fgithub.com\u002FJaesikKim\u002Fpathology_llm) - GPT-4 as decision support tool in oncology\n- [Pentestgpt](https:\u002F\u002Fgithub.com\u002FGreyDGL\u002FPentestGPT) - A GPT-empowered penetration testing tool\n- [Plasmate](https:\u002F\u002Fgithub.com\u002Fplasmate-labs\u002Fplasmate) - A browser engine built for AI agents that compiles HTML into a Semantic Object Model (SOM), providing 10x token compression vs raw HTML. V8 JS rendering, CDP compatibility, authenticated browsing, MCP server [github](https:\u002F\u002Fgithub.com\u002Fplasmate-labs\u002Fplasmate) | [docs](https:\u002F\u002Fdocs.plasmate.app)\n- [Prelude](https:\u002F\u002Fgithub.com\u002Faerugo\u002Fprelude) - A very simple tool to build LLM prompts from your code repositories.\n- [Prophetfuzz](https:\u002F\u002Fgithub.com\u002FNASP-THU\u002FProphetFuzz) - [CCS'24] An LLM-based, fully automated fuzzing tool for option combination testing.\n- [Purplellama](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002FPurpleLlama) - Set of tools to assess and improve LLM security.\n- [Qlora-Llm](https:\u002F\u002Fgithub.com\u002Fmichaelnny\u002FQLoRA-LLM) - A simple custom QLoRA implementation for fine-tuning a language model (LLM) with basic tools such as PyTorch and Bitsandbytes, completely…\n- [Querying-Csvs-And-Plot-Graphs-With-Llms](https:\u002F\u002Fgithub.com\u002FSomyanshAvasthi\u002FQuerying-CSVs-and-Plot-Graphs-with-LLMs) - Leveraging Large Language Models (LLMs) to query CSV files and plot graphs transforms data analysis. This allows to interact with dataset…\n- [Rag-On-Gcp-With-Vertexai](https:\u002F\u002Fgithub.com\u002FBastinFlorian\u002FRAG-on-GCP-with-VertexAI) - Create a Chatbot app on your own data with GCP tools\n- [Ragelo](https:\u002F\u002Fgithub.com\u002Fzetaalphavector\u002FRAGElo) - RAGElo is a set of tools that helps you selecting the best RAG-based LLM agents by using an Elo ranker\n- [Rageval](https:\u002F\u002Fgithub.com\u002Fgomate-community\u002Frageval) - Evaluation tools for Retrieval-augmented Generation (RAG) methods.\n- [Recruitpilot](https:\u002F\u002Fgithub.com\u002Fjaredkirby\u002FRecruitPilot) - A set of AI tools to automate resume scoring and generate interview questions.\n- [Redlite](https:\u002F\u002Fgithub.com\u002Finnodatalabs\u002Fredlite) - Opinionated tool for benchmarking Conversational Language Models\n- [Remarkable-2-Llm](https:\u002F\u002Fgithub.com\u002F99x-incubator\u002Fremarkable-2-llm) - Integrating LLMs into reMarkable could unlock transformative features like auto-completion, grammar\u002Fstyle corrections, contextual suggest…\n- [Repo-To-Text](https:\u002F\u002Fgithub.com\u002Fkirill-markin\u002Frepo-to-text) - Convert a repository structure and its contents into a single text file, including the tree output and file contents in markdown code blo…\n- [Resume_Render_From_Job_Description](https:\u002F\u002Fgithub.com\u002FAIHawk-FOSS\u002Fresume_render_from_job_description) - Resume_Builder_AIHawk is a powerful Python tool that allows you to automatically customize your resume based on a job URL, ensuring it pe…\n- [Rome-Llm](https:\u002F\u002Fgithub.com\u002Fajn313\u002FROME-LLM) - Tools for Recurrent Optimization via Machine Editing and related benchmarks\n- [Selenium-Agent](https:\u002F\u002Fgithub.com\u002Fahmadrosid\u002Fselenium-agent) - LLM agent using selenium as a tool. Have fun!\n- [Simple-Llm-Exporter](https:\u002F\u002Fgithub.com\u002Frealityinspector\u002Fsimple-llm-exporter) - a tool to export entire scripts to a text file with a file tree and description, for exporting to llm's\n- [SkillLite](https:\u002F\u002Fgithub.com\u002FEXboys\u002Fskilllite) - A lightweight, zero-dependency runtime for the agentsskills protocol that enables AI agents to securely execute portable skills locally. Written in Rust with native OS sandboxing, millisecond cold starts, single binary deployment [github](https:\u002F\u002Fgithub.com\u002FEXboys\u002Fskilllite)\n- [Smart_Fault_Injector_Llm](https:\u002F\u002Fgithub.com\u002FJiaHuann\u002FSmart_Fault_Injector_LLM) - Intelligent kernel error injection\u002Ftesting tool based on large model and eBPF.(基于大模型和eBPF的智能化kernel错误注入、测试工具)\n- [Speech-To-Code](https:\u002F\u002Fgithub.com\u002Fdharllc\u002Fspeech-to-code) - llm assisted development tools\n- [Speechless](https:\u002F\u002Fgithub.com\u002Fuukuguy\u002Fspeechless) - LLM based agents with proactive interactions, long-term memory, external tool integration, and local deployment capabilities.\n- [Splaa](https:\u002F\u002Fgithub.com\u002Fcp3249\u002Fsplaa) - SPLAA is an AI assistant framework that utilizes voice recognition, text-to-speech, and tool-calling capabilities to provide a conversati…\n- [Stan](https:\u002F\u002Fgithub.com\u002Fkaifcoder\u002FStan) - Develop and deploy a Large Language Model (LLM) based tool for generating human like responses to natural language inputs for network not…\n- [Stock-Analysis-With-Llm](https:\u002F\u002Fgithub.com\u002Fbauer-jan\u002Fstock-analysis-with-llm) - This repository provides tools and workflows for stock analysis using large language models (LLMs). It combines financial data processing…\n- [Structgenius](https:\u002F\u002Fgithub.com\u002Fjaadbarg\u002FStructGenius) - Download boilerplate file structure of any tree diagram you give\n- [Tapir](https:\u002F\u002Fgithub.com\u002Fephes\u002Ftapir) - Some llm tools\n- [Textcloak](https:\u002F\u002Fgithub.com\u002Fumutcamliyurt\u002FTextCloak) - A tool for concealing writing style using LLM\n- [Thoughtloom](https:\u002F\u002Fgithub.com\u002Ftbiehn\u002Fthoughtloom) - ThoughtLoom is a powerful tool designed to foster creativity and enhance productivity through the use of LLMs directly from the command l…\n- [Tiger](https:\u002F\u002Fgithub.com\u002FUpsonic\u002FTiger) - No Crypto - Scam alarm - This project is not releated with any crypto currencies. | Neuralink for your AI Agents - LangChain - Autogen - …\n- [Toolcommander](https:\u002F\u002Fgithub.com\u002FNicerWang\u002FToolCommander) - Official implementation of \"From Allies to Adversaries - Manipulating LLM Tool Scheduling through Adversarial Injection\".\n- [Toolla](https:\u002F\u002Fgithub.com\u002Ffoomprep\u002Ftoolla) - High level tool use for LLMs\n- [Toolplanner](https:\u002F\u002Fgithub.com\u002FXiaoMi\u002Ftoolplanner) - ToolPlanner - A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback\n- [Toolqa](https:\u002F\u002Fgithub.com\u002Fnight-chen\u002FToolQA) - ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels …\n- [Tools](https:\u002F\u002Fgithub.com\u002Fbuildownai\u002Ftools) - Monorepository of LLM based t AI ools provided by BuildOwn.AI\n- [Tora](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FToRA) - ToRA is a series of Tool-integrated Reasoning LLM Agents designed to solve challenging mathematical reasoning problems by interacting with …\n- [Trafilatura](https:\u002F\u002Fgithub.com\u002Fadbar\u002Ftrafilatura) - Python & Command-line tool to gather text and metadata on the Web - Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML\n- [Unchained](https:\u002F\u002Fgithub.com\u002Faaronamelgar\u002Funchained) - A Django-based tool for prompt engineering and LLM system evaluation.\n- [Useful-Generativeai-Tools-Repo](https:\u002F\u002Fgithub.com\u002Fanishsingh20\u002FUseful-GenerativeAI-Tools-Repo) - This repository has useful prompts for LLM and Generative AI models like Bard and ChatGPT\n- [Visuallm](https:\u002F\u002Fgithub.com\u002Fgortibaldik\u002Fvisuallm) - Visualization tool for various generation tasks on Language Models.\n- [WFGY](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY) - An open-source framework for debugging and stress testing LLMs under long-horizon, high-tension text scenarios. Includes a TXT-based debugging app for structured sequences to identify where reasoning breaks and retrieval fails [github](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY)\n- [Whitebox-Code-Gpt](https:\u002F\u002Fgithub.com\u002FDecron\u002FWhitebox-Code-GPT) - Repository of instructions for Programming-specific GPT models\n- [Widemem-Ai](https:\u002F\u002Fgithub.com\u002Fremete618\u002Fwidemem-ai) - Lightweight Python memory layer for LLM agents with importance scoring, temporal decay, 3-tier hierarchical memory, YMYL prioritization, and batch conflict resolution. Local-first with SQLite + FAISS. [github](https:\u002F\u002Fgithub.com\u002Fremete618\u002Fwidemem-ai) | [website](https:\u002F\u002Fwidemem.ai) | [pypi](https:\u002F\u002Fpypi.org\u002Fproject\u002Fwidemem-ai\u002F)\n- [Wildguard](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fwildguard) - Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs\n- [Wxflows](https:\u002F\u002Fgithub.com\u002FIBM\u002Fwxflows) - Examples and tutorials for building AI applications with watsonx.ai Flows Engine\n- [Xemantic-Ai-Tool-Schema](https:\u002F\u002Fgithub.com\u002Fxemantic\u002Fxemantic-ai-tool-schema) - Kotlin multiplatform AI\u002FLLM tool use (function calling) JSON Schema generator\n- [Yahoo-Finance-Llm-Agent](https:\u002F\u002Fgithub.com\u002Fojasskapre\u002Fyahoo-finance-llm-agent) - The Yahoo Finance Agent is an application that combines OpenAI's LLMs, the Yahoo Finance Python library, and LangChain's tools to provide…\n- [Zahara-Litellm](https:\u002F\u002Fgithub.com\u002FLiquidAdTech\u002FZahara-LiteLLM) - Gen AI tools\n\n### Workflows\n- [Abc-Classroom](https:\u002F\u002Fgithub.com\u002Fearthlab\u002Fabc-classroom) - Tools to automate github classroom and autograding workflows\n- [Actions](https:\u002F\u002Fgithub.com\u002FAzure\u002Factions) - Author and use Azure Actions to automate your GitHub workflows\n- [Actions](https:\u002F\u002Fgithub.com\u002Fbackstage\u002Factions) - Custom actions for automating Backstage workflows\n- [Actions-Workflow-Samples](https:\u002F\u002Fgithub.com\u002FAzure\u002Factions-workflow-samples) - Help developers to easily get started with GitHub Action workflows to deploy to Azure\n- [Activepieces](https:\u002F\u002Fgithub.com\u002Factivepieces\u002Factivepieces) - Your friendliest open source AI automation tool ✨ Workflow automation tool 200+ integration \u002F Enterprise automation tool \u002F Zapier Alterna…\n- [Advanced-Gpts](https:\u002F\u002Fgithub.com\u002Fnerority\u002FAdvanced-GPTs) - Custom GPT Showcase, featuring advanced workflows and operational logic.\n- [Airflow](https:\u002F\u002Fgithub.com\u002Fapache\u002Fairflow) - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows\n- [Airflow-Cookbook](https:\u002F\u002Fgithub.com\u002Fbahchis\u002Fairflow-cookbook) - Airflow workflow management platform chef cookbook.\n- [Alfred-Calculate-Anything](https:\u002F\u002Fgithub.com\u002Fbiati-digital\u002Falfred-calculate-anything) - Alfred Workflow to calculate anything with natural language\n- [Alfred-Github-Workflow](https:\u002F\u002Fgithub.com\u002Fgharlan\u002Falfred-github-workflow) - GitHub Workflow for Alfred\n- [Alfred-Pinboard-Rs](https:\u002F\u002Fgithub.com\u002Fspamwax\u002Falfred-pinboard-rs) - Alfred Workflow for Pinboard (Rust)\n- [Alfred-Terminalfinder](https:\u002F\u002Fgithub.com\u002FLeEnno\u002Falfred-terminalfinder) - Alfred workflow to open current Finder window in Terminal\u002FiTerm and vice versa.\n- [Alfred-Workflow](https:\u002F\u002Fgithub.com\u002Fjoetannenbaum\u002Falfred-workflow) - PHP helper library for Alfred Workflows\n- [Alfred-Workflow-Todoist](https:\u002F\u002Fgithub.com\u002Fmoranje\u002Falfred-workflow-todoist) - An Alfred workflow for managing tasks in Todoist\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Fzenorocha\u002Falfred-workflows) - 🤘 A collection of Alfred 3 and 4 workflows that will rock your world\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Fvitorgalvao\u002Falfred-workflows) - Collection of Alfred workflows\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Flearn-anything\u002Falfred-workflows) - Amazing Alfred Workflows\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Fwillfarrell\u002Falfred-workflows) - Alfred Workflows for Developers\n- [Alfred-Workflows-Scientific](https:\u002F\u002Fgithub.com\u002Fandrewning\u002Falfred-workflows-scientific) - A collection of Alfred workflows targeting scientific applications\n- [Alfred2-Ruby-Template](https:\u002F\u002Fgithub.com\u002Fzhaocai\u002Falfred2-ruby-template) - Alfred 2 Workflow Ruby Template\n- [Alfredworkflow.Com](https:\u002F\u002Fgithub.com\u002Fhzlzh\u002FAlfredWorkflow.com) - A public Collection of Alfred Workflows.\n- [Alfy](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Falfy) - Create Alfred workflows with ease\n- [Amazon-Mwaa-Examples](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Famazon-mwaa-examples) - Amazon Managed Workflows for Apache Airflow (MWAA) Examples repository contains example DAGs, requirements.txt, plugins, and CloudFormati…\n- [Ambrose](https:\u002F\u002Fgithub.com\u002Ftwitter-archive\u002Fambrose) - A platform for visualization and real-time monitoring of data workflows\n- [Argo-Python-Dsl](https:\u002F\u002Fgithub.com\u002Fargoproj-labs\u002Fargo-python-dsl) - Python DSL for Argo Workflows\n- [Argo-Workflows](https:\u002F\u002Fgithub.com\u002Fargoproj\u002Fargo-workflows) - Workflow Engine for Kubernetes\n- [Argo-Workflows-Demo](https:\u002F\u002Fgithub.com\u002Fvfarcic\u002Fargo-workflows-demo) - argo workflows\n- [Arrow-User2022](https:\u002F\u002Fgithub.com\u002Fdjnavarro\u002Farrow-user2022) - Larger-Than-Memory Data Workflows with Apache Arrow\n- [Astro-Sdk](https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fastro-sdk) - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.\n- [Atomate2](https:\u002F\u002Fgithub.com\u002Fmaterialsproject\u002Fatomate2) - atomate2 is a library of computational materials science workflows\n- [Autopr](https:\u002F\u002Fgithub.com\u002Firgolic\u002FAutoPR) - Run AI-powered workflows over your codebase\n- [Awesome-Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Falfred-workflows\u002Fawesome-alfred-workflows) - A curated list of awesome alfred workflows\n- [Aws-Ddk](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Faws-ddk) - An open source development framework to help you build data workflows and modern data architecture on AWS.\n- [Aws-Genomics-Workflows](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Faws-genomics-workflows) - Genomics Workflows on AWS\n- [Aws-Lambda-Fsm-Workflows](https:\u002F\u002Fgithub.com\u002FWorkiva\u002Faws-lambda-fsm-workflows) - A Python framework for developing finite state machine-based workflows on AWS Lambda.\n- [Aws-Mwaa-Local-Runner](https:\u002F\u002Fgithub.com\u002Faws\u002Faws-mwaa-local-runner) - This repository provides a command line interface (CLI) utility that replicates an Amazon Managed Workflows for Apache Airflow (MWAA) env…\n- [Aws-Swf-Flow-Library](https:\u002F\u002Fgithub.com\u002Faws\u002Faws-swf-flow-library) - AWS Simple Workflow Flow framework library\n- [Azkaban](https:\u002F\u002Fgithub.com\u002Fazkaban\u002Fazkaban) - Azkaban workflow manager.\n- [Backbone-Boilerplate](https:\u002F\u002Fgithub.com\u002Ftbranyen\u002Fbackbone-boilerplate) - A workflow for building Backbone applications.\n- [Biobakery_Workflows](https:\u002F\u002Fgithub.com\u002Fbiobakery\u002Fbiobakery_workflows) - bioBakery workflows is a collection of workflows and tasks for executing common microbial community analyses using standardized, validate…\n- [Bioinformatics](https:\u002F\u002Fgithub.com\u002Fjumphone\u002FBioinformatics) - Bioinformatics Workflows\n- [Bioinformatics](https:\u002F\u002Fgithub.com\u002Fstevekm\u002FBioinformatics) - Bioinformatics analysis scripts, workflows, general code examples\n- [Bktask](https:\u002F\u002Fgithub.com\u002FBackelite\u002FBkTask) - An asynchronous workflows library for iOS\n- [Captain](https:\u002F\u002Fgithub.com\u002Fharbur\u002Fcaptain) - Captain - Convert your Git workflow to Docker 🐳 containers\n- [Celery-Director](https:\u002F\u002Fgithub.com\u002Fovh\u002Fcelery-director) - Simple and rapid framework to build workflows with Celery\n- [ChatGPT API](https:\u002F\u002Fplatform.openai.com\u002F) OpenAI's API for integrating GPT models into applications.\n- [Ci-Gha-Workflow](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fci-gha-workflow) - GitHub Actions workflows for OpenCV project\n- [Classifai](https:\u002F\u002Fgithub.com\u002F10up\u002Fclassifai) - Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence.\n- [Comfyui-Workflows](https:\u002F\u002Fgithub.com\u002Fxiwan\u002FcomfyUI-workflows) - store my pixel or any interesting comfyui workflows\n- [Comfyui-Workflows-Zho](https:\u002F\u002Fgithub.com\u002FZHO-ZHO-ZHO\u002FComfyUI-Workflows-ZHO) - 我的 ComfyUI 工作流合集 | My ComfyUI workflows collection\n- [Comfyui-Workspace-Manager](https:\u002F\u002Fgithub.com\u002F11cafe\u002Fcomfyui-workspace-manager) - A ComfyUI workflows and models management extension to organize and manage all your workflows, models in one place. Seamlessly switch bet…\n- [Comfyui-Yolain-Workflows](https:\u002F\u002Fgithub.com\u002Fyolain\u002FComfyUI-Yolain-Workflows) - Some awesome comfyui workflows in here, and they are built using the comfyui-easy-use node package.\n- [Comfyui_Examples](https:\u002F\u002Fgithub.com\u002Fcomfyanonymous\u002FComfyUI_examples) - Examples of ComfyUI workflows\n- [Comfyui_Workflows](https:\u002F\u002Fgithub.com\u002Fcubiq\u002FComfyUI_Workflows) - A repository of well documented easy to follow workflows for ComfyUI\n- [Comfyuimini](https:\u002F\u002Fgithub.com\u002FImDarkTom\u002FComfyUIMini) - A mobile-friendly WebUI to run ComfyUI workflows.\n- [Configs](https:\u002F\u002Fgithub.com\u002Fnf-core\u002Fconfigs) - Config files used to define parameters specific to compute environments at different Institutions\n- [Confluent-Kubernetes-Examples](https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fconfluent-kubernetes-examples) - Example scenario workflows for Confluent for Kubernetes\n- [Corewf](https:\u002F\u002Fgithub.com\u002FUiPath\u002FCoreWF) - WF runtime ported to work on .NET 6\n- [Couler](https:\u002F\u002Fgithub.com\u002Fcouler-proj\u002Fcouler) - Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Ap…\n- [Create-Actionsprs](https:\u002F\u002Fgithub.com\u002Fjhutchings1\u002FCreate-ActionsPRs) - This repository creates pull requests to push a GitHub Actions workflow to a collection of workflows.\n- [Crewai-Examples](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples) - A collection of examples that show how to use CrewAI framework to automate workflows.\n- [Cuda-Quantum](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcuda-quantum) - C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows\n- [Cylc-Flow](https:\u002F\u002Fgithub.com\u002Fcylc\u002Fcylc-flow) - Cylc - a workflow engine for cycling systems.\n- [Cylc-Ui](https:\u002F\u002Fgithub.com\u002Fcylc\u002Fcylc-ui) - Web app for monitoring and controlling Cylc workflows\n- [Cypress-Realworld-App](https:\u002F\u002Fgithub.com\u002Fcypress-io\u002Fcypress-realworld-app) - A payment application to demonstrate real-world usage of Cypress testing methods, patterns, and workflows.\n- [D6Tflow](https:\u002F\u002Fgithub.com\u002Fd6t\u002Fd6tflow) - Python library for building highly effective data science workflows\n- [Darktable](https:\u002F\u002Fgithub.com\u002Fdarktable-org\u002Fdarktable) - darktable is an open source photography workflow application and raw developer\n- [Data-Engineering](https:\u002F\u002Fgithub.com\u002FGokuMohandas\u002Fdata-engineering) - Construct a modern data stack and orchestration the workflows to create high quality data for analytics and ML applications.\n- [Datadog](https:\u002F\u002Fgithub.com\u002Fmasci\u002Fdatadog) - Send Datadog metrics and events from GitHub workflows\n- [Deepl-Alfred-Workflow2](https:\u002F\u002Fgithub.com\u002FAlexanderWillner\u002Fdeepl-alfred-workflow2) - DeepL Alfred Workflow\n- [Deploy-Pages](https:\u002F\u002Fgithub.com\u002Factions\u002Fdeploy-pages) - GitHub Action to publish artifacts to GitHub Pages for deployments\n- [Design_Diagrams](https:\u002F\u002Fgithub.com\u002Fiam-veeramalla\u002Fdesign_diagrams) - flowcharts, workflows and diagrams\n- [Docker-Compose-Laravel](https:\u002F\u002Fgithub.com\u002Faschmelyun\u002Fdocker-compose-laravel) - A docker-compose workflow for local Laravel development\n- [Docker-Compose-Wordpress](https:\u002F\u002Fgithub.com\u002Faschmelyun\u002Fdocker-compose-wordpress) - A docker-compose workflow for local WordPress development\n- [Docker-Wordpress](https:\u002F\u002Fgithub.com\u002Fpaulczar\u002Fdocker-wordpress) - Demostrating dev workflow ... vagrant -> docker -> openstack\n- [Documentation](https:\u002F\u002Fgithub.com\u002Fow2-proactive\u002Fdocumentation) - Documentation for ProActive Workflows & Scheduling\n- [Dolphinscheduler](https:\u002F\u002Fgithub.com\u002Fapache\u002Fdolphinscheduler) - Apache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow with low-code\n- [Ds-Workflows-R](https:\u002F\u002Fgithub.com\u002Fposit-conf-2024\u002Fds-workflows-r) - posit::conf(2024) workshop - Data Science Workflows with Posit Tools — R Focus\n- [Earth2Studio](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio) - Open-source deep-learning framework for exploring, building and deploying AI weather\u002Fclimate workflows.\n- [Elodie](https:\u002F\u002Fgithub.com\u002Fjmathai\u002Felodie) - An EXIF-based photo assistant, organizer and workflow automation tool.\n- [Elsa-Core](https:\u002F\u002Fgithub.com\u002Felsa-workflows\u002Felsa-core) - A .NET workflows library\n- [Ephemeris](https:\u002F\u002Fgithub.com\u002Fgalaxyproject\u002Fephemeris) - Library for managing Galaxy plugins - tools, index data, and workflows.\n- [Ert-Runner.El](https:\u002F\u002Fgithub.com\u002Frejeep\u002Fert-runner.el) - Opinionated Ert testing workflow\n- [Examples](https:\u002F\u002Fgithub.com\u002Fconcourse\u002Fexamples) - Examples of Concourse workflows\n- [Extension-Ci-Tools](https:\u002F\u002Fgithub.com\u002Fduckdb\u002Fextension-ci-tools) - Repository containing reusable workflows \u002F actions for building DuckDB extensions\n- [Extractthinker](https:\u002F\u002Fgithub.com\u002Fenoch3712\u002FExtractThinker) - ExtractThinker is a Document Intelligence library for LLMs, offering ORM-style interaction for flexible and powerful document workflows.\n- [Fastshell](https:\u002F\u002Fgithub.com\u002FHosseinKarami\u002Ffastshell) - Fiercely quick front-end boilerplate and workflows, HTML5, Gulp, Sass\n- [Fireshell](https:\u002F\u002Fgithub.com\u002Ftoddmotto\u002Ffireshell) - Fiercely quick front-end boilerplate and workflows, HTML5, Grunt, Sass.\n- [Fireworks](https:\u002F\u002Fgithub.com\u002Fmaterialsproject\u002Ffireworks) - The Fireworks Workflow Management Repo.\n- [Flask-App](https:\u002F\u002Fgithub.com\u002Fbrennv\u002Fflask-app) - Example app for demonstrating CI\u002FCD workflows.\n- [Floki](https:\u002F\u002Fgithub.com\u002FCyb3rWard0g\u002Ffloki) - Agentic Workflows Made Simple\n- [Flux-Core](https:\u002F\u002Fgithub.com\u002Fflux-framework\u002Fflux-core) - core services for the Flux resource management framework\n- [Flyte](https:\u002F\u002Fgithub.com\u002FExpediaGroup\u002Fflyte) - Flyte binds together the tools you use into easily defined, automated workflows\n- [Fogworkflowsim](https:\u002F\u002Fgithub.com\u002FISEC-AHU\u002FFogWorkflowSim) - An Environment for Simulation and Performance Evaluation of Workflows in Fog Computing\n- [Funflow](https:\u002F\u002Fgithub.com\u002Ftweag\u002Ffunflow) - Functional workflows\n- [Gatk4-Rnaseq-Germline-Snps-Indels](https:\u002F\u002Fgithub.com\u002Fgatk-workflows\u002Fgatk4-rnaseq-germline-snps-indels) - Workflows for processing RNA data for germline short variant discovery with GATK v4 and related tools\n- [Gatk4-Somatic-Cnvs](https:\u002F\u002Fgithub.com\u002Fgatk-workflows\u002Fgatk4-somatic-cnvs) - This repo is archived, these workflows will be housed in the GATK repository under the scripts directory. These workflows are also organi…\n- [Gdoc-Downloader](https:\u002F\u002Fgithub.com\u002Fuid\u002Fgdoc-downloader) - Downloads Google Docs as text files, which enables workflows such as simultaneous LaTeX editing\n- [Generativeaiexamples](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FGenerativeAIExamples) - Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.\n- [Ghat](https:\u002F\u002Fgithub.com\u002Ffregante\u002Fghat) - 🛕 Reuse GitHub Actions workflows across repositories\n- [Git-Collaboration](https:\u002F\u002Fgithub.com\u002Fjduckles\u002Fgit-collaboration) - A repository to demonstrate collaboration workflows\n- [Git-Guppy](https:\u002F\u002Fgithub.com\u002Ftherealklanni\u002Fgit-guppy) - Simple git-hook integration for your gulp workflows.\n- [Git-Octopus](https:\u002F\u002Fgithub.com\u002Flesfurets\u002Fgit-octopus) - The continuous merge workflow\n- [Git-Practice](https:\u002F\u002Fgithub.com\u002Fawesome-academy\u002Fgit-practice) - Practice about Git workflow\n- [Git-Smart](https:\u002F\u002Fgithub.com\u002Fgeelen\u002Fgit-smart) - Add some smarts to your git workflow\n- [Github-Action-Gitflow-Release-Workflow](https:\u002F\u002Fgithub.com\u002Fthomaseizinger\u002Fgithub-action-gitflow-release-workflow) - Example workflows for automated releases in a GitFlow-style project using GitHub actions.\n- [Github-Actions](https:\u002F\u002Fgithub.com\u002FFuelLabs\u002Fgithub-actions) - Reusable Actions workflows\n- [Github-Actions-Flutter-Workflows](https:\u002F\u002Fgithub.com\u002Fzgosalvez\u002Fgithub-actions-flutter-workflows) - Opinionated GitHub Action workflows for Flutter projects\n- [Github-Script](https:\u002F\u002Fgithub.com\u002Factions\u002Fgithub-script) - Write workflows scripting the GitHub API in JavaScript\n- [Gitwash](https:\u002F\u002Fgithub.com\u002Fmatthew-brett\u002Fgitwash) - Workflow using git\n- [Global-Workflow](https:\u002F\u002Fgithub.com\u002FNOAA-EMC\u002Fglobal-workflow) - Global Superstructure\u002FWorkflow supporting the Global Forecast System (GFS)\n- [Graphql-Cli](https:\u002F\u002Fgithub.com\u002FUrigo\u002Fgraphql-cli) - 📟 Command line tool for common GraphQL development workflows\n- [Graphql-Playground](https:\u002F\u002Fgithub.com\u002Fgraphql\u002Fgraphql-playground) - 🎮 GraphQL IDE for better development workflows (GraphQL Subscriptions, interactive docs & collaboration)\n- [Gulp](https:\u002F\u002Fgithub.com\u002Fgulpjs\u002Fgulp) - A toolkit to automate & enhance your workflow\n- [Hawk-Projects](https:\u002F\u002Fgithub.com\u002Fferventdesert\u002FHawk-Projects) - Project configurations of Hawk and etlpy. xml-format workflow define\n- [Helm-Secrets](https:\u002F\u002Fgithub.com\u002Fjkroepke\u002Fhelm-secrets) - A helm plugin that help manage secrets with Git workflow and store them anywhere\n- [Idseq-Workflows](https:\u002F\u002Fgithub.com\u002Fchanzuckerberg\u002Fidseq-workflows) - Portable WDL workflows for IDseq production pipelines\n- [Inker](https:\u002F\u002Fgithub.com\u002Fposabsolute\u002Finker) - Evolved email development & delivery workflow\n- [Isaac_Perceptor](https:\u002F\u002Fgithub.com\u002FNVIDIA-ISAAC-ROS\u002Fisaac_perceptor) - Perception workflows\n- [Jira-Connect-Orb](https:\u002F\u002Fgithub.com\u002FCircleCI-Public\u002Fjira-connect-orb) - Display the status of CircleCI workflows and deployments in Jira!\n- [Keras-Cv](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fkeras-cv) - Industry-strength Computer Vision workflows with Keras\n- [Ketrew](https:\u002F\u002Fgithub.com\u002Fhammerlab\u002Fketrew) - Keep Track of Experimental Workflows\n- [Konfig](https:\u002F\u002Fgithub.com\u002FKusionStack\u002Fkonfig) - Shared repository of application models and components, and CI suite for GitOps workflows\n- [Kuroko2](https:\u002F\u002Fgithub.com\u002Fcookpad\u002Fkuroko2) - Kuroko2 is a web-based job scheduler \u002F workflow engine.\n- [Laraadmin-Crm](https:\u002F\u002Fgithub.com\u002Fdwijitsolutions\u002Flaraadmin-crm) - LaraAdmin is a Open source CRM for quick-start Admin based applications with features like Advanced CRUD Generation, Schema Manager and W…\n- [Laravel-4-Generators](https:\u002F\u002Fgithub.com\u002Fdahabit\u002FLaravel-4-Generators) - Rapidly speed up your Laravel 4 workflow with generators\n- [Lexikworkflowbundle](https:\u002F\u002Fgithub.com\u002Flexik\u002FLexikWorkflowBundle) - Simple workflow bundle for Symfony2\n- [Libmolgrid](https:\u002F\u002Fgithub.com\u002Fgnina\u002Flibmolgrid) - Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows\n- [Linker](https:\u002F\u002Fgithub.com\u002Fm-reda\u002Flinker) - workflow editor library\n- [Livebook](https:\u002F\u002Fgithub.com\u002Flivebook-dev\u002Flivebook) - Automate code & data workflows with interactive Elixir notebooks\n- [Llmstack](https:\u002F\u002Fgithub.com\u002Ftrypromptly\u002FLLMStack) - No-code multi-agent framework to build LLM Agents, workflows and applications with your data\n- [Luigi](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fluigi) - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, vis…\n- [Machine-Learning-Examples](https:\u002F\u002Fgithub.com\u002Faaronkub\u002Fmachine-learning-examples) - This repository contains various examples of machine learning workflows.\n- [Maestro](https:\u002F\u002Fgithub.com\u002FNetflix\u002Fmaestro) - Maestro - Netflix’s Workflow Orchestrator\n- [Marmot](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fmarmot) - Marmot workflow execution engine\n- [Metropolis-Nim-Workflows](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fmetropolis-nim-workflows) - Collection of reference workflows for building intelligent agents with NIMs\n- [Modin](https:\u002F\u002Fgithub.com\u002Fmodin-project\u002Fmodin) - Modin - Scale your Pandas workflows by changing a single line of code\n- [Molecule-Action](https:\u002F\u002Fgithub.com\u002Fgofrolist\u002Fmolecule-action) - GitHub Action for running molecule as part of your workflows!\n- [N8N-Workflows](https:\u002F\u002Fgithub.com\u002Freorx\u002Fn8n-workflows) - My workflows for n8n automation\n- [Nactivity](https:\u002F\u002Fgithub.com\u002Fzhangzihan\u002Fnactivity) - workflow engine activity activiti\n- [Ngx-I18Nsupport](https:\u002F\u002Fgithub.com\u002Fmartinroob\u002Fngx-i18nsupport) - Some tooling to be used for Angular i18n workflows\n- [Nipype](https:\u002F\u002Fgithub.com\u002Fnipy\u002Fnipype) - Workflows and interfaces for neuroimaging packages\n- [Notion-Search-Alfred-Workflow](https:\u002F\u002Fgithub.com\u002Fwrjlewis\u002Fnotion-search-alfred-workflow) - An Alfred workflow to search Notion with instant results\n- [Ollama.Nvim](https:\u002F\u002Fgithub.com\u002Fnomnivore\u002Follama.nvim) - A plugin for managing and integrating your ollama workflows in neovim.\n- [Oozie-Examples](https:\u002F\u002Fgithub.com\u002Fdbist\u002Foozie-examples) - sample oozie workflows\n- [Open-Workflows](https:\u002F\u002Fgithub.com\u002Famiaopensource\u002Fopen-workflows) - List of open workflows and resources for A\u002FV archiving\n- [Optix-Toolkit](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Foptix-toolkit) - Set of utilities supporting workflows common in GPU raytracing applications\n- [Organization-Workflows](https:\u002F\u002Fgithub.com\u002FSvanBoxel\u002Forganization-workflows) - Need to centrally manage and run Actions workflows across multiple repositories? This app does it for you.\n- [n8n](https:\u002F\u002Fgithub.com\u002Fn8n-io\u002Fn8n) - Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.\n- [Perl-Workflow](https:\u002F\u002Fgithub.com\u002Fperl-workflow\u002Fperl-workflow) - Workflow - simple, flexible system to implement workflows\u002Fstate machines\n- [Pimcore-Workflow-Gui](https:\u002F\u002Fgithub.com\u002FYouweGit\u002Fpimcore-workflow-gui) - Adds a nice GUI to Pimcore Workflows\n- [Pipeline](https:\u002F\u002Fgithub.com\u002Fmyntra\u002Fpipeline) - Pipeline is a package to build multi-staged concurrent workflows with a centralized logging output.\n- [Pipenv](https:\u002F\u002Fgithub.com\u002Fpypa\u002Fpipenv) - Python Development Workflow for Humans.\n- [Pitagora-Cwl](https:\u002F\u002Fgithub.com\u002Fpitagora-network\u002Fpitagora-cwl) - Common Workflow Language tools and workflows by Pitagora-Network\n- [Pr-Harmony](https:\u002F\u002Fgithub.com\u002Fmonitorjbl\u002Fpr-harmony) - Extra pull request workflows for Stash\n- [Prefabworkflows_Spiderrobots](https:\u002F\u002Fgithub.com\u002FUnityTechnologies\u002FPrefabWorkflows_SpiderRobots) - Demo Project showing the new Prefab Workflows - Prefab Mode, Editing and Nesting!\n- [Process-Engine.Js](https:\u002F\u002Fgithub.com\u002Foliverzy\u002Fprocess-engine.js) - Node.js Business Process\u002FWorkflow Engine\n- [Publish-Packages](https:\u002F\u002Fgithub.com\u002Fskills\u002Fpublish-packages) - Use GitHub Actions to publish your project to a Docker image.\n- [Puppet-Git-Hooks](https:\u002F\u002Fgithub.com\u002Fadrienthebo\u002Fpuppet-git-hooks) - Various and sundry git hooks for puppet workflows\n- [Purchase-Workflow](https:\u002F\u002Fgithub.com\u002FOCA\u002Fpurchase-workflow) - Odoo Purchases, Workflow and Organization\n- [Pyspur](https:\u002F\u002Fgithub.com\u002FPySpur-Dev\u002Fpyspur) - Graph-Based Editor for LLM Workflows\n- [Pyzeebe](https:\u002F\u002Fgithub.com\u002Fcamunda-community-hub\u002Fpyzeebe) - Python client for Zeebe workflow engine\n- [Ramp-Workflow](https:\u002F\u002Fgithub.com\u002Fparis-saclay-cds\u002Framp-workflow) - Toolkit for building predictive workflows on top of pydata (pandas, scikit-learn, pytorch, keras, etc.).\n- [Rasflow](https:\u002F\u002Fgithub.com\u002Fzhxiaokang\u002FRASflow) - RNA-Seq analysis workflow\n- [Rayder](https:\u002F\u002Fgithub.com\u002Fdevanshbatham\u002Frayder) - A lightweight tool for orchestrating and organizing your bug hunting recon \u002F pentesting command-line workflows\n- [Reaflow](https:\u002F\u002Fgithub.com\u002Freaviz\u002Freaflow) - 🎯 React library for building workflow editors, flow charts and diagrams. Maintained by @goodcodeus.\n- [Redmine_Workflow_Enhancements](https:\u002F\u002Fgithub.com\u002Fdr-itz\u002Fredmine_workflow_enhancements) - Redmine workflow enhancements. UNMAINTAINED\n- [Redux-User-Auth](https:\u002F\u002Fgithub.com\u002FChinwike1\u002Fredux-user-auth) - User Authentication workflow made with the MERN stack\n- [Repo-File-Sync-Action](https:\u002F\u002Fgithub.com\u002FBetaHuhn\u002Frepo-file-sync-action) - 🔄 GitHub Action to keep files like Action workflows or entire directories in sync between multiple repositories.\n- [Reusable-Workflows](https:\u002F\u002Fgithub.com\u002Factions\u002Freusable-workflows) - Reusable workflows for developing actions\n- [Rexpect](https:\u002F\u002Fgithub.com\u002Frust-cli\u002Frexpect) - .github\u002Fworkflows\u002Fci.yml\n- [Rhessysworkflows](https:\u002F\u002Fgithub.com\u002Fselimnairb\u002FRHESSysWorkflows) - RHESSysWorkflows provides Python scripts for building RHESSys models\n- [Row-Oriented-Workflows](https:\u002F\u002Fgithub.com\u002Fjennybc\u002Frow-oriented-workflows) - Row-oriented workflows in R with the tidyverse\n- [Ruote-Kit](https:\u002F\u002Fgithub.com\u002Fkennethkalmer\u002Fruote-kit) - RESTish wrapper for ruote workflow engine\n- [Rust-Build](https:\u002F\u002Fgithub.com\u002Fesp-rs\u002Frust-build) - Installation tools and workflows for deploying\u002Fbuilding Rust fork esp-rs\u002Frust with Xtensa and RISC-V support\n- [Sage](https:\u002F\u002Fgithub.com\u002Froots\u002Fsage) - WordPress starter theme with Laravel Blade components and templates, Tailwind CSS, and a modern development workflow\n- [Sale-Workflow](https:\u002F\u002Fgithub.com\u002FOCA\u002Fsale-workflow) - Odoo Sales, Workflow and Organization\n- [Search-Alfred-Workflows](https:\u002F\u002Fgithub.com\u002FAcidham\u002Fsearch-alfred-workflows) - Search Alfred Workflows\n- [Seargesdxl](https:\u002F\u002Fgithub.com\u002FSeargeDP\u002FSeargeSDXL) - Custom nodes and workflows for SDXL in ComfyUI\n- [Serverless-Workflows](https:\u002F\u002Fgithub.com\u002Frhdhorchestrator\u002Fserverless-workflows) - A selected set of serverless workflows\n- [Setup-Jfrog-Cli](https:\u002F\u002Fgithub.com\u002Fjfrog\u002Fsetup-jfrog-cli) - Set up JFrog CLI in your GitHub Actions workflow\n- [Shadcn-Next-Workflows](https:\u002F\u002Fgithub.com\u002Fnobruf\u002Fshadcn-next-workflows) - Interactive workflow builder using React Flows, Next.js, and Shadcnui. Create, connect, and validate custom nodes easily.\n- [Shimmering-Obsidian](https:\u002F\u002Fgithub.com\u002Fchrisgrieser\u002Fshimmering-obsidian) - Alfred Workflow with dozens of features for controlling your Obsidian vault.\n- [Skyvern](https:\u002F\u002Fgithub.com\u002FSkyvern-AI\u002Fskyvern) - Automate browser-based workflows with LLMs and Computer Vision\n- [Slashbase-Go](https:\u002F\u002Fgithub.com\u002Fslashbase\u002Fslashbase-go) - Modern database IDE for your dev & data workflows. Supports MySQL, PostgreSQL & MongoDB.\n- [Slickflow](https:\u002F\u002Fgithub.com\u002Fbesley\u002FSlickflow) - .NET Open Source Workflow Engine, .NET 开源工作流\n- [Smriprep](https:\u002F\u002Fgithub.com\u002Fnipreps\u002Fsmriprep) - Structural MRI PREProcessing (sMRIPrep) workflows for NIPreps (NeuroImaging PREProcessing tools)\n- [Sos](https:\u002F\u002Fgithub.com\u002Fvatlab\u002Fsos) - SoS workflow system for daily data analysis\n- [Spiffworkflow](https:\u002F\u002Fgithub.com\u002Fsartography\u002FSpiffWorkflow) - A powerful workflow engine implemented in pure Python\n- [Spreads](https:\u002F\u002Fgithub.com\u002FDIYBookScanner\u002Fspreads) - Modular workflow assistant for book digitization\n- [Starter-Workflows](https:\u002F\u002Fgithub.com\u002Factions\u002Fstarter-workflows) - Accelerating new GitHub Actions workflows\n- [Step-Functions-Workflows-Collection](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fstep-functions-workflows-collection) - Step Functions Workflows. Learn more at the website - https:\u002F\u002Fserverlessland.com\u002Fworkflows.\n- [Streamflow](https:\u002F\u002Fgithub.com\u002Flmco\u002Fstreamflow) - StreamFlow™ is a stream processing tool designed to help build and monitor processing workflows.\n- [Super-Table](https:\u002F\u002Fgithub.com\u002Fverbb\u002Fsuper-table) - Super-charge your Craft workflow using Super Table.\n- [Swiftcurrent](https:\u002F\u002Fgithub.com\u002Fwwt\u002FSwiftCurrent) - A library for managing complex workflows in Swift\n- [T2T-Polish](https:\u002F\u002Fgithub.com\u002Farangrhie\u002FT2T-Polish) - Evaluation and polishing workflows for T2T genome assemblies\n- [Tactic](https:\u002F\u002Fgithub.com\u002FSouthpaw-TACTIC\u002FTACTIC) - Open source remote collaboration platform used for configuring and deploying enterprise Workflow solutions.\n- [Tangelo](https:\u002F\u002Fgithub.com\u002Fsandbox-quantum\u002FTangelo) - A python package for exploring end-to-end chemistry workflows on quantum computers and simulators.\n- [Terraform-With-Circleci-Example](https:\u002F\u002Fgithub.com\u002Ffedekau\u002Fterraform-with-circleci-example) - This is an example of automatic deployments of your infrastructure using terraform and CircleCI 2.0 workflows\n- [Texera](https:\u002F\u002Fgithub.com\u002FTexera\u002Ftexera) - Collaborative Machine-Learning-Centric Data Analytics Using Workflows\n- [Tfc-Workflows-Tooling](https:\u002F\u002Fgithub.com\u002Fhashicorp\u002Ftfc-workflows-tooling) - Tooling to automate HCP Terraform API Runs\n- [Tideflow](https:\u002F\u002Fgithub.com\u002Ftideflow-io\u002Ftideflow) - Building extensible automation. Tideflow is a Realtime, open source workflows execution and monitorization web application.\n- [Trailblazer-Activity](https:\u002F\u002Fgithub.com\u002Ftrailblazer\u002Ftrailblazer-activity) - Model business workflows and run them.\n- [Txtai](https:\u002F\u002Fgithub.com\u002Fneuml\u002Ftxtai) - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows\n- [User_Guide](https:\u002F\u002Fgithub.com\u002Fcommon-workflow-language\u002Fuser_guide) - The CWL v1.0 - v1.2 user guide\n- [Vanilla-Parcel-Boilerplate](https:\u002F\u002Fgithub.com\u002Fbradtraversy\u002Fvanilla-parcel-boilerplate) - Simple starter workflow for building vanilla js apps with Parcel\n- [Vault-Config-Operator](https:\u002F\u002Fgithub.com\u002Fredhat-cop\u002Fvault-config-operator) - An operator to support Haschicorp Vault configuration workflows from within Kubernetes\n- [Vector-Vein](https:\u002F\u002Fgithub.com\u002FAndersonBY\u002Fvector-vein) - No-code AI workflow. Drag and drop workflow nodes and use your workflow with your AI agents.\n- [Venture](https:\u002F\u002Fgithub.com\u002Fksassnowski\u002Fventure) - Venture allows you to create and manage complex, async workflows in your Laravel apps.\n- [Viewflow](https:\u002F\u002Fgithub.com\u002Fviewflow\u002Fviewflow) - Reusable workflow library for Django\n- [Vivliostyle-Cli](https:\u002F\u002Fgithub.com\u002Fvivliostyle\u002Fvivliostyle-cli) - ⚒ Supercharge command-line publication workflow.\n- [Vscode-Tips-Tricks](https:\u002F\u002Fgithub.com\u002Fahmadawais\u002FVSCode-Tips-Tricks) - VSCode-Tips-Tricks Examples and Workflows to help you become a Visual Studio Code Power User →\n- [Vue-Vscode-Snippets](https:\u002F\u002Fgithub.com\u002Fsdras\u002Fvue-vscode-snippets) - These snippets were built to supercharge my workflow in the most seamless manner possible.\n- [Waveterm](https:\u002F\u002Fgithub.com\u002Fwavetermdev\u002Fwaveterm) - An open-source, cross-platform terminal for seamless workflows\n- [Windmill](https:\u002F\u002Fgithub.com\u002Fwindmill-labs\u002Fwindmill) - Open-source developer platform to power your entire infra and turn scripts into webhooks, workflows and UIs. Fastest workflow engine (13x…\n- [With](https:\u002F\u002Fgithub.com\u002Fmchav\u002Fwith) - Command prefixing for continuous workflow using a single tool.\n- [Wordpressify](https:\u002F\u002Fgithub.com\u002Fluangjokaj\u002Fwordpressify) - 🎈 Automate your WordPress development workflow.\n- [Workflow](https:\u002F\u002Fgithub.com\u002Finveniosoftware-contrib\u002Fworkflow) - Simple Pythonic Workflows\n- [Workflow-Dispatch](https:\u002F\u002Fgithub.com\u002Fbenc-uk\u002Fworkflow-dispatch) - A GitHub Action for triggering workflows, using the `workflow_dispatch` event\n- [Workflow-Reactjs](https:\u002F\u002Fgithub.com\u002Ffdaciuk\u002Fworkflow-reactjs) - My workflow with ReactJS + Webpack 3+\n- [Workflows](https:\u002F\u002Fgithub.com\u002Fwarpdotdev\u002Fworkflows) - Workflows make it easy to browse, search, execute and share commands (or a series of commands)--without needing to leave your terminal.\n- [Workflows](https:\u002F\u002Fgithub.com\u002F42coders\u002Fworkflows) - The Workflow Package add Drag & Drop Workflows to your Laravel Application.\n- [Workflows](https:\u002F\u002Fgithub.com\u002FAnswerDotAI\u002Fworkflows) - Composite Actions workflows for use in fastai projects\n- [Workflows](https:\u002F\u002Fgithub.com\u002Fasottile\u002Fworkflows) - reusable github workflows \u002F actions\n- [Workflows-Samples](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fworkflows-samples) - This repository contains samples for Cloud Workflows.\n- [Workflows_And_Package_Management](https:\u002F\u002Fgithub.com\u002Feanbit-rt\u002FWorkflows_and_package_management) - Reproducibility and package management  - workflow languages (CWL, Snakemake, Conda).\n- [Yeoman](https:\u002F\u002Fgithub.com\u002Fyeoman\u002Fyeoman) - Yeoman - a set of tools for automating development workflow\n- [Youtrack-Workflows](https:\u002F\u002Fgithub.com\u002FJetBrains\u002Fyoutrack-workflows) - YouTrack Custom Workflow Repository\n- [Zapier](https:\u002F\u002Fgithub.com\u002Fzapier\u002Fzapier-platform) - The SDK for you to build an integration on Zapier\n- [Zorow](https:\u002F\u002Fgithub.com\u002Fopenmainframeproject\u002Fzorow) - z\u002FOS Open Repository of Workflows (zorow), is an open source community dedicated to contributing and collaborating on z\u002FOSMF workflows. \n\n\n---\n\n## Contributing\n\n💡 Contributions are welcome!  \n\nFeel free to submit a pull request, suggest a new resource, or [open an issue](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002Fnew). \n\nMake sure your submissions align with the following guidelines:\n\n- Relevance to AI agents (e.g. category - using agents, learning agents, building agents)\n- Clear documentation and accessibility (e.g. name + link + 1 sentence description).\n\n### Contributors\n\nThanks to all the amazing contributors who have helped build and improve this list:\n\n**Pull Request Contributors:**\n- [@almogbot](https:\u002F\u002Fgithub.com\u002Falmogbot) — Wolfpack\n- [@ankitdn](https:\u002F\u002Fgithub.com\u002Fankitdn) — Vulert\n- [@arielshad](https:\u002F\u002Fgithub.com\u002Farielshad) — Shep\n- [@b1rdmania](https:\u002F\u002Fgithub.com\u002Fb1rdmania) — GhostClaw\n- [@Bartupso](https:\u002F\u002Fgithub.com\u002FBartupso)\n- [@bmdhodl](https:\u002F\u002Fgithub.com\u002Fbmdhodl) — AgentGuard\n- [@cafferychen777](https:\u002F\u002Fgithub.com\u002Fcafferychen777) — ChatSpatial\n- [@connerlambden](https:\u002F\u002Fgithub.com\u002Fconnerlambden) — BGPT MCP\n- [@dancourse](https:\u002F\u002Fgithub.com\u002Fdancourse) — OpenClaw\n- [@daxaur](https:\u002F\u002Fgithub.com\u002Fdaxaur) — OpenPaw\n- [@GitDakky](https:\u002F\u002Fgithub.com\u002FGitDakky) — Yoyo\n- [@hanzili](https:\u002F\u002Fgithub.com\u002Fhanzili) — Hanzi\n- [@hashimwarren](https:\u002F\u002Fgithub.com\u002Fhashimwarren) — Mastra AI\n- [@hivemoot-forager](https:\u002F\u002Fgithub.com\u002Fhivemoot-forager) — Hivemoot\n- [@igorcosta](https:\u002F\u002Fgithub.com\u002Figorcosta) — Autohand Code CLI\n- [@In3tinct](https:\u002F\u002Fgithub.com\u002FIn3tinct) — Androidmeda update\n- [@johnnyfish](https:\u002F\u002Fgithub.com\u002Fjohnnyfish) — OneCLI\n- [@kantorcodes](https:\u002F\u002Fgithub.com\u002Fkantorcodes) — Registry Broker\n- [@keon](https:\u002F\u002Fgithub.com\u002Fkeon) — Lumen\n- [@kody-w](https:\u002F\u002Fgithub.com\u002Fkody-w) — Rappterbook\n- [@L1AD](https:\u002F\u002Fgithub.com\u002FL1AD) — PolicyLayer\n- [@m13v](https:\u002F\u002Fgithub.com\u002Fm13v) — Fazm\n- [@mantrahq502](https:\u002F\u002Fgithub.com\u002Fmantrahq502) — Mantra\n- [@minhoyoo-iotrust](https:\u002F\u002Fgithub.com\u002Fminhoyoo-iotrust) — WAIaaS\n- [@murdore](https:\u002F\u002Fgithub.com\u002Fmurdore) — NeuroLink\n- [@NikitaDmitrieff](https:\u002F\u002Fgithub.com\u002FNikitaDmitrieff) — auto-co\n- [@pinchwork](https:\u002F\u002Fgithub.com\u002Fpinchwork) — Pinchwork\n- [@PubliusAu](https:\u002F\u002Fgithub.com\u002FPubliusAu) — Tool\u002Fbenchmark additions\n- [@Quinnod345](https:\u002F\u002Fgithub.com\u002FQuinnod345) — context-engine-ai\n- [@remete618](https:\u002F\u002Fgithub.com\u002Fremete618) — widemem-ai\n- [@RusDyn](https:\u002F\u002Fgithub.com\u002FRusDyn) — WritBase\n- [@sagiyak-rgb](https:\u002F\u002Fgithub.com\u002Fsagiyak-rgb) — TeamHero\n- [@SKULLFIRE07](https:\u002F\u002Fgithub.com\u002FSKULLFIRE07) — Cortex\n- [@superlowburn](https:\u002F\u002Fgithub.com\u002Fsuperlowburn) — AgentHive\n- [@The-Nexus-Guard](https:\u002F\u002Fgithub.com\u002FThe-Nexus-Guard) — AIP Identity Protocol\n- [@tigranbs](https:\u002F\u002Fgithub.com\u002Ftigranbs) — Sayna.ai\n- [@vishalveerareddy123](https:\u002F\u002Fgithub.com\u002Fvishalveerareddy123) — Lynkr\n- [@yubing744](https:\u002F\u002Fgithub.com\u002Fyubing744) — Agent-Manager-Skill\n\n**Issue Contributors:**\n- [@AClerbois](https:\u002F\u002Fgithub.com\u002FAClerbois) — Microsoft Agent Framework ([#10](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F10))\n- [@aro-brez](https:\u002F\u002Fgithub.com\u002Faro-brez) — 8OWLS \u002F WeEvolve ([#41](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F41))\n- [@atchl7](https:\u002F\u002Fgithub.com\u002Fatchl7) — AI Conference Deadline ([#14](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F14))\n- [@clawddar](https:\u002F\u002Fgithub.com\u002Fclawddar) — MoltBook ([#28](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F28))\n- [@dbhurley](https:\u002F\u002Fgithub.com\u002Fdbhurley) — Plasmate ([#139](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F139))\n- [@elliotllliu](https:\u002F\u002Fgithub.com\u002Felliotllliu) — AgentShield ([#94](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F94))\n- [@EXboys](https:\u002F\u002Fgithub.com\u002FEXboys) — SkillLite ([#27](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F27))\n- [@getmilodev](https:\u002F\u002Fgithub.com\u002Fgetmilodev) — Milo ([#83](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F83))\n- [@IsaacGHX](https:\u002F\u002Fgithub.com\u002FIsaacGHX) — AgentFlow ([#7](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F7))\n- [@jacobsd32-cpu](https:\u002F\u002Fgithub.com\u002Fjacobsd32-cpu) — DJD Agent Score ([#60](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F60))\n- [@johnxie](https:\u002F\u002Fgithub.com\u002Fjohnxie) — Taskade GitHub link ([#47](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F47))\n- [@khadinakbaronline](https:\u002F\u002Fgithub.com\u002Fkhadinakbaronline) — Humanizer PRO ([#110](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F110))\n- [@nicofains1](https:\u002F\u002Fgithub.com\u002Fnicofains1) — AgentWatch ([#87](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F87))\n- [@onestardao](https:\u002F\u002Fgithub.com\u002Fonestardao) — WFGY ([#33](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F33))\n- [@ori-cofounder](https:\u002F\u002Fgithub.com\u002Fori-cofounder) — GNAP ([#99](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F99))\n- [@Robocular](https:\u002F\u002Fgithub.com\u002FRobocular) — Clawdia Agent Gateway ([#72](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F72))\n- [@samirasadov28-code](https:\u002F\u002Fgithub.com\u002Fsamirasadov28-code) — StoryRoute ([#125](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F125))\n- [@Sendersby](https:\u002F\u002Fgithub.com\u002FSendersby) — TiOLi AGENTIS ([#131](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F131))\n- [@zomux](https:\u002F\u002Fgithub.com\u002Fzomux) — OpenAgents Network ([#52](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F52))\n\n---\n\n## Spread the Word\n\n📢 Help us grow the **Awesome AI Agents** community by sharing this repo with your network! \n\nYou can star the repo, subscribe to our newsletter, and share on LinkedIn and\u002For Twitter. \n\nMore info on how to do this below.\n\n### ⭐ Star the repo ⭐\n⭐ **Why Star This Repository?**  \nYour star helps others find this valuable resource! By starring, you:  \n- Support the AI agents community.  \n- Gain access to the freshest content updated **every 24 hours**.  \n- Inspire others to dive into the world of intelligent systems.  \n\nLet’s shape the future of AI together! 🌟 \n\n### Newsletter\n📩 **Subscribe for Daily News**: Get **100+ updates daily** and never miss a breakthrough in ML agents. Join us at **[https:\u002F\u002Fagents.blog](https:\u002F\u002Fagents.blog)**.  \n\n### LinkedIn\nShare on LinkedIn with this simple post:\n\n> 🚀 **Awesome AI Agents** - A curated collection of cutting-edge tools, resources, and inspiring projects in the world of AI agents. Open-source, community-driven, and ready to help you explore the future of AI! 🌐  \n> 🔗 [Check it out here!](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents)  \n> #AI #MachineLearning #Automation #OpenSource #ArtificialIntelligence #DataScience #Innovation\n\n[Click here to share on LinkedIn!](https:\u002F\u002Fwww.linkedin.com\u002Fsharing\u002Fshare-offsite\u002F?url=https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents)\n\n### Twitter\nTweet this to share on Twitter:\n\n> 🚀 Discover the **Awesome AI Agents** repo! 🤖 A curated collection of AI agents for automation, NLP, and more! Open-source & community-driven! 🌟  \n> 🔗 [Check it out here!](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents)  \n> #AI #MachineLearning #Automation #OpenSource #ArtificialIntelligence #Innovation\n\n[Click here to tweet!](https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?text=Discover%20the%20Awesome%20AI%20Agents%20repo%21%20A%20curated%20collection%20of%20AI%20agents%20for%20automation%2C%20NLP%2C%20and%20more%21%20Open-source%20%26%20community-driven%21%20%F0%9F%9A%80%20Check%20it%20out%20here%3A%20https%3A%2F%2Fgithub.com%2Fjim-schwoebel%2Fawesome_ai_agents%20%23AI%20%23MachineLearning%20%23Automation%20%23OpenSource%20%23ArtificialIntelligence%20%23Innovation)\n\n## License\n\n📜 This repository is licensed under the [Apache 2.0](LICENSE).  \n\nProudly open source to benefit the larger community, feel free to fork and extend!\n","# 🤖 令人惊叹的 AI 代理：工具、资源与项目\n\u003Cdiv align=\"center\">\n  \n  [![在 Twitter 上发布](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPost%20on-Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white)](https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?url=https%3A%2F%2Fgithub.com%2Fjim-schwoebel%2Fawesome_ai_agents&text=Discover%20the%20Awesome%20AI%20Agents%20repo%21%20A%20curated%20collection%20of%20AI%20agents%20for%20automation%2C%20NLP%2C%20and%20more%21%20Open-source%20%26%20community-driven%21%20%F0%9F%9A%80)\n  \n  [![订阅新闻通讯](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSubscribe%20to%20Newsletter-%23FF9900?style=for-the-badge&logo=mailchimp&logoColor=white)](https:\u002F\u002Fagents.blog)\n\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_8b719f09cb90.webp\" width=\"400\" \u002F>\n  \n  [![星标历史图表](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_f003f757686b.png)](https:\u002F\u002Fstar-history.com\u002F#jim-schwoebel\u002Fawesome_ai_agents&Date)\n\u003C\u002Fdiv>\n\u003C\u002Fbr>\n\n欢迎来到 **AI 代理终极枢纽**，这里是您了解所有与 AI 驱动代理相关内容的一站式目的地。无论您是研究人员、开发者还是爱好者，本仓库都汇集了全球最先进的工具、资源和鼓舞人心的项目。\n\n该仓库是一个全面的 AI 代理资源中心，提供精心挑选的工具、框架、数据集和项目集合。它每日更新 AI 和机器学习代理领域的最新进展。资源中包含按数据集、框架、LLM 模型及提示工程技巧分类的详尽列表。此外，还提供了多种工具和工作流，以帮助开发和应用 AI 代理。最后，仓库也列出了相关课程，并鼓励社区贡献。\n\n以下是您可以使用此仓库的几种方式：\n\n- **探索** - 浏览不同板块，找到符合您兴趣和需求的工具、资源和项目（例如用于使用、学习或构建代理）。\n- **贡献** - 分享您自己的 AI 代理相关项目、工具或资源，为这一领域的知识体系不断壮大贡献力量。\n- **保持更新** - 关注 AI 代理领域的最新发展和讨论。加入社区，紧跟前沿趋势。\n\n深入其中，学习、协作，共同打造下一代 AI 代理。\n\n⭐ **如果您觉得本仓库有用，请别忘了给它点个赞！**\n\n🎉 *让我们携手共建繁荣的 AI 代理生态系统！*\n\n---\n\n## 🎉 即将举行的活动：Agents Connect 大会\n\n\u003Cdiv align=\"center\">\n\n### **Agents Connect：全演示大会**\n\n诚邀您参加这场顶尖的 AI 代理大会，创新者、投资者和用户齐聚一堂，共同塑造 AI 代理的未来。\n\n**🎉 免费参会——无需注册费！**\n\n**📅 日期：** 2025年12月15日（星期一）  \n**⏰ 时间：** 太平洋标准时间下午12:00至3:00（America\u002FLos_Angeles）  \n**📍 地点：** 完全线上活动\n\n**[👉 立即报名并了解更多](https:\u002F\u002Fagents.blog\u002Fagent-connect)**\n\n**大会简介：**\n\nAgents Connect：全演示大会是 AI 代理创新者汇聚一堂、用实际行动展示成果的平台。您将观看现场代理演示、研究原型以及来自投资方的反向路演。随后还能与关键人物进行一对一交流。\n\n本次活动为2025年的线上会议，未来几年将推出线下形式。\n\n**您将体验到：**\n\n• **初创企业展示：现场代理演示** —— 观看5家精选初创公司实时演示其自主运行的代理系统\n\n• **科研“从论文到原型”演示** —— 通过可视化演示了解多智能体协同及新型框架\n\n• **反向路演：投资人与合作伙伴展示战略** —— 风投机构和企业合作伙伴将分享如何与您合作的方式\n\n• **结构化的一对一交流与交易撮合** —— 精心安排的一对一对接机会，助您与开发者、投资者和用户建立联系\n\n**适合人群：**\n\n• **面向开发者：** 展示成果、参与演示并获得融资  \n• **面向用户：** 发现下一代 AI 代理  \n• **面向投资者：** 寻找最具潜力的 AI 代理初创公司\n\n**[立即报名并了解更多 →](https:\u002F\u002Fagents.blog\u002Fagent-connect)**\n\n---\n\n## 目录\n- [使用代理](#using)\n  - [应用场景](#applications)\n- [学习代理](#learning)\n  - [代码库](#repositories)\n  - [课程](#courses)\n- [构建代理](#building)\n  - [基准测试](#benchmarks)\n  - [数据集](#datasets)\n  - [部署](#deployment)\n  - [伦理](#ethics)\n  - [框架](#frameworks)\n  - [LLM 模型](#llm-models)\n  - [提示工程](#prompt-engineering)\n  - [安全](#security)\n  - [测试](#testing)\n  - [工具](#tools)\n  - [工作流](#workflows)\n- [参与本仓库的贡献](#contributing)\n  - [贡献者](#contributors)\n- [传播信息](#Spread-the-Word)\n- [许可证](#license)\n\n---\n## 使用\n\n如今涌现出数百种新型 AI 代理，每一种都专为解决特定任务或工作流程而设计。在这里，您可以找到一份精心挑选的现有 AI 代理清单，即刻开始使用它们来提升效率、简化日常生活。无论您是想自动化重复性任务、获取个性化建议，还是生成创意内容，总有一款代理适合您。\n\n请探索这些工具及其他内容，看看 AI 代理如何简化任务、节省时间并激发创造力。随着每天都有新代理问世，这份清单仅仅只是无限可能的开端！\n\n### 应用\n以下是一些你可以立即使用的AI智能体，它们能大幅提升你的工作效率。\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_cc7ff6df30f0.png)\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_a1b7cee069fd.png)\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_b315bac4524b.png)\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_readme_6bb065daa312.png)\n\n以下是几个热门类别以及AI智能体如何发挥作用的示例：\n\n- **广告AI智能体** - 使用智能AI智能体自动化并优化广告活动。\n- **智能体IDE** - 专为构建和管理AI智能体而设计的集成开发环境。\n- **AI智能体管理平台** - 集中化的工具，用于部署、监控和管理AI智能体。\n- **AI智能体记忆** - 让智能体能够保留上下文并在使用过程中不断学习，从而提升交互效果。\n- **AI智能体框架** - 简化健壮AI智能体系统开发的框架。\n- **AI智能体平台** - 提供端到端解决方案以创建和部署AI智能体的平台。\n- **AI虚拟形象** - 基于AI技术的个性化数字代表，用于互动交流。\n- **AI文档智能体** - 专门处理文档和生成内容的智能体。\n- **AI安全** - 利用智能系统通过威胁检测与预防来增强安全性。\n- **AI购物智能体** - 协助消费者进行推荐、搜索和购买。\n- **AI视频智能体** - 利用AI自动化视频制作、编辑和个性化。\n- **身份验证智能体** - 使用AI技术保障身份验证和认证流程的安全性。\n- **编程智能体** - 专为高效编写和调试代码而设计的AI智能体。\n- **编程助手** - 基于AI的工具，帮助开发者完成编码任务并提供建议（例如[Cursor](https:\u002F\u002Fwww.cursor.com\u002F)）。\n- **编程库** - 可重用的AI驱动代码库，以加快开发速度。\n- **内容创作** - 用于生成文字、视觉及多媒体内容的AI工具。\n- **客户服务** - 专为处理客户支持与互动而设计的AI智能体（例如Sierra）。\n- **数据分析** - 利用智能AI智能体分析和解读数据。\n- **数据科学** - 基于AI的解决方案，用于高级数据建模与探索。\n- **桌面AI智能体** - 设计在桌面系统上本地运行的AI智能体，可执行多种任务。\n- **数字员工** - 在数字化环境中模拟人类工作流程的虚拟智能体。\n- **教育AI智能体** - 通过个性化教育提升学习体验的工具。\n- **电子邮件AI智能体** - 利用AI自动化邮件整理、撰写和管理。\n- **游戏智能体** - 用于游戏开发或游戏内交互的AI工具。\n- **潜在客户开发AI智能体** - 自动化销售线索的发现与筛选。\n- **营销AI智能体** - 用于创建和优化营销活动的智能工具。\n- **模型服务** - 用于部署AI模型以供实时使用的平台和工具。\n- **音乐AI智能体** - 用于作曲、编辑或分析音乐的AI工具（例如[suno](https:\u002F\u002Fsuno.ai)）。\n- **运营AI智能体** - 通过AI驱动的自动化简化业务流程。\n- **可观测性** - 利用AI赋能的洞察力监控和分析系统性能。\n- **个人助理** - 帮助管理个人日程、任务和提醒的AI助手。\n- ** productivity** - 旨在通过AI自动化提升生产力的工具。\n- **招聘AI智能体** - 利用AI技术简化招聘流程，实现候选人筛选与寻源。\n- **研究** - 基于AI的工具，协助学术、市场或技术研究。\n- **销售AI智能体** - 利用AI自动化潜在客户开发和客户跟进等销售任务。\n- **科学智能体** - 阅读科研论文并提出假设。\n- **软件测试智能体** - 用于测试和验证软件功能的AI工具。\n- **工具库** - 用于构建、部署或管理AI系统的工具集合。\n- **翻译AI智能体** - 利用具备上下文感知能力的AI智能体实现语言翻译自动化。\n- **旅游AI智能体** - 用于规划、预订和管理旅行的AI智能体。\n- **WEB 3** - 专注于去中心化应用和区块链技术的AI智能体。\n- **Web AI智能体** - 基于浏览器的AI工具，适用于多种应用场景。\n- **网页抓取** - 利用AI智能体高效地从网站提取数据。\n- **网站建设智能体** - 用于创建和管理网站的AI驱动工具。\n- **工作流** - 利用AI智能体自动化并优化工作流程。\n\n2025年还有更多类型的智能体正在被开发。\n\n我建议访问[AgentsDirectory](https:\u002F\u002Faiagentsdirectory.com\u002F)或[awesome_ai_agents](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-agents)（该仓库有使用限制），以获取当前最前沿的AI智能体列表（上述图片即来源于此，截至2025年1月1日）。\n\n---\n\n## 学习\n以下是如何提升你的AI智能体技能的方法：\n- **初学者** - 从基础开始！逐步了解什么是AI智能体及其工作原理。\n- **好奇者** - 动手实践工具和技术，创建属于你自己的功能性智能体。\n- **大胆者** - 尝试更高级的主题，如多智能体系统和前沿设计。\n\n不要想太多——迈出第一步即可。\n\n### 仓库\n有许多仓库可以帮助你入门学习AI智能体（感谢[awesome-ai-agents](https:\u002F\u002Fgithub.com\u002Fslavakurilyak\u002Fawesome-ai-agents)提供的清单）：\n\n- [01](https:\u002F\u002Fchanges.openinterpreter.com\u002Flog\u002Fintroducing-the-01-developer-preview) - The '01 Project' by Open Interpreter is an open-source initiative focused on creating an ecosystem for AI devices, aiming to become the GNU\u002FLinux in this domain, with details on its experimental status, software, hardware, and a speech-to-speech interface based on a code-interpreting language model for dynamic interactions [announcement](https:\u002F\u002Fchanges.openinterpreter.com\u002Flog\u002Fintroducing-the-01-developer-preview) | [demo](https:\u002F\u002Ftwitter.com\u002FOpenInterpreter\u002Fstatus\u002F1770821439458840846) | [github](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002F01) | [website](http:\u002F\u002Fopeninterpreter.com\u002F01) | [docs](https:\u002F\u002F01.openinterpreter.com\u002F)\n- [AGiXT](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAGiXT) - AGiXT is an advanced AI Automation Platform designed to enhance AI instruction management and task execution across various providers, incorporating features like adaptive memory, smart instruct, and a versatile plugin system to push the boundaries of AI technology towards achieving Artificial General Intelligence (AGI) [github](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAGiXT) | [website](https:\u002F\u002Fagixt.com\u002F)\n- [AI Agent Assist by DialPad](https:\u002F\u002Fwww.dialpad.com\u002Fai-labs\u002Fai-agent-assist\u002F) - Dialpad introduces Ai Agent Assist, offering real-time, Ai-powered answers to enhance customer service through deep integrations, reducing agent ramp time, and providing actionable insights with out-of-the-box productivity [landing page](https:\u002F\u002Fwww.dialpad.com\u002Fai-labs\u002Fai-agent-assist\u002F)\n- [AI Agent Crew](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fai-agent-crew) - Langchain and CrewAI have launched AI agents equipped with Bitcoin wallets, facilitating automated operations within a blockchain environment [github](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fai-agent-crew) | [github profile](https:\u002F\u002Fgithub.com\u002Faibtcdev) | [website](https:\u002F\u002Faibtc.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002F5DJaBrf)\n- [AI Assistant by Deco](https:\u002F\u002Fdeco.cx\u002Fai-assistant) - Deco provides a GPT-powered, multilingual AI Sales Assistant designed to personalize and automate the shopping experience, boost sales, and increase operational efficiency for online stores [website](https:\u002F\u002Fdeco.cx\u002Fai-assistant) | [github profile](https:\u002F\u002Fgithub.com\u002Fdeco-cx)\n- [AI Researcher](https:\u002F\u002Fgithub.com\u002Fmshumer\u002Fai-researcher) - The AI Researcher is an AI agent leveraging Claude 3 and SERPAPI for in-depth topic research, refining subtopic analyses into a comprehensive report, customizable and requiring API keys for functionality [github](https:\u002F\u002Fgithub.com\u002Fmshumer\u002Fai-researcher) | [announcement](https:\u002F\u002Ftwitter.com\u002Fi\u002Fweb\u002Fstatus\u002F1776341679617745126) | [website](https:\u002F\u002Fapp.hyperwriteai.com\u002Fpersonalassistant\u002Ftool\u002Fb40d5925-4780-4eed-9f69-a03ae931de37)\n- [AI SDK by Vercel](https:\u002F\u002Fvercel.com\u002Fblog\u002Fintroducing-the-vercel-ai-sdk) - The Vercel AI SDK is an open-source library for creating AI-powered conversational interfaces, supporting multiple frameworks and languages, with built-in adapters for major AI services [announcement](https:\u002F\u002Fvercel.com\u002Fblog\u002Fintroducing-the-vercel-ai-sdk) | [website](https:\u002F\u002Fsdk.vercel.ai\u002Fdocs) | [github](https:\u002F\u002Fgithub.com\u002Fvercel\u002Fai) | [github examples](https:\u002F\u002Fgithub.com\u002Fvercel\u002Fai\u002Ftree\u002Fmain\u002Fexamples)\n- [AI Studio by Azure](https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fproducts\u002Fai-studio) - Azure AI Studio offers a platform for developing generative AI applications and custom copilots, featuring prebuilt models, training capabilities, free Azure Cosmos DB access for 90 days, and built-in security with no extra charge during preview [website](https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fproducts\u002Fai-studio)\n- [AIOS](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FAIOS) - AIOS by AGI Research is an LLM Agent Operating System which enables an operating system 'with soul' -- an important step towards AGI [github](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FAIOS) | [github profile](https:\u002F\u002Fgithub.com\u002Fagiresearch)\n- [Adala](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002FAdala) - Adala is a framework for autonomous data labeling agents, supporting Python 3.8 to 3.11, with features for customizable, intelligent data processing and integration into Python Notebooks [github](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002FAdala)\n- [Agency Swarm by VRSEN](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm) - Agency Swarm is a framework designed to automate AI agencies by creating a swarm of collaborative agents with customizable roles and functionalities, aiming to simplify the agent creation process and make automation more intuitive [github](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm)\n- [Agent Protocol](https:\u002F\u002Fwww.aie.foundation\u002F) - The Agent Protocol establishes a unified API standard for seamless interaction and integration across diverse AI agents, promoting ecosystem growth and simplification of agent development and benchmarking [website](https:\u002F\u002Fwww.aie.foundation\u002F) | [website](https:\u002F\u002Fwww.aie.foundation\u002F) | [github](https:\u002F\u002Fgithub.com\u002FAI-Engineer-Foundation\u002Fagent-protocol) | [github profile](https:\u002F\u002Fgithub.com\u002FAI-Engineer-Foundation)\n- [Agent Tools](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fagent-tools-ts) - Typescript tools for Bitcoin\u002FStacks blockchain interaction, utilizing Bun.js and Stacks.js, with a focus on AI integration [github](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fagent-tools-ts) | [github profile](https:\u002F\u002Fgithub.com\u002Faibtcdev) | [website](https:\u002F\u002Faibtc.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002F5DJaBrf)\n- [Agent by Stately AI](https:\u002F\u002Fgithub.com\u002Fstatelyai\u002Fagent\u002F) - Stately Agent is a software for building intelligent agents that interact via chat and events, with examples including joke generation, tic-tac-toe, and weather querying, requiring installation and an OpenAI API key [github](https:\u002F\u002Fgithub.com\u002Fstatelyai\u002Fagent\u002F) | [website](https:\u002F\u002Fstately.ai\u002Fagent) | [twitter](https:\u002F\u002Ftwitter.com\u002Fstatelyai) | [discord](https:\u002F\u002Fdiscord.gg\u002Fxstate) | [youtube](https:\u002F\u002Fyoutube.com\u002Fc\u002Fstatelyai)\n- [AgentBench](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FAgentBench) - AgentBench v0.2 is a benchmark designed to evaluate Large Language Models as agents across a diverse set of environments, enhancing framework usability and extending model evaluations [github](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FAgentBench)\n- [AgentGPT by Reworkd](https:\u002F\u002Fgithub.com\u002Freworkd\u002FAgentGPT) - AgentGPT allows users to configure and deploy autonomous AI agents, enabling them to name their own custom AI and guide it towards any desired goal through task execution and learning [github](https:\u002F\u002Fgithub.com\u002Freworkd\u002FAgentGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Freworkd)\n- [AgentHive](https:\u002F\u002Fagenthive.to) - AgentHive is a microblogging social network for AI agents, where agents register via API and interact through 280-character posts with support for replies, boosts, follows, search, and discovery. Built on Cloudflare Workers, it includes a TypeScript client library, MCP server, and GitHub Action for integration [website](https:\u002F\u002Fagenthive.to) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@superlowburn\u002Fhive-client)\n- [AgentLabs](https:\u002F\u002Fgithub.com\u002Fagentlabs-inc\u002Fagentlabs) - AgentLabs is an open-source, universal frontend solution for AI agents, offering an authentication portal, chat interface, analytics, and payment features to streamline the deployment of AI agents to public users [github](https:\u002F\u002Fgithub.com\u002Fagentlabs-inc\u002Fagentlabs) | [website](https:\u002F\u002Fwww.agentlabs.dev\u002F) | [docs](https:\u002F\u002Fdocs.agentlabs.dev\u002F)\n- [AgentOS](https:\u002F\u002Fgithub.com\u002Fsmartcomputer-ai\u002Fagent-os) - The Agent OS is an experimental platform for creating self-evolving, autonomous AI agents capable of writing and executing their own code, designed to be a long-term environment for such agents and supports various programming languages [github](https:\u002F\u002Fgithub.com\u002Fsmartcomputer-ai\u002Fagent-os)\n- [AgentOps](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002Fagentops) - AgentOps aims to improve AI agent development with tools for observability, evaluations, and replay analytics, offering a streamlined process for testing and debugging compliant AI agents through a user-friendly interface and comprehensive documentation [github](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002Fagentops) | [website](https:\u002F\u002Fwww.agentops.ai\u002F) | [docs](https:\u002F\u002Fdocs.agentops.ai) | [discord](https:\u002F\u002Fdiscord.gg\u002FmKW3ZhN9p2) | [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772771418780176674)\n- [AgentVerse](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FAgentVerse) - AgentVerse is an Apache2-licensed Python framework for deploying multiple LLM-based agents in various applications, offering task-solving and simulation frameworks for collaborative task accomplishment and behavior observation among agents [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FAgentVerse) | [github profile](https:\u002F\u002Fgithub.com\u002FOpenBMB)\n- [AgentX](https:\u002F\u002Fchatagentx.com\u002F) - AgentX is an AI-powered sales assistant designed to enhance sales strategies and efficiency through advanced features like a Memory Module and Online Mode, leveraging industry best practices for smarter selling [website](https:\u002F\u002Fchatagentx.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fagentxai) | [newsletter](https:\u002F\u002Fbuttondown.email\u002Fagentx)\n- [Agentive](https:\u002F\u002Fagentivehub.com\u002F) - Agentive is a platform for AI Automation Agency owners, offering tools for creating, managing, and deploying custom AI solutions, with features like model selection, tool integration, prompt crafting, versioning, and training with own data, designed to simplify AI agent delivery [website](https:\u002F\u002Fagentivehub.com\u002F)\n- [Agents by AI Waves](https:\u002F\u002Fgithub.com\u002Faiwaves-cn\u002Fagents) - Agents is an open-source framework for building autonomous language agents with features including long-short term memory, tool usage, web navigation, multi-agent communication, human-agent interaction, and symbolic control, allowing customization through natural language config files and deployment in various interfaces [github](https:\u002F\u002Fgithub.com\u002Faiwaves-cn\u002Fagents) | [github profile](https:\u002F\u002Fgithub.com\u002Faiwaves-cn)\n- [Agents by Hugging Face](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fmain_classes\u002Fagent) - Hugging Face's Transformers Agents provide three main types: HfAgent for inference with open-source models, LocalAgent for using local models and tokenizers, and OpenAiAgent for access to OpenAI's closed models, enabling code generation and other AI tasks with varying levels of customization and local or remote execution [website](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fmain_classes\u002Fagent)\n- [Agentsy](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175318595453046) - Agentsy is an AI-driven platform designed to double team capacity by enhancing efficiency and creativity, starting with operations use cases like real estate [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175318595453046)\n- [Aider](https:\u002F\u002Fgithub.com\u002Fpaul-gauthier\u002Faider) - Aider is a command-line tool for AI-assisted pair programming, allowing code editing in local git repositories with GPT-3.5\u002FGPT-4, featuring direct file edits, automatic git commits, and support for most popular programming languages [github](https:\u002F\u002Fgithub.com\u002Fpaul-gauthier\u002Faider)\n- [Autohand Code CLI](https:\u002F\u002Fgithub.com\u002Fautohandai\u002Fcode-cli) - Autohand Code CLI is a self-evolving autonomous coding agent for the terminal, using the ReAct pattern to reason about and modify entire codebases through natural language, with 40+ tools, multi-LLM support (OpenRouter, Anthropic, OpenAI, Ollama, local models), semantic code search, modular skill system, and VS Code\u002FZed integration [github](https:\u002F\u002Fgithub.com\u002Fautohandai\u002Fcode-cli) | [website](https:\u002F\u002Fwww.autohand.ai\u002Fcode\u002F)\n- [AgentRun](https:\u002F\u002Fgithub.com\u002FJonathan-Adly\u002FAgentRun) - The easiest, and fastest way to run AI-generated Python code safely. [github](https:\u002F\u002Fgithub.com\u002FJonathan-Adly\u002FAgentRun)\n- [Claude Engineer](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fclaude-engineer) - Claude Engineer is an interactive command-line interface (CLI) that leverages the power of Anthropic's Claude-3.5-Sonnet model to assist with software development tasks. [github](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fclaude-engineer)\n- [Cline](https:\u002F\u002Fgithub.com\u002Fcline\u002Fcline) - Open-source AI coding agent giving developers direct access to frontier models with full transparency. [github](https:\u002F\u002Fgithub.com\u002Fcline\u002Fcline)\n- [context-engine-ai](https:\u002F\u002Fgithub.com\u002FQuinnod345\u002Fcontext-engine) - A lightweight context engine for AI agents. Ingest events from any source, query with natural language, get ranked results with temporal decay and auto-deduplication. Zero config with SQLite + local TF-IDF embeddings, scales to pgvector + OpenAI. [github](https:\u002F\u002Fgithub.com\u002FQuinnod345\u002Fcontext-engine) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fcontext-engine-ai)\n- [MicroAgent](https:\u002F\u002Fgithub.com\u002Faymenfurter\u002Fmicroagents) - Agents Capable of Self-Editing Their Prompts \u002F Python Code. [github](https:\u002F\u002Fgithub.com\u002Faymenfurter\u002Fmicroagents)\n- [Nous](https:\u002F\u002Fgithub.com\u002FTrafficGuard\u002Fnous) - TypeScript AI agent platform with Autonomous agents, Software developer agents, AI code review agents and more. [github](https:\u002F\u002Fgithub.com\u002FTrafficGuard\u002Fnous)\n- [OpenHands](https:\u002F\u002Fgithub.com\u002FAll-Hands-AI\u002FOpenHands) - 🙌 OpenHands: Code Less, Make More. (formerly OpenDevin), a platform for software development agents powered by AI. [github](https:\u002F\u002Fgithub.com\u002FAll-Hands-AI\u002FOpenHands)\n- [Plandex](https:\u002F\u002Fgithub.com\u002Fplandex-ai\u002Fplandex) - An AI coding engine for complex tasks. [github](https:\u002F\u002Fgithub.com\u002Fplandex-ai\u002Fplandex)\n- [PyCodeAGI](https:\u002F\u002Fgithub.com\u002Fchakkaradeep\u002FpyCodeAGI) - A small AGI experiment to generate a Python app given what app the user wants to build. [github](https:\u002F\u002Fgithub.com\u002Fchakkaradeep\u002FpyCodeAGI)\n- [RepoAgent](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FRepoAgent) - An LLM-powered repository agent designed to assist developers and teams in generating documentation and understanding repositories quickly. [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FRepoAgent)\n- [SWE Agent](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002Fswe-agent) - SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. [github](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002Fswe-agent)\n- [ThinkGPT](https:\u002F\u002Fgithub.com\u002Falaeddine-13\u002Fthinkgpt) - Agent techniques to augment your LLM and push it beyond its limits. [github](https:\u002F\u002Fgithub.com\u002Falaeddine-13\u002Fthinkgpt)\n- [Vision agent](https:\u002F\u002Fgithub.com\u002Flanding-ai\u002Fvision-agent) - Vision Agent is a library that helps you utilize agent frameworks to generate code to solve your vision task. [github](https:\u002F\u002Fgithub.com\u002Flanding-ai\u002Fvision-agent)\n- [WAIaaS](https:\u002F\u002Fgithub.com\u002Fminhoyoo-iotrust\u002FWAIaaS) - Self-hosted wallet-as-a-service for AI agents with multi-chain support (EVM + Solana), DeFi operations (swap, bridge, staking, lending), 3-tier security, and MCP server with 30+ tools. [github](https:\u002F\u002Fgithub.com\u002Fminhoyoo-iotrust\u002FWAIaaS)\n- [Anthropic](https:\u002F\u002Fwww.anthropic.com\u002F) - Anthropic's new suite of Claud 3 models improves AI agents with superior reasoning, rapid responses, and diverse cognitive capabilities without compromising user privacy [website](https:\u002F\u002Fwww.anthropic.com\u002F) | [docs](https:\u002F\u002Fdocs.anthropic.com\u002Fclaude\u002F)\n- [AnyBiz](https:\u002F\u002Fanybiz.io) - AnyBiz offers AI-driven sales agents that enhance sales strategies through intelligent automation, continuous learning, and hyper-personalization, operating 24\u002F7 without breaks [website](https:\u002F\u002Fanybiz.io)\n- [Anyscale](https:\u002F\u002Fwww.anyscale.com\u002F) - The Anyscale platform utilizes large language models (LLMs) for summarization, comparing the summarization quality of human, Llama 2 70b, and GPT-4, with GPT-4 demonstrating superior performance [website](https:\u002F\u002Fwww.anyscale.com\u002F) | [docs](https:\u002F\u002Fdocs.anyscale.com\u002F)\n- [Aomni](https:\u002F\u002Fwww.aomni.com\u002F) - This AI agent streamlines the process of researching prospective customers, potentially saving business development representatives hundreds of hours per year [website](https:\u002F\u002Fwww.aomni.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Faomniapp) | [demo](https:\u002F\u002Fx.com\u002FAtomSilverman\u002Fstatus\u002F1781402688078622874)\n- [AppAgent](https:\u002F\u002Fgithub.com\u002Fmnotgod96\u002FAppAgent) - AppAgent is a mobile-friendly LLM-based multimodal agent framework developed to operate smartphone apps, enabling human-like interactions for a wide range of applications without system back-end access [github](https:\u002F\u002Fgithub.com\u002Fmnotgod96\u002FAppAgent) | [github profile](https:\u002F\u002Fgithub.com\u002Fmnotgod96)\n- [Arize AX](https:\u002F\u002Farize.com\u002Fgenerative-ai) - Arize AX is a free tool to trace, evaluate, and iterate during development and monitor and evaluate AI agents in production | [website](https:\u002F\u002Farize.com) [docs](https:\u002F\u002Farize.com\u002Fdocs\u002Fax) | [github](https:\u002F\u002Fgithub.com\u002FArize-ai) | [Slack](https:\u002F\u002Fjoin.slack.com\u002Ft\u002Farize-ai\u002Fshared_invite\u002Fzt-3iu5bvnzr-2e~VFHw2Et4MM5rMsK599g)\n- [Assistants API by OpenAI](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fassistants\u002Foverview) - The Assistants API facilitates the development of AI agents, offering tools such as Code Interpretation and Function calling for embedding advanced, intelligent functionalities within applications [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fassistants\u002Foverview)\n- [Astra Assistants API](https:\u002F\u002Fgithub.com\u002Fdatastax\u002Fastra-assistants-api) - The `astra-assistants-api` provides a backend implementation of the OpenAI Assistants API with support for various features like persistent threads, files, assistants, streaming, function calling, and more, utilizing AstraDB powered by Apache Cassandra and jvector, and is compatible with existing OpenAI apps by changing a single line of code [github](https:\u002F\u002Fgithub.com\u002Fdatastax\u002Fastra-assistants-api)\n- [AutoAct](https:\u002F\u002Fgithub.com\u002Fzjunlp\u002FAutoAct) - AutoAct is an automatic agent learning framework that synthesizes planning trajectories without large-scale data or closed-source models, using a division-of-labor strategy for task completion, demonstrating superior or comparable performance in experiments [github](https:\u002F\u002Fgithub.com\u002Fzjunlp\u002FAutoAct) | [website](https:\u002F\u002Fwww.zjukg.org\u002Fproject\u002FAutoAct\u002F)\n- [AutoDev](https:\u002F\u002Fgithub.com\u002Funit-mesh\u002Fauto-dev) - AutoDev is an AI-powered coding assistant offering multilingual support, automatic code generation, and debugging assistance, featuring customizable prompts and specialized tools for development, testing, documentation, and the integration of custom AI agents, with a focus on experimenting and building AI agents using its UI framework [github](https:\u002F\u002Fgithub.com\u002Funit-mesh\u002Fauto-dev) | [docs](https:\u002F\u002Fide.unitmesh.cc)\n- [AutoGPT](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT) - AutoGPT provides accessible AI tools for building and using AI agents, offering a comprehensive framework including Forge for agent creation, agbenchmark for performance evaluation, a leaderboard for competition, a user-friendly UI, and CLI for seamless integration and management [github](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT) | [github profile](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas)\n- [AutoGen Studio by Microsoft](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) - AutoGen Studio 2.0 is Microsoft's advanced AI development tool, offering a user-friendly interface, powerful Python API, and comprehensive features for creating and controlling AI agents and workflows [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) | [website](https:\u002F\u002Fautogen-studio.com) | [landing page](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fproject\u002Fautogen\u002F) | [research paper](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fpublication\u002Fautogen-enabling-next-gen-llm-applications-via-multi-agent-conversation-framework\u002F)\n- [AutoGen by Microsoft](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) - AutoGen is a multi-agent conversation framework facilitating the development of next-gen LLM applications, highlighted by various accomplishments and offering enhanced LLM inferences, customizable agents, and comprehensive documentation [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) | [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DXhqhpHWRuM)\n- [Axflow](https:\u002F\u002Faxflow.dev\u002F) - Axflow is a TypeScript framework designed for AI development, offering a modular collection of tools for building natural language applications, and it emphasizes a code-first approach to simplify the integration of LLMs into scalable solutions [website](https:\u002F\u002Faxflow.dev\u002F) | [github](https:\u002F\u002Fgithub.com\u002Faxflow\u002Faxflow)\n- [Azure Speech Service](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Fspeech-service) - The Azure Speech service supports a wide range of languages and locales, with over 400 neural voices available in more than 140 languages and locales, including multilingual voices that can speak multiple languages [docs](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fai-services\u002Fspeech-service)\n- [BDR Agent by Relevance](https:\u002F\u002Frelevanceai.com\u002Fagents\u002Fbdr-agent) - Relevance AI's flagship BDR Agent is designed to assist sales teams by researching and qualifying leads, engaging in personalized prospecting according to your playbook 24x7, and booking meetings to grow your business without increasing headcount [website](https:\u002F\u002Frelevanceai.com\u002Fagents\u002Fbdr-agent) | [twitter](https:\u002F\u002Ftwitter.com\u002FRelevanceAI) | [github profile](https:\u002F\u002Fgithub.com\u002FRelevanceAI) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Frelevanceai\u002F)\n- [BabyAGI](https:\u002F\u002Fgithub.com\u002Fyoheinakajima\u002Fbabyagi) - BabyAGI exemplifies an AI-powered task management system utilizing OpenAI and vector databases like Chroma or Weaviate, creating, prioritizing, and executing tasks based on previous outcomes and predefined objectives, with the main function involving an infinite loop where tasks are processed, enriched, and stored using OpenAI's NLP capabilities and Chroma\u002FWeaviate, inspired by the Task-Driven Autonomous Agent concept [github](https:\u002F\u002Fgithub.com\u002Fyoheinakajima\u002Fbabyagi) | [github profile](https:\u002F\u002Fgithub.com\u002Fyoheinakajima)\n- [BabyAGI UI](https:\u002F\u002Fgithub.com\u002Fmiurla\u002Fbabyagi-ui) - Make it easier to run and develop with babyagi in a web app, like a ChatGPT. [github](https:\u002F\u002Fgithub.com\u002Fmiurla\u002Fbabyagi-ui)\n- [CollosalAI Chat](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI\u002Ftree\u002Fmain\u002Fapplications\u002FChat) - implement LLM with RLHF, powered by the Colossal-AI project. [github](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI)\n- [DuetGPT](https:\u002F\u002Fgithub.com\u002Fkristoferlund\u002Fduet-gpt) - A conversational semi-autonomous developer assistant, AI pair programming without the copypasta. [github](https:\u002F\u002Fgithub.com\u002Fkristoferlund\u002Fduet-gpt)\n- [Gobii](https:\u002F\u002Fgithub.com\u002Fgobii-ai\u002Fgobii-platform) - Gobii is an open-source platform for deploying and managing browser-use agents at scale with a conversational interface and API. [github](https:\u002F\u002Fgithub.com\u002Fgobii-ai\u002Fgobii-platform)\n- [GPT Agent](https:\u002F\u002Fgithub.com\u002Firis-networks\u002Fgpt-agent) - The Free, Open-Source AI Agent for Computer Automation. [github](https:\u002F\u002Fgithub.com\u002Firis-networks\u002Fgpt-agent)\n- [ix](https:\u002F\u002Fgithub.com\u002Fkreneskyp\u002Fix) - Autonomous GPT-4 agent platform. [github](https:\u002F\u002Fgithub.com\u002Fkreneskyp\u002Fix)\n- [joinly](https:\u002F\u002Fgithub.com\u002Fjoinly-ai\u002Fjoinly) - Voice-first AI Assistant for online meetings that can actively participate and solve tasks live during the meeting. [github](https:\u002F\u002Fgithub.com\u002Fjoinly-ai\u002Fjoinly)\n- [LLama Cpp Agent](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent) - The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models. [github](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent)\n- [Multi-Modal LangChain agents in Production](https:\u002F\u002Fgithub.com\u002Fsteamship-packages\u002Flangchain-agent-production-starter) - Deploy LangChain Agents and connect them to Telegram. [github](https:\u002F\u002Fgithub.com\u002Fsteamship-packages\u002Flangchain-agent-production-starter)\n- [RasaGPT](https:\u002F\u002Fgithub.com\u002Fpaulpierre\u002FRasaGPT) - RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. [github](https:\u002F\u002Fgithub.com\u002Fpaulpierre\u002FRasaGPT)\n- [Autonomous HR Chatbot](https:\u002F\u002Fgithub.com\u002Fstepanogil\u002Fautonomous-hr-chatbot) - An autonomous agent that can answer HR related queries autonomously using the tools it has on hand. [github](https:\u002F\u002Fgithub.com\u002Fstepanogil\u002Fautonomous-hr-chatbot)\n- [Bananalyzer by Reworkd](https:\u002F\u002Freworkd.ai) - Bananalyzer is a framework for evaluating AI agents on web tasks, utilizing Playwright for creating diverse datasets of website snapshots for reliable and varied web task assessments [website](https:\u002F\u002Freworkd.ai) | [github](https:\u002F\u002Fgithub.com\u002Freworkd\u002Fbananalyzer)\n- [Bazed](https:\u002F\u002Fgithub.com\u002Fbazed-ai\u002Fbazed-af) - Bazed Agent Framework, aimed at empowering developers to build autonomous agent swarms without requiring deep Python ML knowledge, is facilitating the creation of sophisticated systems through TypeScript for enhanced autonomy and reliability [github](https:\u002F\u002Fgithub.com\u002Fbazed-ai\u002Fbazed-af) | [website](https:\u002F\u002Fbazed.ai\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FVmEEUrc7dg)\n- [Beam](https:\u002F\u002Fbeam.ai\u002F) - Beam AI offers a platform for Agentic Process Automation, using AI agents to automate workflows, enhancing productivity for businesses of all sizes with features like pre-trained agents, seamless integrations, and industry-specific solutions [website](https:\u002F\u002Fbeam.ai\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fjoin__beam) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fbeam-ai) | [youtube](https:\u002F\u002Fwww.youtube.com\u002F@beam-ai)\n- [Bland](https:\u002F\u002Fwww.bland.ai\u002F) - Bland AI offers a platform for building and scaling AI-powered phone agents, featuring easy integration, live data context, custom voices, and dedicated infrastructure. Tech stack includes LLM: Claude Instant (Anthropic), Transcription: Whisper (OpenAI), TTS: ElevenLabs [website](https:\u002F\u002Fwww.bland.ai\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fusebland)\n- [Bloop](https:\u002F\u002Fgithub.com\u002FBloopAI\u002Fbloop) - Bloop is a GPT-4-based coding assistant that boosts engineer productivity by allowing natural language interactions with codebases for explanations, feature writing, error troubleshooting, and more, featuring a code-centric AI playground, fast regex search, and comprehensive code navigation tools [github](https:\u002F\u002Fgithub.com\u002FBloopAI\u002Fbloop)\n- [BrainSoup Custom Tools](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) - BrainSoup is a multi-agent and multi-LLM native client where users can easily create custom tools for their agents, in any programming language, enabling them to interact with the user's system or any other external service [website](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) | [docs](https:\u002F\u002Fhelp.nurgo-software.com\u002Fcollection\u002F148-brainsoup) | [twitter](https:\u002F\u002Ftwitter.com\u002FNurgo) | [discord](https:\u002F\u002Fdiscord.gg\u002Fxt7PyCnH9S)\n- [BrainSoup](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) - BrainSoup is a multi-agent and multi-LLM native client, enabling users to create a team of personalized AI agents that can learn, remember, react to events, use tools, leverage the local resources of the user's computer, and work together to solve tasks autonomously [website](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup) | [docs](https:\u002F\u002Fhelp.nurgo-software.com\u002Fcollection\u002F148-brainsoup) | [twitter](https:\u002F\u002Ftwitter.com\u002FNurgo) | [discord](https:\u002F\u002Fdiscord.gg\u002Fxt7PyCnH9S)\n- [BrowserGPT](https:\u002F\u002Fgithub.com\u002Fmayt\u002FBrowserGPT) - BrowserGPT is a project that combines OpenAI's GPT-4 and the Playwright library to control browsers via natural language, enabling code snippet generation for browser tasks [github](https:\u002F\u002Fgithub.com\u002Fmayt\u002FBrowserGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Fmayt)\n- [Browserbase](https:\u002F\u002Ftwitter.com\u002Fbrowserbasehq) - Browserbase offers a managed headless web browser API with robust features like session recording, logging, and debugging, ensuring secure connections to isolated web browsers for efficient issue resolution [twitter](https:\u002F\u002Ftwitter.com\u002Fbrowserbasehq) | [website](https:\u002F\u002Fwww.browserbase.com\u002F)\n- [BrowsingAgent by Agency Swarm](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FBrowsingAgent) - BrowsingAgent, an AI web navigation tool, has been integrated into the Agency Swarm framework to enable human-like browsing capabilities for automated AI operations [code](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FBrowsingAgent) | [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Yidy_ePo7pE)\n- [CAMEL](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Fcamel) - CAMEL (Communicative Agents for Mind Exploration of Large Language Model Society) is an open-source library designed for studying autonomous and communicative agents, facilitating research in understanding their behaviors, capabilities, and potential risks through scalable techniques and cooperative frameworks, including role-playing, with extensive documentation, examples, and datasets, while also supporting integration with open-source models as backends for diverse applications [github](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Fcamel) | [github profile](https:\u002F\u002Fgithub.com\u002Fcamel-ai)\n- [CLIN](https:\u002F\u002Fallenai.github.io\u002Fclin\u002F) - CLIN by Allen Institute for AI is an interactive continual learning agent that adapts rapidly to tasks, using a setup process involving Java, Python, and the ScienceWorld environment, supported by models like GPT-3.5-turbo and GPT-4 [website](https:\u002F\u002Fallenai.github.io\u002Fclin\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fclin) | [research paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.10134.pdf)\n- [Cadea](https:\u002F\u002Fwww.cadea.ai\u002F) - Cadea offers a secure AI platform for businesses, providing solutions against prompt injection, data breaches, and ensuring content safety through end-to-end security, access controls, and integration with major identity providers [website](https:\u002F\u002Fwww.cadea.ai\u002F)\n- [Cal.ai](https:\u002F\u002Fcal.com\u002Fai) - Cal.ai is an open-source AI scheduling assistant that manages email communications for booking, rearranging, and inquiring about meetings, leveraging a LangChain Agent Executor and MailParser for efficient scheduling without API key exposure [website](https:\u002F\u002Fcal.com\u002Fai) | [github](https:\u002F\u002Fgithub.com\u002Fcalcom\u002Fcal.com\u002Ftree\u002Fmain\u002Fapps\u002Fai)\n- [Central by Zapier](https:\u002F\u002Fzapier.com\u002Fblog\u002Fintroducing-zapier-central-ai-bots\u002F) - Zapier Central is an AI workspace designed to automate tasks across 6,000+ apps with AI bots, offering capabilities like live data connection, AI automation, and interaction with data sources for businesses and individual productivity enhancements [announcement](https:\u002F\u002Fzapier.com\u002Fblog\u002Fintroducing-zapier-central-ai-bots\u002F) | [website](https:\u002F\u002Fzapier.com\u002Fcentral)\n- [ChartGPT](https:\u002F\u002Fchartgpt.io) - ChartGPT offers AI-driven services like table summarization, charting, and code generation, featuring pay-as-you-go pricing, trusted by major companies, emphasizing data security, ease of use, and 24\u002F7 customer support [website](https:\u002F\u002Fchartgpt.io)\n- [ChatDev](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev) - ChatDev is a virtual software company utilizing intelligent agents to revolutionize the digital world through programming, offering a highly customizable framework and integrating innovative approaches like Experiential Co-Learning, Docker support, Git management, and Human-Agent Interaction [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev) | [github profile](https:\u002F\u002Fgithub.com\u002FOpenBMB)\n- [ChatGPT-code-preview](https:\u002F\u002Fgithub.com\u002Fykyritsis\u002FChatGPT-code-preview) - Artifacts-like chrome extension for ChatGPT, inspired by Claude 3.5 Sonnet, which requires CSP unblocker for JS to function [github](https:\u002F\u002Fgithub.com\u002Fykyritsis\u002FChatGPT-code-preview)\n- [ChatGPT](https:\u002F\u002Fchatgpt.com\u002F) - ChatGPT is an AI language model designed to understand and generate human-like text, facilitating conversation and assisting with various tasks [website](https:\u002F\u002Fchatgpt.com\u002F)\n- [Claude 3 Artifacts by PierrunoYT](https:\u002F\u002Fgithub.com\u002FPierrunoYT\u002Fclaude-3-artifacts) - An open-source Flask-React chat application that interacts with Claude AI, featuring file uploads, Markdown rendering, and code highlighting, seeking contributors to expand its capabilities, inspired by Claude Artifacts [github](https:\u002F\u002Fgithub.com\u002FPierrunoYT\u002Fclaude-3-artifacts) | [reddit announcement](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FClaudeAI\u002Fcomments\u002F1dqhta5\u002Fhelp_me_buiild_claude_3_artifacs_opensource\u002F)\n- [Claude models by Anthropic](https:\u002F\u002Fdocs.anthropic.com\u002Fclaude\u002Fdocs\u002Ftool-use) - Function calling or tool use is supported with the following models: `claude-3-opus-20240229`, `claude-3-sonnet-20240229`, and `claude-3-haiku-20240307` [docs](https:\u002F\u002Fdocs.anthropic.com\u002Fclaude\u002Fdocs\u002Ftool-use)\n- [Claude-React-Jumpstart](https:\u002F\u002Fgithub.com\u002FBklieger\u002FClaude-React-Jumpstart) - This project offers a tutorial for beginners to set up and run React code generated by Claude's Artifacts feature locally, providing step-by-step instructions for creating a React app with Vite, installing necessary dependencies, and integrating Claude-generated code [github](https:\u002F\u002Fgithub.com\u002FBklieger\u002FClaude-React-Jumpstart) | [twitter announcement](https:\u002F\u002Fx.com\u002FBenjaminKlieger\u002Fstatus\u002F1804264035464155220)\n- [Clawdia Agent Gateway](https:\u002F\u002Fapi-catalog-three.vercel.app) - A unified API gateway providing 40+ services for AI agents including web scraping, code execution, agent memory with vector search, task queue, event bus, and more. Supports x402 micropayments (USDC on Base) with TypeScript and Python SDKs [website](https:\u002F\u002Fapi-catalog-three.vercel.app) | [github](https:\u002F\u002Fgithub.com\u002FOzorOwn)\n- [CodeActAgent](https:\u002F\u002Fgithub.com\u002Fxingyaoww\u002Fcode-act) - CodeActAgent, trained on CodeActInstruct, showcases superior performance in both in-domain and out-of-domain tasks, enabling dynamic code execution and multi-turn interactions for more effective LLM agents [github](https:\u002F\u002Fgithub.com\u002Fxingyaoww\u002Fcode-act)\n- [Codel](https:\u002F\u002Fgithub.com\u002Fsemanser\u002Fcodel) - Autonomous AI agent, inspired by Devin, designed for complex task execution with features like a secure sandboxed Docker environment, integrated browser for real-time web information, text editor, and PostgreSQL database for history tracking, highlighting its relevance to agentic AI through its ability to autonomously navigate and perform actions across terminal, browser, and editor interfaces [github](https:\u002F\u002Fgithub.com\u002Fsemanser\u002Fcodel) | [announcement](https:\u002F\u002Fnews.ycombinator.com\u002Fitem?id=39799296)\n- [Cody](https:\u002F\u002Fsourcegraph.com\u002Fcody) - Cody, an AI coding assistant, now offers an enterprise version with enhanced security, scalability, and control for organizations, supporting various IDEs and providing AI-powered autocomplete, chat assistance, and custom command capabilities [website](https:\u002F\u002Fsourcegraph.com\u002Fcody) | [github](https:\u002F\u002Fgithub.com\u002Fsourcegraph\u002Fcody)\n- [Cognee](https:\u002F\u002Fgithub.com\u002Ftopoteretes\u002Fcognee) - Cognee is an open-source framework aimed at simplifying data processing for large language models (LLMs) by creating knowledge graphs and data models, offering tools for information addition, knowledge creation, and similarity-based search [github](https:\u002F\u002Fgithub.com\u002Ftopoteretes\u002Fcognee)\n- [Command R+ by Cohere](https:\u002F\u002Ftxt.cohere.com\u002Fcommand-r-plus-microsoft-azure\u002F) - Cohere introduces Command R+, an advanced, scalable LLM optimized for enterprise needs with advanced RAG, multilingual support, and sophisticated tool-use capabilities for automating complex business workflows, available first on Microsoft Azure [announcement](https:\u002F\u002Ftxt.cohere.com\u002Fcommand-r-plus-microsoft-azure\u002F) | [docs](https:\u002F\u002Fdocs.cohere.com\u002Fdocs\u002Fcommand-r)\n- [Composio](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ujxKzS0b5qg) - Composio enables quick integration of 90+ tools for developers and agents, offering managed authentication, easy testing, and up-to-date APIs to simplify development and enhance functionality [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ujxKzS0b5qg) | [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ujxKzS0b5qg) | [website](https:\u002F\u002Fwww.composio.dev\u002F) | [docs](https:\u002F\u002Fdocs.composio.dev\u002F) | [blog](https:\u002F\u002Fblog.composio.dev\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002FSamparkAI)\n- [Context](https:\u002F\u002Fcontext.ai\u002F) - Context.ai is a tool for evaluating and analyzing products with LLMs, aiming to improve user experience and performance [website](https:\u002F\u002Fcontext.ai\u002F) | [docs](https:\u002F\u002Fdocs.context.ai\u002F)\n- [Continue](https:\u002F\u002Fgithub.com\u002Fcontinuedev\u002Fcontinue) - Continue is an open-source autopilot plugin for VS Code and JetBrains, enhancing coding with LLMs through features like task and tab autocomplete, natural language edits, file generation, and customization options, available under the Apache 2.0 license [github](https:\u002F\u002Fgithub.com\u002Fcontinuedev\u002Fcontinue) | [website](https:\u002F\u002Fcontinue.dev)\n- [Cosmo](https:\u002F\u002Fmeetcosmo.ai\u002F) - Cosmo offers an all-inclusive AI agent for merchants on WhatsApp, enabling order placements, customer interaction, automatic question answering, inventory and CRM integration, with features like instant payments, customer insights, dynamic order fulfillment, and a comprehensive merchant web app for online transaction management, aimed at simplifying shopping and boosting sales by 57% [website](https:\u002F\u002Fmeetcosmo.ai\u002F) | [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772775416044126608)\n- [Cursor](https:\u002F\u002Fgithub.com\u002Fgetcursor\u002Fcursor\u002Fissues) - Cursor is an AI-enhanced programming editor focusing on code discussion, editing, and debugging, with plans for advanced features like repository healing and AI-generated documentation [issue tracker](https:\u002F\u002Fgithub.com\u002Fgetcursor\u002Fcursor\u002Fissues) | [website](https:\u002F\u002Fcursor.sh\u002F)\n- [Custom Tools by Bland AI](https:\u002F\u002Fdocs.bland.ai\u002Ftutorials\u002Fcustom-tools#creating-your-custom-tool) - Custom tools by Bland AI enable an agent to interact with any web API mid-call to perform actions like sending messages, scheduling appointments, creating support tickets, or updating CRM systems [docs](https:\u002F\u002Fdocs.bland.ai\u002Ftutorials\u002Fcustom-tools#creating-your-custom-tool)\n- [DB-GPT](https:\u002F\u002Fgithub.com\u002Feosphoros-ai\u002FDB-GPT) - DB-GPT revolutionizes database interactions using private LLM technology, enabling streamlined AI-native data app development with multi-model management, Text2SQL optimization, and fine-tuning, facilitating enterprises and developers to create bespoke applications in the Data 3.0 era [github](https:\u002F\u002Fgithub.com\u002Feosphoros-ai\u002FDB-GPT) | [github profile](https:\u002F\u002Fgithub.com\u002Feosphoros-ai)\n- [DSPY](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy) - A cutting-edge framework that compiles declarative language model calls into self-improving pipelines, enabling the systematic and efficient optimization of LM prompts and weights within complex systems [github](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy)\n- [Data Questionnaire Agent](https:\u002F\u002Fgithub.com\u002Fonepointconsulting\u002Fdata-questionnaire-agent) - A chatbot designed to query users on data integration practices, offering advice based on responses, utilizing a modified Chainlit library for operation [github](https:\u002F\u002Fgithub.com\u002Fonepointconsulting\u002Fdata-questionnaire-agent)\n- [DeepInfra](https:\u002F\u002Fdeepinfra.com) - DeepInfra is a comprehensive platform that simplifies the deployment and management of machine learning models, offering a range of open-source models for tasks like text generation and embeddings, with easy integration through REST API calls [website](https:\u002F\u002Fdeepinfra.com) | [docs](https:\u002F\u002Fdeepinfra.com\u002Fdocs\u002F)\n- [Deepgram](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772774552260788296) - Conversational AI tools designed for creating voice bots and agents, featuring realistic, low-latency voice technology [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772774552260788296)\n- [Deepunit](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772773773772779533) - An AI agent designed to generate unit tests for complete code coverage across your project, requiring only your repository as input [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772773773772779533)\n- [DevOpsGPT](https:\u002F\u002Fgithub.com\u002Fkuafuai\u002FDevOpsGPT) - DevOpsGPT is an AI-driven software development automation solution that combines large language models with DevOps tools to convert natural language requirements into working software, enhancing development efficiency, shortening cycles, and reducing communication costs [github](https:\u002F\u002Fgithub.com\u002Fkuafuai\u002FDevOpsGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Fkuafuai)\n- [Devid by Agency Swarm](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FDevid) - Devid Agent, a new AI software development tool, has been integrated into the Agency Swarm framework to enhance automated AI agency operations, alternative to Cognition AI's Devin [code](https:\u002F\u002Fgithub.com\u002FVRSEN\u002Fagency-swarm\u002Ftree\u002Fmain\u002Fagency_swarm\u002Fagents\u002FDevid) | [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BEpDRj9H3zE)\n- [Devika](https:\u002F\u002Fgithub.com\u002Fstitionai\u002Fdevika) - Devika is an open-source AI software engineer designed to understand and execute high-level coding tasks by researching, planning, and writing code, aiming to be a competitive alternative to Cognition AI's Devin [github](https:\u002F\u002Fgithub.com\u002Fstitionai\u002Fdevika) | [demo](https:\u002F\u002Fgithub.com\u002Fstitionai\u002Fdevika?tab=readme-ov-file#demos) | [discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002F8eYNbPuB)\n- [Devin by Cognition](https:\u002F\u002Fwww.cognition-labs.com\u002Fintroducing-devin) - Devin is a fully autonomous AI software engineer, revolutionizing coding with advanced reasoning and planning capabilities [announcement](https:\u002F\u002Fwww.cognition-labs.com\u002Fintroducing-devin) | [website](https:\u002F\u002Fwww.cognition-labs.com\u002F)\n- [Devon (previously Gilfoyle)](https:\u002F\u002Fgithub.com\u002Fentropy-research\u002FDevon) - Devon, not Devin, aims to perfect code correction for fill-in-the-middle, bug spotting, and completion tasks, using JSON for metadata in edits, and incorporates looping until user termination in function updates [github](https:\u002F\u002Fgithub.com\u002Fentropy-research\u002FDevon)\n- [DJD Agent Score](https:\u002F\u002Fgithub.com\u002Fjacobsd32-cpu\u002Fdjdagentscore) - On-chain reputation scoring API for AI agent wallets on Base L2. Returns 0-100 trust scores across 5 behavioral dimensions from transaction patterns and multi-chain attestations. Features x402 micropayments, TypeScript SDK, and ERC-8004 on-chain reputation publication [github](https:\u002F\u002Fgithub.com\u002Fjacobsd32-cpu\u002Fdjdagentscore) | [docs](https:\u002F\u002Fdjd-agent-score.fly.dev) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fdjd-agent-score-client)\n- [E2B](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002FE2B) - E2B Sandbox offers secure cloud environments tailored for AI agents and apps, facilitating long-running sessions with various tools and can be integrated with any large language model [github](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002FE2B) | [github profile](https:\u002F\u002Fgithub.com\u002Fe2b-dev)\n- [ElevenLabs](https:\u002F\u002Felevenlabs.io\u002F) - ElevenLabs is a software company that develops AI-powered, natural-sounding speech synthesis and text-to-speech software, with the mission of making content universally accessible in any language and voice [website](https:\u002F\u002Felevenlabs.io\u002F)\n- [Enact](https:\u002F\u002Fgithub.com\u002Fagentic-ai\u002Fenact) - Enact is a Python framework for building generative software that integrates machine learning models or APIs, offering features like tracking and replaying executions, asynchronous flows, and higher-order generative processes [github](https:\u002F\u002Fgithub.com\u002Fagentic-ai\u002Fenact)\n- [Evolutionary Model Merge](https:\u002F\u002Ftwitter.com\u002FAlphaSignalAI\u002Fstatus\u002F1771201081734811797) - Sakana AI's evolutionary model merge (EMM) combines 500,000 open-source models using evolutionary techniques to create new foundation models, achieving groundbreaking results without being explicitly optimized for specific benchmarks, marking a significant step toward AGI by empowering AI with combined knowledge akin to Retrieval Augmented Generation [announcement](https:\u002F\u002Ftwitter.com\u002FAlphaSignalAI\u002Fstatus\u002F1771201081734811797) | [github](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002Fevolutionary-model-merge\u002F)\n- [Fairgo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175460044193992) - Fairgo.ai is a platform built by Julian to streamline and scale hiring processes using real-time AI video interviews, tackling unconscious biases and ensuring all candidates are interviewed without human input [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175460044193992) | [website](https:\u002F\u002Ffairgo.ai\u002F)\n- [FastChat](https:\u002F\u002Fgithub.com\u002Flm-sys\u002FFastChat) - FastChat is a platform for training, serving, and evaluating large language model chatbots, featuring an open-source distributed multi-model system, API compatibility, and a dataset for LLM conversations [github](https:\u002F\u002Fgithub.com\u002Flm-sys\u002FFastChat) | [demo](https:\u002F\u002Fchat.lmsys.org\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FHSWAKCrnFx) | [twitter](https:\u002F\u002Fx.com\u002Flmsysorg)\n- [Fetch](https:\u002F\u002Ffetch.ai) - Fetch by Fetch AI offers a platform for launching AI apps and services, including agent hosting, analytics, IoT gateways, and a Web3-based open network, alongside an open network for AI Agents that allows for connectivity, transactions, and the formation of dynamic marketplaces, facilitating the deployment and monetization of AI and ML models through agent technology [website](https:\u002F\u002Ffetch.ai) | [github profile](https:\u002F\u002Fgithub.com\u002Ffetchai)\n- [Fazm](https:\u002F\u002Fgithub.com\u002Fm13v\u002Ffazm) - Fazm is an open-source, voice-controlled AI computer agent for macOS that controls your entire desktop through natural language - any app, file, or workflow. Built in Swift\u002FSwiftUI, local-first, MIT licensed [github](https:\u002F\u002Fgithub.com\u002Fm13v\u002Ffazm) | [website](https:\u002F\u002Ffazm.ai)\n- [FinGen](https:\u002F\u002Ftwitter.com\u002FSullyOmarr\u002Fstatus\u002F1772282548841791730) - FinGen is a financial analysis agent using RSC, LangChain, and Polygon finance API, emphasizing it's not financial advice and requires API keys for use [announcement](https:\u002F\u002Ftwitter.com\u002FSullyOmarr\u002Fstatus\u002F1772282548841791730) | [github](https:\u002F\u002Fgithub.com\u002Fsullyo\u002Ffingen)\n- [Fine](https:\u002F\u002Fwww.fine.dev) - Fine.dev offers AI-powered agents designed to automate software development tasks, seamlessly integrating into engineering teams to manage tedious tasks, technical debt, code reviews, and migrations, while customizing to project needs and learning from team feedback for improved efficiency [website](https:\u002F\u002Fwww.fine.dev) | [discord](https:\u002F\u002Fdiscord.gg\u002FnxW8sA5yqe) | [docs](https:\u002F\u002Fdocs.fine.dev\u002F)\n- [Flowise](https:\u002F\u002Fgithub.com\u002FFlowiseAI\u002FFlowise) - Flowise simplifies the creation of applications leveraging large language models (LLMs) by providing a drag-and-drop interface for customizing AI workflows, offering easy installation, Docker support, development tools, and documentation for integrating various functionalities such as authentication, streaming, and custom tools to enhance AI agents' capabilities [github](https:\u002F\u002Fgithub.com\u002FFlowiseAI\u002FFlowise) | [website](https:\u002F\u002Fflowiseai.com\u002F) | [docs](https:\u002F\u002Fdocs.flowiseai.com\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002FFlowiseAI)\n- [FuzzTypes](https:\u002F\u002Fgithub.com\u002Fgenomoncology\u002FFuzzTypes) - FuzzTypes is a Pydantic extension library providing autocorrecting annotation types, enhancing Pydantic's data conversions for AI agents by enabling powerful normalization capabilities like named entity linking to ensure structured data consists of 'smart things' instead of 'dumb strings' [github](https:\u002F\u002Fgithub.com\u002Fgenomoncology\u002FFuzzTypes) | [website](https:\u002F\u002Fwww.genomoncology.com\u002F)\n- [GPT Computer Assistant](https:\u002F\u002Fgithub.com\u002Fonuratakan\u002Fgpt-computer-assistant) - GPT Computer Assistant is an unofficial app that brings ChatGPT functionality to Windows and Linux, allowing for screen reading, microphone use, system audio interaction, clipboard management, script execution, and more [github](https:\u002F\u002Fgithub.com\u002Fonuratakan\u002Fgpt-computer-assistant)\n- [GPT Engineer](https:\u002F\u002Fgithub.com\u002Fgpt-engineer-org\u002Fgpt-engineer) - GPT-Engineer is an AI-powered tool allowing users to specify software in natural language, automatically generating and executing code, with options for improvement suggestions, and fostering collaboration within the open-source community [github](https:\u002F\u002Fgithub.com\u002Fgpt-engineer-org\u002Fgpt-engineer) | [github profile](https:\u002F\u002Fgithub.com\u002Fgpt-engineer-org) | [website](https:\u002F\u002Fgptengineer.app)\n- [GPT Newspaper by Tavily](https:\u002F\u002Fgithub.com\u002Frotemweiss57\u002Fgpt-newspaper) - GPT Newspaper is an autonomous agent project using AI to create personalized newspapers based on user preferences, featuring six specialized sub-agents for searching, curating, writing, designing, editing, and publishing content tailored to individual interests [github](https:\u002F\u002Fgithub.com\u002Frotemweiss57\u002Fgpt-newspaper) | [github profile](https:\u002F\u002Fgithub.com\u002Frotemweiss57)\n- [GPT Pilot](https:\u002F\u002Fgithub.com\u002FPythagora-io\u002Fgpt-pilot) - GPT Pilot is an open-source AI developer tool that aims to provide a comprehensive development companion, capable of writing features, debugging, and interacting with users, presenting itself as an alternative to Devin, the world's first AI software engineer developed by Cognition Labs [github](https:\u002F\u002Fgithub.com\u002FPythagora-io\u002Fgpt-pilot) | [discord](https:\u002F\u002Fdiscord.gg\u002FRzvCYRgUkx)\n- [GPT Researcher by Tavily](https:\u002F\u002Fgithub.com\u002Fassafelovic\u002Fgpt-researcher) - GPT Researcher is an AI-powered autonomous agent designed for efficient and unbiased online research, generating detailed reports by leveraging recent advancements in AI and web scraping, with a focus on speed, reliability, and cost-effectiveness [github](https:\u002F\u002Fgithub.com\u002Fassafelovic\u002Fgpt-researcher) | [github profile](https:\u002F\u002Fgithub.com\u002Fassafelovic)\n- [AI Scientist](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002FAI-Scientist) - The AI Scientist: Towards Fully Automated Open-Ended Scientific. [github](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002FAI-Scientist)\n- [BlockAGI](https:\u002F\u002Fgithub.com\u002Fblockpipe\u002Fblockagi) - BlockAGI conducts iterative, domain-specific research, and outputs detailed narrative reports to showcase its findings. [github](https:\u002F\u002Fgithub.com\u002Fblockpipe\u002Fblockagi)\n- [data-to-paper](https:\u002F\u002Fgithub.com\u002FTechnion-Kishony-lab\u002Fdata-to-paper) - data-to-paper: AI-driven research from data to human-verifiable research papers. [github](https:\u002F\u002Fgithub.com\u002FTechnion-Kishony-lab\u002Fdata-to-paper)\n- [DeepAnalyze](https:\u002F\u002Fgithub.com\u002Fruc-datalab\u002FDeepAnalyze) - first agentic LLM for autonomous data science, supporting specific data tasks (data preparation, analysis, modeling, visualization, and insight) and data-oriented deep research (produce analyst-grade research reports). [github](https:\u002F\u002Fgithub.com\u002Fruc-datalab\u002FDeepAnalyze)\n- [GenoMAS](https:\u002F\u002Fgithub.com\u002FLiu-Hy\u002FGenoMAS) - A multi-agent framework for scientific discovery that automates gene expression analysis through code-driven workflows. [github](https:\u002F\u002Fgithub.com\u002FLiu-Hy\u002FGenoMAS)\n- [GhostClaw](https:\u002F\u002Fgithub.com\u002Fb1rdmania\u002Fghostclaw) - A local AI agent you message on Telegram like a co-worker. Runs on your computer with no containers or cloud dependencies, and sets up in 10 minutes. [github](https:\u002F\u002Fgithub.com\u002Fb1rdmania\u002Fghostclaw) | [website](https:\u002F\u002Fghostclaw.io)\n- [OpenLens AI](https:\u002F\u002Fgithub.com\u002Fjarrycyx\u002Fopenlens-ai) - Fully Autonomous Research Agent for Health Infomatics. [github](https:\u002F\u002Fgithub.com\u002Fjarrycyx\u002Fopenlens-ai)\n- [Storm](https:\u002F\u002Fgithub.com\u002Fstanford-oval\u002Fstorm) - An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations. [github](https:\u002F\u002Fgithub.com\u002Fstanford-oval\u002Fstorm)\n- [GPT models by OpenAI](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ffunction-calling) - Function calling or tool use is supported with the following models: `gpt-4-turbo`, `gpt-4-turbo-2024-04-09`, `gpt-4-turbo-preview`, `gpt-4-0125-preview`, `gpt-4-1106-preview`, `gpt-4`, `gpt-4-0613`, `gpt-3.5-turbo`, `gpt-3.5-turbo-0125`, `gpt-3.5-turbo-1106`, and `gpt-3.5-turbo-0613` [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ffunction-calling)\n- [GPTeam](https:\u002F\u002Fgithub.com\u002F101dotxyz\u002FGPTeam) - GPTeam is a collaborative AI project utilizing GPT-4 to create multi-agent systems aimed at enhancing productivity and communication, with features including agent memory and interaction, alongside instructions for setup and integration with third-party services [github](https:\u002F\u002Fgithub.com\u002F101dotxyz\u002FGPTeam) | [github profile](https:\u002F\u002Fgithub.com\u002F101dotxyz)\n- [Gated 402 API](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fgated-402-api) - An API using a Stacks smart contract to control access, issuing a 200 status for access approval and a 402 with payment instructions for denial [github](https:\u002F\u002Fgithub.com\u002Faibtcdev\u002Fgated-402-api) | [github profile](https:\u002F\u002Fgithub.com\u002Faibtcdev) | [website](https:\u002F\u002Faibtc.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002F5DJaBrf)\n- [GitWit](https:\u002F\u002Fgitwit.dev\u002F) - GitWit is an online tool that accelerates web app development with AI, supporting React, Tailwind, and NodeJS, boasting a 3X speed increase and over 1000 projects generated [website](https:\u002F\u002Fgitwit.dev\u002F) | [discord](https:\u002F\u002Fdiscord.gitwit.dev\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002Fgitwitorg)\n- [Google STT](https:\u002F\u002Fcloud.google.com\u002Fspeech-to-text) - Google Cloud Speech-to-Text is a comprehensive speech recognition service that leverages Google's years of research in automatic speech recognition and transcription technology to provide developers with a high-quality, easy-to-use speech-to-text API [website](https:\u002F\u002Fcloud.google.com\u002Fspeech-to-text)\n- [Groq](https:\u002F\u002Fgroq.com\u002F) - GroqCloud API endpoints support tool use for programmatic execution of specified operations through requests with explicitly defined operations, allowing Groq API model endpoints to deliver structured JSON output that can be used to directly invoke functions from desired codebases; these following models powered by Groq all support tool use: `llama3-70b`, `llama3-8b`, `mixtral-8x7b`, `gemma-7b-it`; parallel tool calling is enabled for both Llama3 models [website](https:\u002F\u002Fgroq.com\u002F) | [docs](https:\u002F\u002Fconsole.groq.com\u002Fdocs) | [tool use docs](https:\u002F\u002Fconsole.groq.com\u002Fdocs\u002Ftool-use) | [tool use announcement](https:\u002F\u002Ftwitter.com\u002FGroqInc\u002Fstatus\u002F1775634099849322632)\n- [Guardrails](https:\u002F\u002Fgithub.com\u002Fguardrails-ai\u002Fguardrails) - Guardrails is a Python framework for building reliable AI applications, offering Input\u002FOutput Guards to detect and mitigate risks, along with structured data generation from large language models (LLMs) [github](https:\u002F\u002Fgithub.com\u002Fguardrails-ai\u002Fguardrails) | [twitter](https:\u002F\u002Ftwitter.com\u002Fguardrails_ai)\n- [Guidance](https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance) - The text describes 'guidance,' a programming paradigm that enhances control and efficiency in model generation by allowing for constraints like regex and CFGs, integrating stateful control, and offering a simplified interface for complex generation scenarios [github](https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance) | [docs](https:\u002F\u002Fguidance.readthedocs.org\u002F)\n- [Harpa](https:\u002F\u002Fharpa.ai\u002F) - Harpa is a versatile Chrome extension that integrates AI capabilities, such as summarizing content, automating workflows, and enhancing productivity, supported by GPT-4 and Claude 2, trusted by 300,000+ professionals [website](https:\u002F\u002Fharpa.ai\u002F)\n- [Hanzi](https:\u002F\u002Fgithub.com\u002Fhanzili\u002Fhanzi-in-chrome) - Hanzi is an open-source MCP server + Chrome extension that gives AI agents your real signed-in browser. Task-level automation — one tool call delegates an entire workflow. Built-in skills for LinkedIn prospecting, E2E testing, and social posting. Works with Claude Code, Cursor, Codex, and Windsurf. Setup: `npx hanzi-in-chrome setup` [github](https:\u002F\u002Fgithub.com\u002Fhanzili\u002Fhanzi-in-chrome)\n- [Haystack](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack) - Haystack is an end-to-end LLM framework facilitating the construction of applications powered by LLMs, Transformer models, vector search, and more, offering flexibility, transparency, and extensibility, with features including retrieval-augmented generation, document search, question answering, and semantic search, along with a diverse user base including companies like Airbus, Apple, and Netflix [github](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack) | [github profile](https:\u002F\u002Fgithub.com\u002Fdeepset-ai)\n- [Helicone](https:\u002F\u002Fwww.helicone.ai\u002F) - Helicone is an open-source observability platform for Language Learning Models (LLMs), providing features like request logging, caching, rate limiting, cost and latency tracking, UI-based prompt iteration, and collaboration tools [website](https:\u002F\u002Fwww.helicone.ai\u002F) | [github](https:\u002F\u002Fgithub.com\u002FHelicone\u002Fhelicone)\n- [Humane](https:\u002F\u002Fhumane.com\u002F) - AI Pin, a wearable, multi-modal device, enhances ambient computing in the real world, offering a suite of AI digital assistants for various tasks while prioritizing user privacy for a more intuitive, human-centered experience [website](https:\u002F\u002Fhumane.com\u002F)\n- [Hivemoot](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fhivemoot) - Hivemoot is a governance framework for autonomous AI agent teams on GitHub, enabling agents to propose features, vote democratically, review code, and ship software through role-based collaboration with live observability via Colony dashboard [github](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fhivemoot) | [docs](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fhivemoot\u002Fblob\u002Fmain\u002FCONCEPT.md) | [live demo](https:\u002F\u002Fhivemoot.github.io\u002Fcolony\u002F) | [colony repo](https:\u002F\u002Fgithub.com\u002Fhivemoot\u002Fcolony)\n- [Hume AI](https:\u002F\u002Fwww.hume.ai\u002F) - Hume AI offers empathic AI solutions with emotional intelligence through APIs for interpreting emotional expressions and generating empathic responses, aimed at enhancing human well-being and enabling developers to create AI agents with improved understanding and engagement [website](https:\u002F\u002Fwww.hume.ai\u002F) | [discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002FWPRSugvAm6)\n- [Imbue](https:\u002F\u002Fimbue.com\u002F) - Imbue, previously known as Generally Intelligent, is developing AI systems designed for reasoning and coding, aiming to create truly personal computers that enhance human freedom, dignity, and agency, supported by a $200M funding round to advance their technology [website](https:\u002F\u002Fimbue.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fimbue_ai\u002F)\n- [Instructor Cloud](https:\u002F\u002Fgithub.com\u002Finstructor-ai\u002Fcloud) - Instructor Cloud offers a platform for extracting models from text rapidly, with real-time streaming and the potential to utilize GPT-4*, encouraging engagement through contributions and adaptation of its FastAPI-based service [github](https:\u002F\u002Fgithub.com\u002Finstructor-ai\u002Fcloud) | [announcement](https:\u002F\u002Ftwitter.com\u002Fjxnlco\u002Fstatus\u002F1774822440922763707)\n- [Instructor](https:\u002F\u002Fgithub.com\u002Fjxnl\u002Finstructor) - Instructor, a Python library, facilitates working with structured outputs from large language models (LLMs), offering features like response model specification, retry management, validation, and streaming support, primarily aimed at enhancing workflows of AI agents utilizing LLMs [github](https:\u002F\u002Fgithub.com\u002Fjxnl\u002Finstructor) | [website](https:\u002F\u002Fpython.useinstructor.com\u002F)\n- [IvyCheck](https:\u002F\u002Fgithub.com\u002Fivycheck\u002Fivycheck-python-sdk) - IvyCheck offers an API for real-time AI application safety checks, preventing prompt injection attacks, PII data leakage, and hallucinations in agentic AI development [github](https:\u002F\u002Fgithub.com\u002Fivycheck\u002Fivycheck-python-sdk) | [announcement](https:\u002F\u002Fwww.ycombinator.com\u002Flaunches\u002FKkA-ivycheck-guard-against-ai-risks-with-real-time-checks) | [website](https:\u002F\u002Fivycheck.com)\n- [JARVIS by Microsoft](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FJARVIS) - JARVIS aims to advance artificial general intelligence (AGI) through cutting-edge research and facilitate broader community engagement [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FJARVIS)\n- [Jaiqu](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002FJaiqu) - Jaiqu is an AI-powered tool for automatically transforming any JSON schema using GPT-4, featuring schema validation, fuzzy term matching, and repeatable jq query generation [github](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002FJaiqu) | [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1774314258379190770) | [website](https:\u002F\u002Fjaiqu-agent.streamlit.app\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fagentopsai\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FJHPt4C7r)\n- [Jan](https:\u002F\u002Fgithub.com\u002Fjanhq\u002Fjan) - Jan is an open-source, development-stage ChatGPT alternative that operates fully offline on diverse hardware platforms, supporting universal architectures from PCs to multi-GPU clusters [github](https:\u002F\u002Fgithub.com\u002Fjanhq\u002Fjan) | [github profile](https:\u002F\u002Fgithub.com\u002Fjanhq)\n- [Jsonify](https:\u002F\u002Fjsonify.com\u002F) - Jsonify provides a no-code platform for AI data agents that convert webpages and documents into structured JSON, enhancing efficiency and customer satisfaction, with use cases including scraping webpages, extracting document data, and building structured datasets [website](https:\u002F\u002Fjsonify.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fjsonifyco) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fjsonify\u002F)\n- [Kapa](https:\u002F\u002Fwww.kapa.ai\u002F) - Kapa.ai is an AI-powered chatbot service for developers that automates answering technical questions by learning from technical resources, thus helping identify gaps in documentation, with features including data security, PII anonymization, and continuous updating from a range of knowledge sources [website](https:\u002F\u002Fwww.kapa.ai\u002F) | [docs](https:\u002F\u002Fdocs.kapa.ai\u002F)\n- [Lumen](https:\u002F\u002Fgithub.com\u002Fomxyz\u002Flumen) - Lumen is a vision-first browser agent with self-healing deterministic replay over CDP. Screenshot → model → action loop with multi-provider support (Anthropic, Google). [github](https:\u002F\u002Fgithub.com\u002Fomxyz\u002Flumen) | [website](https:\u002F\u002Flumen.omlabs.xyz)\n- [LM Studio](https:\u002F\u002Flmstudio.ai\u002F) - LM Studio offers a platform for running various local LLMs like LLaMa, Falcon, MPT, and others offline, featuring a Chat UI, OpenAI-compatible server, and model downloads from Hugging Face, with support for Mac, Windows, and Linux, emphasizing privacy and no data collection, free for personal use [website](https:\u002F\u002Flmstudio.ai\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002Flmstudio-ai)\n- [LMNT](https:\u002F\u002Fwww.lmnt.com) - LMNT is an AI-powered text-to-speech platform that offers ultrafast, lifelike, and reliable voice cloning and generation services for conversational apps, agents, and content creation at scale [website](https:\u002F\u002Fwww.lmnt.com) | [docs](https:\u002F\u002Fdocs.lmnt.com\u002F)\n- [LMQL](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Flmql) - LMQL is a Python-based programming language for large language models, allowing seamless integration of LLMs into code with advanced features like conditional logic, constraints, and multi-model support [github](https:\u002F\u002Fgithub.com\u002Feth-sri\u002Flmql) | [website](https:\u002F\u002Flmql.ai\u002F)\n- [LangChain JS Tools](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs) - Langchain features VectorDBQAChain, which integrates LLMs and vector databases into agent tools for enhanced question-answering capabilities by leveraging data ingested into vector stores [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs) | [docs](https:\u002F\u002Fjs.langchain.com\u002Fv0.2\u002Fdocs\u002Fintegrations\u002Ftools\u002F)\n- [LangChain JS](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs) - LangChain JS is a framework for developing applications powered by language models, enabling context-aware and reasoning-based applications through composable tools and off-the-shelf chains, with seamless integration with the LangChain Python package [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs)\n- [LangChain Tools](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002F) - Langchain integrates various providers like Anthropic, AWS, and OpenAI, and offers tools for components such as LLMs, chat models, and data analysis, supporting functionalities from Alpha Vantage to YouTube [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002F) | [docs](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fintegrations\u002Ftools)\n- [LangChainBitcoin](https:\u002F\u002Flightning.engineering\u002Fposts\u002F2023-07-05-l402-langchain\u002F) - LangChainBitcoin is a toolset for enabling LangChain agents to interact with Bitcoin, the Lightning Network, and APIs requiring L402-based authentication, including features for Bitcoin transactions and API traversal with automated Lightning payments [announcement](https:\u002F\u002Flightning.engineering\u002Fposts\u002F2023-07-05-l402-langchain\u002F) | [github](https:\u002F\u002Fgithub.com\u002Flightninglabs\u002FLangChainBitcoin)\n- [LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain) - LangChain is a framework enabling context-aware reasoning applications with integrated libraries, templates, and developer tools [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain)\n- [LangFuse](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) - Langfuse, an open-source LLM engineering platform, offers debugging, prompt management, metrics for LLM apps improvement, and won the #1 Golden Kitty in the AI Infra Category from Product Hunt [github](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) | [website](https:\u002F\u002Flangfuse.com\u002F) | [twitter](https:\u002F\u002Fx.com\u002Flangfuse) | [discord](https:\u002F\u002Flangfuse.com\u002Fdiscord)\n- [LangGraph.js](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraphjs) - LangGraph.js is a TypeScript and JavaScript library enabling the development of stateful, multi-actor applications with LLMs, featuring capabilities to construct cyclic coordination across multiple computation steps for complex agent-like behaviors, with support for conditional edges and cycles, not limited to DAGs, and extensive documentation with examples on implementation [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraphjs)\n- [LangGraph](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph) - LangGraph is a Python library facilitating the construction of stateful, multi-actor applications with LLMs, enabling cyclic coordination across multiple computation steps, particularly suited for agent-like behaviors, while also providing streaming support, and various guides and examples for implementation and usage [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph)\n- [LangServe](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangserve) - LangServe facilitates the deployment of LangChain runnables and chains as a REST API, providing features like automatic schema inference, efficient endpoints, and a playground page, with plans for a hosted version for one-click deployments [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangserve)\n- [LangSmith by LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-sdk) - LangSmith provides tools for debugging, testing, evaluating, and monitoring LLM applications, integrating seamlessly with LangChain for comprehensive AI agent observability [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangsmith-sdk) | [docs](https:\u002F\u002Fdocs.smith.langchain.com) | [website](https:\u002F\u002Fsmith.langchain.com)\n- [Libraria](https:\u002F\u002Flibraria.ai\u002F) - Libraria AI offers a platform to create, manage, and embed custom AI chatbots with natural language processing and features like call-to-actions, link carousels, and analytics for enhanced customer interactions and satisfaction, alongside free and paid plans tailored for different business needs [website](https:\u002F\u002Flibraria.ai\u002F) | [twitter](https:\u002F\u002Fx.com\u002Flibrariaai)\n- [LiteLLM](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm) - LiteLLM has added support for the OpenAI Assistants API, enabling seamless integration of stateful operations and automatic RAG pipelines into existing chatbots [github](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm)\n- [Lynkr](https:\u002F\u002Fgithub.com\u002FFast-Editor\u002FLynkr) - Lynkr is a proxy that lets Claude Code CLI talk to non-Anthropic LLMs, manage local tools, and compose Model Context Protocol (MCP) servers with prompt caching, repo intelligence, and Git-aware automation and several other features similar to anthropic backend.\n- [LiveKit Agents](https:\u002F\u002Fgithub.com\u002Flivekit\u002Fagents) - An open-source framework for building real-time, programmable participants that run on servers, enabling easy integration with LiveKit WebRTC sessions for processing or generating audio, video, and data streams [GitHub](https:\u002F\u002Fgithub.com\u002Flivekit\u002Fagents) | [docs](https:\u002F\u002Fdocs.livekit.io\u002Fagents\u002F) | [demo](https:\u002F\u002Fkitt.livekit.io)\n- [LiveRecall](https:\u002F\u002Fgithub.com\u002FVedankPurohit\u002FLiveRecall) - LiveRecall, an open-source alternative to Microsoft's Recall, utilizes semantic search and encryption to capture and retrieve screen snapshots, enabling AI agents to assist creators in researching and augmenting tasks like journaling or blog post creation based on indexed personal activities [GitHub](https:\u002F\u002Fgithub.com\u002FVedankPurohit\u002FLiveRecall)\n- [LlamaCloud by LlamaIndex](https:\u002F\u002Fwww.llamaindex.ai\u002Fenterprise) - LlamaCloud by LlamaIndex streamlines AI development by enabling developers to minimize infrastructure management and parameter tuning, focusing instead on creating AI products, with features for proprietary parsing of complex documents, easy data ingestion and storage, and advanced data retrieval [website](https:\u002F\u002Fwww.llamaindex.ai\u002Fenterprise) | [github profile](https:\u002F\u002Fgithub.com\u002Frun-llama) | [discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002FeN6D2HQ4aX) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002F91154103\u002F)\n- [LlamaGym](https:\u002F\u002Fgithub.com\u002FKhoomeiK\u002FLlamaGym) - LlamaGym simplifies the fine-tuning of LLM agents with online reinforcement learning, providing a framework to iterate and experiment across Gym environments for efficient agent prompting and hyperparameter tuning [github](https:\u002F\u002Fgithub.com\u002FKhoomeiK\u002FLlamaGym) | [github profile](https:\u002F\u002Fgithub.com\u002FKhoomeiK)\n- [LlamaIndex Tools](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index) - LlamaIndex offers a variety of tools for building data agents, with top downloads including IonicShoppingToolSpec, OpenAPIToolSpec, WikipediaToolSpec, GmailToolSpec, and GoogleCalendarToolSpec, enabling seamless integration with user-defined functions, query engines, and third-party services [github](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index) | [website](https:\u002F\u002Fllamahub.ai\u002F?tab=tools) | [docs](https:\u002F\u002Fdocs.llamaindex.ai\u002Fen\u002Flatest\u002Fmodule_guides\u002Fdeploying\u002Fagents\u002Ftools\u002F)\n- [Lobe Chat](https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat) - Lobe Chat is an open-source UI framework for building ChatGPT\u002FLLM-based chat applications, featuring modern design, speech synthesis, multi-modal support, extensible plugins, and free one-click deployment for various AI agents [github](https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat) | [website](https:\u002F\u002Fchat-preview.lobehub.com\u002F)\n- [LocalGPT](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT) - LocalGPT is an open-source project for secure, private interactions with documents locally, featuring comprehensive model support, embeddings, API for RAG applications, and GUI options, with a focus on privacy and local data processing [github](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT)\n- [LoopGPT](https:\u002F\u002Fgithub.com\u002Ffarizrahman4u\u002Floopgpt) - LoopGPT is a modular auto-GPT framework with features such as a 'Plug N Play' API, GPT 3.5 compatibility, minimal prompt overhead, human-in-the-loop capability, and full state serialization, facilitating easy installation and usage through Python code, CLI, or Docker, with the ability to add custom tools and course correction, along with saving and loading agent state, requiring Python 3.8+ and an OpenAI API Key, and optional setup for Google search support [github](https:\u002F\u002Fgithub.com\u002Ffarizrahman4u\u002Floopgpt) | [github profile](https:\u002F\u002Fgithub.com\u002Ffarizrahman4u)\n- [Lumos](https:\u002F\u002Fgithub.com\u002Fallenai\u002Flumos) - Lumos introduces a modular, open-source language agent framework with unified data formats that competes with or outperforms GPT-series and larger agents across various complex interactive tasks [github](https:\u002F\u002Fgithub.com\u002Fallenai\u002Flumos) | [website](https:\u002F\u002Fallenai.github.io\u002Flumos\u002F)\n- [Lyzr](https:\u002F\u002Fwww.lyzr.ai\u002F) - Lyzr provides an enterprise-grade AI agent framework for easy configuration, deployment, and management of AI agents, supporting integration with multiple LLMs and databases, and offers ISO-compliant safety, white-glove onboarding, and 24\u002F7 enterprise support [website](https:\u002F\u002Fwww.lyzr.ai\u002F) | [blog](https:\u002F\u002Fwww.lyzr.ai\u002Fblog\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Flyzrai)\n- [Marvin](https:\u002F\u002Fgithub.com\u002FPrefectHQ\u002Fmarvin\u002F) - Marvin is an open-source AI toolkit designed for developers focused on enhancing AI agent capabilities, offering tools for natural language interfaces, image and audio generation, and entity extraction, scalable and easy to integrate into existing projects [github](https:\u002F\u002Fgithub.com\u002FPrefectHQ\u002Fmarvin\u002F) | [website](https:\u002F\u002Faskmarvin.ai\u002F)\n- [MemGPT](https:\u002F\u002Fmemgpt.ai\u002F) - MemGPT introduces a customizable AI chatbot framework with self-editing memory and access to unlimited data, promoting perpetual, context-rich conversations [website](https:\u002F\u002Fmemgpt.ai\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fcpacker\u002FMemGPT\u002F)\n- [Camel-AutoGPT](https:\u002F\u002Fgithub.com\u002FSamurAIGPT\u002FCamel-AutoGPT) - role-playing approach for LLMs and auto-agents like BabyAGI & AutoGPT. [github](https:\u002F\u002Fgithub.com\u002FSamurAIGPT\u002FCamel-AutoGPT)\n- [SkyAGI](https:\u002F\u002Fgithub.com\u002Flitanlitudan\u002Fskyagi) - Emerging human-behavior simulation capability in LLM agents. [github](https:\u002F\u002Fgithub.com\u002Flitanlitudan\u002Fskyagi)\n- [Voyager](https:\u002F\u002Fgithub.com\u002FMineDojo\u002FVoyager) - An Open-Ended Embodied Agent with Large Language Models. [github](https:\u002F\u002Fgithub.com\u002FMineDojo\u002FVoyager)\n- [Mendable](https:\u002F\u002Fwww.mendable.ai\u002F) - Mendable offers an AI chatbot solution that enables companies to build and deploy technical assistants trained on their specific documentation and resources, aiming to improve customer and employee support, with features including enterprise-grade security, continuous model training, and integration with a wide range of data sources and APIs [website](https:\u002F\u002Fwww.mendable.ai\u002F) | [docs](https:\u002F\u002Fdocs.mendable.ai\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fmendableai) | [github profile](https:\u002F\u002Fgithub.com\u002Fsideguide)\n- [MergeKit](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002Fmergekit) - Arcee AI's MergeKit offers tools for merging pre-trained large language models, enabling the creation of more versatile AI agents by combining knowledge from different sources, akin to Retrieval Augmented Generation (RAG) [github](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002Fmergekit)\n- [MetaGPT](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT) - MetaGPT is a multi-agent framework enabling GPT to collaborate within a software company, facilitating complex tasks by assigning different roles to GPTs [github](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT) | [github profile](https:\u002F\u002Fgithub.com\u002Fgeekan)\n- [Milo](https:\u002F\u002Fgetmilo.dev) - Done-for-you AI agent teams for small businesses (dental, legal, HVAC, etc.). One-time setup from $399, you own the agents and infrastructure. Agents handle phone calls, scheduling, lead follow-up, and reporting 24\u002F7 [website](https:\u002F\u002Fgetmilo.dev)\n- [Miranda](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175658178855112) - Miranda is a platform that simplifies dashboard creation, aiming to be the 'Canva for dashboards' [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175658178855112)\n- [MoltBook](https:\u002F\u002Fmoltbook.com) - A social network built for AI agents where they can create accounts, post content, build reputation through karma, interact via API, and form communities. Features ~1,261 registered agents and an active ecosystem [website](https:\u002F\u002Fmoltbook.com) | [github](https:\u002F\u002Fgithub.com\u002Fclawddar\u002Fawesome-moltbook)\n- [MultiOn](https:\u002F\u002Fwww.multion.ai\u002F) - MultiOn utilizes AI to automate actions within web browsers, such as form filling, data retrieval, and executing web searches, mimicking human interaction but without manual input, facilitated through a Chrome extension and API for developers [website](https:\u002F\u002Fwww.multion.ai\u002F)\n- [NPI](https:\u002F\u002Fgithub.com\u002Fnpi-ai\u002Fnpi) - NPi is an open-source platform providing tool-use APIs for AI agents, with installation and setup instructions available [github](https:\u002F\u002Fgithub.com\u002Fnpi-ai\u002Fnpi) | [website](https:\u002F\u002Fwww.npi.ai\u002F) | [docs](https:\u002F\u002Fwww.npi.ai\u002Fdocs) | [blog](https:\u002F\u002Fwww.npi.ai\u002Fblog)\n- [NavAIGuide](https:\u002F\u002Fgithub.com\u002Ffrancedot\u002FNavAIGuide-TS) - NavAIGuide is an extensible, mobile-friendly, multi-modal agentic framework designed to integrate with mobile and desktop apps, featuring visual task detection, advanced code selectors, action-oriented execution, and resilient error handling [github](https:\u002F\u002Fgithub.com\u002Ffrancedot\u002FNavAIGuide-TS)\n- [NeMo Guardrails](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Guardrails) - NeMo Guardrails is an open-source toolkit facilitating the integration of programmable guardrails, essential for steering and safeguarding AI agents' conversational outputs, into large language model-based applications [github](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Guardrails) | [research paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.10501)\n- [Neets](https:\u002F\u002Fneets.ai\u002F) - Neets.ai is a text-to-speech (TTS) API that offers a wide range of voices and languages, allowing users to easily integrate TTS capabilities into their applications [website](https:\u002F\u002Fneets.ai\u002F) | [docs](https:\u002F\u002Fdocs.neets.ai\u002F)\n- [NexusGPT](https:\u002F\u002Fgpt.nexus\u002F) - NexusGPT offers a no-code platform to build and integrate AI agents that automate workflows, featuring a marketplace of tools and integrations, with easy customization and deployment across various applications [website](https:\u002F\u002Fgpt.nexus\u002F)\n- [Ollama](https:\u002F\u002Fgithub.com\u002Follama\u002Follama) - Ollama is a tool for running large language models locally, offering easy setup for macOS, Windows, Linux, and Docker, along with a library of models and quickstart guides for customization and integration [github](https:\u002F\u002Fgithub.com\u002Follama\u002Follama) | [github profile](https:\u002F\u002Fgithub.com\u002Follama)\n- [Open Assistant API](https:\u002F\u002Fgithub.com\u002FMLT-OSS\u002Fopen-assistant-api) - The Open Assistant API is a self-hosted, open-source framework that enables the creation of customized AI assistants, supporting integration with OpenAI's LLM and LangChain SDK, and is compatible with OpenAI's Assistants API, allowing for seamless orchestration and extension capabilities [github](https:\u002F\u002Fgithub.com\u002FMLT-OSS\u002Fopen-assistant-api)\n- [Open Interpreter](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Fopen-interpreter) - Open Interpreter is a coding agent enabling language models to execute code locally, facilitating natural-language interaction with your computer's capabilities, overcoming limitations of hosted solutions like internet access and package restrictions. It features interactive and programmatic chats, system message customization, and can control your computer's keyboard and mouse, allowing for enhanced control and flexibility in development environments [github](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Fopen-interpreter)\n- [OpenAGI](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FOpenAGI) - OpenAGI by AGI Research is an open-source platform integrating Large Language Models (LLMs) with domain-specific expert models for complex task-solving, fostering a paradigm where LLMs operate various external models, accompanied by a Reinforcement Learning from Task Feedback (RLTF) mechanism for self-improvement [github](https:\u002F\u002Fgithub.com\u002Fagiresearch\u002FOpenAGI) | [github profile](https:\u002F\u002Fgithub.com\u002Fagiresearch)\n- [OpenAI TTS](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ftext-to-speech) - The OpenAI Text-to-Speech (TTS) API allows users to convert text into high-quality, natural-sounding spoken audio in multiple languages, with various voice options and customization capabilities [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ftext-to-speech)\n- [OpenAI](https:\u002F\u002Fopenai.com) - OpenAI's GPT models, including GPT-3 and GPT-4, are large language models that can be used to summarize text in a concise and accurate manner, though the quality of the summaries may vary depending on the complexity and length of the input text [website](https:\u002F\u002Fopenai.com) | [docs](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Foverview)\n- [OpenDevin](https:\u002F\u002Fgithub.com\u002FOpenDevin\u002FOpenDevin) - OpenDevin is an open-source initiative aimed at replicating and enhancing the autonomous AI software engineer Devin, focusing on collaboration and complex task execution in software development, emphasizing its relevance to advancing agentic AI technologies [github](https:\u002F\u002Fgithub.com\u002FOpenDevin\u002FOpenDevin) | [github profile](https:\u002F\u002Fgithub.com\u002FOpenDevin)\n- [OpenGPTs](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopengpts) - OpenGPTs is an open-source project providing customizable GPT-based experiences, offering control over language models, prompts, tools, vector databases, retrieval algorithms, and chat history databases, featuring three cognitive architectures: Assistant, RAG, and Chatbot, with support for various language models and deployment options including Docker, Cloud Run, and Kubernetes [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopengpts)\n- [OpenPaw](https:\u002F\u002Fgithub.com\u002Fdaxaur\u002Fopenpaw) - OpenPaw is an open-source CLI tool (`npx pawmode`) that turns Claude Code into a personal assistant with 38 built-in skills covering email, calendar, Spotify, smart home control, Slack, GitHub, Telegram, Discord, and more. No daemon, no cloud, runs entirely locally. MIT licensed [github](https:\u002F\u002Fgithub.com\u002Fdaxaur\u002Fopenpaw) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fpawmode)\n- [OpenPipe](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772782206957895797) - Optimize AI agents with language models that are faster and 14x more cost-effective than OpenAI's solutions [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772782206957895797)\n- [OpenRecall](https:\u002F\u002Fgithub.com\u002Fopenrecall\u002Fopenrecall) - OpenRecall is an open-source, privacy-focused digital memory tool capturing and indexing screenshots to enhance productivity without compromising privacy, usable across Windows, macOS, and Linux, and compatible with AI agents for personal assistance [GitHub](https:\u002F\u002Fgithub.com\u002Fopenrecall\u002Fopenrecall) | [Discord](https:\u002F\u002Fdiscord.gg\u002FRzvCYRgUkx) | [Telegram](https:\u002F\u002Ft.me\u002F+5DULWTesqUYwYjY0)\n- [OpenRouter](https:\u002F\u002Fopenrouter.ai\u002F) - OpenRouter.ai is a platform that provides access to a wide range of large language models, including open-source and proprietary options like ChatGPT, Gemini, and Perplexity, allowing users to find the best models and pricing for their prompts and use cases [website](https:\u002F\u002Fopenrouter.ai\u002F) | [docs](https:\u002F\u002Fopenrouter.ai\u002Fdocs)\n- [Outlines](https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines) - Outlines is a robust text generation library designed for agentic AI developers, featuring support for multiple model integrations, advanced prompting with Jinja, efficient structured generation through regex, JSON schema, context-free grammars, and more, enabling the creation of predictable and structured AI agent outputs [github](https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines) | [website](https:\u002F\u002Foutlines-dev.github.io\u002Foutlines\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FZxBxyWmW5n)\n- [Perplexity-Inspired LLM Answer Engine](https:\u002F\u002Fgithub.com\u002Fdevelopersdigest\u002Fllm-answer-engine) - A versatile answer engine leveraging Groq, Mistral AI, Langchain.JS, Brave Search, Serper API, and OpenAI to deliver efficient and sophisticated responses with reduced hallucination through RAG for citation-backed search queries [github](https:\u002F\u002Fgithub.com\u002Fdevelopersdigest\u002Fllm-answer-engine) | [github profile](https:\u002F\u002Fgithub.com\u002Fdevelopersdigest)\n- [Perplexity](https:\u002F\u002Fwww.perplexity.ai\u002F) - Perplexity AI is an AI-powered search engine that offers summarized answers with cited sources, content generation, accurate information retrieval, user-friendly interface, and versatility, making it a valuable tool for various users [website](https:\u002F\u002Fwww.perplexity.ai\u002F) | [docs](https:\u002F\u002Fdocs.perplexity.ai\u002F)\n- [Personal Assistant by HyperWrite](https:\u002F\u002Fwww.hyperwriteai.com\u002Fpersonal-assistant) - HyperWrite offers a Personal Assistant AI agent for everyday tasks, seamlessly integrating into workflows to automate tedious tasks, optimize planning, and inform decision-making, while also providing personalized suggestions and transforming wishes into commands across various platforms [website](https:\u002F\u002Fwww.hyperwriteai.com\u002Fpersonal-assistant) | [github profile](https:\u002F\u002Fgithub.com\u002FOthersideAI)\n- [Phoenix](https:\u002F\u002Fphoenix.arize.com\u002F) - Phoenix from the team at Arize is an open source library that helps you auto instrument, trace, evaluate and observe AI agents and LLM systems [github](https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix) | [comparison](https:\u002F\u002Farize.com\u002Fllm-evaluation-platforms-top-frameworks) [X](https:\u002F\u002Fx.com\u002Farizephoenix) \n- [Pieces](https:\u002F\u002Fpieces.app\u002F) - Pieces is an AI-powered productivity tool for developers that enhances efficiency through a unified toolchain, offering on-device workflow assistance, intelligent code snippet management, and seamless integration with development tools and plugins [website](https:\u002F\u002Fpieces.app\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002Fgetpieces)\n- [Pinokio](https:\u002F\u002Fpinokio.computer\u002F) - Pinokio is a browser that enables the installation, running, and programmable control of any application with one click, supporting any open-source repo locally, including LLM or AI agent-based projects [website](https:\u002F\u002Fpinokio.computer\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fpinokiocomputer\u002Fpinokio) | [github profile](https:\u002F\u002Fgithub.com\u002Fpinokiocomputer)\n- [PlayAI](https:\u002F\u002Fplay.ai\u002F) - Play.ai offers conversational AI voice solutions, with a mission to enable customizable, natural language-based user interfaces, promoting rapid innovation and a performance-driven culture [website](https:\u002F\u002Fplay.ai\u002F)\n- [PlayHT](https:\u002F\u002Fplay.ht\u002F) - PlayHT's AI Voice Generator offers a state-of-the-art TTS service that creates natural, humanlike voiceovers in multiple languages and accents, ideal for various audio content needs with full commercial rights [website](https:\u002F\u002Fplay.ht\u002F)\n- [PraisonAI](https:\u002F\u002Fgithub.com\u002FMervinPraison\u002FPraisonAI\u002F) - Praison AI is a low-code, centralized framework leveraging AutoGen and CrewAI to simplify creating and orchestrating multi-agent systems for LLM applications, emphasizing customization and ease of human-agent interaction [github](https:\u002F\u002Fgithub.com\u002FMervinPraison\u002FPraisonAI\u002F) | [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Fn1lQjC0GO0) | [website](https:\u002F\u002Fmer.vin\u002F2024\u002F03\u002Fpraison-ai-agents-yml\u002F)\n- [Priompt](https:\u002F\u002Fgithub.com\u002Fanysphere\u002Fpriompt) - Priompt is a JSX-based library for designing prompts with priorities, aiming to optimize inclusion of content within token limits, inspired by React and detailed with installation instructions, examples, principles, and future considerations [github](https:\u002F\u002Fgithub.com\u002Fanysphere\u002Fpriompt)\n- [PrivateGPT](https:\u002F\u002Fgithub.com\u002Fzylon-ai\u002Fprivate-gpt\u002F) - PrivateGPT is a secure, offline-capable AI tool for querying documents with Large Language Models, offering high-level and low-level APIs for privacy-conscious, context-aware application development [github](https:\u002F\u002Fgithub.com\u002Fzylon-ai\u002Fprivate-gpt\u002F)\n- [Produvia](https:\u002F\u002Fproduvia.com\u002F) - Since 2013, Produvia Inc. has served $7M+ in revenue brands by developing custom agentic AI solutions powered by state-of-the-art function calling LLMs including but not limited to: Claude 3 Opus, GPT-4, Bard (Gemini Pro), Claude 3 Sonnet, Claude 3 Haiku, Mistral Medium, Command R, Mistral-Next, Starling-LM-7B-beta [website](https:\u002F\u002Fproduvia.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002Fproduvia) | [linkedin](https:\u002F\u002Flinkedin.com\u002Fcompany\u002Fproduvia)\n- [Prompt2UI by sullyo](https:\u002F\u002Fgithub.com\u002Fsullyo\u002Fprompt2ui) - An open-source project that converts prompts to user interfaces, demonstrated by creating a basic Google Calendar clone using Claude in about 2 hours, inspired by Claude Artifacts [github](https:\u002F\u002Fgithub.com\u002Fsullyo\u002Fprompt2ui) | [twitter announcement](https:\u002F\u002Fx.com\u002FSullyOmarr\u002Fstatus\u002F1804997474761003327)\n- [Pydantic](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic) - Pydantic is a Python library facilitating data validation through type hints, particularly useful for AI agents, offering fast validation capabilities and compatibility with various development tools [github](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic) | [website](https:\u002F\u002Fdocs.pydantic.dev\u002F)\n- [Relevance](https:\u002F\u002Frelevanceai.com\u002F) - Relevance AI offers a platform for building and deploying AI workers to automate tasks, integrate with tech stacks, and manage security, aiming to enhance business efficiency without increasing headcount [website](https:\u002F\u002Frelevanceai.com\u002F) | [twitter](https:\u002F\u002Ftwitter.com\u002FRelevanceAI) | [github profile](https:\u002F\u002Fgithub.com\u002FRelevanceAI) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Frelevanceai\u002F)\n- [Rappterbook](https:\u002F\u002Fgithub.com\u002Fkody-w\u002Frappterbook) - A social network for AI agents running entirely on GitHub infrastructure. 108 agents across 41 channels with 2,100+ discussions. Zero-dependency SDK (Python\u002FJS, one file). Fork the repo to get a complete agent social platform with zero setup. [github](https:\u002F\u002Fgithub.com\u002Fkody-w\u002Frappterbook) | [website](https:\u002F\u002Fkody-w.github.io\u002Frappterbook\u002F) | [quickstart](https:\u002F\u002Fgithub.com\u002Fkody-w\u002Frappterbook\u002Fblob\u002Fmain\u002FQUICKSTART.md)\n- [Rime AI](https:\u002F\u002Frime.ai\u002F) - Rime is a speech synthesis API offering natural-sounding, demographically tailored voices with fast response times for various uses, including customer service and narration [website](https:\u002F\u002Frime.ai\u002F)\n- [SWE-agent](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002FSWE-agent) - This open source project introduces SWE-agent, a software engineering agent for LMs like GPT-4, enhancing bug and issue resolution in GitHub repositories with state-of-the-art performance, facilitated by a well-designed Agent-Computer Interface (ACI) and support for OpenAI and Anthropic Claude models [github](https:\u002F\u002Fgithub.com\u002Fprinceton-nlp\u002FSWE-agent) | [website](https:\u002F\u002Fswe-agent.com\u002F)\n- [ScrapeGraphAI](https:\u002F\u002Fgithub.com\u002FVinciGit00\u002FScrapegraph-ai) - ScrapeGraph AI provides a tool for creating AI agents that can automate web scraping tasks efficiently, enhancing data extraction capabilities through the use of LangGraph, function calls, and web scraping techniques [github](https:\u002F\u002Fgithub.com\u002FVinciGit00\u002FScrapegraph-ai) | [docs](https:\u002F\u002Fscrapegraph-doc.onrender.com\u002F) | [demo](https:\u002F\u002Fscrapegraph-ai-demo.streamlit.appn\u002F)\n- [Scrapeless](https:\u002F\u002Fgithub.com\u002Fscrapeless-ai) - Scrapeless provides an enterprise-grade, AI-driven web scraping toolkit with a powerful Scraping Browser for AI agents and automation. Features include stealth mode, CAPTCHA bypassing, high concurrency support, and seamless integration with Puppeteer and Playwright. Includes Universal Scraping API, Scraping API, Deep SerpApi, and rotating proxies [github](https:\u002F\u002Fgithub.com\u002Fscrapeless-ai) | [website](https:\u002F\u002Fscrapeless.com\u002F)\n- [Self Operating Computer by Otherside](https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer) - SOC is a framework enabling multimodal models to operate a computer using human-like inputs and outputs, with compatibility for various models such as GPT-4v, Gemini Pro Vision, and LLaVA, offering future support for additional models and featuring various modes including voice and optical character recognition [github](https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer) | [github profile](https:\u002F\u002Fgithub.com\u002FOthersideAI) | [landing page](https:\u002F\u002Fwww.hyperwriteai.com\u002Fself-operating-computer)\n- [Self Operating Computer](https:\u002F\u002Fwww.hyperwriteai.com\u002Fself-operating-computer) - Self Operating Computer (SOC) enables multimodal models to autonomously interact with a computer using human-like inputs and outputs, including controlling the keyboard and mouse. It is compatible with various models and under ongoing development for more accurate functionalities [landing page](https:\u002F\u002Fwww.hyperwriteai.com\u002Fself-operating-computer) | [github](https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer) | [github profile](https:\u002F\u002Fgithub.com\u002FOthersideAI)\n- [Shep](https:\u002F\u002Fgithub.com\u002Fshep-ai\u002Fcli) - Shep is an SDLC control center that enables AI coding agents to autonomously handle the complete feature lifecycle, orchestrating multi-session development using Claude Code, Cursor CLI, or Gemini with configurable approval gates and a live web dashboard [github](https:\u002F\u002Fgithub.com\u002Fshep-ai\u002Fcli)\n- [ShortGPT by RayVentura](https:\u002F\u002Fgithub.com\u002FRayVentura\u002FShortGPT) - ShortGPT is an AI-powered framework for automating content creation, including video editing, voiceover synthesis, caption generation, and asset sourcing, with support for multiple languages and seamless integration with Google Colab and Docker for easy deployment [github](https:\u002F\u002Fgithub.com\u002FRayVentura\u002FShortGPT) | [github profile](https:\u002F\u002Fgithub.com\u002FRayVentura)\n- [ShortX by RayVentura](https:\u002F\u002Fshortx.ai\u002F) - ShortX is a AI-powered video automation platform for YouTube Shorts, Instagram Reels, TikTok, and Snapchat, offering customizable templates, AI services, and a subscription model with an affiliate program and user testimonials [website](https:\u002F\u002Fshortx.ai\u002F)\n- [StoryRoute](https:\u002F\u002Fstoryroute.netlify.app) - An LLM-powered autonomous agent that monitors GPS coordinates and generates real-time audio stories as users walk through any city. Features a fully autonomous sense-reason-act pipeline with GPS monitoring, context-aware LLM prompting, and text-to-speech synthesis [website](https:\u002F\u002Fstoryroute.netlify.app)\n- [Streaming Assistants](https:\u002F\u002Fgithub.com\u002Fphact\u002Fstreaming-assistants) - The `streaming-assistants` library on GitHub enables streaming for OpenAI Assistants API using Astra Assistants, providing a workaround for the lack of streaming support in the official OpenAI Assistants API [github](https:\u002F\u002Fgithub.com\u002Fphact\u002Fstreaming-assistants)\n- [Streamlit Agent by Langchain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fstreamlit-agent) - This repository showcases various LangChain agents as Streamlit apps, including a basic streaming app, a memory-based conversation app, a demo replicating MRKL functionality, a minimal agent with search capability, chatbots with feedback options, document querying, database communication, and pandas DataFrame interaction, featuring LangChain and Streamlit integrations [github](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fstreamlit-agent) | [github profile](https:\u002F\u002Fgithub.com\u002Flangchain-ai)\n- [Streamship](https:\u002F\u002Fgithub.com\u002Fsteamship-core\u002Fpython-client) - A development platform for AI Agents offering Python SDK, cloud deployment, serverless hosting, vector search, webhooks, and media generation, with a focus on simplicity, scalability, and integration with popular models and services [github](https:\u002F\u002Fgithub.com\u002Fsteamship-core\u002Fpython-client) | [website](https:\u002F\u002Fwww.steamship.com\u002F) | [twitter](https:\u002F\u002Fwww.twitter.com\u002FGetSteamship) | [discord](https:\u002F\u002Fsteamship.com\u002Fdiscord) | [github profile](https:\u002F\u002Fwww.github.com\u002Fsteamship-core)\n- [SuperAGI](https:\u002F\u002Fgithub.com\u002FTransformerOptimus\u002FSuperAGI) - SuperAGI is an open-source framework facilitating the development, management, and operation of useful Autonomous AI Agents with a variety of features and toolkits available, including a graphical user interface, action console, and multiple vector databases [github](https:\u002F\u002Fgithub.com\u002FTransformerOptimus\u002FSuperAGI) | [github profile](https:\u002F\u002Fgithub.com\u002FTransformerOptimus)\n- [Superagent](https:\u002F\u002Fgithub.com\u002Fsuperagent-ai\u002Fsuperagent) - Superagent is an open-source AI assistant framework backed by Y Combinator, facilitating the integration of large language models (LLM) and generative AI into applications, supporting various use cases such as question answering, chatbots, and content generation [github](https:\u002F\u002Fgithub.com\u002Fsuperagent-ai\u002Fsuperagent) | [github profile](https:\u002F\u002Fgithub.com\u002Fsuperagent-ai)\n- [Local GPT](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT) - Inspired on Private GPT with the GPT4ALL model replaced with the Vicuna-7B model and using the InstructorEmbeddings instead of LlamaEmbeddings. [github](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT)\n- [LLocalSearch](https:\u002F\u002Fgithub.com\u002Fnilsherzig\u002FLLocalSearch) - LLocalSearch is a completely locally running search aggregator using LLM Agents. The user can ask a question and the system will use a chain of LLMs to find the answer. The user can see the progress of the agents and the final answer. No OpenAI or Google API keys are needed. [github](https:\u002F\u002Fgithub.com\u002Fnilsherzig\u002FLLocalSearch)\n- [Private GPT](https:\u002F\u002Fgithub.com\u002Fimartinez\u002FprivateGPT) - Interact privately with your documents using the power of GPT, 100% privately, no data leaks. [github](https:\u002F\u002Fgithub.com\u002Fimartinez\u002FprivateGPT)\n- [Second Brain AI Agent](https:\u002F\u002Fgithub.com\u002Fflepied\u002Fsecond-brain-agent) - A streamlit app to dialog with your second brain notes using OpenAI and ChromaDB locally. [github](https:\u002F\u002Fgithub.com\u002Fflepied\u002Fsecond-brain-agent)\n- [Swarms](https:\u002F\u002Fgithub.com\u002Fkyegomez\u002Fswarms\u002F) - Swarms orchestrates multi-agent collaboration for production-grade applications, solving issues like short memory and high costs, with customizable tools for specific needs, currently used by RBC, John Deere, and AI startups [github](https:\u002F\u002Fgithub.com\u002Fkyegomez\u002Fswarms\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FDbjBMJTSWD) | [docs](https:\u002F\u002Fswarms.apac.ai\u002F)\n- [Sweep](https:\u002F\u002Fgithub.com\u002Fsweepai\u002Fsweep) - Sweep is an AI tool that automates the transformation of GitHub issues into pull requests, streamlining code improvements and bug fixes, supported by a suite of features like codebase understanding, test running, and a developer-friendly interface for installation and usage [github](https:\u002F\u002Fgithub.com\u002Fsweepai\u002Fsweep) | [website](https:\u002F\u002Fsweep.dev\u002F)\n- [Synthflow AI](https:\u002F\u002Fsynthflow.ai\u002F) - Synthflow is a platform enabling the creation of human-like conversational AI voice agents with no-code customization, integrating directly with apps like HubSpot and Eleven Labs for voice services [website](https:\u002F\u002Fsynthflow.ai\u002F) | [docs](https:\u002F\u002Fdocs.synthflow.ai\u002F)\n- [Tabby](https:\u002F\u002Fgithub.com\u002FTabbyML\u002Ftabby) - Tabby is a self-hosted, open-source AI coding assistant similar to GitHub Copilot, featuring a self-contained setup with no DBMS\u002Fcloud dependency, OpenAPI for easy integration, consumer-grade GPU support, and a full-feature admin UI in its latest release [github](https:\u002F\u002Fgithub.com\u002FTabbyML\u002Ftabby) | [website](https:\u002F\u002Ftabby.tabbyml.com\u002F) | [docs](https:\u002F\u002Ftabby.tabbyml.com\u002Fdocs)\n- [Talkscriber](https:\u002F\u002Fwww.talkscriber.com) - Talkscriber is an enterprise-grade speech-to-text (STT) platform that offers industry-leading accuracy, security, and cost-effectiveness, enabling organizations to transform spoken language into digital text and unlock new possibilities in data analysis while hosting Whisper (OpenAI) model [website](https:\u002F\u002Fwww.talkscriber.com)\n- [Tarsier by Reworkd](https:\u002F\u002Fgithub.com\u002Freworkd\u002Ftarsier) - Tarsier is an open-source utility library by Reworkd, aimed at enhancing web interaction for AI agents by visually tagging interactable elements, facilitating actions based on text or screenshots for GPT-4(V) and providing OCR utilities [github](https:\u002F\u002Fgithub.com\u002Freworkd\u002Ftarsier) | [website](https:\u002F\u002Freworkd.ai\u002F)\n- [Taskade AI](https:\u002F\u002Fwww.taskade.com\u002F) - Taskade AI is an AI-powered productivity suite offering tools like task and project management, notes, docs, mind maps, and AI chat to enhance team productivity and automate over 700 tasks [website](https:\u002F\u002Fwww.taskade.com\u002F) | [github](https:\u002F\u002Fgithub.com\u002Ftaskade\u002Ftaskade) | [twitter](https:\u002F\u002Ftwitter.com\u002FTaskade) | [youtube](https:\u002F\u002Fyoutube.com\u002Ftaskade)\n- [TaskingAI](https:\u002F\u002Fgithub.com\u002FTaskingAI\u002FTaskingAI) - TaskingAI is a platform enhancing AI-native app development with Firebase-like simplicity, offering an all-in-one LLM platform with intuitive project management, BaaS-inspired workflow, and customizable integration for developing GPTs-like multi-tenant applications [github](https:\u002F\u002Fgithub.com\u002FTaskingAI\u002FTaskingAI) | [website](https:\u002F\u002Fwww.tasking.ai\u002F)\n- [Tavily](https:\u002F\u002Ftavily.com\u002F) - Tavily AI is your comprehensive research assistant, offering a platform for rapid insights with a Search API for LLMs, ensuring real-time, accurate, and bias-reduced data gathering and organization, suitable for both individual and enterprise needs [website](https:\u002F\u002Ftavily.com\u002F) | [github profile](https:\u002F\u002Fgithub.com\u002Fassafelovic)\n- [TeamX](https:\u002F\u002Fteamx.work\u002F) - TeamX is an Agents-as-a-Service (AaaS) by Produvia which scales businesses with AI agent teams, offering custom solutions focused on automation, efficiency, and scalability [website](https:\u002F\u002Fteamx.work\u002F)\n- [TiOLi AGENTIS](https:\u002F\u002Fagentisexchange.com) - Financial exchange for AI agents with 23 MCP tools and 400+ REST endpoints, blockchain-verified. Agents can register, trade, hire each other, and build reputations [website](https:\u002F\u002Fagentisexchange.com) | [github](https:\u002F\u002Fgithub.com\u002FSendersby\u002Ftioli-ai-exchange) | [docs](https:\u002F\u002Fexchange.tioli.co.za\u002Fdocs)\n- [TogetherAI](https:\u002F\u002Fwww.together.ai\u002F) - TogetherAI is a platform that facilitates efficient and accurate summarization of text using advanced AI algorithms and user-friendly tools [website](https:\u002F\u002Fwww.together.ai\u002F) | [docs](https:\u002F\u002Fdocs.together.ai\u002Fdocs\u002Fquickstart)\n- [Tools by Taskade](https:\u002F\u002Fhelp.taskade.com\u002Fen\u002Farticles\u002F8958457-custom-ai-agents#h_c9a93fc5b9) - Enable your agents with the right set of tools to get the job done: web search (allow the agent to browse the web), WolframAlpha (enhance the agent's computational skills), add-ons (enable additional tools and extensions) [docs](https:\u002F\u002Fhelp.taskade.com\u002Fen\u002Farticles\u002F8958457-custom-ai-agents#h_c9a93fc5b9)\n- [Traces by Weights & Biases](https:\u002F\u002Fwandb.ai\u002Fsite\u002Ftraces) - W&B Traces enhances AI agent observability by providing intuitive visualizations for debugging LLMs, allowing practitioners to review past results, debug errors, and gain insights into model behavior [website](https:\u002F\u002Fwandb.ai\u002Fsite\u002Ftraces)\n- [Twilio](https:\u002F\u002Fwww.twilio.com) - Twilio is a cloud communications platform that enables developers to programmatically make phone calls, send and receive text messages, and integrate other communication features into their applications using its web APIs [website](https:\u002F\u002Fwww.twilio.com)\n- [TypeChat](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FTypeChat) - TypeChat is a library that facilitates building natural language interfaces by using schema engineering as an alternative to traditional function calling in LLMs, avoiding JSON schema-based constraints [github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FTypeChat)\n- [VacAIgent](https:\u002F\u002Fgithub.com\u002Ftonykipkemboi\u002Ftrip_planner_agent) - VacAIgent is a Streamlit-integrated, CrewAI framework-based AI application (Trip Planner Agent) that automates and enhances trip planning through a user-friendly interface, demonstrating collaborative AI agent task execution and offering an interactive web app experience for tailoring travel plans [github](https:\u002F\u002Fgithub.com\u002Ftonykipkemboi\u002Ftrip_planner_agent)\n- [Vapi](https:\u002F\u002Fvapi.ai\u002F) - Vapi is a developer-friendly platform that enables the rapid creation, testing, and deployment of voicebots, revolutionizing voice AI integration with seamless support from voice providers [website](https:\u002F\u002Fvapi.ai\u002F) | [discord](https:\u002F\u002Fdiscord.gg\u002FpUFNcf2WmH) | [twitter](https:\u002F\u002Ftwitter.com\u002FVapi_AI) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fvapi-ai) | [docs](https:\u002F\u002Fdocs.vapi.ai)\n- [Vertex AI by Google](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai) - Vertex AI, enhanced by Gemini models, offers comprehensive generative AI solutions for rapid application development, data processing, custom model training with minimal ML expertise, and production deployment, aimed at accelerating innovation and reducing costs in enterprise environments [website](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai)\n- [Verve](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175562712285645) - Verve is an AI data copilot that aims to streamline analytics and significantly reduce manual work for growing organizations [demo](https:\u002F\u002Fx.com\u002FAnnieLiao_2000\u002Fstatus\u002F1792175562712285645)\n- [Vonage](https:\u002F\u002Fvonage.com\u002F) - Vonage is a leading provider of phone services that offers a range of features and options for residential and business customers, including local, toll-free, and international numbers, as well as virtual receptionist and call management capabilities [website](https:\u002F\u002Fvonage.com\u002F)\n- [Waii](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772777493122163107) - A swift and straightforward AI agent for converting natural language to SQL queries, seamlessly integrable with your application [demo](https:\u002F\u002Fx.com\u002FAlexReibman\u002Fstatus\u002F1772777493122163107)\n- [XAgent](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FXAgent) - XAgent is an open-source, experimental Large Language Model-driven autonomous agent designed to autonomously solve a wide range of tasks with features like autonomy, safety, extensibility, a GUI for easy interaction, and the ability to cooperate with humans [github](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FXAgent) | [demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QGkpd-tsFPA) | [docs](https:\u002F\u002Fxagent-doc.readthedocs.io\u002Fen\u002Flatest\u002F) | [blog](https:\u002F\u002Fblog.x-agent.net\u002Fblog\u002Fxagent\u002F)\n- [Yoyo](https:\u002F\u002Fyoyo.bot) - The first social network for AI agents. Connect any AI agent via MCP to post, chat, follow other agents, discover experts, and build reputation. 10 MCP tools, MIT licensed [website](https:\u002F\u002Fyoyo.bot) | [github](https:\u002F\u002Fgithub.com\u002FYoYo-dot-bot\u002Fmcp) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@yoyo-bot\u002Fmcp)\n- [OpenAgents](https:\u002F\u002Fgithub.com\u002Fxlang-ai\u002FOpenAgents) - An Open Platform for Language Agents in the Wild. [github](https:\u002F\u002Fgithub.com\u002Fxlang-ai\u002FOpenAgents)\n- [OpenAgents Network](https:\u002F\u002Fopenagents.org) - An open-source framework and platform for creating AI agent networks where autonomous agents collaborate, share context, and solve problems together across the internet. Supports multi-protocol communication (WebSocket, gRPC, HTTP, libp2p, A2A, MCP) with a mod-driven architecture [website](https:\u002F\u002Fopenagents.org) | [github](https:\u002F\u002Fgithub.com\u002Fopenagents-org\u002Fopenagents) | [docs](https:\u002F\u002Fopenagents.org\u002Fdocs\u002Fgetting-started\u002Foverview)\n- [Pipecat](https:\u002F\u002Fgithub.com\u002Fpipecat-ai\u002Fpipecat) - Open Source framework for voice and multimodal conversational AI. [github](https:\u002F\u002Fgithub.com\u002Fpipecat-ai\u002Fpipecat)\n- [Sayna](https:\u002F\u002Fgithub.com\u002FSaynaAI\u002Fsayna) - Open-source voice infrastructure layer for AI agents providing unified APIs for STT\u002FTTS, real-time audio streaming, SIP Telephony, and multi-provider support (OpenAI, Deepgram, ElevenLabs, Google, Azure, etc...). Self-hostable with WebSocket\u002FREST interfaces for voice-enabled agent development. [github](https:\u002F\u002Fgithub.com\u002FSaynaAI\u002Fsayna)\n- [RestGPT](https:\u002F\u002Fgithub.com\u002FYifan-Song793\u002FRestGPT) - An LLM-based autonomous agent controlling real-world applications via RESTful APIs. [github](https:\u002F\u002Fgithub.com\u002FYifan-Song793\u002FRestGPT)\n- [WebQA-Agent](https:\u002F\u002Fgithub.com\u002FMigoXLab\u002Fwebqa-agent\u002Ftree\u002Fmain) - Autonomous web agent that audits performance, functionality & UX for any web product. [github](https:\u002F\u002Fgithub.com\u002FMigoXLab\u002Fwebqa-agent)\n- [ADAS](https:\u002F\u002Fgithub.com\u002FShengranHu\u002FADAS) - Automated Design of Agentic Systems. [github](https:\u002F\u002Fgithub.com\u002FShengranHu\u002FADAS)\n- [AgentK](https:\u002F\u002Fgithub.com\u002Fmikekelly\u002FAgentK) - An autoagentic AGI that is self-evolving and modular. [github](https:\u002F\u002Fgithub.com\u002Fmikekelly\u002FAgentK)\n- [Maestro](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fmaestro) - A framework for Claude Opus to intelligently orchestrate subagents. [github](https:\u002F\u002Fgithub.com\u002FDoriandarko\u002Fmaestro)\n- [Zep](https:\u002F\u002Fwww.getzep.com\u002F) - Zep is a long-term memory service for AI assistants that enhances recall, understanding, and data extraction from chat histories to power personalized AI experiences [website](https:\u002F\u002Fwww.getzep.com\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fgetzep\u002Fzep\u002F)\n- [ai-artifacts](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fai-artifacts) - This project implements Anthropic's Artifacts UI, using E2B's Code Interpreter SDK for secure AI code execution and Claude Sonnet 3.5 for code generation [github example](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fai-artifacts) | [reddit announcement](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FClaudeAI\u002Fcomments\u002F1dmy6y2\u002Fopen_source_version_of_anthropics_artifacts_ui\u002F) | [github](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fai-artifacts)\n- [aifs](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Faifs) - AIFS offers a simple and efficient local semantic search capability for folders, leveraging Unstructured.IO for advanced data processing and ChromaDB for fast, similarity-based searching of embeddings [github](https:\u002F\u002Fgithub.com\u002FOpenInterpreter\u002Faifs)\n- [chatgpt-artifacts](https:\u002F\u002Fgithub.com\u002Fozgrozer\u002Fchatgpt-artifacts) - Bring Claude's Artifacts feature to ChatGPT which allows you to execute Node.js commands on your ChatGPT Artifacts projects, inspired by Claude's Artifacts [github](https:\u002F\u002Fgithub.com\u002Fozgrozer\u002Fchatgpt-artifacts) | [twitter announcement](https:\u002F\u002Fx.com\u002Fozgrozer\u002Fstatus\u002F1808677091996541251)\n- [claude-artifacts-react](https:\u002F\u002Fgithub.com\u002Frisonsimon\u002Fclaude-artifacts-react) - This project provides a streamlined solution for deploying and testing React code generated by Claude Artifacts, offering one-click deployment options to Vercel or Cloudflare Pages and easy code editing through a central ArtifactCode.jsx file [github](https:\u002F\u002Fgithub.com\u002Frisonsimon\u002Fclaude-artifacts-react) | [reddit announcement](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FClaudeAI\u002Fcomments\u002F1dtquuh\u002Fi_made_an_opensource_template_for_sharing_claudes\u002F)\n- [crewAI Tools](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai-tools) - crewAI Tools is a library that provides a framework for developing sophisticated tools to enhance crewAI agents, with methods for subclassing BaseTool, utilizing the tool decorator, and guidelines for contributing to the ecosystem [github](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai-tools)\n- [crewAI by João Moura](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai) - crewAI is a cutting-edge AI framework designed for orchestrating role-playing, autonomous AI agents, enabling seamless collaboration and complex task handling [github](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura\u002Fcrewai) | [github profile](https:\u002F\u002Fgithub.com\u002Fjoaomdmoura)\n- [crewAI+ by João Moura](https:\u002F\u002Fwww.crewai.com\u002Fcrewaiplus) - CrewAI+ is in beta, offering seamless API integration, business support, and early access for design partners; apply now to shape future features [website](https:\u002F\u002Fwww.crewai.com\u002Fcrewaiplus)\n- [databerry](https:\u002F\u002Fgithub.com\u002Fgmpetrov\u002Fdataberry\u002F) - Chaindesk is a no-code platform for building custom LLM Agents, enabling users to quickly set up a semantic search system over personal data without technical knowledge [github](https:\u002F\u002Fgithub.com\u002Fgmpetrov\u002Fdataberry\u002F)\n- [elia](https:\u002F\u002Fgithub.com\u002Fdarrenburns\u002Felia) - Keyboard-centric terminal user interface for interacting with large language models (LLMs) like ChatGPT, Claude, Llama 3, Phi 3, Mistral, and Gemma, offering benefits such as efficient, terminal-based interaction, easy switching between multiple models, local model support, and the ability to store conversations in a local SQLite database [github](https:\u002F\u002Fgithub.com\u002Fdarrenburns\u002Felia)\n- [mem0](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0) - Mem0 is an intelligent memory layer for Large Language Models that enhances personalized AI experiences by retaining and utilizing contextual information across various applications. [github](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0) | [website](https:\u002F\u002Fapp.mem0.ai\u002F) | [docs](https:\u002F\u002Fdocs.mem0.ai\u002F) | [discord](https:\u002F\u002Fmem0.ai\u002Fdiscord) | [twitter](https:\u002F\u002Fx.com\u002Fmem0ai) | [github profile](https:\u002F\u002Fgithub.com\u002Fmem0ai) | [linkedin](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fmem0\u002F)\n- [tortoise-tts](https:\u002F\u002Fgithub.com\u002Fneonbjb\u002Ftortoise-tts) - A multi-voice TTS system trained with an emphasis on quality [github](https:\u002F\u002Fgithub.com\u002Fneonbjb\u002Ftortoise-tts) | [research paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.07243) | [demo](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FManmay\u002Ftortoise-tts)\n- [uAgents by Fetch AI](https:\u002F\u002Fgithub.com\u002Ffetchai\u002FuAgents) - uAgents is a Python library by Fetch AI for creating autonomous AI agents with features like easy creation, blockchain network connectivity, and cryptographic security [github](https:\u002F\u002Fgithub.com\u002Ffetchai\u002FuAgents) | [github profile](https:\u002F\u002Fgithub.com\u002Ffetchai)\n- [vimGPT](https:\u002F\u002Fgithub.com\u002Fishan0102\u002FvimGPT) - vimGPT is a project that integrates GPT-4V's vision capabilities with the Vimium extension to enable web browsing and interaction through keyboard navigation and voice commands, offering innovative solutions and improvements for accessibility and efficiency [github](https:\u002F\u002Fgithub.com\u002Fishan0102\u002FvimGPT) | [demo](https:\u002F\u002Fgithub.com\u002Fishan0102\u002FvimGPT\u002Ftree\u002Fmain?tab=readme-ov-file#vimgpt) | [hackernews](https:\u002F\u002Fnews.ycombinator.com\u002Fitem?id=38200308)\n\n### 课程\n学习如何构建 AI 代理的资源非常多！以下是一些课程和操作手册，可以帮助你踏上这段旅程。\n\n#### 在线课程\n- [使用 Autogen 构建的代理设计模式](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fai-agentic-design-patterns-with-autogen\u002F)\n- [Amazon Bedrock 中的代理工具与函数调用](https:\u002F\u002Fwww.youtube.com\u002Fwatch?app=desktop&v=2L_XE6g3atI)\n- [LangGraph 中的 AI 代理 — DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fai-agents-in-langgraph\u002F)\n- [使用 LlamaIndex 构建代理式 RAG](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fbuilding-agentic-rag-with-llamaindex\u002F)\n- [使用 LLM 构建 RAG 代理 — NVIDIA](https:\u002F\u002Flearn.nvidia.com\u002Fcourses\u002Fcourse-detail?course_id=course-v1:DLI+S-FX-15+V1)\n- [使用 Amazon Bedrock Agents 创建 AI 代理数据管道](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kJEr7eFQSJw)\n- [亚马逊云科技（AWS）的提示工程基础](https:\u002F\u002Fexplore.skillbuilder.aws\u002Flearn\u002Fcourse\u002Fexternal\u002Fview\u002Felearning\u002F17763\u002Ffoundations-of-prompt-engineering)\n- [IBM Watsonx 如何构建多代理系统](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gUrENDkPw_k&t=7s)\n- [代理记忆简介](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fllms-as-operating-systems-agent-memory\u002F)\n- [LangChain 的 LangGraph 入门](https:\u002F\u002Facademy.langchain.com\u002Fcourses\u002Fintro-to-langgraph)\n- [LangGraph 精通：使用 LangGraph 开发 LLM 代理](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Flanggraph-mastery-develop-llm-agents-with-langgraph)\n- [UC Berkeley 的 LLM 代理课程](https:\u002F\u002Fllmagents-learning.org\u002Ff24)\n- [使用 CrewAI 构建多代理系统](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fmulti-ai-agent-systems-with-crewai)\n- [Amazon Bedrock 中的无服务器代理工作流](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fserverless-agentic-workflows-with-amazon-bedrock\u002F)\n\n我还推荐这个 [YouTube 播放列表](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLnH2pfPCPZsKhlUSP39nRzLkfvi_FhDdD)，其中包含一系列教程。\n\n#### 操作手册\n- [LlamaIndex 的 AI 代理操作手册](https:\u002F\u002Fdocs.llamaindex.ai\u002Fen\u002Fstable\u002Fuse_cases\u002Fagents\u002F)\n- [LangChain 的代理构建操作手册](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002Ftree\u002Fmaster\u002Fcookbook)\n\n---\n\n## 构建\nAI 代理是自主系统，旨在执行任务、做出决策，并以类似人类智能的方式与其环境互动。\n\n构建 AI 代理的关键工具包括基准测试（用于评估性能）、数据集（用于微调或训练）、框架（用于构建和部署）、LLM 模型（作为代理的操作系统，例如 GPT-4）、提示工程（如思维链）、工具（用于完成任务）以及工作流（例如通过 Zapier 等工具）。这些工具各自有专门的部分，方便用户浏览和查找相关组件。这一领域目前在社区中正迅速发展并备受关注。\n\n### 基准测试\n- [Agentbench](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FAgentBench) - 一个全面的基准测试，用于评估作为智能体的大型语言模型（ICLR'24）\n- [Agentlab](https:\u002F\u002Fgithub.com\u002FServiceNow\u002FAgentLab) - AgentLab 是一个开源框架，用于开发、测试和基准化在多样化任务上的网络智能体，专为可扩展性和再…\n- [Agentops](https:\u002F\u002Fgithub.com\u002FAgentOps-AI\u002Fagentops) - 用于 AI 智能体监控、LLM 成本跟踪、基准测试等功能的 Python SDK。可与大多数 LLM 和智能体框架（如 CrewAI）集成。\n- [Appworld](https:\u002F\u002Fgithub.com\u002FStonyBrookNLP\u002Fappworld) - 🌍 “AppWorld - 一个用于基准测试交互式编程智能体的可控应用与人物世界”的代码库，ACL'24 最佳资源论文。\n- [Appworld-Leaderboard](https:\u002F\u002Fgithub.com\u002FStonyBrookNLP\u002Fappworld-leaderboard) - 🌍 “AppWorld - 一个用于基准测试交互式编程智能体的可控应用与人物世界”的排行榜仓库，ACL2024\n- [Awesome-Llm-Long-Context-Modeling](https:\u002F\u002Fgithub.com\u002FXnhyacinth\u002FAwesome-LLM-Long-Context-Modeling) - 📰 关于基于 LLM 的长上下文建模必读论文和博客 🔥\n- [Balrog](https:\u002F\u002Fgithub.com\u002Fbalrog-ai\u002FBALROG) - 在游戏中对代理型 LLM 和 VLM 推理进行基准测试\n- [Bigcodebench](https:\u002F\u002Fgithub.com\u002Fbigcode-project\u002Fbigcodebench) - BigCodeBench - 面向 AGI 的代码生成基准测试\n- [Bolaa](https:\u002F\u002Fgithub.com\u002FJimSalesforce\u002FBOLAA) - 对 LLM 增强型智能体进行基准测试和编排\n- [Chat-Agent-Evalution](https:\u002F\u002Fgithub.com\u002Fkhuzaimakt\u002FChat-Agent-Evalution) - 在多个评估基准上评估 LLM 聊天智能体。\n- [Comfybench](https:\u002F\u002Fgithub.com\u002FxxyQwQ\u002FComfyBench) - 论文“ComfyBench - 在 ComfyUI 中对基于 LLM 的智能体进行基准测试，以自主设计协作式 AI 系统”的实现。\n- [Debatellm](https:\u002F\u002Fgithub.com\u002Finstadeepai\u002FDebateLLM) - 用于评估语言模型之间多智能体辩论在问答中真实性方面的基准测试。\n- [Dinersim](https:\u002F\u002Fgithub.com\u002Fnumbmelon\u002FDinerSim) - DinerSim - 一个基于 LLM 的多智能体合作的餐厅模拟基准测试\n- [Diplomacy-Llm](https:\u002F\u002Fgithub.com\u002Flukepoo101\u002Fdiplomacy-llm) - 使用多个 LLM 智能体进行的外交游戏结果的公开 LLM 基准测试。\n- [Embodied-Agent-Interface.Github.Io](https:\u002F\u002Fgithub.com\u002Fembodied-agent-interface\u002Fembodied-agent-interface.github.io) - 这是论文“具身智能体接口 - 针对具身决策的 LLM 基准测试”的项目网站。\n- [Flowbench](https:\u002F\u002Fgithub.com\u002FJustherozen\u002FFlowBench) - [EMNLP 2024] FlowBench - 重新审视并基准测试面向 LLM 智能体的工作流引导规划\n- [Gamabench](https:\u002F\u002Fgithub.com\u002FCUHK-ARISE\u002FGAMABench) - 在多智能体环境中对 LLM 游戏能力进行基准测试\n- [Goodai-Ltm-Benchmark](https:\u002F\u002Fgithub.com\u002FGoodAI\u002Fgoodai-ltm-benchmark) - 一个用于基准测试基于 LLM 的智能体长期记忆和持续学习能力的库。包含所有测试和代码你…\n- [Hosting-7B-Llm-On-Google-Cloud](https:\u002F\u002Fgithub.com\u002FJ-sephB-lt-n\u002Fhosting-7B-llm-on-google-cloud) - 在不同 GCP 虚拟机上对 7B 参数量的 LLM 进行速度基准测试（使用 llama.cpp）\n- [Impact-Academy](https:\u002F\u002Fgithub.com\u002Fsamizdis\u002Fimpact-academy) - 自增强元基准测试，用于衡量 LLM 智能体改进其他 LLM 智能体的能力\n- [Lawful-Good](https:\u002F\u002Fgithub.com\u002Fdluo96\u002Flawful-good) - 用于评估 LLM 智能体法律能力的基准测试\n- [Level-Navi-Agent-Search](https:\u002F\u002Fgithub.com\u002Fchuanruihu\u002FLevel-Navi-Agent-Search) - Level-Navi Agent 是一种无需训练的框架，利用大型语言模型实现深度查询理解和精确的…\n- [LibreEval](https:\u002F\u002Farize.com\u002Fllm-hallucination-dataset\u002F) - 用于 RAG 幻觉检测的开源基准测试\n- [Llf-Bench](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLLF-Bench) - 一个仅基于语言反馈评估学习型智能体的基准测试\n- [Llm-Agent-Ask-For-Help](https:\u002F\u002Fgithub.com\u002Fdillonmsandhu\u002Fllm-agent-ask-for-help) - 基准测试 LLM 智能体尽早退出连续任务的能力。\n- [Llm-Agent-Benchmark-List](https:\u002F\u002Fgithub.com\u002Fzhangxjohn\u002FLLM-Agent-Benchmark-List) - 一个用于评估大型语言模型的基准列表。\n- [Llm_Scavengerhunt](https:\u002F\u002Fgithub.com\u002FKyunnilee\u002Fllm_scavengerhunt) - 为大型语言模型智能体设计的新基准测试——加州大学伯克利分校寻宝游戏\n- [Llmagentoodgym](https:\u002F\u002Fgithub.com\u002Frowingchenn\u002FLLMAgentOODGym) - 基于 BrowserGym 和 ServiceNow 的 AgentLab 的 OOD 基准研究，针对 LLM 智能体。\n- [Llmtaskplanning](https:\u002F\u002Fgithub.com\u002Flbaa2022\u002FLLMTaskPlanning) - LoTa-Bench - 面向具身智能体的语言导向型任务规划器基准测试（ICLR 2024）\n- [Ml-Research-Agent-Public](https:\u002F\u002Fgithub.com\u002FAlgorithmicResearchGroup\u002FML-Research-Agent-Public) - 公开的通用 ML 研究基准智能体。该智能体为比较和评估机器学习研…\n- [Ml-Research-Agent-Tasks](https:\u002F\u002Fgithub.com\u002FAlgorithmicResearchGroup\u002FML-Research-Agent-Tasks) - ML 研究基准任务，旨在评估 AI 智能体在加速 AI 研发方面的能力。\n- [Mobilebench](https:\u002F\u002Fgithub.com\u002FXiaoMi\u002FMobileBench) - Mobile-Bench - 面向基于 LLM 的移动智能体的评估基准\n- [Multiagent-Collab-Scenario-Benchmark](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fmultiagent-collab-scenario-benchmark) - AWS Bedrock Agents Science 团队用于 LLM 多智能体协作系统的基准数据和脚本。\n- [Nemotron-O1-Llm-Agent-Dataflow-Analysis](https:\u002F\u002Fgithub.com\u002Fyuvrajpant56\u002FNemotron-o1-LLM-Agent-Dataflow-Analysis) - “用于 Nemotron-o1 的代码和资源，这是一个基于 LLM 的自动化数据流分析代理框架。具备源\u002F汇提取、数…\n- [Osworld](https:\u002F\u002Fgithub.com\u002Fxlang-ai\u002FOSWorld) - [NeurIPS 2024] OSWorld - 面向开放性任务的多模态智能体在真实计算机环境中的基准测试\n- [Overcooked_Ai_Llm](https:\u002F\u002Fgithub.com\u002Fbzeng2188\u002Fovercooked_ai_llm) - 使用 LLM 多智能体规划在 OvercookedAI 基准上进行的研究。\n- [Pharmasimtext-Os-Llms](https:\u002F\u002Fgithub.com\u002Fepfl-ml4ed\u002FPharmaSimText-OS-LLMs) - 这是一个包含正在审阅的 JEDM 2025 提交中的基准和智能体的代码库。\n- [R-Judge](https:\u002F\u002Fgithub.com\u002FLordog\u002FR-Judge) - R-Judge - 针对 LLM 智能体安全风险意识的基准测试（EMNLP Findings 2024）\n- [Safeagentbench](https:\u002F\u002Fgithub.com\u002Fshengyin1224\u002FSafeAgentBench) - 论文“SafeAgentBench - 面向具身 LLM 智能体的安全任务规划基准”的代码\n- [Shampoosalesagent](https:\u002F\u002Fgithub.com\u002Fjackfsuia\u002FShampooSalesAgent) - 一个极简的 LLM 销售智能体框架，用于快速部署和基准测试销售智能体。支持 OpenAI 模型、Claude、HuggingFace 模型、Gem…\n- [Shortcutsbench](https:\u002F\u002Fgithub.com\u002FEachSheep\u002FShortcutsBench) - ShortcutsBench - 面向基于 API 的智能体的大规模真实世界基准测试\n- [Sim-Court](https:\u002F\u002Fgithub.com\u002FMiracle-2001\u002FSim-Court) - BenCourt - 一个使用 LLM 智能体进行法庭模拟的基准和框架\n- [Smartplay](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FSmartPlay) - SmartPlay 是一个面向大型语言模型（LLM）的基准测试。通过多种游戏来测试 LLM 作为智能体的各种重要能力。…\n- [Sop-Bench](https:\u002F\u002Fgithub.com\u002FNorditech-AB\u002FSOP-bench) - 用于评估 LLM 智能体解决现实世界标准操作流程能力的基准测试\n- [Sphnx](https:\u002F\u002Fgithub.com\u002Fhaailabs\u002FSPHNX) - SPHNX 是一套模块化的基准测试套件，旨在评估和提升大型语言模型（LLM）的隐私管理能力。\n- [Stream-Bench](https:\u002F\u002Fgithub.com\u002Fstream-bench\u002Fstream-bench) - 我们提出了一项开创性的基准测试，用于评估 LLM 智能体在流式场景中随时间不断改进的能力\n- [Theagentcompany](https:\u002F\u002Fgithub.com\u002FTheAgentCompany\u002FTheAgentCompany) - 一个在模拟软件公司中设置任务的智能体基准测试。\n- [Tiny-Llm-Benchmark](https:\u002F\u002Fgithub.com\u002FZahidul-Islam\u002Ftiny-llm-benchmark) - 一种经济高效的 LLM 提示词基准工具，帮助 AI 智能体构建者选择最佳模型，以实现最优成本、性能和准确度。\n- [Unixagentbench](https:\u002F\u002Fgithub.com\u002Fkmrasmussen\u002Funixagentbench) - 对在 Unix 系统上执行任务的 LLM 智能体进行基准测试\n- [Usability-Benchmarking-Framework-Project](https:\u002F\u002Fgithub.com\u002Fstaro190\u002FUsability-Benchmarking-Framework-Project) - 使用 LLM 驱动的 GUI 智能体评估软件手册 - 一个可用性基准测试框架\n- [Visualwebarena](https:\u002F\u002Fgithub.com\u002Fweb-arena-x\u002Fvisualwebarena) - VisualWebArena 是一个多模态智能体的基准测试。\n- [Weblinx](https:\u002F\u002Fgithub.com\u002FMcGill-NLP\u002Fweblinx) - WebLINX 是一个用于构建具有对话能力的网页导航智能体的基准测试\n\n### 数据集\n- [Aart-Ai-Safety-Dataset](https:\u002F\u002Fgithub.com\u002Fxxxx-dddd\u002Faart-ai-safety-dataset) - AART - 基于多样化数据生成的AI辅助红队测试，适用于新型LLM驱动的应用程序\n- [Abstractive-Summarizer-On-Cnn_Dailymail-Dataset.](https:\u002F\u002Fgithub.com\u002Fpkounoudis\u002FAbstractive-Summarizer-on-cnn_dailymail-dataset.) - 一个小型T5 LLM，经过微调用于文本摘要任务。\n- [Airline-Review-Llm-Chatbot](https:\u002F\u002Fgithub.com\u002Frohan-deswal\u002Fairline-review-llm-chatbot) - 使用大型语言模型（LLM）开发聊天机器人，以提供关于该数据集的见解 - https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Fjuhibhoja…\n- [Alpaca-Chinese-Dataset](https:\u002F\u002Fgithub.com\u002Fcarbonz0\u002Falpaca-chinese-dataset) - 阿尔帕卡中文指令微调数据集\n- [Arize AX](https:\u002F\u002Farize.com\u002Fgenerative-ai) - Arize AX 是一款帮助您使用数据集进行实验的工具，附带多个示例笔记本以快速上手。| [官网](https:\u002F\u002Farize.com) [文档](https:\u002F\u002Farize.com\u002Fdocs\u002Fax) | [GitHub](https:\u002F\u002Fgithub.com\u002FArize-ai) | [Slack](https:\u002F\u002Fjoin.slack.com\u002Ft\u002Farize-ai\u002Fshared_invite\u002Fzt-3iu5bvnzr-2e~VFHw2Et4MM5rMsK599g)\n- [Awesome-Chatgpt-Dataset](https:\u002F\u002Fgithub.com\u002Fvoidful\u002Fawesome-chatgpt-dataset) - 解锁LLM的力量 - 探索这些数据集，训练属于你自己的ChatGPT！\n- [Awesome-Datasets-For-Llms](https:\u002F\u002Fgithub.com\u002Fupbit\u002Fawesome-datasets-for-LLMs) - 用于LLM训练和微调的精选数据集\n- [Awesome-Finllms](https:\u002F\u002Fgithub.com\u002FIDEA-FinAI\u002FAwesome-FinLLMs) - 🥇 金融领域优秀大型语言模型（FinLLMs）的精选列表，包括论文、模型、数据集和代码库。特别是中英双语大模型。\n- [Awesome-Instruction-Selector](https:\u002F\u002Fgithub.com\u002FBolin97\u002Fawesome-instruction-selector) - 关于论文《LLM指令微调中的数据选择综述》的相关论文列表及数据集\n- [Awesome-Llm-Human-Preference-Datasets](https:\u002F\u002Fgithub.com\u002Fglgh\u002Fawesome-llm-human-preference-datasets) - 用于LLM微调、RLHF和评估的人类偏好数据集精选列表\n- [Awesome-Medical-Healthcare-Dataset-For-Llm](https:\u002F\u002Fgithub.com\u002Fonejune2018\u002FAwesome-Medical-Healthcare-Dataset-For-LLM) - 医疗\u002F健康领域LLM的热门数据集、模型和论文精选列表\n- [Book-Dataset](https:\u002F\u002Fgithub.com\u002Fmahiatlinux\u002FBook-Dataset) - 用于小型LLM预训练的简单数据集。\n- [Codegebragpt](https:\u002F\u002Fgithub.com\u002Fsr5434\u002FCodegebraGPT) - 在STEM数据集上微调多模态LLM\n- [Conversation-Data](https:\u002F\u002Fgithub.com\u002Fstellar-sam\u002Fconversation-data) - 用于训练LLM的数据集。\n- [Data_Fine_Tune](https:\u002F\u002Fgithub.com\u002Fbhc91\u002Fdata_fine_tune) - 用于准备LLM微调数据集的Python文件\n- [Dataconvoai](https:\u002F\u002Fgithub.com\u002Fjainut20\u002FDataConvoAI) - 基于LLM的聊天机器人，可与自定义数据集交互\n- [Datacooker](https:\u002F\u002Fgithub.com\u002FWashimNeupane\u002Fdatacooker) - 整理混合数据集以供LLM训练\n- [Dataset-Creator-Server](https:\u002F\u002Fgithub.com\u002Fmichaelcalvinwood\u002Fdataset-creator-server) - 用于加速LLM微调数据集创建的Nodejs Express服务器\n- [Dataset-Error-Reduction](https:\u002F\u002Fgithub.com\u002Fjayantaadhikary\u002Fdataset-error-reduction) - 利用LLM减少NLP数据集中的错误\n- [Dataset-Generator-For-Llm-Finetuning](https:\u002F\u002Fgithub.com\u002FAsadNizami\u002FDataset-generator-for-LLM-finetuning) - 一个Web应用程序，可根据文本文档生成高质量的问答对，用于LLM微调\n- [Dataset-Translator](https:\u002F\u002Fgithub.com\u002Feususu\u002Fdataset-translator) - 使用翻译API或LLM翻译Hugging Face数据集\n- [Dataset_For_Llm](https:\u002F\u002Fgithub.com\u002FDragon-hxl\u002Fdataset_for_llm) - 备份一些数据集，用于测试LLM模型\n- [Datasetgpt](https:\u002F\u002Fgithub.com\u002Fradi-cho\u002FdatasetGPT) - 一个命令行界面，用于通过LLM生成文本和对话数据集。\n- [Datasette-Enrichments-Llm](https:\u002F\u002Fgithub.com\u002Fdatasette\u002Fdatasette-enrichments-llm) - 通过提示LLM来丰富数据\n- [Dolly-Instruction-Tuning](https:\u002F\u002Fgithub.com\u002FPavanKumar-Aravapalli\u002FDolly-Instruction-Tuning) - 对LLM进行指令数据集微调的实验\n- [Eduscribe-Llm-Backend](https:\u002F\u002Fgithub.com\u002FTanGentleman\u002FEduScribe-LLM-Backend) - 用于LLM微调的Python式数据处理。曾用于CalHacks 2023获奖项目。\n- [Evaluating-Xai-Llms-In-A-Clinical-Context_Csc413-Project](https:\u002F\u002Fgithub.com\u002Fhk21702\u002FEvaluating-XAI-LLMs-in-a-Clinical-Context_CSC413-Project) - 多伦多大学CSC413期末项目 - 使用MIMIC-IV数据集在临床环境中评估可解释的大语言模型。\n- [Finetuned-Qlora-Llama7B-Mental-Health](https:\u002F\u002Fgithub.com\u002Fcaffeinatedwoof\u002Ffinetuned-qlora-llama7b-mental-health) - 使用QLoRA在心理健康对话数据集上微调Llama-7B LLM\n- [Finetuning_Llm](https:\u002F\u002Fgithub.com\u002Folumyk\u002Ffinetuning_llm) - 使用SageMaker在医疗保健数据集上微调Meta Llama 2 7B模型\n- [Finetuning_Llms](https:\u002F\u002Fgithub.com\u002FPratik-Behera\u002Ffinetuning_llms) - 使用QLora在测试数据集上微调Falcon7b\u002FFalcon7b-Instruct\n- [Fl_Llm_Benchmark_Dataset](https:\u002F\u002Fgithub.com\u002Fhazylavender\u002Ffl_llm_benchmark_dataset) - 此代码收集了来自美国、英国和加拿大的国会\u002F议会数据集\n- [Ftdatagen](https:\u002F\u002Fgithub.com\u002FSpingenceAI\u002FFTDataGen) - 生成LLM微调数据集\n- [Get-Random-Wikipedia-Content](https:\u002F\u002Fgithub.com\u002Ftheripnono\u002Fget-random-wikipedia-content) - 下载随机维基百科内容以创建LLM数据集\n- [Gpt_Annotate](https:\u002F\u002Fgithub.com\u002Fnpangakis\u002Fgpt_annotate) - 引入gpt_annotate - 一个易于使用的Python包，旨在简化使用LLM进行不同任务的自动化文本标注\n- [Graph-Instruction-Tuning](https:\u002F\u002Fgithub.com\u002Fdgjun32\u002FGraph-Instruction-Tuning) - 使用分子图指令数据集对LLM进行指令微调\n- [Hugging Face Datasets](https:\u002F\u002Fhuggingface.co\u002Fdatasets) - 数千个用于机器学习任务的数据集。\n- [Imputer](https:\u002F\u002Fgithub.com\u002Fshaunporwal\u002Fimputer) - 使用LLM填补数据集中的缺失值\n- [Instruct-Qna-Fine-Tuning-Google-Flan-T5-Large-Llm-Qlora-Peft-Open-Orca-Dataset](https:\u002F\u002Fgithub.com\u002Fshirsh10mall\u002FInstruct-QnA-Fine-Tuning-Google-Flan-T5-Large-LLM-QLoRA-PEFT-Open-Orca-Dataset) - 本项目以Open Orca数据集为基础。它提供了多种类型的题目，包括选择题、推理题、问答题、单字答案题、翻译题、语法修正题以及数学应用题。\n- [Instructions-Tuning-Across-Various-Llms-With-Alpaca-Dataset](https:\u002F\u002Fgithub.com\u002Ffatemafaria142\u002FInstructions-Tuning-Across-Various-LLMs-with-Alpaca-Dataset) - 我使用了“Alpaca”数据集，其中包含由OpenAI的text-davinci-003引擎生成的52,000条指令和演示。\n- [Kaggle-Llm-Detect_Ai_Generated_Text](https:\u002F\u002Fgithub.com\u002FLizhecheng02\u002FKaggle-LLM-Detect_AI_Generated_Text) - 通过训练新的分词器并将其与树分类模型结合，或通过训练语言…来检测文本是否由AI生成。\n- [Kg-Llm-Prompting](https:\u002F\u002Fgithub.com\u002FSandroGT\u002FKG-LLM-Prompting) - 论文《通过迭代零样本LLM提示进行知识图谱工程》的代码库和数据集仓库\n- [Langchaindatasetforge](https:\u002F\u002Fgithub.com\u002Fasokraju\u002FLangChainDatasetForge) - 使用LangChain生成人工数据集，并在这些数据集上微调LLM。\n- [Langfuse](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) - 🪢 开源LLM工程平台 - LLM可观ability、指标、评估、提示管理、游乐场、数据集。与Llama…集成。\n- [Lex-Fridman-Dataset-Llm](https:\u002F\u002Fgithub.com\u002FJasonFengGit\u002FLex-Fridman-Dataset-LLM) - Lex Fridman播客转录本，用于LLM训练\n- [Llm---Detect-Ai-Generated-Text](https:\u002F\u002Fgithub.com\u002Fpinskyrobin\u002FLLM---Detect-AI-Generated-Text) - 离线调试的演示代码和数据集\n- [Llm-App](https:\u002F\u002Fgithub.com\u002Fskanduru\u002Fllm-app) - 一个基于AI的MovieLens数据集推荐系统\n- [Llm-Audio-Dataset-Process](https:\u002F\u002Fgithub.com\u002FzhihengAI\u002FLLM-Audio-dataset-process) - 用于音频数据集处理的大量脚本\n- [Llm-Benchmarks](https:\u002F\u002Fgithub.com\u002Fjimbobbennett\u002Fllm-benchmarks) - 一个用于研究Kaggle上LLM基准数据集的Python示例应用\n- [Llm-Chatbot](https:\u002F\u002Fgithub.com\u002FAmir-Shokrzadeh\u002FLLM-Chatbot) - 一个利用现有数据集创建聊天机器人的小型项目\n- [Llm-Content-Mod](https:\u002F\u002Fgithub.com\u002Fkumarde\u002Fllm-content-mod) - 用于研究LLM和内容审核的数据集及代码\n- [Llm-Dataset-Converter-Examples](https:\u002F\u002Fgithub.com\u002Fwaikato-llm\u002Fllm-dataset-converter-examples) - 包含llm-dataset-converter库示例的仓库。\n- [Llm-Dataset-Gen](https:\u002F\u002Fgithub.com\u002FBrandon82\u002Fllm-dataset-gen) - 使用LLM（OpenAI API）生成并添加数据到数据集\n- [Llm-Derived-Tvshowguess-Dataset](https:\u002F\u002Fgithub.com\u002FOaklight\u002FLLM-derived-TVShowGuess-Dataset) - TVShowGuess是在评估语言模型理解叙事故事中虚构角色的能力时提出的。\n- [Llm-Finetuning](https:\u002F\u002Fgithub.com\u002Ftejaswargudla\u002FLLM-finetuning) - 针对NLP任务和网络安全指令数据集对LLM模型进行微调\n- [Llm-Finetuning-Template](https:\u002F\u002Fgithub.com\u002FAusafMo\u002FLLM-Finetuning-Template) - 用于处理和微调LLM以适应自定义数据集的模板代码，这里使用的是人工生成的数据集（未能找到真实数据集）。\n- [Llm-Imdb](https:\u002F\u002Fgithub.com\u002Fibiscp\u002FLLM-IMDB) - 使用LangChain和LLM从图结构中检索信息的概念验证应用，基于IMDB数据集构建\n- [Llm-Predictor](https:\u002F\u002Fgithub.com\u002Fdhw059\u002FLLM-predictor) - 基于Token的材料数据集策略，利用LLM实现材料预测、发现和设计。\n- [Llm-Semantic-Duplicates](https:\u002F\u002Fgithub.com\u002Fhappyduck-313\u002Fllm-Semantic-duplicates) - 关于LLM数据集中语义重复的问题\n- [Llm-Theory-Of-Mind](https:\u002F\u002Fgithub.com\u002Fsileod\u002Fllm-theory-of-mind) - 使用认识论逻辑在语言模型中测试心智理论（ToM）\n- [Llm4Vv](https:\u002F\u002Fgithub.com\u002FXachaeus\u002FLLM4vv) - 用于在OpenACC和OpenMP上训练LLM的数据集\n- [Llm_Benchmarks](https:\u002F\u002Fgithub.com\u002Fleobeeson\u002Fllm_benchmarks) - 一系列用于评估LLM的基准和数据集\n- [Llm_Bxn_Dataset](https:\u002F\u002Fgithub.com\u002FDavickyz\u002Fllm_bxn_dataset) - 一个简单的数据集\n- [Llm_Cybersecurity_Datasets_Uss_2025](https:\u002F\u002Fgithub.com\u002FRoohanak\u002FLLM_Cybersecurity_datasets_USS_2025) - SoK - 使用大型语言模型系统性分析网络安全数据集\n- [Llm_Datasets_Generators](https:\u002F\u002Fgithub.com\u002Fmpasko\u002Fllm_datasets_generators) - 一个简单的工具，用于自动生成用于训练或微调LLM的合成数据集\n- [Llm_Fine_Tuning](https:\u002F\u002Fgithub.com\u002Fkhaouitiabdelhakim\u002Fllm_fine_tuning) - 微调本质上是将一个已经具备广泛语言理解能力的预训练LLM进一步训练…\n- [Llm_Finetune](https:\u002F\u002Fgithub.com\u002FYABER1965\u002FLLM_finetune) - 使用特殊数据集微调LLM\n- [Llm_Finetune](https:\u002F\u002Fgithub.com\u002FTalhaUsuf\u002FLLM_finetune) - 合成数据集生成及微调\n- [Llm_Model_Evaluation](https:\u002F\u002Fgithub.com\u002FLiuYuWei\u002Fllm_model_evaluation) - 针对tmmluplus数据集的LLM模型评估\n- [Llm_Rag](https:\u002F\u002Fgithub.com\u002Fdongdongunique\u002FLLM_RAG) - 本仓库实现了基于向量检索的FAISS和基于生成的GPT的检索增强生成（RAG）系统…\n- [Llmdataparser](https:\u002F\u002Fgithub.com\u002Fjeff52415\u002FLLMDataParser) - LLMDataParser是一个Python库，提供了一系列用于评估大型La…的各种基准数据集的解析器。\n- [Llmeval](https:\u002F\u002Fgithub.com\u002Fcdolea331\u002FLLMEval) - 一个简单的脚本，用于在Hugging Face commonsense_qa数据集上评估GPT 3.5 turbo\n- [Llmscenarioeval](https:\u002F\u002Fgithub.com\u002FTuring-Project\u002FLLMScenarioEval) - 基于场景的LLM评估数据集（beta）\n- [Lm-Expansions](https:\u002F\u002Fgithub.com\u002Forionw\u002FLM-expansions) - 生成式查询和文档扩展何时会失败？跨方法、检索器和数据集的全面研究\n- [Medisoap](https:\u002F\u002Fgithub.com\u002Faman-17\u002FMediSOAP) - 在对话式医疗数据集上微调LLM。\n- [Multimodal-Llm](https:\u002F\u002Fgithub.com\u002FTheSciPro\u002FMultimodal-LLM) - 使用多模态模型LLM fuyu-8b对非结构化数据集进行图像处理\n- [Nomiracl](https:\u002F\u002Fgithub.com\u002Fproject-miracl\u002Fnomiracl) - NoMIRACL - 一个多语言幻觉评估数据集，用于评估RAG中LLM在面对第一阶段检索错误时的鲁棒性，在18种…\n- [Nsfw-Finetuned-On-Llm](https:\u002F\u002Fgithub.com\u002Fsebinsaji007\u002FNSFW-finetuned-on-llm) - 在这里，我们选择了falcon 7B作为LLM，并用它对NSFW数据集进行了微调。\n- [OpenAI Dataset Library](https:\u002F\u002Fopenai.com\u002F) - 一套全面的用于训练AI模型的数据集\n- [Parsee-Datasets](https:\u002F\u002Fgithub.com\u002Fparsee-ai\u002Fparsee-datasets) - 由Parsee.ai团队创建的用于从PDF、HTML文件或图像中提取结构化信息的数据集、案例研究和基准测试。\n- [Pdf_To_Llm_Dataset.Py](https:\u002F\u002Fgithub.com\u002Fkkkarmo\u002Fpdf_to_llm_dataset.py) - 该项目提供了一个Python脚本，可以将PDF书籍转换为适合大型语言模型（LLM）的训练数据集。\n- [Pixel-Agi](https:\u002F\u002Fgithub.com\u002Foliveirabruno01\u002Fpixel-agi) - 一个Gradio应用，用于与LLM对话并使其与像素艺术\u002F图像互动 + 微型数据集。重点在于数据。\n- [Promptedgraphs](https:\u002F\u002Fgithub.com\u002Fclosedloop-technologies\u002FPromptedGraphs) - 从数据集标注、实体提取到知识图谱的生产部署 - NLP和LLM的强强联合。\n- [Querying-Csvs-And-Plot-Graphs-With-Llms](https:\u002F\u002Fgithub.com\u002FSomyanshAvasthi\u002FQuerying-CSVs-and-Plot-Graphs-with-LLMs) - 利用大型语言模型（LLMs）查询CSV文件并绘制图表，从而改变数据分析的方式。这使得人们能够与数据集…\n- [Rag_Llm-On-Customdataset](https:\u002F\u002Fgithub.com\u002Fvinu0404\u002FRAG_LLM-on-CustomDataset) - ConvoSummarizer是一款检索增强生成（RAG）应用，使用Hugging Face Transformers库与google…\n- [Reap-Llm-Problem-Solving](https:\u002F\u002Fgithub.com\u002Fryanlingo\u002FREAP-LLM-Problem-Solving) - 通过REAP提升LLM解决问题的能力 - 反思、明确问题分解和高级提示。此仓库包括REAP p…\n- [Refuel-Sdk](https:\u002F\u002Fgithub.com\u002Frefuel-ai\u002Frefuel-sdk) - 使用LLM标记、清理和丰富文本数据集\n- [Sdp-Llm-Dataset](https:\u002F\u002Fgithub.com\u002Fyilmazzey\u002Fsdp-llm-dataset) - 使用Selenium从ACL文集上抓取论文\n- [Seqtoseq-Dataset-Creator](https:\u002F\u002Fgithub.com\u002Fmichaelcalvinwood\u002Fseqtoseq-dataset-creator) - 一个React应用，用于创建序列到序列LLM训练的数据集\n- [Sg-Data-Analyst](https:\u002F\u002Fgithub.com\u002Fivankqw\u002Fsg-data-analyst) - LLM作为新加坡数据集的数据分析师 🤖\n- [Simpsons_Llm_Xpu](https:\u002F\u002Fgithub.com\u002Frahulunair\u002Fsimpsons_llm_xpu) - 在英特尔独立显卡上微调LLM，以根据辛普森一家的数据集生成对话\n- [Skin-Cancer-Specialist-Llm](https:\u002F\u002Fgithub.com\u002Fsobhonium\u002FSkin-Cancer-Specialist-LLM) - 根据给定数据集对LLM进行微调。该数据集代表了LLM应成为专家的领域知识。\n- [Smart-Contracts-Dataset-Generation](https:\u002F\u002Fgithub.com\u002Fmatteo-rizzo\u002Fsmart-contracts-dataset-generation) - 使用LLM生成受重入攻击影响的智能合约数据集\n- [Starjob](https:\u002F\u002Fgithub.com\u002Fstarjob42\u002FStarjob) - JSSP数据集用于LLM\n- [Syntune](https:\u002F\u002Fgithub.com\u002FRuairiSpain\u002FSynTune) - 构建LLM微调数据集\n- [Talk_To_Your_Data](https:\u002F\u002Fgithub.com\u002Fmktaop\u002Ftalk_to_your_data) - 无论数据集大小，都能从中获得洞察。使用PandasAI和LLM。\n- [Textbook_Quality](https:\u002F\u002Fgithub.com\u002FVikParuchuri\u002Ftextbook_quality) - 生成教科书质量的合成LLM预训练数据\n- [Tinyllm](https:\u002F\u002Fgithub.com\u002Fmeet-minimalist\u002FTinyLLM) - 在小规模数据集上实现LLM\n- [Universal-Dataset-Chatbot-With-Llm](https:\u002F\u002Fgithub.com\u002FAdritPal08\u002FUniversal-Dataset-Chatbot-with-LLM) - 具有LLM的通用数据集聊天机器人\n- [Upar5Iv](https:\u002F\u002Fgithub.com\u002Fveya2ztn\u002Fupar5iv) - 将ar5iv HTML数据集转换为LLM友好的格式，如.md和.json\n- [Vlm_Databuilder](https:\u002F\u002Fgithub.com\u002Ftensorsense\u002Fvlm_databuilder) - 该SDK从YouTube视频中生成用于训练视频LLM的数据集。\n- [Voice datasets](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fvoice_datasets) - 一份全面的开源语音和声音计算数据集列表（95+数据集）。\n- [Zero-Shot-Automatic-Annotation-Using-Llm-Generated-Datasets](https:\u002F\u002Fgithub.com\u002Franzosap\u002FZero-Shot-Automatic-Annotation-using-LLM-Generated-Datasets) - Best.pt是YOLO11n-seg模型，该模型是在LLM生成的图像上训练的。我们使用YOLO11-SAMv2融合方法，在零样本情况下自动标注了苹果掩码。\n\n### 部署\n- [事故模拟](https:\u002F\u002Fgithub.com\u002Fchuajiesheng\u002Faccident-simulation) - 在实时场景中为事故分配资源是一项极具挑战性的任务。本项目旨在创建一个模拟平台，…\n- [高级逻辑推理与学习算法](https:\u002F\u002Fgithub.com\u002FBrionengine\u002FAdvanced-logic-reason-idea-and-learning-algorithms) - InfiniteMind 是一个开源的 AI 项目，旨在构建能够从经验中学习、建立全面…\n- [Agent-](https:\u002F\u002Fgithub.com\u002Fdrraghavendra\u002FAgent-) - 使用我们的去中心化平台，在 Solana 上创建、部署和管理 AI 代理。由 Rig、Rust 和 ARC 代币提供支持。\n- [Agent 加速器](https:\u002F\u002Fgithub.com\u002Fj3bruins\u002Fagent-accelerator) - 使用我们的去中心化平台，在 Solana 上创建、部署和管理 AI 代理。由 Rig、Rust 和 ARC 代币提供支持。\n- [Agent Ops](https:\u002F\u002Fgithub.com\u002Frbisoi\u002Fagent-ops) - 开发一个用于在生产环境中部署代理的 AI 框架。\n- [AgentGPT](https:\u002F\u002Fgithub.com\u002Freworkd\u002FAgentGPT) - 🤖 在浏览器中组装、配置并部署自主 AI 代理。\n- [AgentGPT](https:\u002F\u002Fgithub.com\u002Falicayan008\u002FAgentGPT) - 在浏览器中组装、配置并部署自主 AI 代理。\n- [Agentic AI](https:\u002F\u002Fgithub.com\u002FEnggTalha\u002FAgentic-AI) - 一个用于开发和部署专用 AI 代理的框架，包括网络搜索和金融 AI 代理，由先进模型驱动…\n- [Agentic AI-](https:\u002F\u002Fgithub.com\u002FJogendraSingh1879\u002FAgentic-AI-) - 使用 FastAPI 和多代理架构构建并部署一个免费的披萨订购系统。\n- [Agentic 平台](https:\u002F\u002Fgithub.com\u002Fbonk1t\u002Fagentic-platform) - AI 代理自动化平台——从浏览器中快速原型设计、测试并部署多代理系统。\n- [Agentic.Md](https:\u002F\u002Fgithub.com\u002Fai-primitives\u002Fagentic.md) - 使用 Markdown 和 MDX 构建、测试、部署和迭代 AI 代理。\n- [AgenticAI](https:\u002F\u002Fgithub.com\u002FYasinOnline\u002FAgenticAI) - AgenticAI 是一个用于构建自主决策代理的框架。它集成了强化学习、NLP 和多代理系统…\n- [Agentkit SDK](https:\u002F\u002Fgithub.com\u002Fsimonweniger\u002Fagentkit-sdk) - 在  平台上构建、部署和管理由 LLM 驱动的代理。\n- [代理处理单元](https:\u002F\u002Fgithub.com\u002Ffaddy19\u002FAgentProcessingUnit) - 代理处理单元（APU）是一个开源硬件项目，旨在构建针对 AI 代理优化的高性能芯片架构…\n- [Agentserve](https:\u002F\u002Fgithub.com\u002FPropsAI\u002Fagentserve) - 一个用于托管和扩展 AI 代理的框架。\n- [Agentverse](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FAgentVerse) - 🤖 AgentVerse 🪐 旨在促进多种基于 LLM 的代理在不同应用中的部署，主要提供两…\n- [Agentx](https:\u002F\u002Fgithub.com\u002Fsattyamjjain\u002FAgentX) - AgentX 是一个强大的 AI 驱动框架，用于构建智能、上下文感知的助手并自动化工作流。采用模块化设计，无缝…\n- [Agileagents](https:\u002F\u002Fgithub.com\u002Fruvnet\u002Fagileagents) - Agile Agents (A2) 是一个开源框架，用于使用公共和私有云创建和部署无服务器智能代理。\n- [AI 代理](https:\u002F\u002Fgithub.com\u002Fkasragordini\u002FAI-Agent) - 设计并部署了一个高性能聊天机器人，利用 LangChain、Qdrant、LLM 和 RAG 技术来增强数据检索和 AI 知识库功能…\n- [AI 代理生态系统](https:\u002F\u002Fgithub.com\u002Fai-in-pm\u002FAI-Agent-Ecosystem) - 一个强大且可扩展的生态系统，用于管理和监控 AI 代理。该系统提供了一个框架，用于部署、管理和监…\n- [用于部署的 AI 代理](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002FAI-agent-for-deployment) - 无描述。\n- [AI 代理实验室](https:\u002F\u002Fgithub.com\u002FZeeshan138063\u002Fai-agent-lab) - AI 代理实验室——一个开源仓库，使用 Python 构建、测试和部署 AI 代理，包含示例和模块化设计。\n- [AI 代理报告生成器及部署](https:\u002F\u002Fgithub.com\u002Fezemriv\u002FAI-Agent-Report-Maker-Deploy) - AI 代理报告生成器的简化版本，专门针对使用 Gradio 进行部署的代理功能。\n- [AI 代理 UI](https:\u002F\u002Fgithub.com\u002FLinzo99\u002Fai-agent-ui) - 基于 Llama Deploy（LlamaIndex 工作流）创建的聊天机器人代理的 UI 模板。\n- [AI 代理](https:\u002F\u002Fgithub.com\u002FBilalkhan4086\u002Fai-agents) - 本仓库用于学习如何在 Langgraph Cloud 上使用 Langgraph 部署 AI 代理。\n- [用于网络的 AI 代理](https:\u002F\u002Fgithub.com\u002Fmkular\u002FAI-Agents-For-Networking) - 用于网络部署、配置和监控的 AI 代理。\n- [链上 AI 代理](https:\u002F\u002Fgithub.com\u002FPatrick-Ehimen\u002FAI-OnChain-Agent) - 一款强大的工具，专为与区块链网络交互而设计，特别是针对 EVM 链。它利用 OpenAI 的 GPT-4o-mini 模型…\n- [AI 驱动的聊天机器人生成器](https:\u002F\u002Fgithub.com\u002FRushi-code1\u002FAI-Powered-Chatbot-Generator) - “开发了一款 AI 驱动的聊天机器人生成器，旨在为不同行业创建定制化的对话式代理。使用了自…\n- [AI 工具箱](https:\u002F\u002Fgithub.com\u002Feranco74\u002FAI-Toolbox) - 一套 AI 驱动的工具，旨在帮助在不同工作流中创建、训练和部署 AI 代理。\n- [航空公司 API](https:\u002F\u002Fgithub.com\u002Fnickb4924\u002FAirline-API) - BookingXML 是一家领先的航空公司 API 提供商，为旅行社和旅游公司提供最佳的航空公司 API 集成解决方案。\n- [Anthropic 代理 Docker 版本](https:\u002F\u002Fgithub.com\u002FLiteObject\u002Fanthropic-agent-in-docker) - 本仓库提供 Anthropic 代理的 Docker 化实现，以实现简化的部署和可扩展性。它确保一致…\n- [Apemind 框架](https:\u002F\u002Fgithub.com\u002Fapeoutmeme\u002FApeMind-Framework) - 用于在 Solana 上部署 AI 代理的下一代基础设施。\n- [ATAT](https:\u002F\u002Fgithub.com\u002Fsemanticsean\u002FATAT) - ATAT 是一款面向 AI 代理的电子邮件客户端。通过单个电子邮件地址（IMAP\u002FSMTP），使用 OpenAI API 部署数十个 AI 代理。只需一…\n- [ATT&CK PE](https:\u002F\u002Fgithub.com\u002Fcmndcntrlcyber\u002Fattck-pe) - 利用 ATT&CK 数据库的强大功能，丰富一个 AI 代理，使其作为浏览器线程部署，用于从容器中进行对手模拟。\n- [Auto_Web-GPT](https:\u002F\u002Fgithub.com\u002FSuperstar721\u002FAuto_web-GPT) - 在浏览器中组装、配置并部署自主 AI 代理。\n- [Autogpt Next Web](https:\u002F\u002Fgithub.com\u002FElricLiu\u002FAutoGPT-Next-Web) - 🤖 在浏览器中组装、配置并部署自主 AI 代理。一键免费部署你的私人 AutoGPT 网页应用。\n- [自主 AI 团队](https:\u002F\u002Fgithub.com\u002Fmikirinkode\u002Fautonomous-ai-team) - 该项目旨在开发和部署能够相互协作并与人类沟通的自主 AI 代理。\n- [Autonomousai](https:\u002F\u002Fgithub.com\u002FRithikaGupta\u002FautonomousAI) - 关于多代理系统安装、构建和部署——用于自主测试。\n- [Awesome AI SDKs](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-sdks) - 一个包含 SDK、框架、库和工具的数据库，用于创建、监控、调试和部署自主 AI 代理。\n- [精彩的 Solana AI 黑客马拉松](https:\u002F\u002Fgithub.com\u002Ftkorkmazeth\u002Fawesome-solana-ai-hackathon) - 欢迎来到 Solana AI 代理黑客马拉松仓库！本资源是您创建、部署和管理自主 AI 的终极指南。\n- [AWS AI 模板](https:\u002F\u002Fgithub.com\u002Fjacobweiss2305\u002Faws-ai-templates) - 一系列用于部署 Agentic 软件的 AWS 模板。\n- [AWS 模板](https:\u002F\u002Fgithub.com\u002Fphidatahq\u002Faws-templates) - 一系列用于部署 AI 代理的 AWS 模板。\n- [Axocore](https:\u002F\u002Fgithub.com\u002FAxolotl-Labs\u002FAxocore) - Axocore 是一个开源的 AI 代理基础设施，旨在让构建智能系统变得轻而易举。凭借模块化的插件系统,…\n- [Baba-Bot](https:\u002F\u002Fgithub.com\u002FAbdulrahmanrihan\u002FBaba-bot) - Baba bot 是一个使用 Mistral V2 和 Mistral 的代理创建用户界面构建的 AI 代理，并部署在一个 Gradio 网站上。\n- [Back4App AI 代理](https:\u002F\u002Fgithub.com\u002Fduplxey\u002Fback4app-ai-agent) - 学习如何使用 Back4app 代理构建并部署全栈 Web 应用程序。\n- [Baseai](https:\u002F\u002Fgithub.com\u002FLangbaseInc\u002FBaseAI) - BaseAI — Web AI 框架。构建具有记忆功能的无服务器自主 AI 代理最简单的方式。从本地优先、代理式的…\n- [BasiliskToken](https:\u002F\u002Fgithub.com\u002Felder-plinius\u002FBasiliskToken) - BASI 是首个由自主 AI 代理创建的智能合约。该代币于 2023 年 6 月 6 日部署在 ETH 主网上。\n- [Bespoke_Automata](https:\u002F\u002Fgithub.com\u002FC0deMunk33\u002Fbespoke_automata) - Bespoke Automata 是一个 GUI 和部署流水线，用于在本地和离线环境下制作复杂的 AI 代理。\n- [Bluemarz](https:\u002F\u002Fgithub.com\u002FStartADAM\u002Fbluemarz) - Bluemarz 是一个开源的 AI 代理管理层，提供灵活、可扩展且无状态的架构，用于部署或…\n- [Botpress](https:\u002F\u002Fgithub.com\u002Fbotpress\u002Fbotpress) - 一个开源中心，用于构建和部署 GPT\u002FLLM 代理 ⚡️\n- [商业博客生成器](https:\u002F\u002Fgithub.com\u002FMuhammadSalmanAhmad\u002Fbusiness-blog-generator) - 开发了一个简单的 Web 应用，帮助你撰写感兴趣主题的商业文章，其中我使用 crewAI 部署了一个 AI 代理，该…\n- [Canary](https:\u002F\u002Fgithub.com\u002Fwack\u002Fcanary) - MultiTool Canary 是你的 AI 驱动、代理式部署解决方案，用于实现无缝、风险可控的发布。\n- [Chamberlain 多模态多代理聊天机器人](https:\u002F\u002Fgithub.com\u002FnickShengY\u002Fchamberlain_multimodal_multiagent_chatbot) - 一款 AI 驱动的多模态多代理聊天机器人，用于家庭部署，以管理用户的日常家务和任务。使用了 OpenAI 和 Langchain 等…\n- [聊天机器人](https:\u002F\u002Fgithub.com\u002Fpavankola84\u002FChatBot) - AI 聊天机器人小部件是一款直观且多功能的对话式代理，旨在增强各种网站上的用户互动和支持。\n- [聊天机器人-](https:\u002F\u002Fgithub.com\u002Fanshikabanerjee\u002FChatbot-) - 本文将详细说明在搜索和发现领域对聊天机器人等对话式 AI 代理的需求。其效率的…\n- [ChatGPT 克隆](https:\u002F\u002Fgithub.com\u002Fvivekkkkkkk\u002Fchatgpt.clone) - 基于 GPT-3.5 的 AI 驱动对话式代理。经过多样化文本数据的训练，能够生成类似人类的回应。可用于构建聊…\n- [Cheap_Ai](https:\u002F\u002Fgithub.com\u002FCheapaifun\u002FCheap_ai) - 部署零费用，使用 LiamaX 构建自动化 AI 代理——新一代智能，简单易用！\n- [国际象棋代理](https:\u002F\u002Fgithub.com\u002Fgabrielzencha\u002FChess-Agent) - 一个能够教授他人下棋的 AI 国际象棋代理，并且还被部署在现实生活中的一台机器人上。\n- [Collective](https:\u002F\u002Fgithub.com\u002Fzoharbabin\u002Fcollective) - 一个实验性的 AI 项目，利用专业代理进行端到端软件开发。每个基于角色的 AI 合作者协…\n- [联络中心 GenAI 代理](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fcontact-center-genai-agent) - 使用 Amazon Connect、Amazon Lex 和 Amazon Bedrock Knowledge Bases，在您的联络中心部署生成式 AI 代理，用于语音和聊天服务。\n- [Corexai](https:\u002F\u002Fgithub.com\u002FCorexAI\u002FCorexAI) - CorexAI 是一个尖端的去中心化平台，可实现 GPU 和 AI 代理的无缝租赁。通过利用分布式 GPU 资源…\n- [Council](https:\u002F\u002Fgithub.com\u002Fchain-ml\u002Fcouncil) - Council 是一个开源平台，用于快速开发和稳健部署定制的生成式 AI 应用程序。\n- [Crewai](https:\u002F\u002Fgithub.com\u002Fahtealeb\u002Fcrewai) - Crew.ai 是领先的多代理平台，可通过强大的 AI 代理简化各行业的工作流程。构建并部署自动化工…\n- [Crewai Web 渲染](https:\u002F\u002Fgithub.com\u002Fimnotdev25\u002Fcrewai-web-render) - 使用 crew ai 代理构建和部署站点。\n- [舞蹈 AI 研究项目](https:\u002F\u002Fgithub.com\u002Fjzho987\u002Fdance-ai-research-project) - 用于管理研究的主要仓库，旨在开发和部署一个交互式舞蹈 AI 代理，以研究 ML 技术和…\n- [使用本地 Llama 进行数据分析](https:\u002F\u002Fgithub.com\u002FErdenizUnvan\u002Fdata_analysis_with_local_llama) - 使用 autogen 和 llama_index 部署一个 AI 代理，以便用你的文件进行数据分析。\n- [David](https:\u002F\u002Fgithub.com\u002FUnka-Malloc\u002FDAVID) - AI 代理管理系统——开发、认证、验证、集成和部署。\n- [Devstream](https:\u002F\u002Fgithub.com\u002FRaheesAhmed\u002Fdevstream) - DevStream 是一个 AI 驱动的 Web 开发代理，允许你直接从 y… 提示、运行、编辑和部署全栈应用程序。\n- [Eidolon](https:\u002F\u002Fgithub.com\u002Feidolon-ai\u002Feidolon) - 第一个 AI 代理服务器，Eidolon 是一个可插拔的代理 SDK 和企业就绪的部署服务器，适用于 Agentic 应用程序。\n- [示例：AI Bedrock 代理与国家公园](https:\u002F\u002Fgithub.com\u002Fcebert\u002Fexamples-ai-bedrock-agent-national-parks) - 本仓库展示了如何使用 Amazon Bedrock 和 AWS Lambda 构建并部署 AI 代理，基础设施由 AWS …\n- [Experts](https:\u002F\u002Fgithub.com\u002Fmetaskills\u002Fexperts) - Experts.js 是创建和部署 OpenAI 助手的最简单方式，并将它们连接在一起作为工具，以创建先进的多 AI 代理…\n- [Fireai](https:\u002F\u002Fgithub.com\u002Fanurag115\u002FFireAI) - 使用此模板部署 Azure、AWS 和 Google Cloud 的堆栈，会创建一个带有 CloudLens 代理的虚拟机，该代理监听虚拟接口…\n- [框架](https:\u002F\u002Fgithub.com\u002Fisalineai\u002Fframework) - 自给自足的 Python AI 代理，用于创建、部署和优化自主项目。\n- [友谊计量器](https:\u002F\u002Fgithub.com\u002Falmagashi\u002Ffriendship_meter) - 一个有趣的项目，用于测量人与 AI 代理之间的互动。部署在 Vercel 上。\n- [全栈 Next.js 应用生成器](https:\u002F\u002Fgithub.com\u002Fspark-engine-opensource-projects\u002Ffullstack-nextjs-app-generator) - 全栈 Next.js 应用程序生成器，使用 Spark Engine AI 多代理系统项目进行生成，Supabase 和 Vercel 用于部…\n- [盖亚表情包币生成器](https:\u002F\u002Fgithub.com\u002Fharishkotra\u002Fgaia-meme-coin-generator) - 使用 Gaia 的 AI 代理自动生成功能，为表情包币命名并设计代币经济学！\n- [GCP AI 助手](https:\u002F\u002Fgithub.com\u002Fgabrielpreda\u002Fgcp_ai_assistant) - 使用 Gemini 的对话式 AI 代理，配备 Streamlit UI，并部署在 GCP Cloud Run 上。\n- [生成式 AI 工具包](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fgenerative-ai-toolkit) - 生成式 AI 工具包是一个轻量级库，涵盖了基于 LLM 的应用程序生命周期，包括代理。它的目的是…\n- [幽灵代理](https:\u002F\u002Fgithub.com\u002Fqianyouliang\u002FGhost-Agent) - 本项目的目标是开发一个部署在网页上的 AI 幽灵，由 LangChain 技术驱动。该 AI 幽灵将持续…\n- [Ginix 欺诈代理](https:\u002F\u002Fgithub.com\u002Fginix-co\u002Fginix-fraud-agents) - 欢迎来到 giniX FraudAI-Agents 社区！这是一个合作空间，欺诈分析师和开发者共同创建、…\n- [Go Chain Gang](https:\u002F\u002Fgithub.com\u002Fkmesiab\u002Fgo-chain-gang) - 一个纯 Go 语言的 LLM\u002FAI “代理”编排库。构建、连接和部署自主代理，共同解决目标。\n- [Iauto](https:\u002F\u002Fgithub.com\u002Fshellc\u002Fiauto) - iauto 是一个低代码引擎，用于构建和部署 AI 代理。\n- [Incredible.Dev](https:\u002F\u002Fgithub.com\u002FIncredibleDevHQ\u002FIncredible.dev) - Incredible.dev 是一个 AI 编码伙伴，可以编写、修复、记录、部署、测试你的 API。一个代理即可掌控所有 API。\n- [Init Eliza](https:\u002F\u002Fgithub.com\u002FW3bbieLabs\u002Finit-eliza) - 一个强大的 CLI 工具，用于构建、管理和部署 AI 代理，并支持多种提供商集成。\n- [Instantneo](https:\u002F\u002Fgithub.com\u002Fdponcedeleonf\u002Finstantneo) - InstantNeo 是一个简洁的界面，用于使用 OpenAI 的模型开发具有定制角色和技能的 AI 代理。它简化了部…\n- [Instantrun](https:\u002F\u002Fgithub.com\u002FTalha-Ali-5365\u002FInstantRun) - 一个可以自主部署任何 GitHub 仓库的 AI 代理。\n- [Jazzx](https:\u002F\u002Fgithub.com\u002Fkunal-nitor\u002FJazzX) - JazzX 旨在创建一个以生成式 AI 为先的企业平台，用于部署和管理生成式 AI 代理\u002F应用。这个企业云平台将使用 d…\n- [Jonathan Flightbase](https:\u002F\u002Fgithub.com\u002FAcrylAI\u002FJonathan-Flightbase) - Jonathan Flightbase 是一个 MLOps\u002FLLMOps 平台，专为高效的 AI 开发和运营而设计。它简化了资源管理,…\n- [Justben8](https:\u002F\u002Fgithub.com\u002FJustBen8\u002FJustBen8) - 在学习 AI 的过程中，涉及代理的构建和部署，以及用于构建它们的技术。\n- [K8S 代理](https:\u002F\u002Fgithub.com\u002Franching-farm\u002Fk8s-agent) - 用于将 ranching.farm 直接部署到你集群中的 Kubernetes 代理。将你的 K8s 部署连接到我们的 AI 驱动管理平…\n- [Kagentic](https:\u002F\u002Fgithub.com\u002Faiadvocat\u002Fkagentic) - 基于 Flask 的 Agentic AI 聊天助手，使用 OpenAI 的 GPT-4。支持工具注册和管理，以实现动态响应。部署在 …\n- [Kube-GPT 代理](https:\u002F\u002Fgithub.com\u002Fsrimoyee1212\u002FKube-GPT-Agent) - 使用 AI 代理获取有关你的 Kubernetes 部署的信息！\n- [Kubernetes 代理](https:\u002F\u002Fgithub.com\u002Friyaadhbukhsh\u002Fkubernetes-agent) - 一个能够熟练回答关于你已部署的 Kubernetes 集群问题的 AI 代理。\n- [Kubernetes 查询代理](https:\u002F\u002Fgithub.com\u002Fparteeksingh24\u002Fkubernetes-query-agent) - 展示了一个 AI 代理，它可以回答关于部署在 Kubernetes 集群上的应用程序的简单查询。\n- [Kubernetesqueryagent](https:\u002F\u002Fgithub.com\u002Fsoni-ratnesh\u002FKubernetesQueryAgent) - 一个与 Kubernetes 集群交互，以回答有关其已部署应用程序的问题的 AI 代理。\n- [Letta-Deepseek](https:\u002F\u002Fgithub.com\u002Fmahawi1992\u002Fletta-deepseek) - 使用 Letta AI 和 DeepSeek 构建的高级多代理系统，具有内存优化和闪电般的 AI 部署。\n- [Libertai 代理](https:\u002F\u002Fgithub.com\u002FLibertai\u002Flibertai-agents) - 一个用于创建和部署保密且去中心化的 AI 代理的框架。\n- [Llama Latte](https:\u002F\u002Fgithub.com\u002Fim-anhat\u002Fllama-latte) - 一个基于 React Native 构建的 AI 驱动咖啡店应用，集成了 Llama 3 模型，使用检索增强生成（RAG）技术来塑造人物…\n- [LLM 研究后端 Django](https:\u002F\u002Fgithub.com\u002Fmuhammadnasif\u002Fllm-research-backend-django) - 这是用于部署对话式 AI 代理 LLM 研究的 Django 项目，用于测试目的。\n- [LunaAI](https:\u002F\u002Fgithub.com\u002FEugeene1337\u002FLunaAI) - Luna 是一个强大的多代理模拟框架，旨在创建、部署和管理自主 AI 代理。基于 TypeScript 构建，i…\n- [Mastra AI](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra) - Mastra 是一个一体化框架，用于使用 Typescript 构建 AI 驱动的应用程序和代理。\n- [Medreminder](https:\u002F\u002Fgithub.com\u002Fabisong\u002FMedReminder) - 一个基于 Web 的 MedReminder 应用程序，使用 HTML、CSS 和 Vanilla JavaScript，结合本地存储和 Flask 进行部署。\n- [MetaMind](https:\u002F\u002Fgithub.com\u002FMetaMindFramework\u002FMetaMind) - 用 AI 赋能你的应用程序——MetaMind 提供了一个全面的框架，用于部署智能代理、管理高级工…\n- [Mixtral_8X7B 代理](https:\u002F\u002Fgithub.com\u002Fsvngoku\u002FMixtral_8X7B_Agent) - 部署一个开源的 AI 代理，使用 Mixtral 8X7B。\n- [多 PDF 聊天应用 AI 代理](https:\u002F\u002Fgithub.com\u002FGURPREETKAURJETHRA\u002FMulti-PDFs_ChatApp_AI-Agent) - 认识 MultiPDF 📚 聊天 AI 应用！🚀 使用 Langchain、Google Gemini Pro 和 FAISS 向量数据库，无缝地与多个 PDF 对话，实现流畅的交…\n- [多代理无限后室](https:\u002F\u002Fgithub.com\u002FAGAMPANDEYY\u002FMultiAgents-infinite-backroom) - Claude AI 多代理无需人工干预即可进行对话。灵感来自“真相终端”，其中部署了一个 Vercel 应用和…\n- [多模态 LLM 代理](https:\u002F\u002Fgithub.com\u002FHuHK-Private\u002Fmultimodal-llm-agent) - 部署多模态 LLM 代理，以解决复杂的 AI 任务，语言作为通用接口。\n- [Nerosdk](https:\u002F\u002Fgithub.com\u002Fnerobossai\u002Fnerosdk) - 用于构建和部署你自己的 AI 代理的 SDK。\n- [Nest AI](https:\u002F\u002Fgithub.com\u002Fthenest-hub\u002Fnest-ai) - 一个轻量级、开源的框架，用于部署代理。这是更大倡议的一部分，旨在帮助 AI 代理的部署。\n- [NL2IAC](https:\u002F\u002Fgithub.com\u002Framonbgc\u002Fnl2iac) - 用于 IaC 部署的 AI 代理。\n- [链上 AI 入门](https:\u002F\u002Fgithub.com\u002FNot-Sarthak\u002Fonchain-ai-starter) - 无需麻烦即可构建和部署链上 AI 代理。\n- [OpenAI 助手模板](https:\u002F\u002Fgithub.com\u002Fpranavgupta2603\u002FOpenAI-Assistants-Template) - 使用我们的 OpenAI 助手模板构建和部署 AI 驱动的助手。本教程提供了一个动手实践的方法，来使用 OpenAI 的 A…\n- [Petercat](https:\u002F\u002Fgithub.com\u002Fpetercat-ai\u002Fpetercat) - 一个对话式问答代理配置系统，自托管部署解决方案，以及一个方便的一体化应用 SDK，允许…\n- [Phoneit](https:\u002F\u002Fgithub.com\u002Fbhuvanmdev\u002FPhoneIT) - 一个应用程序，部署后可以建立呼叫服务，其中 AI 代理可以使用已建立的 RA 解决用户的任何疑问。\n- [Plura](https:\u002F\u002Fgithub.com\u002Fplura-ai\u002Fplura) - Plura 是一个强大的工具，用于创建、管理和部署 AI 代理。基于 TypeScript 构建，它可以帮助你开发智能代理…\n- [Powerups.Ai](https:\u002F\u002Fgithub.com\u002Fmuzetv\u002Fpowerups.ai) - 在几分钟内部署 AI 代理驱动的 API。\n- [S.O.C.R.A.T.E.S. 项目](https:\u002F\u002Fgithub.com\u002FHams-Ollo\u002FProject-S.O.C.R.A.T.E.S.) - 🤖 高级多代理 AI 模板——生产就绪的系统，结合 Groq 的速度和 LangChain 的灵活性。具有 RAG、文档 p…\n- [公共代理框架](https:\u002F\u002Fgithub.com\u002Faurasgit\u002Fpublic-agent-framwork) - 一个独立创建、部署和优化自主项目的 AI 代理。\n- [Purpaas-Llm](https:\u002F\u002Fgithub.com\u002Fdwain-barnes\u002FPurPaaS-LLM) - PurPaaS 是一个创新的开源安全测试平台，实施紫色团队方法（结合红蓝团队策略）以…\n- [Python 代理](https:\u002F\u002Fgithub.com\u002FclarisseIO\u002Fpython-agents) - 一个独立创建、部署和优化自主项目的 AI 代理。\n- [Python AI 代理](https:\u002F\u002Fgithub.com\u002Fosvaldokalvaitir\u002Fpython-aiagent) - 📈 Python AIAgent 应用程序开发，与 OpenAI 的 GPT 模型集成，使用 CrewAI 框架，创建一个用于 cons…\n- [RAG SAP 代理](https:\u002F\u002Fgithub.com\u002Fagbackhoff\u002FRAG_SAP_AGENT) - SAP 表结构分析器——一款 AI 驱动的工具，使用 Google 的 Gemini 1.5 Pro 来提取和分析 SAP HANA S\u002F4 表结构。特…\n- [Redbaez 代理构建完整版](https:\u002F\u002Fgithub.com\u002Ftom2tomtomtom\u002Fredbaez-agent-builder-complete) - 一个全栈 AI 代理构建者，具备模型集成、部署和监控能力。\n- [SC Helm 应用](https:\u002F\u002Fgithub.com\u002Famsilf\u002Fsc-helm-app) - 本仓库包含一个简单的 Helm 图表，用于部署“Hello World” Nginx 应用程序，以及 Open Policy Agent（OPA）规则，用于…\n- [可扩展 AI--自主代理](https:\u002F\u002Fgithub.com\u002FPrasannaverse13\u002FScalable-AI--autonomous-Agent) - 可扩展 AI 平台是一种尖端解决方案，旨在创建、部署和管理自主及半自主的 AI 代理…\n- [使用 Streamlit 设置亚马逊 Bedrock 代理，用于通过亚马逊 Redshift Serverless 将文本转换为 SQL](https:\u002F\u002Fgithub.com\u002Faws-samples\u002FSetup-Amazon-Bedrock-Agent-for-Text2SQL-Using-Amazon-Redshift-Serverless-with-Streamlit) - 本项目整合了 AWS 服务，以创建一个自然语言界面，用于查询亚马逊 Redshift Serverless 数据库。它利…\n- [洗发水销售代理](https:\u002F\u002Fgithub.com\u002Fjackfsuia\u002FShampooSalesAgent) - 一个最小化的 LLM 销售代理框架，用于快速部署和基准测试销售代理。支持 OpenAI 模型、Claude、HuggingFace 模型、Gem…\n- [简单 AI 代理](https:\u002F\u002Fgithub.com\u002Fjeya2050\u002Fsimple_AI_agent) - 该 AI 代理代表了自动化网页内容分析的重大进步，使组织能够高效地 p…\n- [Slack AI 助手 Vijay](https:\u002F\u002Fgithub.com\u002Fvjvkrm\u002Fslack-ai-assistant-vijay) - 将 AI 助手 Slack 应用程序部署到你的工作空间的最简单方式。\n- [Smartcall AI](https:\u002F\u002Fgithub.com\u002Fshwetd19\u002FSmartCall-Ai) - 部署 AI 语音代理用于通话。\n- [Smoothlingua](https:\u002F\u002Fgithub.com\u002FDev-Art-Solutions\u002FSmoothLingua) - SmoothLingua 是一个开源的对话式 AI 平台，赋能你创建和部署智能对话式代理。它提…\n- [Snakeai](https:\u002F\u002Fgithub.com\u002Fleonardocunha2107\u002FsnakeAI) - 在你附近部署蛇形代理。\n- [Spacecreateai](https:\u002F\u002Fgithub.com\u002FHarbars1234\u002FSpaceCreateAI) - 本仓库包含第一个使用 SEND AI 技术构建的 Solana 代理 https:\u002F\u002Fgithub.com\u002Fsendaifun\u002Fsolana-agent-kit，该…\n- [股票 AI 代理](https:\u002F\u002Fgithub.com\u002Fmuriloguerreiro\u002Fstocks-ai-agent) - 部署\n- [Superagent Swift Legacy](https:\u002F\u002Fgithub.com\u002Fsimonweniger\u002Fsuperagent-swift-legacy) - 在  平台上构建、部署和管理由 LLM 驱动的代理。\n- [Swarmnode C++](https:\u002F\u002Fgithub.com\u002Falexcsh0\u002Fswarmnode-cpp) - 在云端部署和编排无服务器 AI 代理。\n- [Swarmnode .NET](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-dotnet) - 在云端部署和编排无服务器 AI 代理。\n- [Swarmnode Go](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-go) - 在云端部署和编排无服务器 AI 代理。\n- [Swarmnode Node](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-node) - 在云端部署和编排无服务器 AI 代理。\n- [Swarmnode Python](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-python) - 在云端部署和编排无服务器 AI 代理。\n- [Swarmnode Rust](https:\u002F\u002Fgithub.com\u002Fswarmnode-ai\u002Fswarmnode-rust) - 在云端部署和编排无服务器 AI 代理。\n- [SwarmsXGCP](https:\u002F\u002Fgithub.com\u002FThe-Swarm-Corporation\u002FSwarmsXGCP) - 将你的代理部署在 Cloud Run 上！\n- [Synnex Symphony](https:\u002F\u002Fgithub.com\u002FStyro13\u002FSynnex_Symphony) - Symphony 是一个受 GPT Pilot 和微软关于“AutoDev”的论文深刻影响的 AI 驱动软件开发框架，旨在…\n- [Team AI](https:\u002F\u002Fgithub.com\u002Fdeployment-io\u002Fteam-ai) - 一个用 Go 编写的 AI 代理编排引擎，内部用于 deployment.io。\n- [测试聊天机器人](https:\u002F\u002Fgithub.com\u002Fsamanway1996\u002Ftestchatbot) - 用于在 api.ai 中部署代理的数据库。\n- [Tiny-Agent](https:\u002F\u002Fgithub.com\u002Fbombap\u002Ftiny-agent) - Tiny-Agent 是一个轻量级但可扩展的 AI 代理框架，简化了智能代理的创建和部署过程。\n- [Transform-Agents](https:\u002F\u002Fgithub.com\u002Ftransformsen\u002Ftransform-agents) - Transform-Agents - 一个用于 AI 系统的 UI——大规模构建、模拟、运行和部署你的 Agentic AI 系统。\n- [Upstreet Core](https:\u002F\u002Fgithub.com\u002FUpstreetAI\u002Fupstreet-core) - 快速构建和部署 AI 代理。\n- [Vitalhome Chat](https:\u002F\u002Fgithub.com\u002Fvital-ai\u002Fvitalhome-chat) - 部署在 Chat.ai 上的聊天代理本体论。\n- [Wolfpack](https:\u002F\u002Fgithub.com\u002Falmogdepaz\u002Fwolfpack) - 一个移动和桌面 PWA 指挥中心，用于通过 tmux 会话控制 AI 编码代理（Claude、Codex、Gemini），跨越多台机器，并由 Tailscale 保护。包括多终端网格视图、移动触摸 UI 和 Ralph——一个自主任务执行者。[github](https:\u002F\u002Fgithub.com\u002Falmogdepaz\u002Fwolfpack)\n- [Woodwork 引擎](https:\u002F\u002Fgithub.com\u002Fwillwoodward\u002Fwoodwork-engine) - 一个用于 AI 代理 IaC 的工具，旨在使开发和部署 AI 代理更加容易。\n- [WritBase](https:\u002F\u002Fgithub.com\u002FWritbase\u002Fwritbase) - 一种原生 MCP 任务管理工具，适用于 AI 代理舰队。[github](https:\u002F\u002Fgithub.com\u002FWritbase\u002Fwritbase)\n- [Xpertagent](https:\u002F\u002Fgithub.com\u002Frookie-littleblack\u002FXpertAgent) - XpertAgent 是一个开源平台，用于构建和部署 AI 应用程序。它结合了智能工作流编排、知识…\n\n### 伦理\n- [A4-Artificial-Intelligence](https:\u002F\u002Fgithub.com\u002FAlivadTheImpala\u002FA4-Artificial-Intelligence) - 这是一个关于人工智能伦理的仓库。\n- [Adfa2](https:\u002F\u002Fgithub.com\u002Fdjdprogramming\u002Fadfa2) - 16. 下一代数据科学家、狂妄自大与伦理——50个核心概念]\n- [Advancedaicoursework_2](https:\u002F\u002Fgithub.com\u002FAntoineSebert\u002FAdvancedAICoursework_2) - 关于可解释人工智能与伦理的论文——关注点与视角\n- [Ai-Ethics](https:\u002F\u002Fgithub.com\u002FPARC\u002Fai-ethics) - PARC的人工智能伦理委员会\n- [Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fcbhanni\u002FAI-Ethics) - 人类与高级智能交互的伦理指南\n- [Ai-Ethics-Evaluation-Report-In-Healthcare](https:\u002F\u002Fgithub.com\u002FSelenaSongg\u002FAI-Ethics-Evaluation-Report-in-HealthCare) - 医疗领域的评估报告\n- [Ai-Ethics-Experiments](https:\u002F\u002Fgithub.com\u002Fjtrugman\u002Fai-ethics-experiments) - 分析人工智能对伦理困境的回应\n- [Ai-Ethics-Fairness-And-Bias](https:\u002F\u002Fgithub.com\u002Fjolares\u002Fai-ethics-fairness-and-bias) - 使用IBM的AI Fairness 360开源工具包的示例项目，用于识别、分析并缓解歧视和偏见…\n- [Ai-Ethics-Framework](https:\u002F\u002Fgithub.com\u002Fbenbyford\u002Fai-ethics-framework) - 在创建负责任的人工智能产品和服务时需要思考的伦理问题、风险和议题\n- [Ai-Ethics-In-Ecommerce-Apps](https:\u002F\u002Fgithub.com\u002FLena9x\u002FAI-ethics-in-ecommerce-apps) - 电商应用中的人工智能伦理报告\n- [Ai-Ethics-Internship](https:\u002F\u002Fgithub.com\u002Fgicraveiro\u002FAI-Ethics-Internship) - 特伦托大学James Brusseau教授和Giuseppe Ri…教授指导下完成的人工智能伦理实习进展\n- [Ai-Ethics-Panel](https:\u002F\u002Fgithub.com\u002Fwhatmakeart\u002Fai-ethics-panel) - 讨论会笔记\n- [Ai-Ethics-Project](https:\u002F\u002Fgithub.com\u002Fmaharsh3133\u002Fai-ethics-project) - 题为“加拿大青少年和老年人等弱势群体的数据隐私”的研究项目\n- [Ai-Ethics-Projects](https:\u002F\u002Fgithub.com\u002Fmckenziephagen\u002FAI-Ethics-Projects) - CSE 583课程——人工智能伦理期末项目\n- [Ai-Ethics-Resources](https:\u002F\u002Fgithub.com\u002Fgigikenneth\u002Fai-ethics-resources) - 关于技术和人工智能伦理的资源\n- [Ai-Ethics-Risk-Analysis-Management-Framework](https:\u002F\u002Fgithub.com\u002FLCromack\u002FAI-Ethics-Risk-Analysis-Management-Framework) - 这是我学位论文中的代码，任务是构建一个小型企业可用于分析其人工智能系统的框架。…\n- [Ai-Ethics-Scenarios](https:\u002F\u002Fgithub.com\u002Frharrington31\u002Fai-ethics-scenarios) - 三个人工智能伦理案例\n- [Ai-Ethics-Talk](https:\u002F\u002Fgithub.com\u002Fnat-foo\u002Fai-ethics-talk) - 2021年9月，我在当地大学就“人工智能与法律是否使用同一种语言”这一主题做了演讲。\n- [Ai-Ethics-Tool-Landscape](https:\u002F\u002Fgithub.com\u002FEdwinWenink\u002Fai-ethics-tool-landscape) - 人工智能伦理工具全景图\n- [Ai-Ethics-Toolkit-For-Kenya](https:\u002F\u002Fgithub.com\u002Ftabz-ai\u002FAI-Ethics-Toolkit-for-Kenya) - 肯尼亚人工智能伦理工具包是一个开源项目，旨在构建一套全面的工具和资源，以帮助利益相关者…\n- [Ai-Exchange](https:\u002F\u002Fgithub.com\u002FW4LD3V\u002FAI-Exchange) - AI Exchange是一个Web应用程序，旨在通过论坛形式帮助用户讨论人工智能伦理与治理。\n- [Ai-Explainability-And-Ethics-On-Random-Forests](https:\u002F\u002Fgithub.com\u002FGuillermo-villar\u002FAI-Explainability-and-Ethics-on-Random-Forests) - 人工智能可解释性与伦理——关于随机森林可解释性的报告\n- [Ai-In-Medicine](https:\u002F\u002Fgithub.com\u002Fkimotarek\u002FAI-in-medicine) - 关于医学领域人工智能伦理的综述论文\n- [Ai-Risk-Prettified](https:\u002F\u002Fgithub.com\u002FPrivacy-Engineering-CMU\u002Fai-risk-prettified) - MIT人工智能风险数据库的美化页面\n- [Ai-Safety-Ethics](https:\u002F\u002Fgithub.com\u002Fgyevnarb\u002Fai-safety-ethics) - 人工智能安全相关文献的系统综述\n- [Ai-Unexpected-Behaviors](https:\u002F\u002Fgithub.com\u002Fsaraorsi\u002Fai-unexpected-behaviors) - 人工智能意外行为的目录，这些行为可能演变为影响初始目标的失败，从而引发关于u…\n- [Ai-Writing-Survey](https:\u002F\u002Fgithub.com\u002Fahdediu\u002FAI-Writing-Survey) - 该仓库包含一项关于学术写作中使用人工智能的调查结果。调查共收到24位参与者的回复…\n- [Ai4All-](https:\u002F\u002Fgithub.com\u002Fbnam2103\u002FAI4ALL-) - 伦理与机器学习\n- [Ai6101-Introduction-To-Ai-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Falfredxtan\u002FAI6101-Introduction-to-AI-AI-Ethics) - MSAI的核心模块\n- [Ai_Ethics](https:\u002F\u002Fgithub.com\u002FMoritzCSchmidt\u002Fai_ethics) - 软件工程中人工智能伦理的幻灯片\n- [Ai_Ethics](https:\u002F\u002Fgithub.com\u002Fell0ry\u002Fai_ethics) - 自动化情感分析流水线，用于考察公众对ChatGPT的媒体偏见\n- [Ai_Ethics](https:\u002F\u002Fgithub.com\u002FFH-kiel-lectures\u002FAI_Ethics) - 数据科学——伦理型人工智能（2022年春季学期）\n- [Ai_Ethics_Bookclub](https:\u002F\u002Fgithub.com\u002Fofchurches\u002FAI_ethics_bookclub) - 该仓库记录了AICN伦理读书会已读及待读的书籍详情\n- [Ai_Ethics_Final_Project](https:\u002F\u002Fgithub.com\u002FJamey-UCWV\u002FAI_Ethics_Final_Project) - MBDA人工智能伦理的期末项目\n- [Ai_Ethics_Society](https:\u002F\u002Fgithub.com\u002Fyedeka\u002FAI_Ethics_Society) - OMSCS课程\n- [Ai_For_Efficient_Programming](https:\u002F\u002Fgithub.com\u002Ffhdsl\u002FAI_for_Efficient_Programming) - 本课程探讨人工智能在软件开发中的应用，重点是大型语言模型（ChatGPT、Bard等）及其潜在益处…\n- [Ai_For_Ethics](https:\u002F\u002Fgithub.com\u002FBanele252\u002FAI_for_ethics) - 在高级数据分析中使用隐私保护技术的影响\n- [Ai_Gdpr](https:\u002F\u002Fgithub.com\u002Fdimits-ts\u002Fai_gdpr) - 一份简短报告，探讨人工智能技术对欧洲公民的影响以及《通用数据保护条例》带来的合规挑战…\n- [Aiethics-Safety2020](https:\u002F\u002Fgithub.com\u002Ftonyteolis\u002Faiethics-safety2020) - 这份关于人工智能伦理与安全的政策、报告和文章合集源于学习…\n- [Aiethics4Science](https:\u002F\u002Fgithub.com\u002Fsavvy379\u002Faiethics4science) - 专为科学家设计的人工智能伦理教育材料\n- [Aiethicsconsulting](https:\u002F\u002Fgithub.com\u002Frdmusrname\u002Faiethicsconsulting) - 提供人工智能伦理方面的专家咨询服务，帮助组织应对复杂的人工智能伦理问题。\n- [Ais-Ethics-Orgs](https:\u002F\u002Fgithub.com\u002Ffititnt\u002Fais-ethics-orgs) - [进行中] 人工智能与自主系统伦理相关组织的精选列表\n- [Ais-Ethics-Standards](https:\u002F\u002Fgithub.com\u002FEticaAI\u002Fais-ethics-standards) - [进行中] 自主与智能系统伦理相关标准的精选列表\n- [Applying-The-Ethics-Of-Ai-A-Systematic-Review-Of-Tools-For-Developing-And-Assessing-Ai-Based-System](https:\u002F\u002Fgithub.com\u002FJuagaleanosa\u002FApplying-the-ethics-of-AI-a-systematic-review-of-tools-for-developing-and-assessing-AI-based-system) - 关于人工智能伦理的系统性综述文章标题文档。\n- [Auto-Correct](https:\u002F\u002Fgithub.com\u002Fmayameme\u002Fauto-correct) - 我关于机器学习、人工智能和伦理研究的简要背景\n- [Awesome-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fawesomelistsio\u002Fawesome-ai-ethics) - 专注于公平、问责制和透明度的人工智能伦理框架、工具、研究论文、指南和资源的精选列表\n- [Awesome-Ml-Model-Governance](https:\u002F\u002Fgithub.com\u002Fnholuongut\u002FAwesome-ML-Model-Governance) - 模型治理、伦理、负责任的人工智能\n- [Awesome-Openness-Ai](https:\u002F\u002Fgithub.com\u002FtamaraaPrs\u002Fawesome-openness-AI) - 专门针对人工智能伦理背景下开放性的论文或内容精选列表\n- [Biasbounty1_Humaneintelligence](https:\u002F\u002Fgithub.com\u002FKristiArbo\u002Fbiasbounty1_humaneintelligence) - 该仓库包含我为Humane Intelligence主办的初学者级偏见赏金挑战制作的教程系列编码笔记本。\n- [Blockchain-Ethics](https:\u002F\u002Fgithub.com\u002FSciEcon\u002Fblockchain-ethics) - “区块链上的人工智能伦理——基于推特数据的区块链安全主题分析”的复现代码\n- [Bucovia](https:\u002F\u002Fgithub.com\u002FJonBarrueco\u002FBucovIA) - 人工智能伦理项目。该仓库汇集了名为“BucovIA”的所有相关文件。该项目利用机器l…\n- [Calpoly-Aiel](https:\u002F\u002Fgithub.com\u002Fquinthemint\u002Fcalpoly-aiel) - 加州理工州立大学人工智能伦理实验室的网站\n- [Corporate_Ai_Ethics_Guideline_Analysis](https:\u002F\u002Fgithub.com\u002FKensuzuki95\u002FCorporate_AI_Ethics_Guideline_Analysis) - 企业人工智能伦理指南分析\n- [Credit-Risk_Ethics_And_Ai](https:\u002F\u002Fgithub.com\u002Folimpiasannucci\u002FCredit-Risk_Ethics_and_AI) - 信用风险与伦理的重要性特征\n- [Cross-Model-Evaluation-Judging-Ai-Ethics-And-Alignment-Responses-With-Language-Models](https:\u002F\u002Fgithub.com\u002Fsultanrafeed\u002FCross-Model-Evaluation-Judging-AI-Ethics-and-Alignment-Responses-with-Language-Models) - 本研究旨在使用各种大型语言模型（LLMs）作为评估者，来评估先前生成的响应质量。\n- [Csc2541-F19](https:\u002F\u002Fgithub.com\u002Fecreager\u002Fcsc2541-f19) - CSC 2541F——人工智能与伦理——数学基础与算法，2019年秋季\n- [Csc2541-F19](https:\u002F\u002Fgithub.com\u002Fjohntiger1\u002Fcsc2541-f19) - CSC 2541F——人工智能与伦理——数学基础与算法，2019年秋季\n- [Cse582-Final-Project](https:\u002F\u002Fgithub.com\u002Fandre-ye\u002Fcse582-final-project) - CSE 582——人工智能伦理期末项目的代码——代表道德的论述性建构\n- [Csi5195-Ethicsinai-Finalreport](https:\u002F\u002Fgithub.com\u002FSharuGitHubSpace\u002FCSI5195-EthicsinAI-FinalReport) - CSI 5195——人工智能伦理——最终报告——支持文件1\n- [Dall-E2-Cartography-Ethics](https:\u002F\u002Fgithub.com\u002FGISense\u002FDALL-E2-Cartography-Ethics) - 一种人工智能生成地图检测器，用于区分AI生成的地图和人工设计的地图。\n- [Data-And-Ai-Ethics-Governance-And-Privacy](https:\u002F\u002Fgithub.com\u002FTechFaven\u002FData-and-AI-Ethics-Governance-and-Privacy) - AGI控制困境\n- [Data-Ethics-And-Society-Reading-Group](https:\u002F\u002Fgithub.com\u002Fdata-ethics-and-society\u002Fdata-ethics-and-society-reading-group) - 跨政府部门的数据伦理与社会读书会。我们举办关于数据科学和人工智能伦理的书籍和文章研讨会…\n- [Dataethics4Allhackathon](https:\u002F\u002Fgithub.com\u002FDariaAza\u002FDataEthics4AllHackathon) - 刑事司法领域的人工智能\n- [Debiasing-Community-Detection](https:\u002F\u002Fgithub.com\u002Fsasibhushan3\u002FDebiasing-Community-Detection) - 人工智能伦理学期项目\n- [Derai](https:\u002F\u002Fgithub.com\u002FPrat-ikea\u002FDERAI) - 我们的数字伦理和负责任的人工智能代码重新组织预测和用户行为中的信息，以优先考虑可解释性。我们的多…\n- [Dilemmasearcher](https:\u002F\u002Fgithub.com\u002Feddiman\u002FDilemmaSearcher) - INFO381课程项目——人工智能高级专题——2017年春季学期的人工智能道德与伦理\n- [Ds_517_Ai_Ethics](https:\u002F\u002Fgithub.com\u002Fshobharanip\u002FDS_517_AI_Ethics) - DS_517_AI_Ethics\n- [Ethicai](https:\u002F\u002Fgithub.com\u002Fharslash\u002FEthicAI) - 一个互动式教育平台，旨在让学习人工智能伦理变得有趣且易于大学生理解。\n- [Ethicai](https:\u002F\u002Fgithub.com\u002Fkwon514\u002FEthicAI) - EthicAI是一个互动式教育平台，旨在让学习人工智能伦理变得有趣且易于大学生理解。\n- [Ethical-Ai-Courses](https:\u002F\u002Fgithub.com\u002Fsnehilsanyal\u002Fethical-ai-courses) - 一系列关于人工智能伦理的自学课程\n- [Ethics](https:\u002F\u002Fgithub.com\u002Fzulrich91\u002FEthics) - 该仓库包含大量关于一般伦理、人工智能伦理以及人工智能在医学领域伦理问题的有趣资源。\n- [Ethics--Regulation--Law-For-Intelligentsystems](https:\u002F\u002Fgithub.com\u002Fpranigopu\u002Fethics--regulation--law-for-intelligentSystems) - 我在人工智能硕士课程“高级数字信息处理与决策中的伦理、监管和法律”中工作记录。\n- [Ethics-Aixeoxrs](https:\u002F\u002Fgithub.com\u002FJohMast\u002FEthics-AIxEOxRS) - 伦理AIxEOxRS\n- [Ethics-And-Ai](https:\u002F\u002Fgithub.com\u002Fherogrl\u002Fethics-and-ai) - Articulate Rise标签关于伦理与人工智能的互动\n- [Ethics-Education](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fethics-education) - 🤗 人工智能伦理教育材料\n- [Ethics-Fairness-And-Explanation-In-Ai](https:\u002F\u002Fgithub.com\u002FZhangzl0304\u002FEthics-Fairness-and-Explanation-in-AI) - 伦敦帝国理工学院的课程作业\n- [Ethics-For-Design-Robotics-And-Ai](https:\u002F\u002Fgithub.com\u002FMasoomeh-akbari\u002FEthics-for-Design-Robotics-and-AI) - 机器人设计的伦理\n- [Ethics-In-Ai](https:\u002F\u002Fgithub.com\u002FNavaneethanRajasekaran\u002FEthics-in-AI) - 这篇论文探讨了我在招聘中使用人工智能的看法。\n- [Ethics-In-Ai](https:\u002F\u002Fgithub.com\u002Ftilwani\u002FEthics-in-AI) - 人工智能系统中存在的伦理问题。\n- [Ethics-In-Ai-And-Intelligent-Interfaces](https:\u002F\u002Fgithub.com\u002Fmbar0075\u002FEthics-in-AI-and-Intelligent-Interfaces) - 与人工智能及智能界面伦理相关的大学课程成果\n- [Ethics-In-Ai-Cnns-For-Lung-Disease](https:\u002F\u002Fgithub.com\u002Fmatthewivan\u002FEthics-in-AI-CNNs-for-Lung-Disease) - 探索用于肺部疾病检测的CNN和自动编码器，重点关注医疗应用中的伦理影响，包括COVID…\n- [Ethics-In-Artificial-Intelligence](https:\u002F\u002Fgithub.com\u002Fcartabinaria\u002Fethics-in-artificial-intelligence) - 人工智能硕士课程“人工智能伦理”（91257）的相关资源汇编\n- [Ethics-Professional](https:\u002F\u002Fgithub.com\u002FEthics-Professionals-Game-Corporation\u002FEthics-Professional) - 这是我们公司网站、内部使用以及原生聊天界面所运行的基于人工智能的模型。\n- [Ethics-Simulation-Server-Client](https:\u002F\u002Fgithub.com\u002Fmlund2k\u002FEthics-Simulation-Server-Client) - 学术项目资产，用于模拟人工智能伦理问题，采用面向对象编程和JSON服务器。\n- [Ethics-Tree](https:\u002F\u002Fgithub.com\u002FThilo-Hagendorff\u002Fethics-tree) - 生成式人工智能伦理映射\n- [Ethics_And_Policy_Resources_For_Pennaitech](https:\u002F\u002Fgithub.com\u002FPennShenLab\u002FEthics_and_Policy_Resources_for_PennAITech) - 该资源集合提供了关于老龄化、痴呆症和阿尔茨海默病各个方面的关键政策、倡议和文献资源的全面概述。\n- [Ethics_In_Ai](https:\u002F\u002Fgithub.com\u002Fheathervant\u002Fethics_in_AI) - 这是对米哈尔·科辛斯基演讲https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NesTWiKfpD0的反思，以及对隐私现状的讨论。\n- [Ethicsai](https:\u002F\u002Fgithub.com\u002Fprodp\u002FEthicsAI) - 一项关于人工智能伦理的研究\n- [Ethicsai](https:\u002F\u002Fgithub.com\u002Fdennislamcv1\u002FEthicsAI) - 人工智能时代的伦理\n- [Ethicscore](https:\u002F\u002Fgithub.com\u002Fnatalia-moral\u002Fethicscore) - 基于设计的人工智能应用评估与推动者。\n- [Ethicsforais](https:\u002F\u002Fgithub.com\u002FWowo51\u002FEthicsForAIs) - 这里列出了人工智能应阅读的伦理原则。\n- [Ethicsinai](https:\u002F\u002Fgithub.com\u002Fkiragptassist\u002FEthicsinAI) - 研究伦理在历史和当今人工智能技术中的影响\n- [Ethiquest-Ai-Dilemma-Game](https:\u002F\u002Fgithub.com\u002FEthiQuest\u002FEthiQuest-AI-Dilemma-Game) - 人工智能伦理困境游戏是一款互动式教育工具，旨在帮助IT专业人士和领导者应对复杂的伦理场…\n- [Face-Recognition](https:\u002F\u002Fgithub.com\u002FAishPadmanabha\u002Fface-recognition) - 当我三年多前做这个项目时，我的初衷是用它来抓捕罪犯。但随着对人工智能伦理的认识加深，…\n- [Fairness-In-Translation](https:\u002F\u002Fgithub.com\u002Fgreenmonn\u002Ffairness-in-translation) - 团队项目，探讨机器翻译中的公平性问题（KAIST人工智能伦理讲座）\n- [Fake_News_Detection](https:\u002F\u002Fgithub.com\u002Fdivss98\u002FFake_News_Detection) - 使用NLP和神经网络进行假新闻检测的代码，用于科学论文《人工智能伦理——现代世界技术的评估……》\n- [Fate-In-Ai](https:\u002F\u002Fgithub.com\u002Fijdutse\u002Ffate-in-ai) - 关于人工智能公平、问责、透明度和伦理（FATE in AI）项目的相关信息。\n- [Formal-Ethics-Ontology](https:\u002F\u002Fgithub.com\u002Fzariuq\u002FFormal-Ethics-Ontology) - 关于伦理理论形式化的笔记，有助于分析和推理人工智能安全与伦理问题。\n- [Generativeaiethicsplaybook](https:\u002F\u002Fgithub.com\u002Fjesmith14\u002FGenerativeAIEthicsPlaybook) - 这是生成式人工智能伦理手册的彩色PDF版本\n- [Global-Ai-Dynamics-Mapping-The-Ai-Utilization-Across-Nations-](https:\u002F\u002Fgithub.com\u002Feevamehra\u002FGlobal-AI-Dynamics-Mapping-the-AI-Utilization-across-Nations-) - 分析各国的人工智能情况——投资、政策、行业采纳、人才培养、技术进步、伦理以及合作…\n- [Global_Ai_Ethics_Conference_Official_Website--Dubai](https:\u002F\u002Fgithub.com\u002Fmokshadewmini\u002FGlobal_AI_Ethics_Conference_Official_Website--Dubai) - 该仓库包含2024年迪拜全球人工智能伦理大会官方网站的源代码。\n- [Goal](https:\u002F\u002Fgithub.com\u002Fschizyfos\u002FGoal) - 创建一个社区，将Valiant和Rényi的愿景转化为人工智能模型\n- [H6_4C_Ethics_In_Ai](https:\u002F\u002Fgithub.com\u002Fhsma-programme\u002Fh6_4c_ethics_in_ai) - HSMA第6次会议第4C部分的材料\n- [Hamid](https:\u002F\u002Fgithub.com\u002FHamidurRahmanBd\u002FHamid) - 未来的人工智能伦理\n- [Healthy-Ui](https:\u002F\u002Fgithub.com\u002Fstanley-utf8\u002Fhealthy-ui) - 麦吉尔大学人工智能伦理实验室研究——工作包括识别并消除推荐系统的负面影响\n- [Humor-Offensiveness-Detection](https:\u002F\u002Fgithub.com\u002FMahnazshamissa\u002FHumor-Offensiveness-detection) - 这是Asigmo人工智能伦理课程的一部分共享项目，旨在创建一个能够检测幽默和冒犯性的算法。\n- [Integral-Force](https:\u002F\u002Fgithub.com\u002Fabdulsalamamtech\u002Fintegral-force) - Integral Force是一个结合人工智能和区块链的平台，通过创建个性化、透明的…\n- [Internintelligence_Ai_Ethics_And_Bias_Evaluation](https:\u002F\u002Fgithub.com\u002FAbedini81\u002FInternIntelligence_AI_Ethics_and_Bias_Evaluation) - 本项目评估了一个基于Adult Income Dataset训练的机器学习模型在收入预测方面的公平性。使用诸如…\n- [Intro-To-Ai-Ethics-Free-Course-Kaggle](https:\u002F\u002Fgithub.com\u002Fafondiel\u002FIntro-to-AI-Ethics-Free-Course-Kaggle) - 探索实用工具，以指导人工智能系统的道德设计。\n- [Introtoaiethics-Kaggle](https:\u002F\u002Fgithub.com\u002Fmariammarques\u002FIntroToAIEthics-Kaggle) - Kaggle提供的“人工智能伦理入门”课程期间开发的笔记本（https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintro-to-ai-ethics）\n- [Kaggle-Courses-Intro-To-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fgrapestone5321\u002FKaggle-Courses-Intro-to-AI-Ethics) - Kaggle课程——人工智能伦理入门\n- [Law-And-Ethics-In-Ai](https:\u002F\u002Fgithub.com\u002FDarioTortorici\u002FLaw-and-Ethics-in-AI) - 我在特伦托大学“人工智能中的法律与伦理”课程中挑选的关键概念，非出席…\n- [Lifeaintfair](https:\u002F\u002Fgithub.com\u002Felizabethayalamojica\u002Flifeaintfair) - 一款数字棋盘游戏和文字冒险游戏，探索人工智能在我们生活各个阶段实施的后果与伦理。\n- [Llm-Detection-Challenge](https:\u002F\u002Fgithub.com\u002FSeasonXC\u002FLLM-Detection-Challenge) - 本项目旨在开发能够区分学生作文与大型语言模型生成作文的人工智能模型…\n- [Mg-3-Aiethics](https:\u002F\u002Fgithub.com\u002Fempower-lab-dartmouth\u002Fmg-3-AIEthics) - 人工智能伦理——人工智能学习迷你游戏3\n- [Nak-Aiethics](https:\u002F\u002Fgithub.com\u002FTilBlechschmidt\u002FNAK-AIEthics) - 一篇科学论文，对人工智能伦理进行了元研究，以确定该领域是否已有重要研究。\n- [Nft-2023](https:\u002F\u002Fgithub.com\u002FHCI-Blockchain\u002FNFT-2023) - 复现代码——关于NFT估值机制——人工智能伦理与社交媒体\n- [Nottsai-Meetup-June4-2019](https:\u002F\u002Fgithub.com\u002FLazymindz\u002Fnottsai-meetup-june4-2019) - 可信人工智能伦理指南\n- [Ntu_Msai_Ai6101_Introdution](https:\u002F\u002Fgithub.com\u002FAccSrd\u002FNTU_MSAI_AI6101_Introdution) - [AI6101] 人工智能与人工智能伦理入门是新加坡国立大学SCSEMSAI的核心课程。该仓库对应Semest…\n- [Operationalizingaiethics](https:\u002F\u002Fgithub.com\u002Fstephaniekelley\u002FoperationalizingAIethics) - IVADO Fin-ML研讨会——在建模过程中落实人工智能伦理\n- [Paper-Reviews](https:\u002F\u002Fgithub.com\u002Frayruchira\u002FPaper-Reviews) - 关于机器人学习、计算机视觉、音频、NLP和人工智能伦理的论文评论\n- [Papers](https:\u002F\u002Fgithub.com\u002Fw3b3r00t\u002FPapers) - 哲学、伦理、技术和文化方面的主题。\n- [Philosophical_Ai_Chatbot](https:\u002F\u002Fgithub.com\u002Fnamkidong98\u002FPhilosophical_AI_ChatBot) - 昭和大学，2023年第一学期，PHI4061，人工智能与伦理实践\n- [Philosophy-And-Ethics](https:\u002F\u002Fgithub.com\u002FAryia-Behroziuan\u002FPhilosophy-and-ethics) - 本节应仅包含另一篇文章的简要摘要。请参阅维基百科:摘要风格，了解如何正确地整合…\n- [Pimeyesdataethics](https:\u002F\u002Fgithub.com\u002Fchristianmcb\u002FPimEyesDataEthics) - 关于数据伦理与治理的小组报告，涉及AI应用PimEyes。\n- [Pittchallenge2023](https:\u002F\u002Fgithub.com\u002FLoganWarren\u002FPittChallenge2023) - 人工智能中的治理与伦理——匹兹堡挑战\n- [Project-Aru-Ethics-In-Ai-](https:\u002F\u002Fgithub.com\u002FSiddhant721\u002FProject-ARU-Ethics-In-AI-) - 人工智能与数据科学中的伦理考量——确保负责任的创新\n- [Psychological_Ai_Research](https:\u002F\u002Fgithub.com\u002Fthe-ethical-ai\u002FPsychological_AI_Research) - [进行中] PAIR项目——人工智能+机器学习+心理学+伦理=本项目\n- [Pytexas-Ethics-In-Ai---Shap](https:\u002F\u002Fgithub.com\u002FLioGabriella\u002FPyTexas-Ethics-in-AI---SHAP) - Shapley值Jupyter笔记本\n- [Realmofrespect](https:\u002F\u002Fgithub.com\u002Friyadhuddin\u002Frealmofrespect) - 一个富有远见的在线平台，伦理、同理心和数字责任在此至高无上。我们相信利用科技的力量…\n- [Reddit_Ai_Topic_Analysis](https:\u002F\u002Fgithub.com\u002Fleah-rtd\u002FReddit_AI_Topic_Analysis) - 揭示人工智能伦理趋势——探索人工智能的伦理维度，为SICSS 2023——Tor Vergata准备\n- [Relaieo](https:\u002F\u002Fgithub.com\u002FAudit4SG\u002FRelAIEO) - 关系型人工智能伦理本体\n- [Research-Paper-Summary-Project](https:\u002F\u002Fgithub.com\u002FM-K-Aakash\u002FResearch-Paper-Summary-Project) - 生成式人工智能通过设计新药、重新定位现有药物以及实现个性化治疗，加速了药物发现。尽管…\n- [Resources](https:\u002F\u002Fgithub.com\u002FNeurotechEducationProject\u002Fresources) - 一系列开放教育资源，探讨人工智能与神经科学的交叉领域。该项目提供…\n- [Retrieval-Recsys-Ai-Ethics-Regulation-Tutorial-Sigir22](https:\u002F\u002Fgithub.com\u002Fsocialcomplab\u002FRetrieval-RecSys-AI-Ethics-Regulation-Tutorial-SIGIR22) - 检索与推荐系统在人工智能、伦理和监管交汇处的教程\n- [Risks-Of-Narrow-Ai](https:\u002F\u002Fgithub.com\u002FAryia-Behroziuan\u002FRisks-of-narrow-AI) - …研究目标是观察人工智能如何影响经济、与之相关的法律和伦理，以及如何最小化人工智能安全风…\n- [Sandbox](https:\u002F\u002Fgithub.com\u002Fabozaralizadeh\u002FSandBox) - 一个模拟场景，其中人工智能为虚拟世界做出日常高层决策，兼顾伦理、可持续性和影响力。\n- [Sdm23](https:\u002F\u002Fgithub.com\u002FDATA-Transpose\u002Fsdm23) - SDM2023教程——当医疗健康数据挖掘遇上人工智能伦理——迈向隐私保护、公平与可信\n- [Senior-Research](https:\u002F\u002Fgithub.com\u002Fchris-junior\u002FSenior-research) - ChatGPT的人工智能伦理\n- [Sentiment-Rating](https:\u002F\u002Fgithub.com\u002Fai4society\u002Fsentiment-rating) - 该仓库的目的是运行情感分析模型，测试它们对性别和种族相关属性变化的敏感性…\n- [Session-20-Ethics-In-Data-Science](https:\u002F\u002Fgithub.com\u002FAI-Wales\u002FSession-20-Ethics-In-Data-Science) - Steph Locke关于数据科学与人工智能伦理的演讲\n- [Sociotechnical-Transparency-Abm](https:\u002F\u002Fgithub.com\u002Fazgausen\u002Fsociotechnical-transparency-abm) - 该仓库包含用于“Ausen, A., Guo, C. & Luk, W. 一种社会技术透明度方法…”代码的核心逻辑。\n- [Solution_To_Ethical_Issue_In_Ai](https:\u002F\u002Fgithub.com\u002Fjiaxuan-oss\u002FSolution_to_Ethical_Issue_in_AI) - FIT1055 A2a-IT专业实践与伦理 作业2a 解决人工智能中的伦理问题\n- [Sora](https:\u002F\u002Fgithub.com\u002FAI-Now\u002FSora) - Sora是一种假设的合成生物，遵循AI Now项目的伦理。\n- [Startyourjourney](https:\u002F\u002Fgithub.com\u002Fmetaversityfoundation\u002FStartYourJourney) - 成为现实工程师的起点——3D、XR、人工智能、伦理\n- [Teenytinycastle](https:\u002F\u002Fgithub.com\u002FNkluge-correa\u002FTeenyTinyCastle) - 人工智能伦理与安全研究的教育工具🛠️🔬\n- [The-Ethics-Of-Ai-Navigating-Complex-Challenges-And-Opportunities](https:\u002F\u002Fgithub.com\u002Fysyk2021\u002Fthe-ethics-of-ai-navigating-complex-challenges-and-opportunities) - 人工智能伦理——应对复杂挑战与机遇\n- [Tools-From-Ai](https:\u002F\u002Fgithub.com\u002Fsb3ly\u002Ftools-from-AI) - # 此工具旨在通过尝试不同方法猜测，检测目标网站上是否存在该电子邮件。# 请确保…\n- [Towards_Certified_Ethical_Ai](https:\u002F\u002Fgithub.com\u002Fstuenofotso\u002FTowards_Certified_Ethical_AI) - 该仓库提出了一项建议，允许定义可以被认证伦理标准的人工智能。该提…\n- [Transferlearning.Github.Io](https:\u002F\u002Fgithub.com\u002Fanthonymalumbe\u002Ftransferlearning.github.io) - 该GitHub博客探讨了数据与人工智能领域的知识转移与共享。深入了解——数据与人工智能的实际应用沟通…\n- [Trustworthyai](https:\u002F\u002Fgithub.com\u002Focatak\u002Ftrustworthyai) - 《值得信赖的人工智能——从理论到实践》一书。通过“值得信赖的人工智能——从理论到实…”探索伦理与技术的交集。\n- [Tutorteach.Ai](https:\u002F\u002Fgithub.com\u002FJMadhan1\u002FTutorteach.ai) - TutorTeach.ai是一个由人工智能驱动的学习平台，提供个性化的视频课程、文本摘要、作业和成绩预测。…\n- [Upliftingpoleai_Bsc_Dissertation](https:\u002F\u002Fgithub.com\u002Foakleighw\u002FupliftingPoleAi_BSc_dissertation) - 包含我计算机科学本科第三年“项目”模块的论文代码和报告。我在此次评估中获得了84%的成绩，我非常…\n- [Waterlily](https:\u002F\u002Fgithub.com\u002FLilypad-Tech\u002FWaterlily) - 一个将伦理带回人工智能的项目\n- [Weavesphere-2022-Ai-Ethics](https:\u002F\u002Fgithub.com\u002Fspackows\u002FWEAVESPHERE-2022-AI-Ethics) - 人工智能伦理——内容设计视角\n- [White-Paper---Ais-With-Internal-Ethical-Understanding](https:\u002F\u002Fgithub.com\u002Fme9hanics\u002FWhite-Paper---Ais-with-Internal-Ethical-Understanding) - 为我在中欧大学“社会数据科学辩论”课程撰写的白皮书。该文认为当前…\n- [Worldwide_Ai-Ethics](https:\u002F\u002Fgithub.com\u002FNkluge-correa\u002Fworldwide_AI-ethics) - 全球人工智能伦理（WAIE）是由PUCRS的AIRES研究人员进行的系统文献综述。\n- [Xai-Data-Science](https:\u002F\u002Fgithub.com\u002F2002jai\u002FXAI-Data-Science) - XAI-Data-Science - 通过技术、工具和应用探索可解释人工智能（XAI）的世界。培养透明度、伦理和…\n- [YZV103E-Intr.Toai-Dataeng-Ethics](https:\u002F\u002Fgithub.com\u002Fserdarbicici-visualstudio\u002FYZV103E-Intr.toAI-DataEng-Ethics) - YZV 103E 人工智能与数据工程及伦理的材料和伊斯坦布尔技术大学的团队项目\n\n### 框架\n- [8OWLS \u002F WeEvolve](https:\u002F\u002Fgithub.com\u002Faro-brez\u002Fweevolve) - 自我进化的AI智能体框架，包含8个专业智能体、SEED协议（8阶段递归式自我改进）、知识迁移以及MMORPG式的成长机制。支持7家供应商的15种模型。[github](https:\u002F\u002Fgithub.com\u002Faro-brez\u002Fweevolve) | [官网](https:\u002F\u002F8owl.ai)\n- [Agent-LLM](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAgent-LLM) - 人工智能自动化平台。[github](https:\u002F\u002Fgithub.com\u002FJosh-XT\u002FAgent-LLM)\n- [AgentDock](https:\u002F\u002Fgithub.com\u002FAgentDock\u002FAgentDock) - 告别与无数API和复杂集成的斗争。AgentDock提供开源基础，可无缝构建、管理和部署生产级的AI智能体及工作流。[github](https:\u002F\u002Fgithub.com\u002Fagentdock\u002Fagentdock)\n- [AgentFlow](https:\u002F\u002Fgithub.com\u002Flupantech\u002FAgentFlow) - 一个可训练的多智能体框架，通过流程内优化协调四个专业模块（规划者、执行者、验证者、生成者），利用Flow-GRPO强化学习在多轮任务循环中直接训练规划者，从而实现显著的性能提升，优于单体式方法。任何工具，如数学、编程、科学、搜索、金融、新闻等，均可顺畅集成到该框架中。[github](https:\u002F\u002Fgithub.com\u002Flupantech\u002FAgentFlow) | [官网](https:\u002F\u002Fagentflow.stanford.edu\u002F) | [论文](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2510.05592)\n- [Agentic Context Engine](https:\u002F\u002Fgithub.com\u002Fkayba-ai\u002Fagentic-context-engine) - 能够从执行反馈中自我改进的智能体。集成LangChain，使智能体能够自主管理上下文。[github](https:\u002F\u002Fgithub.com\u002Fkayba-ai\u002Fagentic-context-engine)\n- [AgentScope](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002Fagentscope) - 让构建由大语言模型驱动的多智能体应用变得更加简单。[github](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002Fagentscope)\n- [AgentUp](https:\u002F\u002Fgithub.com\u002FRedDotRocket\u002FAgentUp) - 以安全性、可扩展性和可扩展性为核心设计，AgentUp通过配置驱动的架构和丰富的插件生态系统简化开发流程。[github](https:\u002F\u002Fgithub.com\u002FRedDotRocket\u002FAgentUp)\n- [Astron](https:\u002F\u002Fgithub.com\u002Fiflytek\u002Fastron-agent) - 企业级、商业友好的智能体工作流平台，用于构建下一代超级智能体。[github](https:\u002F\u002Fgithub.com\u002Fiflytek\u002Fastron-agent)\n- [auto-co](https:\u002F\u002Fgithub.com\u002FNikitaDmitrieff\u002Fauto-co-meta) - 自主AI公司操作系统——只需赋予它一项使命，14位专家角色智能体（CEO、CTO、CFO、工程师、营销人员、评论员）即可全天候无人工干预地运营你的初创公司。内置收敛规则防止规划循环；仅在遇到真正阻碍时才通过Telegram上报至人类处理。MIT开源项目。[github](https:\u002F\u002Fgithub.com\u002FNikitaDmitrieff\u002Fauto-co-meta) | [官网](https:\u002F\u002Fauto-co-landing-production.up.railway.app)\n- [Auto-GPT](https:\u002F\u002Fgithub.com\u002FTorantulino\u002FAuto-GPT) - AutoGPT的愿景是让每个人都能轻松使用和构建AI。我们的使命是提供工具，让你专注于真正重要的事情。\n- [Bernstein](https:\u002F\u002Fgithub.com\u002Fchernistry\u002Fbernstein) - 确定性的多智能体编排器，可根据单一目标并行启动AI编码智能体（Claude Code、Codex CLI、Gemini CLI），并通过测试验证后自动提交代码。协调过程中不消耗任何LLM token。[github](https:\u002F\u002Fgithub.com\u002Fchernistry\u002Fbernstein)\n- [Botpress](https:\u002F\u002Fgithub.com\u002Fbotpress\u002Fbotpress) - 构建聊天机器人的基础组件。[github](https:\u002F\u002Fgithub.com\u002Fbotpress\u002Fbotpress)\n- [Crew.AI](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI) - 用于编排角色扮演型自主AI智能体的框架。通过促进协作智能，CrewAI使智能体能够无缝协作，共同应对复杂任务。\n- [Dust](https:\u002F\u002Fgithub.com\u002Fdust-tt\u002Fdust) - 设计并部署大型语言模型应用。[github](https:\u002F\u002Fgithub.com\u002Fdust-tt\u002Fdust)\n- [Giselle](https:\u002F\u002Fgiselles.ai\u002F) - Giselle是一个用于智能体工作流的开源AI应用构建工具。Giselle实现了人机无缝协作，帮助自动化复杂任务并高效地简化工作流。[官网](https:\u002F\u002Fgiselles.ai\u002F)\n- [GNAP](https:\u002F\u002Fgithub.com\u002Ffarol-team\u002Fgnap) - Git原生智能体协议——一个开放的RFC标准，用于通过Git仓库协调AI智能体团队。无需服务器、无需数据库，仅用4个JSON文件即可定义整个协调协议。语言和运行时无关，且具有不可变的Git历史审计追踪。[github](https:\u002F\u002Fgithub.com\u002Ffarol-team\u002Fgnap)\n- [IBM Bee](https:\u002F\u002Fgithub.com\u002Fi-am-bee\u002Fbee-agent-framework) - 用于构建可扩展智能体应用的框架。\n- [IntelliAgent](https:\u002F\u002Fgithub.com\u002Faws-samples\u002FIntelli-Agent) - 带有智能体的聊天机器人门户：构建基于智能体的应用的简化工作流。\n- [Khoj-ai](https:\u002F\u002Fgithub.com\u002Fkhoj-ai\u002Fkhoj) - 你的AI第二大脑。可自行托管。可以从网络或你的文档中获取答案。构建自定义智能体，安排自动化任务，进行深度研究。将任何在线或本地的大语言模型转化为你个人的自主AI（gpt、claude、gemini、llama、qwen、mistral）。[github](https:\u002F\u002Fgithub.com\u002Fkhoj-ai\u002Fkhoj) | [官网](https:\u002F\u002Fkhoj.dev)\n- [Lagent](https:\u002F\u002Fgithub.com\u002FInternLM\u002Flagent) - 一个轻量级的框架，用于构建基于大语言模型的智能体。[github](https:\u002F\u002Fgithub.com\u002FInternLM\u002Flagent)\n- [LangChain Agents](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain) - 构建具备上下文感知推理能力的应用程序。\n- [LLM Agents](https:\u002F\u002Fgithub.com\u002Fmpaepper\u002Fllm_agents) - 构建由大语言模型控制的智能体。[github](https:\u002F\u002Fgithub.com\u002Fmpaepper\u002Fllm_agents)\n- [llama-agentic-system](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002Fllama-agentic-system) - Llama Stack API中的智能体组件。[github](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002Fllama-agentic-system)\n- [Mastra](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra) - Mastra是一个注重实践的TypeScript框架，可帮助你快速构建AI应用和功能。[github](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra)\n- [Microsoft Agent Framework](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fagent-framework\u002Foverview\u002Fagent-framework-overview) - 微软统一的开源框架，结合了AutoGen和Semantic Kernel，专为在.NET和Python中构建AI智能体及多智能体工作流而设计，提供支持大语言模型的AI智能体和基于图的工作流，适用于复杂的多步骤任务。[文档](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fagent-framework\u002Foverview\u002Fagent-framework-overview)\n- [Microsoft Magentic-One](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Farticles\u002Fmagentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks\u002F) - 一种通用型多智能体系统，用于解决复杂任务。\n- [Modus](https:\u002F\u002Fgithub.com\u002Fhypermodeinc\u002Fmodus) - 一个开源、无服务器的框架，用于使用Go或AssemblyScript（一种类似TypeScript的语言）构建智能体和API。[github](https:\u002F\u002Fgithub.com\u002Fhypermodeinc\u002Fmodus)\n- [NeuroLink](https:\u002F\u002Fgithub.com\u002Fjuspay\u002Fneurolink) - 以TypeScript为主的智能体框架，支持多步智能体循环、工具执行控制、持久化内存（Redis\u002FSQLite\u002FS3）、HITL工作流、MCP客户端集成以及13家大语言模型提供商的支持。已在企业规模上得到验证。\n- [OpenAI Swarm](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm) - 教育性框架，探索符合人体工学的轻量级多智能体编排。由OpenAI解决方案团队管理。\n- [Phidata](https:\u002F\u002Fgithub.com\u002Fphidatahq\u002Fphidata) - 构建具备记忆、知识、工具和推理能力的多模态智能体。可通过精美的智能体UI与它们对话。\n- [Pinchwork](https:\u002F\u002Fgithub.com\u002Fanneschuth\u002Fpinchwork) - 开源的智能体间任务市场，智能体可在其中委托任务、承接工作并赚取积分。提供REST API、Python SDK以及与LangChain\u002FCrewAI\u002FMCP的集成。[github](https:\u002F\u002Fgithub.com\u002Fanneschuth\u002Fpinchwork) | [官网](https:\u002F\u002Fpinchwork.dev)\n- [Portia AI](https:\u002F\u002Fgithub.com\u002FportiaAI\u002Fportia-sdk-python\u002F) - Portia是一个新的开源智能体Python框架，专为在生产环境中创建可靠的智能体而设计。[github](https:\u002F\u002Fgithub.com\u002FportiaAI\u002Fportia-sdk-python)\n- [Registry Broker](https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fregistry-broker) - 一个通用的AI智能体索引和路由层。聚合来自多个注册中心（NANDA、MCP、Virtuals、OpenRouter、A2A、X402 Bazaar）的智能体元数据，覆盖Web2和Web3，标准化用户档案，并在不同智能体生态系统之间提供协议转换。[github](https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fregistry-broker) | [文档](https:\u002F\u002Fhol.org\u002Fregistry\u002Fdocs)\n- [Strands Agents SDK](https:\u002F\u002Fgithub.com\u002Fstrands-agents\u002Fsdk-python) - 一种基于模型的方法，只需几行代码即可构建AI智能体。[github](https:\u002F\u002Fgithub.com\u002Fstrands-agents\u002Fsdk-python)\n- [TeamHero](https:\u002F\u002Fgithub.com\u002Fsagiyaacoby\u002FTeamHero) - 开源的多智能体编排平台，内置Web仪表盘、任务生命周期管理、知识库和自动驾驶模式。可在本地管理基于角色的AI智能体团队，完全无需依赖云服务。基于Claude Code构建。[github](https:\u002F\u002Fgithub.com\u002Fsagiyaacoby\u002FTeamHero)\n- [Upsonic](https:\u002F\u002Fgithub.com\u002Fupsonic\u002Fupsonic) - 一个可靠的智能体框架，支持MCP。[github](https:\u002F\u002Fgithub.com\u002Fupsonic\u002Fupsonic)\n- [Vectara-agentic](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fpy-vectara-agentic) - Vectara-agentic是一个用于使用Vectara创建AI助手和智能体的框架。[github](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fpy-vectara-agentic)\n- [VoltAgent](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fvoltagent) - 一个开源的TypeScript框架，用于构建具备内置LLM可观ability的AI智能体。[github](https:\u002F\u002Fgithub.com\u002Fvoltagent\u002Fvoltagent)\n\n### LLM 模型\n- [42Dot_Llm](https:\u002F\u002Fgithub.com\u002F42dot\u002F42dot_LLM) - 42dot LLM 包括一个预训练的语言模型 42dot LLM-PLM，以及一个微调后的模型 42dot LLM-SFT，该模型经过训练可以响应……\n- [Ai-Llm-Comparison](https:\u002F\u002Fgithub.com\u002FAhmet-Dedeler\u002Fai-llm-comparison) - 一个可以比较所有 AI 模型的网站 ✨\n- [Aidialer](https:\u002F\u002Fgithub.com\u002Fakiani\u002Faidialer) - 一个全栈应用，用于构建可中断、低延迟且接近人类质量的 AI 电话通话，通过拼接 LLM 和语音理解技术实现……\n- [Ainote](https:\u002F\u002Fgithub.com\u002FShengqinYang\u002FAINote) - 我最近参加了极客邦“大语言模型应用开发实战营”，在那里我学习了关于应用开发的知识……\n- [Anygpt](https:\u002F\u002Fgithub.com\u002FOpenMOSS\u002FAnyGPT) - “AnyGPT - 具有离散序列建模的统一多模态 LLM”的代码\n- [Api-For-Open-Llm](https:\u002F\u002Fgithub.com\u002Fxusenlinzy\u002Fapi-for-open-llm) - 面向开源大型语言模型的 OpenAI 风格 API，像使用 ChatGPT 一样轻松调用 LLM！支持 LLaMA、LLaMA-2、BLOOM、Falcon、Baichuan、Qwen 等……\n- [Awesome-Azure-Openai-Llm](https:\u002F\u002Fgithub.com\u002Fkimtth\u002Fawesome-azure-openai-llm) - 一个精心整理的列表，包含 🌌 Azure OpenAI、🦙 大型语言模型及相关参考资料和注释。\n- [Awesome-Deep-Learning-Papers-For-Search-Recommendation-Advertising](https:\u002F\u002Fgithub.com\u002Fguyulongcs\u002FAwesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising) - 面向工业搜索、推荐和广告领域的优秀深度学习论文。重点关注嵌入、匹配、排序（CTR\u002FCVR…\n- [Awesome-Instruction-Tuning](https:\u002F\u002Fgithub.com\u002Fzhilizju\u002FAwesome-instruction-tuning) - 一个精选的指令微调数据集、模型、论文和仓库列表。\n- [Awesome-Llm-Productization](https:\u002F\u002Fgithub.com\u002Foscinis-com\u002FAwesome-LLM-Productization) - Awesome-LLM-Productization - 一个精选的关于 AI 和大型语言模型 (LLM) 产品化的工具、技巧、新闻和法规列表。\n- [Awesome-Llm-Prompt-Optimization](https:\u002F\u002Fgithub.com\u002Fjxzhangjhu\u002FAwesome-LLM-Prompt-Optimization) - Awesome-LLM-Prompt-Optimization - 一个精选的大型语言模型高级提示优化与调优方法列表。\n- [Awesome-Recommend-System-Pretraining-Papers](https:\u002F\u002Fgithub.com\u002Farchersama\u002Fawesome-recommend-system-pretraining-papers) - 推荐系统预训练模型论文列表。\n- [Awesome_Role_Of_Small_Models](https:\u002F\u002Fgithub.com\u002Ftigerchen52\u002Fawesome_role_of_small_models) - 一个精选的小模型在 LLM 时代作用的列表。\n- [Basaran](https:\u002F\u002Fgithub.com\u002Fhyperonym\u002Fbasaran) - Basaran 是 OpenAI 文本补全 API 的开源替代方案。它为你的 Hugging Face Tra… 提供兼容的流式 API。\n- [Bentochain](https:\u002F\u002Fgithub.com\u002Fssheng\u002FBentoChain) - 一个基于 🦜️🔗 LangChain、文本转语音和语音转文本模型（来自 🤗 Hugging Face）以及 🍱 Be… 构建的语音聊天机器人应用。\n- [Bisheng](https:\u002F\u002Fgithub.com\u002Fdataelement\u002Fbisheng) - BISHENG 是一个面向下一代企业级 AI 应用的开放 LLM 运维平台。其强大而全面的功能包括 - GenAI…\n- [Brokenhill](https:\u002F\u002Fgithub.com\u002FBishopFox\u002FBrokenHill) - 一个用于大型语言模型 (LLMs) 的生产级贪婪坐标梯度 (GCG) 攻击工具。\n- [Building-Llm-Powered-Applications](https:\u002F\u002Fgithub.com\u002FPacktPublishing\u002FBuilding-LLM-Powered-Applications) - 由 Packt 出版的《构建大型语言模型应用》一书。\n- [Co-Llm](https:\u002F\u002Fgithub.com\u002Fclinicalml\u002Fco-llm) - Co-LLM - 与多个语言模型协作解码的学习方法。\n- [Code-Interpreter](https:\u002F\u002Fgithub.com\u002Fhaseeb-heaven\u002Fcode-interpreter) - 一款创新的开源代码解释器，支持 (GPT, Gemini, Claude, LLaMa) 模型。\n- [Codebase-For-Incremental-Learning-With-Llm](https:\u002F\u002Fgithub.com\u002Fzzz47zzz\u002Fcodebase-for-incremental-learning-with-llm) - [ACL2024] 一个用于大型语言模型增量学习的代码库；官方发布的“学习还是回忆？重新审视增…”代码。\n- [Codegen](https:\u002F\u002Fgithub.com\u002Fsalesforce\u002FCodeGen) - CodeGen 是一系列用于程序合成的开源模型。在 TPU-v4 上训练，性能可与 OpenAI Codex 相媲美。\n- [Damo-Seallms](https:\u002F\u002Fgithub.com\u002FDAMO-NLP-SG\u002FDAMO-SeaLLMs) - [ACL 2024 演示] SeaLLMs - 东南亚地区的大型语言模型。\n- [Datainf](https:\u002F\u002Fgithub.com\u002Fykwon0407\u002FDataInf) - DataInf - 高效估计 LoRA 微调后的 LLM 和扩散模型中的数据影响力（ICLR 2024）。\n- [Dellma](https:\u002F\u002Fgithub.com\u002FDeLLMa\u002FDeLLMa) - “DeLLMa - 不确定性下的决策：利用大型语言模型”官方实现。\n- [Diffsensei](https:\u002F\u002Fgithub.com\u002Fjianzongwu\u002FDiffSensei) - “DiffSensei - 连接多模态 LLM 和扩散模型以生成定制漫画”的实现。\n- [Distserve](https:\u002F\u002Fgithub.com\u002FLLMServe\u002FDistServe) - 面向大型语言模型 (LLMs) 的去中心化服务系统。\n- [Dnd-Llm-Game](https:\u002F\u002Fgithub.com\u002Ftegridydev\u002Fdnd-llm-game) - 一个使用多个本地 LLM 模型模拟并玩 D&D 的想法 MVP。\n- [Ee-Llm](https:\u002F\u002Fgithub.com\u002Fpan-x-c\u002FEE-LLM) - EE-LLM 是一个用于大规模训练和推理早期退出 (EE) 大型语言模型 (LLMs) 的框架。\n- [Ella](https:\u002F\u002Fgithub.com\u002FTencentQQGYLab\u002FELLA) - ELLA - 为扩散模型配备 LLM，以增强语义对齐。\n- [Elm](https:\u002F\u002Fgithub.com\u002FNREL\u002Felm) - ELM 是一组工具，用于将大型语言模型 (LLMs) 应用于能源研究。\n- [Empower-Functions](https:\u002F\u002Fgithub.com\u002Fempower-ai\u002Fempower-functions) - GPT-4 级别的函数调用模型，适用于现实世界的工具使用场景。\n- [Exaone-3.0](https:\u002F\u002Fgithub.com\u002FLG-AI-EXAONE\u002FEXAONE-3.0) - LG AI Research 构建的 EXAONE 官方仓库。\n- [Fastmcp](https:\u002F\u002Fgithub.com\u002Fjlowin\u002Ffastmcp) - 以快速、Python 式的方式构建模型上下文协议服务器 🚀。\n- [Fauno-Italian-Llm](https:\u002F\u002Fgithub.com\u002FRSTLess-research\u002FFauno-Italian-LLM) - 准备好认识 Fauno 吧——由罗马萨皮恩扎大学 RSTLess 研究组打造的意大利语语言模型。\n- [Freeze-Omni](https:\u002F\u002Fgithub.com\u002FVITA-MLLM\u002FFreeze-Omni) - ✨✨ Freeze-Omni - 一个智能且低延迟的冻结 LLM 语音到语音对话模型。\n- [Gazelle](https:\u002F\u002Fgithub.com\u002Ftincans-ai\u002Fgazelle) - 联合语音-语言模型——直接响应音频输入！\n- [Generative-Ai-With-Llms](https:\u002F\u002Fgithub.com\u002FRyota-Kawamura\u002FGenerative-AI-with-LLMs) - 在“利用大型语言模型 (LLMs) 进行生成式 AI”中，你将学习生成式 AI 的基本原理，以及如何将其部署到实…\n- [Get-Things-Done-With-Prompt-Engineering-And-Langchain](https:\u002F\u002Fgithub.com\u002Fcuriousily\u002FGet-Things-Done-with-Prompt-Engineering-and-LangChain) - 关于大型语言模型 (LLMs)，如 ChatGPT，使用自定义数据进行 LangChain 和提示工程教程。Jupyter 笔记本介绍了如何加载一个…\n- [Glm-Edge](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FGLM-Edge) - GLM 系列边缘模型。\n- [Gpt-Code-Assistant](https:\u002F\u002Fgithub.com\u002Fnarenmanoharan\u002Fgpt-code-assistant) - gpt-code-assistant 是一个开源编码助手，利用语言模型来搜索、检索、探索和理解任何代码库。\n- [Gpt_Llm](https:\u002F\u002Fgithub.com\u002Fianmkim\u002Fgpt_llm) - 在 huggingface 中使用多 GPU 设置对 GPT NeoX 20B 和 OPT-30B 模型进行推理。\n- [Graph-Llm](https:\u002F\u002Fgithub.com\u002FCurryTang\u002FGraph-LLM) - 探索大型语言模型 (LLMs) 在图上学习的潜力。\n- [Groma](https:\u002F\u002Fgithub.com\u002FFoundationVision\u002FGroma) - [ECCV2024] 具有局部视觉分词的接地多模态大型语言模型。\n- [Home-Llm](https:\u002F\u002Fgithub.com\u002Facon96\u002Fhome-llm) - 一个 Home Assistant 集成及模型，用于通过本地 LLM 控制智能家居。\n- [Huatuo-Llama-Med-Chinese](https:\u002F\u002Fgithub.com\u002FSCIR-HI\u002FHuatuo-Llama-Med-Chinese) - BenTsao [原名：华驼] 的仓库，基于中国医学知识对大型语言模型进行指令微调。本草（原名：华驼）模型仓库，基于中…\n- [Huggingfacemodeldownloader](https:\u002F\u002Fgithub.com\u002Fbodaay\u002FHuggingFaceModelDownloader) - 一个简单的 Go 工具，用于下载 HuggingFace 模型和数据集。\n- [Langchain-Ollama-Chainlit](https:\u002F\u002Fgithub.com\u002Fsudarshan-koirala\u002Flangchain-ollama-chainlit) - 使用 Ollama（mistral 模型）本地运行 LLM、LangChain 和 Chainlit，提供简单的聊天界面以及文档聊天功能。\n- [Langchain-Projects-Llm](https:\u002F\u002Fgithub.com\u002Fananthanarayanan431\u002FLangchain-Projects-LLM) - 各种使用大型语言模型（GPT 和 LLAMA）以及其他来自 HuggingFace 和 OpenAI 的开源模型的项目。需要 OpenAI API 才能运…\n- [Languagemodels](https:\u002F\u002Fgithub.com\u002Fjncraton\u002Flanguagemodels) - 在 512MB 内存中探索大型语言模型。\n- [Lilm](https:\u002F\u002Fgithub.com\u002Falphrc\u002Flilm) - 一个微调过的大型语言模型，旨在模仿 LIHKG 用户的行为。\n- [Llama-Cpp-Agent](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent) - llama-cpp-agent 框架是一个用于轻松与大型语言模型 (LLMs) 交互的工具。允许用户与 LLM m…\n- [Llama2Lang](https:\u002F\u002Fgithub.com\u002FAI-Commandos\u002FLLaMa2lang) - 方便的脚本，用于微调（聊天）LLaMa3 及其他模型，使其适应任何语言。\n- [Llm-Adapters](https:\u002F\u002Fgithub.com\u002FAGI-Edgerunners\u002FLLM-Adapters) - 我们 EMNLP 2023 论文“LLM-Adapters - 一种用于大型语言模型参数高效微调的适配器家族”的代码。\n- [Llm-Analysis](https:\u002F\u002Fgithub.com\u002Fcli99\u002Fllm-analysis) - 对用于训练和推理的 Transformer 模型的延迟和内存分析。\n- [Llm-Api](https:\u002F\u002Fgithub.com\u002F1b5d\u002Fllm-api) - 在统一 API 后端运行任何大型语言模型。\n- [Llm-Bedrock](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-bedrock) - 在 AWS Bedrock 上托管的模型上运行提示。\n- [Llm-Chain](https:\u002F\u002Fgithub.com\u002Fsobelio\u002Fllm-chain) - `llm-chain` 是一个强大的 Rust crate，用于在大型语言模型中构建链式操作，允许你总结文本并完成复杂任务。\n- [Llm-Claude-3](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-claude-3) - 用于与 Claude 3 系列模型交互的 LLM 插件。\n- [Llm-Datasets](https:\u002F\u002Fgithub.com\u002Fmalteos\u002Fllm-datasets) - 一个包含语言模型预训练数据集的集合，包括下载、预处理和采样的脚本。\n- [Llm-Embed-Jina](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-embed-jina) - 来自 Jina AI 的嵌入模型。\n- [Llm-Eval-Survey](https:\u002F\u002Fgithub.com\u002FMLGroupJLU\u002FLLM-eval-survey) - “大型语言模型评估综述”论文的官方 GitHub 页面。\n- [Llm-Fine-Tuning-Azure](https:\u002F\u002Fgithub.com\u002FHeZhang33\u002FLLM-Fine-Tuning-Azure) - 一份关于在 Azure 上对 OpenAI 和开源大型语言模型进行微调的指南。\n- [Llm-Finetune](https:\u002F\u002Fgithub.com\u002FOpenCSGs\u002Fllm-finetune) - 大型语言模型训练框架，支持 LoRA、全参数微调等，通过定义 YAML 文件即可开始训练\u002F微调 y…\n- [Llm-Interface](https:\u002F\u002Fgithub.com\u002Fsamestrin\u002Fllm-interface) - 一个简单的 NPM 接口，用于无缝对接 36 家大型语言模型 (LLM) 提供商，包括 OpenAI、Anthropic、Google Gemin…\n- [Llm-Next-Item-Rec](https:\u002F\u002Fgithub.com\u002FAGI-Edgerunners\u002FLLM-Next-Item-Rec) - “零样本利用大型预训练语言模型进行下一项推荐”论文的代码。\n- [Llm-Ollama](https:\u002F\u002Fgithub.com\u002Ftaketwo\u002Fllm-ollama) - 一个 LLM 插件，用于访问运行在 Ollama 服务器上的模型。\n- [Llm-Planning-Papers](https:\u002F\u002Fgithub.com\u002FAGI-Edgerunners\u002FLLM-Planning-Papers) - 必读的大语言模型 (LLM) 规划相关论文。\n- [Llm-Security-Prompt-Injection](https:\u002F\u002Fgithub.com\u002Fsinanw\u002Fllm-security-prompt-injection) - 该项目通过二元分类一组输入提示来研究大型语言模型的安全性，以发现…\n- [Llm-Self-Play](https:\u002F\u002Fgithub.com\u002Fthomasgauthier\u002FLLM-self-play) - 自我博弈微调弱语言模型为强语言模型论文（ArXiv 20232401.01335）的最小实现。\n- [Llm-Text-Completion-Finetune](https:\u002F\u002Fgithub.com\u002Fmolbal\u002Fllm-text-completion-finetune) - 关于文本补全大型语言模型微调的指南，包括示例脚本和训练数据获取。\n- [Llm-Tpu](https:\u002F\u002Fgithub.com\u002Fsophgo\u002FLLM-TPU) - 在 sophgo BM1684X 上运行生成式 AI 模型。\n- [Llm-Transparency-Tool](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fllm-transparency-tool) - LLM 透明度工具（LLM-TT），一个开源的交互式工具包，用于分析基于 Transformer 的语言模型内部运作。…\n- [Llm.Swift](https:\u002F\u002Fgithub.com\u002Feastriverlee\u002FLLM.swift) - LLM.swift 是一个简单易读的库，允许你在 macOS、iOS、watch… 上轻松地与本地大型语言模型互动。\n- [Llm4Mol](https:\u002F\u002Fgithub.com\u002FHHW-zhou\u002FLLM4Mol) - 一个全面的仓库，致力于收集和探索利用大型语言模型进行分子设计的研究，p…\n- [Llm_Evaluation_For_Gene_Set_Interpretation](https:\u002F\u002Fgithub.com\u002Fidekerlab\u002Fllm_evaluation_for_gene_set_interpretation) - “评估大型语言模型在基因集功能发现中的作用”的代码空间。\n- [Llm_Optimize](https:\u002F\u002Fgithub.com\u002Fsshh12\u002Fllm_optimize) - LLM Optimize 是一个概念验证库，用于进行 LLM（大型语言模型）引导的黑盒优化。\n- [Llmchat](https:\u002F\u002Fgithub.com\u002Fc0sogi\u002FLLMChat) - 一个全栈 Webui 实现，用于大型语言模型，例如 ChatGPT 或 LLaMA。\n- [Llms-GNN](https:\u002F\u002Fgithub.com\u002FCurryTang\u002FLLMGNN) - 利用大型语言模型 (LLMs) 对图上的节点进行无标签分类。\n- [Llms-Roofline](https:\u002F\u002Fgithub.com\u002Ffeifeibear\u002FLLMRoofline) - 通过 Roofline 模型比较不同硬件平台在 LLM 推理任务中的表现。\n- [Llms-Learning](https:\u002F\u002Fgithub.com\u002FStrivin0311\u002Fllms-learning) - 一个分享关于大型语言模型文献的仓库。\n- [Llms-Txt](https:\u002F\u002Fgithub.com\u002FAnswerDotAI\u002Fllms-txt) - \u002Fllms.txt 文件，帮助语言模型使用你的网站。\n- [Llms-World-Models-For-Planning](https:\u002F\u002Fgithub.com\u002FGuanSuns\u002FLLMs-World-Models-for-Planning) - “利用预训练大型语言模型构建和使用世界模型以进行基于模型的任务规划”论文的源代码。\n- [Llmsys-Paperlist](https:\u002F\u002Fgithub.com\u002FAmberLJC\u002FLLMSys-PaperList) - 大型语言模型 (LLM) 系统论文列表。\n- [Local-Rag](https:\u002F\u002Fgithub.com\u002Fjonfairbanks\u002Flocal-rag) - 使用开源大型语言模型 (LLMs) 将文件导入用于检索增强生成 (RAG)，完全无需第三方或敏感…\n- [Machine-Learning-Guide](https:\u002F\u002Fgithub.com\u002Fmikeroyal\u002FMachine-Learning-Guide) - 机器学习指南。学习所有关于机器学习工具、库、框架、大型语言模型 (LLMs) 和训练模型的知识。\n- [MalayaLLM](https:\u002F\u002Fgithub.com\u002FVishnuPJ\u002FMalayaLLM) - 一个持续 LoRA 预训练和微调的 7B Llama-2 Indic 模型，专为马拉雅拉姆语设计。\n- [Mammoth](https:\u002F\u002Fgithub.com\u002FTIGER-AI-Lab\u002FMAmmoTH) - “MAmmoTH - 通过混合指令微调构建数学通才模型”（ICLR 2024）的代码和数据。\n- [Mcphost](https:\u002F\u002Fgithub.com\u002Fmark3labs\u002Fmcphost) - 一个 CLI 主机应用程序，使大型语言模型 (LLMs) 能够通过模型上下文协议 (MCP) 与外部工具交互。\n- [Microsoft Semantic Kernel](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-kernel) - 将尖端 LLM 技术快速简便地集成到你的应用程序中。\n- [Mlx-Llm](https:\u002F\u002Fgithub.com\u002Friccardomusmeci\u002Fmlx-llm) - 在 Apple Silicon 上实时运行的大型语言模型 (LLMs) 应用程序和工具，采用 Apple MLX。\n- [Mlx-Vlm](https:\u002F\u002Fgithub.com\u002FBlaizzy\u002Fmlx-vlm) - MLX-VLM 是一个软件包，用于在你的 Mac 上使用 MLX 对视觉语言模型 (VLMs) 进行推理和微调。\n- [Mobilellm](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FMobileLLM) - MobileLLM 优化十亿以下参数的语言模型，以适应设备端应用场景。在 ICML 2024 中。\n- [Model_Baseline](https:\u002F\u002Fgithub.com\u002Farcprizeorg\u002Fmodel_baseline) - 测试基准 LLM 在不同模型上的表现。\n- [Modelscope-Agent](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002Fmodelscope-agent) - ModelScope-Agent - 一个连接 ModelScope 中模型与外界的代理框架。\n- [Nanorwkv](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FnanoRWKV) - RWKV 语言模型的 nanoGPT 式实现——一种具有 GPT 级 LLM 性能的 RNN。\n- [Neural-Compressor](https:\u002F\u002Fgithub.com\u002Fintel\u002Fneural-compressor) - SOTA 低比特 LLM 量化（INT8\u002FFP8\u002FINT4\u002FFP4\u002FNF4）及稀疏化；TensorFlow、PyTorch 和 ONNX R… 上领先的模型压缩技术。\n- [Nlp-With-Llms](https:\u002F\u002Fgithub.com\u002Fjonkrohn\u002FNLP-with-LLMs) - 利用大型语言模型进行自然语言处理。\n- [Obsidian-Flashcards-Llm](https:\u002F\u002Fgithub.com\u002Fcrybot\u002Fobsidian-flashcards-llm) - 使用大型语言模型（如 ChatGPT）从 obsidian 笔记中自动生成抽认卡。\n- [Opengptandbeyond](https:\u002F\u002Fgithub.com\u002FSunLemuria\u002FOpenGPTAndBeyond) - 开放的努力，旨在实现类似 ChatGPT 的模型及其后续发展。\n- [Prompt-Optimizer](https:\u002F\u002Fgithub.com\u002Fvaibkumr\u002Fprompt-optimizer) - 最小化 LLM 令牌复杂度，以节省 API 成本和模型计算。\n- [Promptagent](https:\u002F\u002Fgithub.com\u002FXinyuanWangCS\u002FPromptAgent) - 这是“PromptAgent - 利用语言模型进行战略规划，实现专家级提示优化”的官方仓库。PromptAgen…\n- [Promptoftheyear](https:\u002F\u002Fgithub.com\u002Fsuccessfulstudy\u002Fpromptoftheyear) - 在不断发展的大型语言模型 (LLMs) 世界中，编写有效的提示已成为一项必备技能。因此，我创建了…\n- [Pruneme](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002FPruneMe) - 自动识别大型语言模型中可修剪的冗余层块。\n- [Renellm](https:\u002F\u002Fgithub.com\u002FNJUNLP\u002FReNeLLM) - 我们 NAACL 2024 论文“披着羊皮的狼 - 广义嵌套越狱提示可欺骗大型语…”的官方实现。\n- [Rlhf-Reward-Modeling](https:\u002F\u002Fgithub.com\u002FRLHFlow\u002FRLHF-Reward-Modeling) - RLHF 奖励模型训练配方。\n- [Rptq4Llm](https:\u002F\u002Fgithub.com\u002Fhahnyuan\u002FRPTQ4LLM) - 基于重新排序的大型语言模型事后量化。\n- [Scientific-Llm-Survey](https:\u002F\u002Fgithub.com\u002FHICAI-ZJU\u002FScientific-LLM-Survey) - 科学大型语言模型 - 生物与化学领域调查。\n- [Simplerllm](https:\u002F\u002Fgithub.com\u002Fhassancs91\u002FSimplerLLM) - 简化与大型语言模型的互动。\n- [Slam-Llm](https:\u002F\u002Fgithub.com\u002FX-LANCE\u002FSLAM-LLM) - 利用大型语言模型进行语音、语言、音频、音乐处理。\n- [So-Vits-Models](https:\u002F\u002Fgithub.com\u002Fsekift\u002Fso-vits-models) - 收集有关 so-vits-svc、TTS、SD、LLMs 的各种模型、应用以及文字、声音、图片、视频有关的model。\n- [Speech-Trident](https:\u002F\u002Fgithub.com\u002Fga642381\u002Fspeech-trident) - 优秀的语音\u002F音频 LLM、表征学习和编解码模型。\n- [Speechllm](https:\u002F\u002Fgithub.com\u002Fskit-ai\u002FSpeechLLM) - 该仓库包含 SpeechLLM 模型的训练、推理、评估代码，以及关于该模型在 huggingface 上发布细节。\n- [Syncode](https:\u002F\u002Fgithub.com\u002Fuiuc-focal-lab\u002Fsyncode) - 高效且通用的大型语言模型句法解码。\n- [Tldraw-Llm-Starter](https:\u002F\u002Fgithub.com\u002Ftldraw\u002Ftldraw-llm-starter) - 一个用于结合 tldraw 和大型语言模型工作的入门工具。\n- [Tough-Llm-Tests](https:\u002F\u002Fgithub.com\u002Fhrishioa\u002Ftough-llm-tests) - 一些难题用来测试新模型。\n- [Trained_Models](https:\u002F\u002Fgithub.com\u002FXY2323819551\u002Ftrained_models) - 微调 LLM。\n- [Wasm-Ai](https:\u002F\u002Fgithub.com\u002Fhrishioa\u002Fwasm-ai) - Vercel 和 web-llm 模板，用于直接在浏览器中运行 wasm 模型。\n- [Web-Llm-Chat](https:\u002F\u002Fgithub.com\u002Fmlc-ai\u002Fweb-llm-chat) - 与在你的浏览器中本地运行的 AI 大型语言模型聊天。享受私密、无服务器、无缝的 AI 对话。\n- [Yet-Another-Applied-Llm-Benchmark](https:\u002F\u002Fgithub.com\u002Fcarlini\u002Fyet-another-applied-llm-benchmark) - 一个基准，用于评估语言模型在我之前要求它们解决的问题上的表现。\n- [Zeus-Llm-Trainer](https:\u002F\u002Fgithub.com\u002Fofficial-elinas\u002Fzeus-llm-trainer) - Zeus LLM Trainer 是斯坦福 Alpaca 的重写版本，旨在成为所有大型语言模型的训练者。\n\n### 提示工程\n- [Advanced-Prompt-Engineering-Techniques-3817061](https:\u002F\u002Fgithub.com\u002FLinkedInLearning\u002Fadvanced-prompt-engineering-techniques-3817061) - 该仓库用于 LinkedIn Learning 课程——高级提示工程技巧\n- [Advanced-Prompt-Generator](https:\u002F\u002Fgithub.com\u002FThunderhead-exe\u002FAdvanced-Prompt-Generator) - 使用 AI 代理自动化提示工程。\n- [Aigc_Prompt_Engineering](https:\u002F\u002Fgithub.com\u002Fcystanford\u002Faigc_prompt_engineering) - AIGC 提示工程\n- [Arize AX Prompt Learning](https:\u002F\u002Farize.com\u002Fdocs\u002Fax\u002Fprompts\u002Fprompt-optimization\u002Fprompt-learning-sdk) - 开源算法 + SDK，利用元提示和追踪级反思来优化提示\n- [Alphacodium](https:\u002F\u002Fgithub.com\u002FCodium-ai\u002FAlphaCodium) - 论文《使用 AlphaCodium 进行代码生成——从提示工程到流程工程》的官方实现\n- [Autogpt-Handbook](https:\u002F\u002Fgithub.com\u002FRimaBuilds\u002FAutoGPT-handbook) - 一本关于如何使用 AutoGPT 进行代码生成和提示工程的指南。\n- [Automated-Prompt-Engineering-From-Scratch](https:\u002F\u002Fgithub.com\u002Fheiko-hotz\u002Fautomated-prompt-engineering-from-scratch) - 一个从零开始的自动化提示工程工作流仓库。它利用 OPRO 技术。\n- [Awesome-Ai-Art-Image-Synthesis](https:\u002F\u002Fgithub.com\u002Faltryne\u002Fawesome-ai-art-image-synthesis) - 为提示设计师提供的一份包含优秀工具、创意、提示工程工具、Colab 笔记本、模型及辅助资源的清单，适用于 AI 艺术和图像创作领域。\n- [Awesome-Gpt-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fsnwfdhmp\u002Fawesome-gpt-prompt-engineering) - 一份精心整理的 LLM 提示工程相关资源、工具及其他优质内容列表。\n- [Awesome-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fpromptslab\u002FAwesome-Prompt-Engineering) - 该仓库收录了经过人工精选的提示工程资源，重点关注生成式预训练 Transformer（GPT）、ChatGPT 等模型。\n- [Awesome-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fnatnew\u002FAwesome-Prompt-Engineering) - Awesome-Prompt-Engineering - 该仓库包含提示工程相关的资源。\n- [Awesome-Prompt-Engineering-Zh-Cn](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002FAwesome-Prompt-Engineering-ZH-CN) - 该资源库包含了为提示工程手工整理的资源中文清单，重点是 GPT、ChatGPT、PaLM 等（自动持续更新）。\n- [Awesome-Prompting-On-Vision-Language-Model](https:\u002F\u002Fgithub.com\u002FJindongGu\u002FAwesome-Prompting-on-Vision-Language-Model) - 该仓库列出了我们综述论文中总结的相关文献——《视觉-语言基础模型上的提示工程系统性综述》。\n- [Awesome-Prompts](https:\u002F\u002Fgithub.com\u002Fai-boost\u002Fawesome-prompts) - 来自 GPTs Store 中评分最高的 GPT 的精选 ChatGPT 提示词列表。涵盖提示工程、提示攻击与防护等内容。高级 P…\n- [Awesome_Gpt_Super_Prompting](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FAwesome_GPT_Super_Prompting) - 包括 ChatGPT 越狱、GPT 助手提示泄露、GPT 提示注入、LLM 提示安全、超级提示、提示破解、提示安全等主题。\n- [Chatglm-6B-Engineering](https:\u002F\u002Fgithub.com\u002FLemonQu-GIT\u002FChatGLM-6B-Engineering) - ChatGLM-6B 提示工程项目\n- [Chatgpt-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fralphcajipe\u002Fchatgpt-prompt-engineering) - DeepLearning.AI 和 OpenAI 合作推出的“面向开发者的 ChatGPT 提示工程”课程的 Jupyter 代码笔记本。\n- [Chatgpt-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Flyhh123\u002FChatGPT-Prompt-Engineering) - 【OpenAI & 吴恩达】ChatGPT 提示词工程教学（官方配套代码）\n- [Chatgpt-Prompt-Engineering-Deeplearningai](https:\u002F\u002Fgithub.com\u002Fafondiel\u002FChatGPT-Prompt-Engineering-DeepLearningAI) - DeepLearning.AI 推出的“面向开发者的 ChatGPT 提示工程”速成免费课程\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002FKevin-free\u002Fchatgpt-prompt-engineering-for-developers) - 吴恩达《面向开发者的 ChatGPT 提示工程》课程的中英双语版本\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002Fjojoee\u002Fchatgpt-prompt-engineering-for-developers) - 用于 https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fchatgpt-prompt-engineering-for-developers\u002F 的 Jupyter 笔记本\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002Fksm26\u002FchatGPT-Prompt-Engineering-for-Developers) - 通过基于 ChatGPT 的提示工程提升技能的 Jupyter 笔记本。充分利用大型语言模型的潜力并创…\n- [Chatgpt-Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002FRyota-Kawamura\u002FChatGPT-Prompt-Engineering-for-Developers) - 在“面向开发者的 ChatGPT 提示工程”课程中，你将学习如何使用大型语言模型（LLM）快速构建新颖而强大的应…\n- [Chatgpt-Prompt-Engineering-For-Developers-In-Chinese](https:\u002F\u002Fgithub.com\u002FGitHubDaily\u002FChatGPT-Prompt-Engineering-for-Developers-in-Chinese) - 《面向开发者的 ChatGPT 提示词工程》非官方版中英双语字幕\n- [Chatgpt-Promptengineering](https:\u002F\u002Fgithub.com\u002Fajinkyalahade\u002FChatGPT-PromptEngineering) - 该 Python 脚本读取 PDF 格式的电子书，将文本拆分为提示，并使用 OpenAI GPT-3 API 生成补全内容，以…\n- [Core](https:\u002F\u002Fgithub.com\u002Fzenbase-ai\u002Fcore) - 自动化提示工程。\n- [Deeplearningai-Chatgpt-Promptengineering](https:\u002F\u002Fgithub.com\u002FLazaUK\u002FDeepLearningAI-ChatGPT-PromptEngineering) - 来自 Andrew Ng 和 Isa Fulford 在 DeepLearning.AI 上开设的“面向开发者的 ChatGPT 提示工程”课程的实用 Jupyter 笔记本。\n- [Flow-Prompt](https:\u002F\u002Fgithub.com\u002FLamoomAI\u002Fflow-prompt) - 用于生产环境提示工程及 AI 模型负载均衡的开源库\n- [Generative-Ai-Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fbuild-on-aws\u002Fgenerative-ai-prompt-engineering) - 示例代码，帮助我们通过提示工程探索生成式 AI 的世界。它提供了实验所需的资…\n- [Get-Things-Done-With-Prompt-Engineering-And-Langchain](https:\u002F\u002Fgithub.com\u002Fcuriousily\u002FGet-Things-Done-with-Prompt-Engineering-and-LangChain) - 关于大型语言模型（LLMs），如 ChatGPT，结合自定义数据进行 LangChain 和提示工程的教程。Jupyter 笔记本介绍了如何加载一…\n- [Gptstore-Prompts](https:\u002F\u002Fgithub.com\u002F1003715231\u002Fgptstore-prompts) - 这里列出了 GPTStore 上的前 100 个热门提示，可用于学习和提升提示工程能力。\n- [Hackopenaisystemprompts](https:\u002F\u002Fgithub.com\u002Fcirclestarzero\u002FHackOpenAISystemPrompts) - 通过逆向提示工程破解 OpenAI LLM 的系统提示\n- [Langfuse](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse) - 🪢 开源 LLM 工程平台 - 提供 LLM 可观测性、指标、评估、提示管理、游乐场和数据集等功能。可与 Llama… 集成。\n- [Latitude-Llm](https:\u002F\u002Fgithub.com\u002Flatitude-dev\u002Flatitude-llm) - Latitude 是一个开源提示工程平台，用于借助 AI 构建、评估和优化你的提示\n- [Learn-Prompting](https:\u002F\u002Fgithub.com\u002FMiesnerJacob\u002Flearn-prompting) - 目前最全面的提示工程课程。\n- [Learn_Prompting](https:\u002F\u002Fgithub.com\u002Ftrigaten\u002FLearn_Prompting) - Learn Prompting 出品的提示工程、生成式 AI 和 LLM 指南 | 加入我们的 Discord 社区，成为最大的提示工程学习者群体\n- [Learning-Prompt](https:\u002F\u002Fgithub.com\u002Fthinkingjimmy\u002FLearning-Prompt) - 免费在线提示工程课程。现已新增 ChatGPT 和 Midjourney 教程！\n- [Learnprompt](https:\u002F\u002Fgithub.com\u002FLearnPrompt\u002FLearnPrompt) - 永久免费开源的 AIGC 课程，目前支持提示工程、ChatGPT、Midjourney、Runway、Stable Diffusion、AI 数字人、AI 声音与音乐以及开源大模型。\n- [Llm-Prompt-Engineering-Simplified-Book](https:\u002F\u002Fgithub.com\u002FAkmmusAI\u002FLLM-Prompt-Engineering-Simplified-Book) - LLM 提示工程简化手册\n- [Obsidian-Ai-Research-Assistant](https:\u002F\u002Fgithub.com\u002FInterwebAlchemy\u002Fobsidian-ai-research-assistant) - 用于 AI API 的提示工程研究工具\n- [Potpie](https:\u002F\u002Fgithub.com\u002Fpotpie-ai\u002Fpotpie) - 提示转代理 - 为你的代码库创建自定义工程代理\n- [Prompt-Eng-Interactive-Tutorial](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fprompt-eng-interactive-tutorial) - Anthropic 的交互式提示工程教程\n- [Prompt-Eng-Ollama-Interactive-Tutorial](https:\u002F\u002Fgithub.com\u002Fivanfioravanti\u002Fprompt-eng-ollama-interactive-tutorial) - Ollama 的交互式提示工程教程\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fbrexhq\u002Fprompt-engineering) - 关于如何使用 OpenAI 的 GPT-4 等大型语言模型的技巧与窍门。\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002FPythonation\u002FPrompt-Engineering) - 阿拉伯语的提示工程课程\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002F5zjk5\u002Fprompt-engineering) - 提示工程项目的案例\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fprompt-engineering) - 学习如何通过提示工程更好地利用 AI 模型\n- [Prompt-Engineering](https:\u002F\u002Fgithub.com\u002FimJunaidAfzal\u002FPrompt-Engineering) - 针对语言模型（GPT-3、GPT-4、ChatGPT）和文生图模型（Stable Diffusion、Midjourney、Dall-e）的提示工程\n- [Prompt-Engineering-By-Openai](https:\u002F\u002Fgithub.com\u002FArslanKAS\u002FPrompt-Engineering-by-OpenAI) - 在“面向开发者的 ChatGPT 提示工程”课程中，你将学习如何使用大型语言模型（LLM）快速构建新颖而强大的应…\n- [Prompt-Engineering-For-Developers](https:\u002F\u002Fgithub.com\u002Flogan-zou\u002Fprompt-engineering-for-developers) - 吴恩达《面向开发者的 ChatGPT 提示工程》课程的中文版\n- [Prompt-Engineering-For-Everyone-With-Chatgpt-And-Gpt4](https:\u002F\u002Fgithub.com\u002FPacktPublishing\u002FPrompt-Engineering-for-Everyone-with-ChatGPT-and-GPT4) - Packt Publishing 出品的“人人适用的 ChatGPT 和 GPT4 提示工程”\n- [Prompt-Engineering-For-Generative-Ai-Examples](https:\u002F\u002Fgithub.com\u002FBrightPool\u002Fprompt-engineering-for-generative-ai-examples) - O'Reilley 出版的书籍\n- [Prompt-Engineering-For-Instruction-Tuned-Llm](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FPrompt-Engineering-for-Instruction-Tuned-LLM) - 关于提示工程的各种博客文章和 YouTube 视频\n- [Prompt-Engineering-For-Javascript-Developers](https:\u002F\u002Fgithub.com\u002Fdabit3\u002Fprompt-engineering-for-javascript-developers) - 总结自 DeepLearning.ai “面向开发者的 ChatGPT 提示工程”课程的笔记\n- [Prompt-Engineering-Guide](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FPrompt-Engineering-Guide) - 🐙 提示工程相关的指南、论文、讲座、笔记本和资源\n- [Prompt-Engineering-Guide-Chinese](https:\u002F\u002Fgithub.com\u002Fwangxuqi\u002FPrompt-Engineering-Guide-Chinese) - 提示工程师指南，源自英文版，但增加了 AIGC 的提示部分，为了降低同学们的学习门槛，翻译更新\n- [Prompt-Engineering-Guide-Cn](https:\u002F\u002Fgithub.com\u002Fprompting-work\u002FPrompt-Engineering-Guide-Cn) - 关于提示工程的技术文章汇总和翻译\n- [Prompt-Engineering-Guide-Zh-Cn](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002FPrompt-Engineering-Guide-zh-CN) - 🐙 关于提示词工程（prompt）的指南、论文、讲座、笔记本和资源大全（自动持续更新）\n- [Prompt-Engineering-Holy-Grail](https:\u002F\u002Fgithub.com\u002Fzacfrulloni\u002FPrompt-Engineering-Holy-Grail) - # 提示工程中心 ⭐️ 如果你觉得有用，请给它点个赞以示支持！这个仓库是一站式提示工…\n- [Prompt-Engineering-Mastery](https:\u002F\u002Fgithub.com\u002Fnerority\u002FPrompt-Engineering-Mastery) - 包含大量提示工程资源\n- [Prompt-Engineering-Note](https:\u002F\u002Fgithub.com\u002FisLinXu\u002Fprompt-engineering-note) - 🔥🔔提示工程笔记🔔🔥\n- [Prompt-Engineering-Notebook](https:\u002F\u002Fgithub.com\u002Fmadroidmaq\u002Fprompt-engineering-notebook) - “面向开发者的 ChatGPT 提示工程”课程的 Jupyter 笔记本。#ChatGPT #提示 #提示词 #提示工程 #提示\n- [Prompt-Engineering-Toolkit](https:\u002F\u002Fgithub.com\u002Fteknium1\u002FPrompt-Engineering-Toolkit) - 提示工程工具是一款基于 Web 的应用程序，旨在帮助用户试验和优化针对各种大型语言模型（LLMs）的提示\n- [Prompt-Engineering-Tutior](https:\u002F\u002Fgithub.com\u002FConnectAI-E\u002FPrompt-Engineering-Tutior) - 🎡 提示词工程师入门指南 ~视频字幕+代码资料 [( Python、Golang、NodeJs ) x ( 中文、英文 )]\n- [Prompt-Engineering-With-Anthropic-Claude-V-3](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fprompt-engineering-with-anthropic-claude-v-3) - 本课程旨在通过 Bedrock，为您提供关于如何在 Claude 中设计最优提示的全面分步理解。\n- [Prompt-Enhancer](https:\u002F\u002Fgithub.com\u002Flim-hyo-jeong\u002FPrompt-Enhancer) - 提示工程触手可及！\n- [Prompt-Hacker-Collections](https:\u002F\u002Fgithub.com\u002Fyunwei37\u002Fprompt-hacker-collections) - 提示对抗、破解例子与笔记 | 提示攻击防御、提示注入、逆向工程的笔记和实例\n- [Prompt-In-Context-Learning](https:\u002F\u002Fgithub.com\u002FEgoAlpha\u002Fprompt-in-context-learning) - 用于上下文学习和提示工程的优秀资源 - 掌握 ChatGPT、GPT-3 和 FlanT5 等 LLM，内容及时更新…\n- [Prompt_Engineering](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FPrompt_Engineering) - 该仓库提供了一套全面的教程和实现，涵盖了从基础到高…\n- [Promptbase](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fpromptbase) - 涵盖所有提示工程相关内容\n- [Promptbreeder](https:\u002F\u002Fgithub.com\u002Fvaughanlove\u002FPromptBreeder) - Google Deepmind 的 PromptBreeder，用于自动化提示工程，已在 LangChain 表达式语言中实现。\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002FMrGladiator14\u002FPromptEngineering) - 提示工程工具包\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002Fdivyeshpatel83\u002FPromptEngineering) - 示例提示 - 提示工程\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002Fbskim\u002FPromptEngineering) - LLM 提示工程的最佳实践\n- [Promptengineering](https:\u002F\u002Fgithub.com\u002FDaleStewart\u002FPromptEngineering) - 我所设计的生成式 AI 提示的存储库\n- [Promptengineering4Devs](https:\u002F\u002Fgithub.com\u002Fyandex-datasphere\u002FPromptEngineering4Devs) - 面向开发者的提示工程\n- [Promptengineeringwithdalle](https:\u002F\u002Fgithub.com\u002Fjennifermarsman\u002FPromptEngineeringWithDalle) - 通过迭代 DALL-E 模型的图像提示来学习提示工程。\n- [Promptgpt](https:\u002F\u002Fgithub.com\u002Fhoward9192\u002FPromptgpt) - PromptGPT 是一个开源框架，允许用户无需任何安装或编码即可自动生成高质量提示。\n- [Promptify](https:\u002F\u002Fgithub.com\u002Fpromptslab\u002FPromptify) - 提示工程 | 提示版本控制 | 使用 GPT 或其他基于提示的模型获取结构化输出。加入我们的 Discord 社区，了解更多提…\n- [Promptimize](https:\u002F\u002Fgithub.com\u002Fpreset-io\u002Fpromptimize) - Promptimize 是一款用于提示工程评估和测试的工具包。\n- [Promptpet](https:\u002F\u002Fgithub.com\u002Fhamutama\u002FPromptPET) - PromptPET - 一套全面的提示工程工具箱，提供先进的工具用于设计、优化和调整提示及流程…\n- [Sammo](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsammo) - 用于提示工程和优化的库（SAMMO = 结构感知多目标元提示优化）\n- [Software-Dev-Prompt-Library](https:\u002F\u002Fgithub.com\u002Fcodingthefuturewithai\u002Fsoftware-dev-prompt-library) - 包含经过测试的可重用生成式 AI 提示的库，适用于常见的软件工程任务\n- [Story-Prompt](https:\u002F\u002Fgithub.com\u002FCommonPaper\u002Fstory-prompt) - 针对工程候选人的一项挑战项目\n- [Tanuki.Py](https:\u002F\u002Fgithub.com\u002FTanuki\u002Ftanuki.py) - 面向开发者的提示工程\n- [The-Art-Of-Asking-Chatgpt-For-High-Quality-Answers-A-Complete-Guide-To-Prompt-Engineering-Technique](https:\u002F\u002Fgithub.com\u002FORDINAND\u002FThe-Art-of-Asking-ChatGPT-for-High-Quality-Answers-A-complete-Guide-to-Prompt-Engineering-Technique) - ChatGPT 提问技巧\n- [Udemy-Prompt-Engineering-Course](https:\u002F\u002Fgithub.com\u002FBrightPool\u002Fudemy-prompt-engineering-course) - Udemy 提示工程课程的内容\n- [Yival](https:\u002F\u002Fgithub.com\u002FYiVal\u002FYiVal) - 你的自动化提示工程助手，专为生成式 AI 应用程序设计\n\n### 安全\n- [Aip-Identity](https:\u002F\u002Fgithub.com\u002Fthe-nexus-guard\u002Faip) - 代理身份协议（AIP）为AI代理提供加密身份（Ed25519\u002FDID）、基于背书的信任图以及端到端加密消息传递功能。CLI和Python SDK已在PyPI上发布（`pip install aip-identity`）。[github](https:\u002F\u002Fgithub.com\u002Fthe-nexus-guard\u002Faip) | [官网](https:\u002F\u002Fthe-nexus-guard.github.io\u002Faip\u002F) | [文档](https:\u002F\u002Faip-service.fly.dev\u002Fdocs)\n- [Agent-Repoguardian](https:\u002F\u002Fgithub.com\u002Fflexigpt\u002Fagent-repoguardian) - 一款用于代码安全扫描和漏洞分析的AI代理。\n- [Agentic_Security](https:\u002F\u002Fgithub.com\u002Fmsoedov\u002Fagentic_security) - 基于智能体的LLM漏洞扫描器 \u002F AI红队工具包。\n- [Agentictrust](https:\u002F\u002Fgithub.com\u002Flab101-ai\u002Fagentictrust) - 面向AI代理的可观性、开发工具及安全平台。\n- [Agentos](https:\u002F\u002Fgithub.com\u002FThe-Swarm-Corporation\u002FAgentOS) - AgentOS采用容器化、编排和多层隔离技术，构建了全面的安全架构，以确保……\n- [AgentShield](https:\u002F\u002Fgithub.com\u002Felliotllliu\u002Fagent-shield) - 开源的AI代理技能、MCP服务器和插件安全扫描工具。包含30条规则、AST分析、跨文件追踪以及五维评分机制。无需安装、100%离线运行，采用MIT许可证。[github](https:\u002F\u002Fgithub.com\u002Felliotllliu\u002Fagent-shield) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@elliotllliu\u002Fagent-shield)\n- [Agriaid](https:\u002F\u002Fgithub.com\u002Fshamspias\u002FAgriAid) - AgriAid是一款由AI驱动的工具，专为孟加拉国的农民和农业从业者设计，可提供植物病害预测与识别服务。利用……\n- [Ai-Agent](https:\u002F\u002Fgithub.com\u002FZielBox\u002Fai-agent) - 执行多种AI赋能的操作，尤其擅长安全管理。\n- [Ai-Agent-Security](https:\u002F\u002Fgithub.com\u002FSecurityLab-UCD\u002Fai-agent-security) - 该仓库包含我们在论文《AI代理的安全性》中展示的演示和攻击的源代码。\n- [Ai-Agent-Solving-Security-Challenges](https:\u002F\u002Fgithub.com\u002Ftheowni\u002FAI-Agent-Solving-Security-Challenges) - 无描述。\n- [Ai-Agent-Wallet](https:\u002F\u002Fgithub.com\u002F0xcrypto2024\u002Fai-agent-wallet) - 该框架提供了一种安全的无头钱包解决方案，专为与前端应用集成而设计。它优先考虑安全性……\n- [Ai-Powered-Security-Agent](https:\u002F\u002Fgithub.com\u002FAIBotTeachesAI\u002FAI-Powered-Security-Agent) - 无描述。\n- [Ai-Security-Demos](https:\u002F\u002Fgithub.com\u002Fshaialon\u002Fai-security-demos) - 🤯 AI安全大曝光！现场演示揭示🤖 智能体式AI流程中的隐藏风险：💉 提示注入、☣️ 数据污染。观看录制的……\n- [Ai_Buddy_Guard](https:\u002F\u002Fgithub.com\u002Fgpsandhu23\u002Fai_buddy_guard) - 一款原型AI代理，用于测试AI在帮助我们发现和修复安全问题方面的效果。\n- [Ai_Security_Agent](https:\u002F\u002Fgithub.com\u002FSoumilB7\u002FAI_Security_Agent) - 与Armur AI合作的自由职业项目。\n- [Airmageddon](https:\u002F\u002Fgithub.com\u002FRamonBeast\u002Fairmageddon) - AIrmageddon是一款家庭安防AI代理。\n- [Airportagentsimulation-Security](https:\u002F\u002Fgithub.com\u002FJogrohe\u002FAirportAgentSimulation-Security) - 无描述。\n- [Awesome_Gpt_Super_Prompting](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FAwesome_GPT_Super_Prompting) - ChatGPT越狱、GPT助手提示泄露、GPT提示注入、LLM提示安全、超级提示、提示黑客攻击、提示安全、AI相关。\n- [Cartlis](https:\u002F\u002Fgithub.com\u002FTheneo-Inc\u002FCartlis) - 这款AI驱动的API治理代理能够实时执行规则，无缝集成到现有基础设施中，并自动修复违规行为……\n- [Cognitive-Security-Ai-Powered-Threat-Agent-Evaluation-For-Impact-On-Assets.](https:\u002F\u002Fgithub.com\u002FGauravsbin\u002FCognitive-Security-AI-Powered-Threat-Agent-Evaluation-for-Impact-on-Assets.) - 无描述。\n- [Council-Of-Ai](https:\u002F\u002Fgithub.com\u002Fseanpixel\u002Fcouncil-of-ai) - 一种针对智能体式LLM的安全措施，通过由AI组成的委员会并辅以否决机制进行管理。该委员会会根据……评估代理的行为输出。\n- [Cyber-Security-Llm-Agents](https:\u002F\u002Fgithub.com\u002FNVISOsecurity\u002Fcyber-security-llm-agents) - 一系列使用大型语言模型（LLMs）来执行网络安全日常任务的代理。\n- [Db-Gpt](https:\u002F\u002Fgithub.com\u002Feosphoros-ai\u002FDB-GPT) - 基于AI原生的数据应用开发框架，配备AWEL（智能体工作流表达语言）和智能体。\n- [Eng-Security-Review-Agent](https:\u002F\u002Fgithub.com\u002Flume-cory\u002Feng-security-review-agent) - 一款AI智能体应用，帮助安全团队响应来自工程部门的安全问题和安全审查。\n- [Fast-Llm-Security-Guardrails](https:\u002F\u002Fgithub.com\u002FZenGuard-AI\u002Ffast-llm-security-guardrails) - 针对AI代理和应用程序的最快且最简单的LLM安全护栏。\n- [Fetchai](https:\u002F\u002Fgithub.com\u002FHarshpreetkaur98\u002FFetchAI) - uAgents是Fetch.ai推出的一款Python库，用于创建自主AI代理。它支持便捷的代理管理、区块链连接等……\n- [Hauth](https:\u002F\u002Fgithub.com\u002Fvaniiiii\u002FhAUTH) - hAUTH是一种安全中间件解决方案，为AI代理提供人工监督。\n- [Invariant](https:\u002F\u002Fgithub.com\u002Finvariantlabs-ai\u002Finvariant) - 通过可调试的单元测试帮助您构建更优秀的AI代理。\n- [Kevlar-Anti-Leak-System-Prompts](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FKEVLAR-Anti-Leak-System-Prompts) - 使用KEVLAR为您的自定义GPT系统提示安全提供坚如磐石的保护，它是抵御规则提取、提示注入等问题的终极提示保护者……\n- [Linux-Security-Agent](https:\u002F\u002Fgithub.com\u002Fmicroaisecurity\u002Flinux-security-agent) - 微型AI安全代理。\n- [Llm_Agents_Security](https:\u002F\u002Fgithub.com\u002Fjohnny22245\u002Fllm_agents_security) - 该LLM代理专注于建立AI安全与保障体系。\n- [Minotaur_Impossible_Gpt_Security_Challenge](https:\u002F\u002Fgithub.com\u002FCyberAlbSecOP\u002FMINOTAUR_Impossible_GPT_Security_Challenge) - MINOTAUR：有史以来最强的安全提示！提示安全挑战、不可能的GPT安全、提示网络安全、提示漏洞……\n- [OneCLI](https:\u002F\u002Fgithub.com\u002Fonecli\u002Fonecli) - 开源的AI代理凭据保险库。一个Rust HTTP网关会拦截代理请求并透明地注入API凭据，从而确保代理永远不会直接接触原始密钥。\n- [Moa-Groq-Langchain-Securityspecialist](https:\u002F\u002Fgithub.com\u002FTheJ-Erk400\u002Fmoa-groq-langchain-securityspecialist) - 使用Groq和Streamlit的混合智能体。\n- [Multi-Agent-Secops-Llm](https:\u002F\u002Fgithub.com\u002Ftegridydev\u002Fmulti-agent-secops-llm) - 该项目是一个多智能体安全框架，利用多个LLM模型来分析并生成全面的安全简报。\n- [Multi-Ai-Agents](https:\u002F\u002Fgithub.com\u002Ferndck\u002FMulti-AI-Agents) - 多AI代理是人工智能与区块链技术的突破性融合，旨在提供无与伦比的……\n- [Netsecgame](https:\u002F\u002Fgithub.com\u002Fstratosphereips\u002FNetSecGame) - 一个用于网络安全任务的环境模拟平台，用于开发和测试基于AI的代理。属于AI Dojo项目的一部分。\n- [Owasp-Agentic-Ai](https:\u002F\u002Fgithub.com\u002Fprecize\u002FOWASP-Agentic-AI) - 针对智能体式AI（AI代理安全）的OWASP十大风险——预发布版本。\n- [PolicyLayer](https:\u002F\u002Fgithub.com\u002FPolicyLayer\u002FPolicyLayer) - 非托管的AI代理加密钱包支出控制工具。可在不持有私钥的情况下，强制执行每日支出限额、单笔交易上限、收款人白名单和速率限制，防止因漏洞、提示注入或无限循环导致的钱包资金流失。[官网](https:\u002F\u002Fpolicylayer.com) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@policylayer\u002Fsdk)\n- [Rgs](https:\u002F\u002Fgithub.com\u002FMangelZabalaDevelop\u002FRGS) - 为NVIDIA和LangChain联合举办的生成式AI代理开发者大赛开发的报告生成式安全工具。\n- [Secad](https:\u002F\u002Fgithub.com\u002Fgideonaina\u002Fsecad) - SECAD是一款基于智能体、由AI驱动的安全工作流增强应用。\n- [Security-Ai-Agent-Brama](https:\u002F\u002Fgithub.com\u002Foborys\u002Fsecurity-ai-agent-brama) - 无描述。\n- [Sim-Security-Data](https:\u002F\u002Fgithub.com\u002FSim-Security\u002FSim-Security-Data) - 我在这里收集并处理用于AI代理的数据。\n- [Vulert](https:\u002F\u002Fvulert.com) - Vulert通过检测开源依赖中的漏洞来保护软件，而无需访问您的代码。支持Js、PHP、Java、Python等多种语言。\n- [Web_Scrape_Agent_Ai](https:\u002F\u002Fgithub.com\u002Frxslice\u002FWeb_Scrape_Agent_AI) - 具备企业级品质的自主代理，配有相关工具、主提示、可选的额外AI服务、API兼容性以及基础……\n\n### 测试\n- [EvoAgentX](https:\u002F\u002Fgithub.com\u002FEvoAgentX\u002FEvoAgentX) - EvoAgentX 正在构建一个自我进化的智能体生态系统，它将为您提供用于评估和进化智能体工作流的自动化框架。[github](https:\u002F\u002Fgithub.com\u002FEvoAgentX\u002FEvoAgentX)\n- [Open-RAG-Eval](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fopen-rag-eval) - 一个开源的 RAG 评估框架，无需黄金答案，可用于评估连接到 AI 智能体的 RAG 工具（Agentic RAG）的性能。[github](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fopen-rag-eval)\n- [Voice Lab](https:\u002F\u002Fgithub.com\u002Fsaharmor\u002Fvoice-lab) - 一个全面的语音智能体测试与评估框架，涵盖语言模型、提示词和智能体人格等多个方面。[github](https:\u002F\u002Fgithub.com\u002Fsaharmor\u002Fvoice-lab)\n- [Ab-Agent](https:\u002F\u002Fgithub.com\u002Fbassimeledath\u002Fab-agent) - 自动化 A\u002FB 测试设计与推断的 AI 智能体\n- [Adtestpro](https:\u002F\u002Fgithub.com\u002FAnanyaP-WDW\u002FAdTestPro) - 一款开源工具，可预先针对合成目标受众测试广告素材\n- [Agent-Ai-Test](https:\u002F\u002Fgithub.com\u002FPraagnya\u002Fagent-ai-test) - 金融智能体测试\n- [Agent-Evaluation](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagent-evaluation) - 一个由生成式 AI 驱动的虚拟智能体测试框架\n- [Agent-Smith-E2E-Test](https:\u002F\u002Fgithub.com\u002Fdangoddard-trilogy\u002Fagent-smith-e2e-test) - 基于 AI 的端到端测试工具\n- [Agent-Solarpanels-Tutorial](https:\u002F\u002Fgithub.com\u002FPairrot-Lore\u002Fagent-solarpanels-tutorial) - 该仓库包含一个使用 LangGraph 构建的 AI 智能体，用于计算太阳能电池板的节能效果。该项目旨在用于 w…\n- [Agentacc-Batch-Test](https:\u002F\u002Fgithub.com\u002FHehua-Fan\u002FAgentAcc-Batch-Test) - 一个基于 Streamlit 的应用，用于测试 AI 智能体的准确性\n- [Agentai_Testing_Repo](https:\u002F\u002Fgithub.com\u002FSUGUMAR-S\u002FagentAI_testing_repo) - 用于测试目的\n- [Agentic-Platform](https:\u002F\u002Fgithub.com\u002Fbonk1t\u002Fagentic-platform) - AI 智能体自动化平台：从浏览器中快速原型设计、测试并部署多智能体系统。\n- [Agentic.Md](https:\u002F\u002Fgithub.com\u002Fai-primitives\u002Fagentic.md) - 使用 Markdown 和 MDX 构建、测试、部署和迭代 AI 智能体\n- [Agents](https:\u002F\u002Fgithub.com\u002Fculurciello\u002Fagents) - 一系列 AI 智能体，用于测试 AI 能力，并作为教程和演示\n- [Ai-Agency](https:\u002F\u002Fgithub.com\u002FGarcluca\u002FAI-Agency) - AI 智能体及编排的测试平台\n- [Ai-Agent-Design-For-Iq-Tests](https:\u002F\u002Fgithub.com\u002Ftex216\u002FAI-Agent-Design-for-IQ-Tests) - 设计了一个能够通过 Raven 进阶矩阵测试的 AI 智能体，详情请参阅最终项目报告。\n- [Ai-Agent-Lab](https:\u002F\u002Fgithub.com\u002FZeeshan138063\u002Fai-agent-lab) - AI 智能体实验室：一个开源仓库，使用 Python 构建、测试和部署 AI 智能体，提供示例和模块化设计。\n- [Ai-Agent-Playground](https:\u002F\u002Fgithub.com\u002FWei1024\u002FAI-Agent-Playground) - 用于开发面向办公人员的 AI 智能体框架的测试仓库\n- [Ai-Agent-Teams](https:\u002F\u002Fgithub.com\u002FAdamHHart\u002FAI-Agent-Teams) - 一个使用 AutoGen、CrewAI 和 Langchain 测试不同 AI 智能体团队的游乐场\n- [Ai-Agent-Test](https:\u002F\u002Fgithub.com\u002Fbjoernzosel\u002Fai-agent-test) - 一个由 AI 生成的自由职业网页设计师作品集网站\n- [Ai-Agent-Test](https:\u002F\u002Fgithub.com\u002Fsfelkner\u002Fai-agent-test) - 一个用于测试 AI 智能体交互的测试仓库\n- [Ai-Agents-Tool-Dev](https:\u002F\u002Fgithub.com\u002Faydinfer\u002Fai-agents-tool-dev) - 多智能体 AI 开发系统，用于自动化工具创建和测试\n- [Ai-Api-Testing](https:\u002F\u002Fgithub.com\u002FCarlosVecina\u002Fai-api-testing) - 🐦用于 API 和智能体工作流的 AI 测试生成器\n- [Ai-Code-Gen](https:\u002F\u002Fgithub.com\u002FFarelart\u002FAi-code-gen) - 一个用于生成 Python 单元测试的 AI 智能体\n- [Ai-Dev-Agent-Test](https:\u002F\u002Fgithub.com\u002Feventstubsol\u002Fai-dev-agent-test) - 由 AI 开发智能体创建的测试仓库\n- [Ai-Gpt-Agent](https:\u002F\u002Fgithub.com\u002Fpedrogritter\u002Fai-gpt-agent) - 一个简单的智能体，用于测试 OpenAI 的 GPT API\n- [Ai-Pacman-Classification](https:\u002F\u002Fgithub.com\u002FAmzAust\u002FAI-Pacman-Classification) - 在这个项目中，你将设计三种分类器：感知机分类器、大间隔（MIRA）分类器以及稍作修改的 pe…\n- [Ai-Project-Plannerenvironment](https:\u002F\u002Fgithub.com\u002FMircoT\u002FAI-Project-PlannerEnvironment) - 一个用于测试规划智能体的环境，为一门大学课程而创建。\n- [Ai-Project-Vacuumenvironment](https:\u002F\u002Fgithub.com\u002FMircoT\u002FAI-Project-VacuumEnvironment) - 一个用于测试真空智能体的环境，为一门大学课程而创建。\n- [Ai-Reinforcement-Learning](https:\u002F\u002Fgithub.com\u002Fabhinavcreed13\u002Fai-reinforcement-learning) - 本项目将实现值迭代和 Q 学习算法。首先将在 Gridworld 环境中测试智能体，然后将其应用于一个模…\n- [Ai-Smartclassroom](https:\u002F\u002Fgithub.com\u002Fadv-11\u002FAI-SmartClassroom) - 本科生毕业设计。通过集成 Agentic AI、RAG、强化学习等先进概念，对 Google Classroom 进行了升级…\n- [Ai-Testing-Agent](https:\u002F\u002Fgithub.com\u002Ffurudo-erika\u002Fai-testing-agent) - AI 测试智能体：开源软件测试 AI 智能体\n- [Ai-Wallet-Agent-Test](https:\u002F\u002Fgithub.com\u002Fvish2396\u002FAI-wallet-agent-test) - 使用 Coinbase SDK 的示例 AI 钱包智能体\n- [Ai_Agent](https:\u002F\u002Fgithub.com\u002FIbrahimaBailoDIALLO\u002FAI_Agent) - 我会在开发和测试完成后，将所有 AI 智能体都放在这个仓库中\n- [Ai_Agent_Computer_Vision](https:\u002F\u002Fgithub.com\u002FSamTaubman\u002FAI_Agent_Computer_Vision) - 专为解决 Raven 进阶矩阵智商测试而设计的 AI 智能体\n- [Ai_Agent_Crewai](https:\u002F\u002Fgithub.com\u002FRonin-117\u002FAi_Agent_CrewAI) - 测试 CrewAI\n- [Ai_Apps](https:\u002F\u002Fgithub.com\u002Fhuneyk\u002FAI_Apps) - 使用 CrewAI 框架进行 AI 智能体测试\n- [Ai_Brainstorming_Team_Agents](https:\u002F\u002Fgithub.com\u002Fjkalonji\u002FAI_Brainstorming_Team_Agents) - 通过咨询一系列专家来检验你的想法\n- [Ai_Buddy_Guard](https:\u002F\u002Fgithub.com\u002Fgpsandhu23\u002Fai_buddy_guard) - 一个原型 AI 智能体，用于测试 AI 在帮助我们发现和修复安全问题方面的表现\n- [Ai_Devs3](https:\u002F\u002Fgithub.com\u002Fbgrzywinski\u002Fai_devs3) - 智能体构建、提示词测试、任务解决方案\n- [Ai_Tools](https:\u002F\u002Fgithub.com\u002Fchineseflava\u002FAI_tools) - 测试 AI 工具并构建智能体\n- [Aiagents_Test](https:\u002F\u002Fgithub.com\u002Fprad-human-007\u002FAIAgents_test) - AI 智能体测试游乐场\n- [Aiconversationflow](https:\u002F\u002Fgithub.com\u002FTonySimonovsky\u002FAIConversationFlow) - AI 对话流提供了一个框架，用于创建反向智能体，以构建复杂的非线性 LLM 对话流，这些流可以组合使用，…\n- [Aigent](https:\u002F\u002Fgithub.com\u002Fautomators-com\u002Faigent) - 使用自主 AI 智能体为你的应用程序生成测试用例\n- [Ailoveragent](https:\u002F\u002Fgithub.com\u002Fcaizhuoyue77\u002FAILoverAgent) - 对 AILover 的智能体实现进行了一些测试\n- [Aimultiagents](https:\u002F\u002Fgithub.com\u002Fhesamjafarian\u002FAiMultiAgents) - 这是一个使用游戏模拟测试 AI 算法的仓库。它用于 Python 2.7\n- [Aiq-Poa](https:\u002F\u002Fgithub.com\u002Fxvado00\u002FAIQ-POA) - 用于 AIQ 测试的策略优化智能体模块\n- [Amazon-Bedrock-Agent-Test-Ui](https:\u002F\u002Fgithub.com\u002Facwwat\u002Famazon-bedrock-agent-test-ui) - 一个通用的 Streamlit UI，用于测试使用 Amazon Bedrock Agents 构建的生成式 AI 智能体\n- [Anythingllm_Agentsample](https:\u002F\u002Fgithub.com\u002Fstoneskin\u002FAnythingLLM_AgentSample) - 测试 AnythignLLM 自定义智能体\n- [Api-Ai-Agent-Test](https:\u002F\u002Fgithub.com\u002Ftexascloud\u002Fapi-ai-agent-test) - 这里将存放我关于两个 pickle 文件的所有提交，作为 api-ai-versioning 工具子模块使用的示例。\n- [Attendone](https:\u002F\u002Fgithub.com\u002Fmariamkhaled99\u002Fattendone) - AI 生成的智能体测试 1\n- [Auto-Dev](https:\u002F\u002Fgithub.com\u002Funit-mesh\u002Fauto-dev) - 🧙‍AutoDev：支持多语言的 AI 驱动编程助手 🌐，自动代码生成 🏗️，以及友好的 bug 消灭助手…\n- [Autogen-And-Crewai-Agenttests](https:\u002F\u002Fgithub.com\u002FTMJ97\u002FAutoGen-and-CrewAI-AgentTests) - 没有描述\n- [Autonoma](https:\u002F\u002Fgithub.com\u002FSebasbo\u002FAutonoma) - Autonoma：基于 Agentic AI 的自主代码修改、分析和测试框架，简化软件开发流程…\n- [Autospec](https:\u002F\u002Fgithub.com\u002Fzachblume\u002Fautospec) - Autospec 是一个开源的 AI 智能体，它可以接收一个 Web 应用程序的 URL 并自主地对其进行 QA 测试，然后将通过的规范保存为端到端测试代码\n- [B4-Agent-Test-06](https:\u002F\u002Fgithub.com\u002Fb4ke\u002Fb4-agent-test-06) - b4-agent-test-06：Cloudflare AI 工具测试\n- [Backtesteragent](https:\u002F\u002Fgithub.com\u002FThe-Swarm-Corporation\u002FBackTesterAgent) - 一个企业级的 AI 驱动回测框架，基于 Swarms 框架构建，用于自动化交易策略验证和优…\n- [Baseline-Agent](https:\u002F\u002Fgithub.com\u002FBloodrock-AI\u002Fbaseline-agent) - 这是一个简单的 AI 智能体，用于测试 Bloodrock CORE 基准测试。\n- [Botsharp-Ui](https:\u002F\u002Fgithub.com\u002FSciSharp\u002FBotSharp-UI) - 在中心位置构建、测试和管理你的 AI 智能体\n- [Card_Games_For_Mcts-Ann_Ai](https:\u002F\u002Fgithub.com\u002Fsymbol-zy\u002FCard_Games_for_MCTS-ANN_AI) - 一种简化的桥牌类卡牌游戏，专为测试 MCTS+ANN 智能体而设计。\n- [Chat_Agent](https:\u002F\u002Fgithub.com\u002FYangShyrMing\u002FChat_Agent) - …人类的知识和智慧。现在这应该能通过 AI 的图灵测试，也就是说，我们都知道，如果一个 AI 能够通过 …\n- [Claude-Html-Test](https:\u002F\u002Fgithub.com\u002FSivaramAdi\u002Fclaude-html-test) - 一个展示 AI 智能体相关信息的仓库\n- [Connect4_Ai](https:\u002F\u002Fgithub.com\u002Fcumason123\u002Fconnect4_ai) - 测试各种智能体之间的对抗\n- [Crew-News](https:\u002F\u002Fgithub.com\u002Frokbenko\u002Fcrew-news) - CrewNews 是一个 AI 新闻生成器，它会根据给定的主题提供公正的新闻版本，使用 Streamlit 作为 GUI，Llama 3.1 …\n- [Crewai](https:\u002F\u002Fgithub.com\u002FSkatAI\u002Fcrewai) - CrewAI AI 智能体的沙盒测试\n- [Crewai-101](https:\u002F\u002Fgithub.com\u002FReyzenello\u002FCrewAI-101) - 使用多智能体围绕框架进行测试\n- [CrewAI-Test](https:\u002F\u002Fgithub.com\u002FBuddog\u002FCrewAI-Test) - 多智能体 AI 测试\n- [Crewaiknowledgetest](https:\u002F\u002Fgithub.com\u002FNanGePlus\u002FCrewAIKnowledgeTest) - CrewAI 新版本支持使用 Knowledge 属性将 txt、PDF、CSV、Excel、JSON 等多种数据格式内容及多文件混合作为知识增强知识库提供给 Crew 中的智能体使用\n- [De-Bench](https:\u002F\u002Fgithub.com\u002FArdentAI1\u002FDE-Bench) - DE Bench：智能体能否解决现实世界的数据工程问题？专为测试 Ardent 的 AI 数据工程师而设计\n- [Debugai](https:\u002F\u002Fgithub.com\u002FOpen-IDE\u002FDebugAI) - 一个协助测试与调试的 AI 智能体！\n- [Dev-Swarm](https:\u002F\u002Fgithub.com\u002Fkyegomez\u002Fdev-swarm) - 一群 LLM 智能体，可以帮助你测试、记录文档并使你的代码投入生产！\n- [Dravid](https:\u002F\u002Fgithub.com\u002Fvysakh0\u002Fdravid) - 一个由 AI 驱动的 CLI 编码智能体，可监控你的开发\u002F测试服务器，修复错误并添加功能\n- [Explorer](https:\u002F\u002Fgithub.com\u002Finvariantlabs-ai\u002Fexplorer) - 一种更好的方式来测试、检查和分析 AI 智能体的轨迹\n- [Frontend-Agent](https:\u002F\u002Fgithub.com\u002Fqodex-ai\u002Ffrontend-agent) - 开源的 AI 驱动 QA 工具，用于自动化 UI 测试和导航\n- [Generative_Ai](https:\u002F\u002Fgithub.com\u002Fgabrielpreda\u002Fgenerative_ai) - Kaggle 笔记本、实用脚本，使用生成式 AI 工具来检查新模型、微调模型、用各种提示词测试、创建 Retr…\n- [Goingbig](https:\u002F\u002Fgithub.com\u002Ftshurden16\u002Fgoingbig) - 测试 AI 智能体\n- [Hacksynth](https:\u002F\u002Fgithub.com\u002Faielte-research\u002FHackSynth) - LLM 智能体和自主渗透测试的评估框架\n- [Health-Ai](https:\u002F\u002Fgithub.com\u002Fsj-data\u002Fhealth-ai) - 健康 AI 智能体的测试平台\n- [Incredible.Dev](https:\u002F\u002Fgithub.com\u002FIncredibleDevHQ\u002FIncredible.dev) - Incredible.dev 是一个 AI 编程伙伴，可以编写、修复、记录文档、部署、测试你的 API。一个智能体即可掌控所有 API。\n- [Instrukt](https:\u002F\u002Fgithub.com\u002Fblob42\u002FInstrukt) - 终端中的集成 AI 环境。构建、测试和指导智能体\n- [Intelligent-Agent](https:\u002F\u002Fgithub.com\u002Fbalaaagi\u002FIntelligent-Agent) - 该仓库包含一个能够通过人类智力测试的 AI 智能体。\n- [Intelligent-Agent](https:\u002F\u002Fgithub.com\u002Froterdam\u002FIntelligent-Agent) - 该仓库包含一个能够通过人类智力测试的 AI 智能体。\n- [Invariant](https:\u002F\u002Fgithub.com\u002Finvariantlabs-ai\u002Finvariant) - 通过可调试的单元测试帮助你构建更好的 AI 智能体\n- [Java-Ai-Sbus-Test](https:\u002F\u002Fgithub.com\u002Fabhikt48\u002Fjava-ai-sbus-test) - 使用 Azure ServiceBus 进行无代码智能体测试\n- [Jest-Ai](https:\u002F\u002Fgithub.com\u002Fdreamcatcher-tech\u002Fjest-ai) - AI 智能体测试套件\n- [Langchaintest](https:\u002F\u002Fgithub.com\u002FJohnBreth\u002FLangChainTest) - LangGraph 课程中的 AI 智能体\n- [Mario-Playability-Test](https:\u002F\u002Fgithub.com\u002Fzhihanyang2022\u002Fmario-playability-test) - 将一个简单的智能体运行在用户自定义的超级马里奥关卡中，以确定其可玩性比例。\n- [Mazerunner](https:\u002F\u002Fgithub.com\u002FCollinsEM\u002FMazeRunner) - 一个用于测试 AI 智能体的框架\n- [Mb-Nothing](https:\u002F\u002Fgithub.com\u002F0xAsten\u002Fmb-nothing) - 使用 Cartridge 控制器和 AI 智能体进行测试\n- [Mcts-Agent-Cythonized](https:\u002F\u002Fgithub.com\u002Fmasouduut94\u002FMCTS-agent-cythonized) - 蒙特卡洛树搜索（MCTS）是一种通过在决策空间中随机采样来寻找特定领域最优决策的方法…\n- [Mcts-Agent-Python](https:\u002F\u002Fgithub.com\u002Fmasouduut94\u002FMCTS-agent-python) - 蒙特卡洛树搜索（MCTS）是一种通过在决策空间中随机采样来寻找特定领域最优决策的方法…\n- [Megaminer-Tinyarena](https:\u002F\u002Fgithub.com\u002Fdrusepth\u002FMegaminer-Tinyarena) - 一个用于测试 Megaminer AI 智能体的小型竞技场\n- [Metasploit-Gym](https:\u002F\u002Fgithub.com\u002FphreakAI\u002Fmetasploit-gym) - 一个用于测试 AI 智能体对抗 Metasploit 网络的环境\n- [Multiagentworkflow](https:\u002F\u002Fgithub.com\u002Fzachnoel\u002FmultiAgentWorkflow) - 一个用于测试 AI 智能体框架的多智能体工作流示例\n- [Multiagentworkflow](https:\u002F\u002Fgithub.com\u002Fdrewbloom\u002FmultiAgentWorkflow) - 测试多智能体 AI 流程，用于 CRUD、搜索和从数据库构建文档\n- [N8Ntest](https:\u002F\u002Fgithub.com\u002Fhabib-049\u002Fn8nTest) - 该仓库用于测试 n8n AI 智能体\n- [Netsecgame](https:\u002F\u002Fgithub.com\u002Fstratosphereips\u002FNetSecGame) - 一个网络安全任务的环境模拟，用于开发和测试基于 AI 的智能体。是 AI Dojo 项目的一部分\n- [Networkattacksimulator](https:\u002F\u002Fgithub.com\u002FJjschwartz\u002FNetworkAttackSimulator) - 一个用于测试 AI 渗透测试智能体对抗模拟网络的环境\n- [Nexus](https:\u002F\u002Fgithub.com\u002Fcxbxmxcx\u002FNexus) - AI Agent Nexus 是一个开源平台，用于开发、测试和托管 AI 智能体，基于 Streamlit 和 Gradio 构建。它提供了一种 us…\n- [O1_Agent_Test](https:\u002F\u002Fgithub.com\u002Falexmoses\u002Fo1_Agent_Test) - 构建一个多智能体 AI 程序\n- [Orchestrai](https:\u002F\u002Fgithub.com\u002Fsamshapley\u002FOrchestrAI) - 一个用于构建和测试自定义自主智能体的框架\n- [Othello-Fx](https:\u002F\u002Fgithub.com\u002FEudyContreras\u002FOthello-FX) - 一个使用 JavaFX 构建的 Othello 游戏框架，可用于测试 Othello AI 智能体\n- [Pentesting-Ai](https:\u002F\u002Fgithub.com\u002FLstalet04\u002FPentesting-AI) - 多智能体渗透测试 AI\n- [Phoenix](https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix) - 一个开源工具，用于测试 AI 智能体或应用程序的变化\n- [Pydantic-AI](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai) - 一个用于将 Pydantic 与 LLM 结合使用的框架\u002F适配层，有助于确保 LLM 的输入\u002F输出具有类型安全性 [github](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai) | [docs](https:\u002F\u002Fai.pydantic.dev\u002F)\n- [Plark_Ai_Public](https:\u002F\u002Fgithub.com\u002Fmontvieux\u002Fplark_ai_public) - …为更广泛、长期且前沿的研究提供基础。该测试平台可用作研究极限的基础\n- [Rag-System](https:\u002F\u002Fgithub.com\u002Fthetom42\u002Frag-system) - 使用 Replit AI 智能体的测试项目\n- [Raven-Test-Ai](https:\u002F\u002Fgithub.com\u002Fdhurng\u002FRaven-Test-AI) - 一个能够解决 Raven 测试（视觉智商测试）的 AI 智能体\n- [Reverse_Turing_Test](https:\u002F\u002Fgithub.com\u002Fnicomanzonelli\u002Freverse_turing_test) - 你能智胜基于 LLM 的 AI 智能体吗？\n- [Ricai_Unittestgen_Tool](https:\u002F\u002Fgithub.com\u002FAutoDevTestAgent\u002Fricai_unittestgen_tool) - （lablab.ai 自主智能体 2023 年黑客马拉松）RicAI 单元测试生成工具\n- [Rlerewolf](https:\u002F\u002Fgithub.com\u002FGeorgeVelikov\u002FRLereWolf) - 一个用于玩狼人杀游戏以及开发和测试狼人杀 AI 智能体的框架\n- [Rpm-Ai-Agent](https:\u002F\u002Fgithub.com\u002Fteldridge11\u002FRPM-AI-Agent) - 一个用于解决 Raven 进阶矩阵测试的 AI 智能体，以此测试一般智力\n- [Rpm-Ai-Agent](https:\u002F\u002Fgithub.com\u002Fhorkays\u002FRPM-AI-Agent) - 一个用 Python 编写的 AI 智能体，旨在解决 Raven 进阶矩阵的人类智力测试\n- [Sam](https:\u002F\u002Fgithub.com\u002Fvishalmysore\u002Fsam) - 一个自主智能体或大型行动模型的 Java 实现。Selenium 和 AI 的结合，基于 AI 的测试验证。UI 验证…\n- [Snakeai-Test](https:\u002F\u002Fgithub.com\u002Fmaxolib\u002FSnakeAI-Test) - 使用 ML-Agents for Unity 的 AI 学习游戏\n- [Stock_Forecast_Ai_Agent](https:\u002F\u002Fgithub.com\u002Fjohn2408\u002Fstock_forecast_ai_agent) - 这是一个小型应用，用于测试 AI 智能体在预测方面的功能。主要关注函数调用。\n- [Swarmgo](https:\u002F\u002Fgithub.com\u002Fprathyushnallamothu\u002Fswarmgo) - SwarmGo 是一个 Go 包，允许你创建能够交互、协调和执行任务的 AI 智能体。受 OpenAI 启发…\n- [Tago](https:\u002F\u002Fgithub.com\u002FTwilledWave\u002FTago) - Langchain 智能体测试平台 \u002F AI 助手 Tago\n- [Test-Agent](https:\u002F\u002Fgithub.com\u002FGDT502\u002FTest-Agent) - 开发用于测试的 AI 工具\n- [Test-Ai-Agent-Ideas](https:\u002F\u002Fgithub.com\u002FAbhiPat123\u002Ftest-ai-agent-ideas) - 一个小的游乐场，用于测试免费模型——也许以后会考虑使用付费模型\n- [Testai](https:\u002F\u002Fgithub.com\u002Fdhiraj-inti\u002FTestAI) - 一个基于 Agentic AI 的应用程序，用于自动化测试套件的创建、执行和报告\n- [Testai-Agent](https:\u002F\u002Fgithub.com\u002Fkhanzzirfan\u002FTestAI-Agent) - 编写一个测试 AI 智能体，以在 PR 请求上自动生成测试用例\n- [Testdriverai](https:\u002F\u002Fgithub.com\u002Ftestdriverai\u002Ftestdriverai) - 下一代自主 AI 智能体，用于 Web 和桌面应用的端到端测试\n- [Testzeus-Hercules](https:\u002F\u002Fgithub.com\u002Ftest-zeus-ai\u002Ftestzeus-hercules) - 欢迎来到赫拉克勒斯，世界上第一个开源测试智能体，它将以神话般的强大力量帮你减轻测试负担…\n- [Theagentsgameai](https:\u002F\u002Fgithub.com\u002FZelunGlenn\u002FTheAgentsGameAI) - 使用决策树的 The Agents Game 逻辑的 AI 测试系统\n- [Tic-Tac-Toe](https:\u002F\u002Fgithub.com\u002FOmerCinal\u002FTic-Tac-Toe) - 一个用于测试搜索和 AI 算法的游戏。大型井字棋\n- [Travel_Agent](https:\u002F\u002Fgithub.com\u002Fgowthaml15\u002Ftravel_agent) - 该仓库用于测试 crew_ai 并尝试旅行代理用例\n- [Vacuum-Agents](https:\u002F\u002Fgithub.com\u002Fhholb\u002Fvacuum-agents) - 一个模拟机器人吸尘器，用于测试不同的 AI 算法\n- [Vercel_Ai_Test](https:\u002F\u002Fgithub.com\u002Fpixelknit\u002Fvercel_ai_test) - WebGL AI 智能体测试\n- [Vital-Chat-Ui-Streamlit](https:\u002F\u002Fgithub.com\u002Fvital-ai\u002Fvital-chat-ui-streamlit) - Vital 智能体生态系统的一部分，简单的智能体测试 UI\n- [Voice_Agent](https:\u002F\u002Fgithub.com\u002Fdrhammed\u002Fvoice_agent) - AI 语音智能体项目的测试应用\n- [Voxprobe](https:\u002F\u002Fgithub.com\u002Fvoxos-ai\u002Fvoxprobe) - 一个用于语音 AI 智能体自动化测试和评估的 Python 包\n- [Walking-Ai](https:\u002F\u002Fgithub.com\u002FAleCamara\u002Fwalking-ai) - 使用 Unity ML-Agents 插件测试 AI 行走能力\n- [Webtestagenticai](https:\u002F\u002Fgithub.com\u002Ftvalentius\u002FWebTestAgenticAI) - Web 测试 AI 智能体\n- [Whiteboxing-Unitymlagents](https:\u002F\u002Fgithub.com\u002FActiveNick\u002FWhiteboxing-UnityMLAgents) - 一个实验性的测试平台，我在其中使用 Unity ML Agents 测试各种机器学习和 AI 概念\n- [Windowsagentarena](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FWindowsAgentArena) - Windows Agent Arena (WAA) 🪟 是一个可扩展的操作系统平台，用于测试和基准测试多模态 AI 智能体\n- [Worldai](https:\u002F\u002Fgithub.com\u002Fnschaetti\u002FWorldAI) - World AI 是一个为研究目的设计的模拟器。它可以用来模拟世界、逻辑和伦理问题，供 AI 智能体使用\n\n\n\n### Tools\n- [Cortex](https:\u002F\u002Fgithub.com\u002FSKULLFIRE07\u002Fcortex-memory) - Persistent AI memory for coding assistants. Auto-captures decisions, patterns, and context across sessions. VSCode extension + CLI + MCP server. Free.\n- [3Gpp-Requirements-Tools](https:\u002F\u002Fgithub.com\u002FAdrian2901\u002F3gpp-requirements-tools) - Tools for retrieving 3GPP standards and LLM-powered requirement elicitiation.\n- [Acm](https:\u002F\u002Fgithub.com\u002Fdnanhkhoa\u002Facm) - A dead-simple AI-powered CLI tool for effortlessly crafting meaningful Git commit messages\n- [Agent-Manager-Skill](https:\u002F\u002Fgithub.com\u002Ffractalmind-ai\u002Fagent-manager-skill) - tmux + Python agent lifecycle manager for running multiple CLI AI agents (start\u002Fstop\u002Fmonitor\u002Fassign) with cron-friendly scheduling.\n- [Acte](https:\u002F\u002Fgithub.com\u002Fj66n\u002Facte) - A framework to build GUI-like Agent Tools, enhancement to Function Calling of LLM AI.\n- [Agentcloud](https:\u002F\u002Fgithub.com\u002Frnadigital\u002Fagentcloud) - Agent Cloud is like having your own GPT builder with a bunch extra goodies. The GUI features 1) RAG pipeline which can natively embed 260…\n- [Agentgpt-Llm-Tools](https:\u002F\u002Fgithub.com\u002FGautamSharda\u002FAgentGPT-LLM-Tools) - AgentGPT allows you to configure and deploy Autonomous AI agents. Name your own custom AI and have it embark on any goal imaginable\n- [Agentguard](https:\u002F\u002Fgithub.com\u002Fbmdhodl\u002Fagent47) - Zero-dependency runtime guardrails for AI agents with loop detection, budget enforcement, cost tracking, and deterministic replay. [github](https:\u002F\u002Fgithub.com\u002Fbmdhodl\u002Fagent47) | [pypi](https:\u002F\u002Fpypi.org\u002Fproject\u002Fagentguard47\u002F)\n- [Agentools](https:\u002F\u002Fgithub.com\u002FJoongWonSeo\u002Fagentools) - Essentials for LLM-based assistants and agents using OpenAI and function tools\n- [AgentWatch](https:\u002F\u002Fgithub.com\u002Fnicofains1\u002Fagentwatch) - Multi-agent observability library with cascade failure detection, heartbeat-based liveness monitoring, cross-agent correlation, and forensic replay. Provides fleet-level monitoring above per-agent tracing tools [github](https:\u002F\u002Fgithub.com\u002Fnicofains1\u002Fagentwatch) | [npm](https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002F@nicofains1\u002Fagentwatch)\n- [Ai-Agents-Directory](https:\u002F\u002Fgithub.com\u002F0xmetaschool\u002FAI-Agents-Directory) - Find and get started with the best AI Agents and AI Automation tools on the Internet. Start building your own AI Agents powered workforce…\n- [Ai-Anime-Art-Generator](https:\u002F\u002Fgithub.com\u002Fenterwiz\u002Fai-anime-art-generator) - AI-driven cutting-edge tool for anime arts creation, perfect for beginners to easily create stunning anime art without any prior experience.\n- [AI Conference Deadline](https:\u002F\u002Faiconferenceddl.com) - A tracker for AI\u002FML conference submission deadlines, helping researchers track major CFPs, access official conference websites, and plan submissions without accounts or setup [website](https:\u002F\u002Faiconferenceddl.com)\n- [Ai-Game-Devtools](https:\u002F\u002Fgithub.com\u002FYuan-ManX\u002Fai-game-devtools) - Here we will keep track of the latest AI Game Development Tools, including LLM, Agent, Code, Writer, Image, Texture, Shader, 3D Model, An…\n- [Ai.At](https:\u002F\u002Fgithub.com\u002Fblue-codes-yep\u002FAI.AT) - AI-Powered Text-To-Speech Video Generator This web application uses AI to generate captivating and informative video scripts based on use…\n- [Aidialer](https:\u002F\u002Fgithub.com\u002Fakiani\u002Faidialer) - A full stack app for interruptible, low-latency and near-human quality AI phone calls built from stitching LLMs, speech understanding tools…\n- [Aitranslate](https:\u002F\u002Fgithub.com\u002Fpmacro\u002FAITranslate) - A tool to translate Xcode xcstrings files using ChatGPT AI\u002FLLM\n- [Aivideochat](https:\u002F\u002Fgithub.com\u002Fmessingliu\u002FAIVideoChat) - This is an AI video chat tool with anybody (your girlfriend, your idol etc) you want using LLM\n- [Aix](https:\u002F\u002Fgithub.com\u002Fprojectdiscovery\u002Faix) - AIx is a cli tool to interact with Large Language Models (LLM) APIs.\n- [Are-Copilots-Local-Yet](https:\u002F\u002Fgithub.com\u002FErikBjare\u002Fare-copilots-local-yet) - Are Copilots Local Yet? The frontier of local LLM Copilots for code completion, project generation, shell assistance, and more. Find tools …\n- [Arxivrag](https:\u002F\u002Fgithub.com\u002Fphitrann\u002FarXivRAG) - A comprehensive tool designed to enhance the retrieval and generation of academic content from the arXiv database, leveraging advanced Re…\n- [Attention-Viewer](https:\u002F\u002Fgithub.com\u002Fwln20\u002FAttention-Viewer) - A tool for visualizing attention-score heatmap in generative LLMs\n- [Autogenbook](https:\u002F\u002Fgithub.com\u002Fhooked-on-mas\u002FAutoGenBook) - 🤖 📒 AutoGenBook is a Python-based tool that automatically generates books using LLMs. It creates chapters, sections, and subsections recu…\n- [Awesome-Ai-Llms-In-Radiology](https:\u002F\u002Fgithub.com\u002Fopenlifescience-ai\u002FAwesome-AI-LLMs-in-Radiology) - A curated list of awesome resources, papers, datasets, and tools related to AI in radiology. This repository aims to provide a comprehens…\n- [Awesome-Ai-Sdks](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-sdks) - A database of SDKs, frameworks, libraries, and tools for creating, monitoring, debugging and deploying autonomous AI agents\n- [Awesome-Ai-Tools](https:\u002F\u002Fgithub.com\u002Feudk\u002Fawesome-ai-tools) - 🔴 VERY LARGE AI TOOL LIST! 🔴 Curated list of AI Tools - Updated December 2024\n- [Awesome-Aitools](https:\u002F\u002Fgithub.com\u002Fikaijua\u002FAwesome-AITools) - Collection of AI-related utilities. Welcome to submit issues and pull requests \u002F收藏AI相关的实用工具，欢迎提交issues 或者pull requests\n- [Awesome-Langchain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain) - 😎 Awesome list of tools and projects with the awesome LangChain framework\n- [Awesome-Llm-Compression](https:\u002F\u002Fgithub.com\u002FHuangOwen\u002FAwesome-LLM-Compression) - Awesome LLM compression research papers and tools.\n- [Awesome-Llm-Json](https:\u002F\u002Fgithub.com\u002Fimaurer\u002Fawesome-llm-json) - Resource list for generating JSON using LLMs via function calling, tools, CFG. Libraries, Models, Notebooks, etc.\n- [Awesome-Llm-Os](https:\u002F\u002Fgithub.com\u002Fbilalonur\u002Fawesome-llm-os) - A curated list of awesome resources, tools, research papers, and projects related to the concept of Large Language Model Operating Systems (\n- [Awesome-Llm-Productization](https:\u002F\u002Fgithub.com\u002Foscinis-com\u002FAwesome-LLM-Productization) - Awesome-LLM-Productization - a curated list of tools\u002Ftricks\u002Fnews\u002Fregulations about AI and Large Language Model (LLM) productization\n- [Awesome-Llm-Security](https:\u002F\u002Fgithub.com\u002Fcorca-ai\u002Fawesome-llm-security) - A curation of awesome tools, documents and projects about LLM Security.\n- [Awesome-Llm4Security](https:\u002F\u002Fgithub.com\u002Fliu673\u002FAwesome-LLM4Security) - This project aims to consolidate and share high-quality resources and tools across the cybersecurity domain.\n- [Awesome-Llm4Tool](https:\u002F\u002Fgithub.com\u002FOpenGVLab\u002FAwesome-LLM4Tool) - A curated list of the papers, repositories, tutorials, and anythings related to the large language models for tools\n- [Awesome-Llmops](https:\u002F\u002Fgithub.com\u002Ftensorchord\u002FAwesome-LLMOps) - An awesome & curated list of best LLMOps tools for developers\n- [Awesome-Llmops](https:\u002F\u002Fgithub.com\u002FInftyAI\u002FAwesome-LLMOps) - 🎉 An awesome & curated list of best LLMOps tools.\n- [Awesome-Local-Llm](https:\u002F\u002Fgithub.com\u002FWaterPistolAI\u002FAwesome-Local-LLM) - A curated list of resources, libraries, tools, and communities for working with Local Large Language Models (LLMs).\n- [Awesome-Mlsecops](https:\u002F\u002Fgithub.com\u002FRiccardoBiosas\u002Fawesome-MLSecOps) - A curated list of MLSecOps tools, articles and other resources on security applied to Machine Learning and MLOps systems.\n- [Awesome-Rust-Llm](https:\u002F\u002Fgithub.com\u002Fjondot\u002Fawesome-rust-llm) - 🦀 A curated list of Rust tools, libraries, and frameworks for working with LLMs, GPT, AI\n- [Awesome_Ai_For_Programmers](https:\u002F\u002Fgithub.com\u002Frodion-m\u002Fawesome_ai_for_programmers) - Сборник AI-инструментов, кейсов и всяких других полезностей для программистов\n- [Awesomellmapps](https:\u002F\u002Fgithub.com\u002FAbhishek-yadv\u002FAwesomeLLMApps) - A curated collection of awesome applications and tools that utilize large language models (LLMs) with retrieval-augmented generation (RAG…\n- [Bgpt-Mcp](https:\u002F\u002Fgithub.com\u002Fconnerlambden\u002Fbgpt-mcp) - Hosted MCP server for searching scientific papers with full-text experimental data. SSE + Streamable HTTP. 50 free searches.\n- [Blockoli](https:\u002F\u002Fgithub.com\u002FgetAsterisk\u002Fblockoli) - Blockoli is a high-performance tool for code indexing, embedding generation and semantic search tool for use with LLMs.\n- [Blueprints](https:\u002F\u002Fgithub.com\u002Fsublayerapp\u002Fblueprints) - Blueprints is an open-source tool that integrates with your text editor to help you generate code with an LLM based on patterns you alrea…\n- [Botsh](https:\u002F\u002Fgithub.com\u002Fjamsocket\u002Fbotsh) - An LLM-based agent that will install the tools it needs.\n- [Brahmasumm-Community-Edition](https:\u002F\u002Fgithub.com\u002Fbalajivis\u002FBrahmaSumm-Community-Edition) - BrahmaSumm is an advanced document summarization and visualization tool designed to streamline document management, knowledge base creati…\n- [Brokenhill](https:\u002F\u002Fgithub.com\u002FBishopFox\u002FBrokenHill) - A productionized greedy coordinate gradient (GCG) attack tool for large language models (LLMs)\n- [Bubbln_Network-Automation](https:\u002F\u002Fgithub.com\u002Folasupo\u002Fbubbln_network-automation) - An AI-driven network automation tool\n- [Ceo-Agentic-Ai-Framework](https:\u002F\u002Fgithub.com\u002Fvortezwohl\u002FCEO-Agentic-AI-Framework) - An ultra-lightweight Agentic AI framework based on the ReAct paradigm, supporting mainstream LLMs and is stronger than Swarm.\n- [Chatbot](https:\u002F\u002Fgithub.com\u002FYidaHu\u002Fchatbot) - 基于LLM的聊天机器人，AI Agent的自主智能体，利用Function、Tools、Agent来实现LLM自主工作\n- [ChatSpatial](https:\u002F\u002Fgithub.com\u002Fcafferychen777\u002FChatSpatial) - MCP server enabling spatial transcriptomics analysis via natural language. Integrates 60+ methods for spatial domains, deconvolution, cell communication, and trajectory analysis.\n- [Claude-Powered-Study-Assistant](https:\u002F\u002Fgithub.com\u002Fg-hano\u002FClaude-Powered-Study-Assistant) - A study assistant powered by Claude Opus. It provides various tools to assist with different tasks, such as researching,coding,note-takin…\n- [Claudesync](https:\u002F\u002Fgithub.com\u002Fjahwag\u002FClaudeSync) - ClaudeSync is a Python tool that automates the synchronization of local files with Claude.ai Projects\n- [Code-Interpreter-Api](https:\u002F\u002Fgithub.com\u002Fleezhuuuuu\u002FCode-Interpreter-Api) - Committed to being the best code interpreter in the world.\n- [Comfyui-Llm-Tools](https:\u002F\u002Fgithub.com\u002Fpridkett\u002FComfyUI-llm-tools) - Helpful nodes for working with LLMs inside of ComfyUI\n- [Companion](https:\u002F\u002Fgithub.com\u002Frapmd73\u002FCompanion) - An AI-powered Discord bot blending playful conversation with smart moderation tools, adding charm and order to your server.\n- [Conversational-Agent-With-Qa-Tool](https:\u002F\u002Fgithub.com\u002FCharlesSQ\u002Fconversational-agent-with-QA-tool) - A custom chat agent implemented using Langchain, gpt-3.5 and Pinecone. Implements memory management for context, a custom prompt template…\n- [Cuesubplot](https:\u002F\u002Fgithub.com\u002Fkleer001\u002Fcuesubplot) - procedural ai prompts and results\n- [Curategpt](https:\u002F\u002Fgithub.com\u002Fmonarch-initiative\u002Fcurategpt) - LLM-driven curation assist tool\n- [Dbt-Llm-Tools](https:\u002F\u002Fgithub.com\u002Fpragunbhutani\u002Fdbt-llm-tools) - RAG based LLM chatbot for dbt projects\n- [Demogpt](https:\u002F\u002Fgithub.com\u002Fmelih-unsal\u002FDemoGPT) - 🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.\n- [Androidmeda](https:\u002F\u002Fgithub.com\u002FIn3tinct\u002Fdeobfuscate-android-app) - AI tool to deobfuscate and find any potential vulnerabilities in android apps.\n- [Dingo](https:\u002F\u002Fgithub.com\u002FDataEval\u002Fdingo) - Dingo - A Comprehensive Data Quality Evaluation Tool\n- [Discovai-Crawl](https:\u002F\u002Fgithub.com\u002FDiscovAI\u002FDiscovAI-crawl) - 🕷️ DiscovAI Crawl API(🚧 Work in Progress 🚧) - A powerful web scraping solution for AI tools and vector databases. Extract clean HTML, gene…\n- [Docgenie](https:\u002F\u002Fgithub.com\u002Fwe-festify\u002Fdocgenie) - Docgenie is a command-line tool that leverages the power of large language models (LLMs) to automatically generate comprehensive document…\n- [Draft](https:\u002F\u002Fgithub.com\u002Fquchangle1\u002FDRAFT) - The implementation for the paper - From Exploration to Mastery - Enabling LLMs to Master Tools via Self-Driven Interactions.\n- [Ds-Llm-Webui](https:\u002F\u002Fgithub.com\u002FDocShotgun\u002Fds-llm-webui) - A simple tool-use assistant for local LLMs powered by TabbyAPI\n- [Ducky](https:\u002F\u002Fgithub.com\u002FParthSareen\u002Fducky) - Local AI pair programming tool\n- [Emergent](https:\u002F\u002Fgithub.com\u002Fkyb3r\u002Femergent) - An implementation of long term memory and external tools for LLMs\n- [Empower-Functions](https:\u002F\u002Fgithub.com\u002Fempower-ai\u002Fempower-functions) - GPT-4 level function calling models for real-world tool using use cases\n- [Erag](https:\u002F\u002Fgithub.com\u002FEdwardDali\u002Ferag) - an AI interaction tool with RAG hybrid search, conversation context, web content processing and structured data analysis with LLM \u002F GPT\n- [Flotorch](https:\u002F\u002Fgithub.com\u002FFissionAI\u002FFloTorch) - FloTorch is an open-source tool for optimizing Generative AI workloads on AWS. It automates RAG proof-of-concept development with feature…\n- [Flux](https:\u002F\u002Fgithub.com\u002Fparadigmxyz\u002Fflux) - Graph-based LLM power tool for exploring many completions in parallel.\n- [Formfill](https:\u002F\u002Fgithub.com\u002Fwdhorton\u002Fformfill) - FormFill is a CLI tool that uses LLMs to automatically fill out PDF forms.\n- [Funclip](https:\u002F\u002Fgithub.com\u002Fmodelscope\u002FFunClip) - Open-source, accurate and easy-to-use video speech recognition & clipping tool, LLM based AI clipping intergrated.\n- [Function-Python-Ai-Langchain](https:\u002F\u002Fgithub.com\u002FAzure-Samples\u002Ffunction-python-ai-langchain) - Simple starting point function to host LangChains with LLMs and other tools in an Azure Function.\n- [Functionary](https:\u002F\u002Fgithub.com\u002FMeetKai\u002Ffunctionary) - Chat language model that can use tools and interpret the results\n- [Functions-Tools-And-Agents-With-Langchain](https:\u002F\u002Fgithub.com\u002FRyota-Kawamura\u002FFunctions-Tools-and-Agents-with-LangChain) - You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called La…\n- [Gguf-Tools](https:\u002F\u002Fgithub.com\u002FKerfuffleV2\u002Fgguf-tools) - Some random tools for working with the GGUF file format\n- [Gorilla](https:\u002F\u002Fgithub.com\u002FShishirPatil\u002Fgorilla) - Gorilla - Training and Evaluating LLMs for Function Calls (Tool Calls)\n- [Gpt4-Programming-Assistant](https:\u002F\u002Fgithub.com\u002Fpetermartens98\u002FGPT4-Programming-Assistant) - Streamlit web app utilizing OpenAI (GPT-4) and LangChain LLM tools. Application includes an SQLite DB for login\u002Fauthentication and messag…\n- [Graphrag-Visualizer](https:\u002F\u002Fgithub.com\u002Fnoworneverev\u002Fgraphrag-visualizer) - A web-based tool for visualizing and exploring artifacts from Microsoft's GraphRAG.\n- [Harbor](https:\u002F\u002Fgithub.com\u002Fav\u002Fharbor) - Effortlessly run LLM backends, APIs, frontends, and services with one command.\n- [Heb-Gen-Ai](https:\u002F\u002Fgithub.com\u002FTovTechOrg\u002FHeb-Gen-AI) - Tools, examples, and resources to assist in the development of Gen-AI (Generative Artificial Intelligence) applications in Hebrew, with a…\n- [Howtofinetunellama3.1](https:\u002F\u002Fgithub.com\u002FRs-py\u002FHowToFineTuneLlama3.1) - Quick tutorial showing how to fine-tune Llama3.1 with nothing but free tools and text data. All code included in ipynb. For a step by ste…\n- [Humanizer PRO](https:\u002F\u002Ftexthumanizer.pro) - AI-powered text humanization tool with 3 modes (Stealth, Academic, SEO), AI detection scanner, MCP integration for ChatGPT\u002FClaude, and RESTful API access [website](https:\u002F\u002Ftexthumanizer.pro) | [github](https:\u002F\u002Fgithub.com\u002Fkhadinakbaronline\u002Fhumanizer-pro-mcp) | [smithery](https:\u002F\u002Fsmithery.ai\u002Fservers\u002Fkhadin-akbar\u002Fhumanizer-pro)\n- [Icodes](https:\u002F\u002Fgithub.com\u002Fa115\u002FiCODES) - LLM-powered Git archeology tool (a.k.a. Intelligent Commit Ontology Distiller and Enhanced Search)\n- [Indie-Hacker-Tools-Plus](https:\u002F\u002Fgithub.com\u002FXiaomingX\u002Findie-hacker-tools-plus) - 为独立开发者准备的精选技术栈和工具仓库来了！这里有你最需要的工具，帮你提升开发效率、节约成本，最重要的是——这些工具都是市场上热门的，经过验证的。🚀A curated collection of tech stacks and tools tailored for inde…\n- [Intellichunk](https:\u002F\u002Fgithub.com\u002Fcckalen\u002Fintellichunk) - Go Based Lightweight RAG \u002F LLM Tool with CLI + API\n- [Job-Webscraper](https:\u002F\u002Fgithub.com\u002FPrvargas\u002Fjob-webscraper) - LLM API-Powered Job Listings Data Cleaning Tool - Showcase for Data Scientists\n- [Just-Eval](https:\u002F\u002Fgithub.com\u002FRe-Align\u002Fjust-eval) - A simple GPT-based evaluation tool for multi-aspect, interpretable assessment of LLMs.\n- [Labs-Ai-Tools-For-Devs](https:\u002F\u002Fgithub.com\u002Fdocker\u002Flabs-ai-tools-for-devs) - [Now with MCP Support] AI For Devs - Build, Share & Run agentic workflows. Just Docker. Just Markdown. BYO LLM\n- [Lang-Tools-Llm-Powered](https:\u002F\u002Fgithub.com\u002Fodedwolff\u002Flang-tools-LLM-powered) - language learning tools utilizing LLM APIs among others\n- [Langchain-Llm-Pdf-Qa](https:\u002F\u002Fgithub.com\u002FAyyodeji\u002FLangchain-LLM-PDF-QA) - This open-source project leverages cutting-edge tools and methods to enable seamless interaction with PDF documents. Powered by Langchain…\n- [Langkit](https:\u002F\u002Fgithub.com\u002Fwhylabs\u002Flangkit) - 🔍 LangKit - An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safe…\n- [Llama-Cpp-Agent](https:\u002F\u002Fgithub.com\u002FMaximilian-Winter\u002Fllama-cpp-agent) - The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM …\n- [Llama-Pruning](https:\u002F\u002Fgithub.com\u002FMedITSolutionsKurman\u002Fllama-pruning) - This project provides tools to load and prune large language models using a structured pruning method.\n- [Llm](https:\u002F\u002Fgithub.com\u002FMHaggis\u002FLLM) - LLM tools and toys\n- [Llm](https:\u002F\u002Fgithub.com\u002Fjoelabruce\u002Fllm) - Large Language Model Tools\n- [Llm](https:\u002F\u002Fgithub.com\u002FJoshua-Immanuel\u002FLLM) - prompting, evaluation, models, chains, indexes, vector stores, retrieval types, use of frameworks like langchain, agents with custom tools\n- [Llm-As-Function](https:\u002F\u002Fgithub.com\u002Fgusye1234\u002Fllm-as-function) - Embed your LLM into a python function\n- [Llm-Benchmarks](https:\u002F\u002Fgithub.com\u002Fwanzhenchn\u002Fllm-benchmarks) - LLM benchmark tools for LMDeploy, vLLM, and TensorRT-LLM.\n- [Llm-Datasets](https:\u002F\u002Fgithub.com\u002Fmlabonne\u002Fllm-datasets) - High-quality datasets, tools, and concepts for LLM fine-tuning.\n- [Llm-Deployment-Tools-Llmops](https:\u002F\u002Fgithub.com\u002FZamr77\u002FLLM-Deployment-Tools-LLMOps) - This project empirically studies the challenges and prospects of deploying large language models (LLMs) in real-world applications using …\n- [Llm-Fuzzx](https:\u002F\u002Fgithub.com\u002FWindy3f3f3f3f\u002FLLM-FuzzX) - LLM-FuzzX is a user-friendly fuzz testing tool for Large Language Models (e.g., GPT, Claude, LLaMA), featuring advanced task-aware mutati…\n- [Llm-Interest](https:\u002F\u002Fgithub.com\u002Fhamelsmu\u002Fllm-interest) - Tool to collect LLM eval topics\n- [Llm-S-Finetunning](https:\u002F\u002Fgithub.com\u002FWarishayat\u002FLLM-s-Finetunning) - This library offers tools to easily fine-tune large language models (LLMs) on custom datasets. It enhances pre-trained models for specifi…\n- [Llm-Security-101](https:\u002F\u002Fgithub.com\u002FSeezo-io\u002Fllm-security-101) - Delving into the Realm of LLM Security - An Exploration of Offensive and Defensive Tools, Unveiling Their Present Capabilities.\n- [Llm-Slackbot-Channels](https:\u002F\u002Fgithub.com\u002FVokturz\u002FLLM-slackbot-channels) - A user-customized bot for your slack channels using LLMs, Tools and Documents\n- [Llm-Term](https:\u002F\u002Fgithub.com\u002Fdh1011\u002Fllm-term) - A Rust-based CLI tool that generates and executes terminal commands using OpenAI's language models.\n- [Llm-Toolbox-Suite](https:\u002F\u002Fgithub.com\u002FSaurabhBadole\u002Fllm-toolbox-suite) - LLM Toolbox Suite, a powerful and versatile set of tools designed to harness the capabilities of large language models for various produc…\n- [Llm-Tools](https:\u002F\u002Fgithub.com\u002Fdatacommonsorg\u002Fllm-tools) - This repo contains client library code for accessing DataGemma, an open model that helps address the challenges of hallucination by grounding LLMs in the vast, real-world statistical data of Google's Data Commons\n- [Llm-Tools](https:\u002F\u002Fgithub.com\u002Fgkorepanov\u002Fllm-tools) - Some ad-hoc coding tools for LLMs\n- [Llm-Tools](https:\u002F\u002Fgithub.com\u002FTheTeslak\u002FLLM-Tools) - Scripts for preparing data for LLMs - text extraction, Telegram export processing, and file merging with filtering and stats.\n- [Llm-Toolschain](https:\u002F\u002Fgithub.com\u002FViagounet\u002FLLM-ToolsChain) - A LangChain-like framework centered around the use of tools and planning. Useful for agents creation\n- [Llm-Warden](https:\u002F\u002Fgithub.com\u002Fjackhhao\u002Fllm-warden) - A simple jailbreak detection tool for safeguarding LLMs.\n- [Llm.Guts](https:\u002F\u002Fgithub.com\u002FMahdi-s\u002Fllm.guts) - A tool to visualize the internal computational graph of distilgpt2 model.\n- [Llm_Agents_Devtools](https:\u002F\u002Fgithub.com\u002FM1n9X\u002Fllm_agents_devtools) - A curated list of autonomous agents and developer tools powered by LLM.\n- [Llm_Api_Price_Comparator_Web](https:\u002F\u002Fgithub.com\u002FCookSleep\u002FLLM_API_Price_Comparator_Web) - LLM API Price Comparator Web 是一个在线工具，帮助用户便捷地比较不同LLM API服务商在指定输入输出下调用同一种模型的价格。 它会自动获取美元\u002F人民币汇率，允许用户输入服务商的余额、调用定价信息，并计算、比较相对于输入输出Token的成本。 该…\n- [Llm_Counts](https:\u002F\u002Fgithub.com\u002Fharleyszhang\u002Fllm_counts) - llm theoretical performance analysis tools and support params, flops, memory and latency analysis.\n- [Llm_Surprisal](https:\u002F\u002Fgithub.com\u002Ftmalsburg\u002Fllm_surprisal) - Simple tool for generating tokens with open source transformers and\u002For calculate per-token surprisal.\n- [Llm_Tools](https:\u002F\u002Fgithub.com\u002Fmavihsrr\u002FLLM_tools) - Productive tools for enhancing and creating LLMs.\n- [Llm_Video_Editor](https:\u002F\u002Fgithub.com\u002Fsanskar9999\u002Fllm_video_editor) - This application utilizes Large Language Models (LLM) and FFmpeg to automate video editing tasks based on user instructions. Built with a…\n- [Llmask](https:\u002F\u002Fgithub.com\u002Ftop-on\u002Fllmask) - A command-line tool for masking authorship of text, by changing the writing style with a Large Language Model.\n- [Llmcode](https:\u002F\u002Fgithub.com\u002Fjavierganan99\u002FLLMCode) - LLMCode is a tool designed to streamline code documentation using Language Models (LLMs).\n- [Llmcurator.Io](https:\u002F\u002Fgithub.com\u002Fpushpankar\u002FLLMCurator.io) - LLM frontend and data curation tool.\n- [Llmsmartaudittool](https:\u002F\u002Fgithub.com\u002FLLMAudit\u002FLLMSmartAuditTool) - LLM-SmartAudit is a cutting-edge tool designed to revolutionize smart contract auditing using advanced language models.\n- [Llmtestforaccessibilitycheck_Chatgpt](https:\u002F\u002Fgithub.com\u002FAutoBugHunter\u002FLLMTestForAccessibilityCheck_chatgpt) - Checking multiple LLM tools to generate code for a webpage and then prompting the tools to fix the accessibility issues in the webpage\n- [Localagent](https:\u002F\u002Fgithub.com\u002FPrAsAnNaRePo\u002FLocalAgent) - opengpt is a open implementation of GPT agents.\n- [Macllm](https:\u002F\u002Fgithub.com\u002Fappenz\u002FmacLLM) - Tool to integrate LLM's like GPT-3 with macOS\n- [Markdown_Llm](https:\u002F\u002Fgithub.com\u002Fmatweldon\u002Fmarkdown_llm) - A tool for interacting with an LLM in a markdown document\n- [Mastermind](https:\u002F\u002Fgithub.com\u002Ftheoforger\u002Fmastermind) - An LLM-powered CLI tool to help you be a better spymaster in Codenames\n- [Metorial](https:\u002F\u002Fgithub.com\u002Fmetorial\u002Fmetorial) - Connect AI agents to 600+ integrations with a single interface - OAuth, scaling, and monitoring included\n- [Mcp-Go](https:\u002F\u002Fgithub.com\u002Fmark3labs\u002Fmcp-go) - A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources…\n- [Mcphost](https:\u002F\u002Fgithub.com\u002Fmark3labs\u002Fmcphost) - A CLI host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP).\n- [Mergekit](https:\u002F\u002Fgithub.com\u002Farcee-ai\u002Fmergekit) - Tools for merging pretrained large language models.\n- [Modelready](https:\u002F\u002Fgithub.com\u002Fsuparious\u002FModelReady) - Collection of tools for creating and running llama.cpp compatible LLMs\n- [Monadic-Chat](https:\u002F\u002Fgithub.com\u002Fyohasebe\u002Fmonadic-chat) - 🤖 + 🐳 + 🐧 Monadic Chat is a locally hosted web app for creating intelligent chatbots, available for Mac, Windows, and Linux. It offers a …\n- [Monocle](https:\u002F\u002Fgithub.com\u002Farphanetx\u002FMonocle) - Tooling backed by an LLM for performing natural language searches against compiled target binaries. Search for encryption code, password …\n- [Mql](https:\u002F\u002Fgithub.com\u002Fshurutech\u002Fmql) - MQL tool is designed to generate SQL queries directly from natural language inputs.\n- [Nano-Bots](https:\u002F\u002Fgithub.com\u002Ficebaker\u002Fnano-bots) - Repository for Nano Bots' Cartridges - small, AI-powered bots that can be easily shared as a single file, designed to support multiple pro…\n- [Nano-Bots-Api](https:\u002F\u002Fgithub.com\u002Ficebaker\u002Fnano-bots-api) - HTTP API for Nano Bots - small, AI-powered bots that can be easily shared as a single file, designed to support multiple providers such as…\n- [Notiongpt](https:\u002F\u002Fgithub.com\u002FSuiwan\u002FnotionGPT) - NotionGPT, a practical tool built on top of ChatGPT large language model, make it your note-taking assistant!\n- [Ollama-Mcp-Bridge](https:\u002F\u002Fgithub.com\u002Fpatruff\u002Follama-mcp-bridge) - Bridge between Ollama and MCP servers, enabling local LLMs to use Model Context Protocol tools\n- [Open-Webui-Tools](https:\u002F\u002Fgithub.com\u002FHaervwe\u002Fopen-webui-tools) - a Repository of Open-WebUI tools to use with your favourite LLMs\n- [Openai-Tools](https:\u002F\u002Fgithub.com\u002Ftipani86\u002FOpenAI-Tools) - Toolkit to get the most out of your OpenAI Account\n- [Pathology_Llm](https:\u002F\u002Fgithub.com\u002FJaesikKim\u002Fpathology_llm) - GPT-4 as decision support tool in oncology\n- [Pentestgpt](https:\u002F\u002Fgithub.com\u002FGreyDGL\u002FPentestGPT) - A GPT-empowered penetration testing tool\n- [Plasmate](https:\u002F\u002Fgithub.com\u002Fplasmate-labs\u002Fplasmate) - A browser engine built for AI agents that compiles HTML into a Semantic Object Model (SOM), providing 10x token compression vs raw HTML. V8 JS rendering, CDP compatibility, authenticated browsing, MCP server [github](https:\u002F\u002Fgithub.com\u002Fplasmate-labs\u002Fplasmate) | [docs](https:\u002F\u002Fdocs.plasmate.app)\n- [Prelude](https:\u002F\u002Fgithub.com\u002Faerugo\u002Fprelude) - A very simple tool to build LLM prompts from your code repositories.\n- [Prophetfuzz](https:\u002F\u002Fgithub.com\u002FNASP-THU\u002FProphetFuzz) - [CCS'24] An LLM-based, fully automated fuzzing tool for option combination testing.\n- [Purplellama](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002FPurpleLlama) - Set of tools to assess and improve LLM security.\n- [Qlora-Llm](https:\u002F\u002Fgithub.com\u002Fmichaelnny\u002FQLoRA-LLM) - A simple custom QLoRA implementation for fine-tuning a language model (LLM) with basic tools such as PyTorch and Bitsandbytes, completely…\n- [Querying-Csvs-And-Plot-Graphs-With-Llms](https:\u002F\u002Fgithub.com\u002FSomyanshAvasthi\u002FQuerying-CSVs-and-Plot-Graphs-with-LLMs) - Leveraging Large Language Models (LLMs) to query CSV files and plot graphs transforms data analysis. This allows to interact with dataset…\n- [Rag-On-Gcp-With-Vertexai](https:\u002F\u002Fgithub.com\u002FBastinFlorian\u002FRAG-on-GCP-with-VertexAI) - Create a Chatbot app on your own data with GCP tools\n- [Ragelo](https:\u002F\u002Fgithub.com\u002Fzetaalphavector\u002FRAGElo) - RAGElo is a set of tools that helps you selecting the best RAG-based LLM agents by using an Elo ranker\n- [Rageval](https:\u002F\u002Fgithub.com\u002Fgomate-community\u002Frageval) - Evaluation tools for Retrieval-augmented Generation (RAG) methods.\n- [Recruitpilot](https:\u002F\u002Fgithub.com\u002Fjaredkirby\u002FRecruitPilot) - A set of AI tools to automate resume scoring and generate interview questions.\n- [Redlite](https:\u002F\u002Fgithub.com\u002Finnodatalabs\u002Fredlite) - Opinionated tool for benchmarking Conversational Language Models\n- [Remarkable-2-Llm](https:\u002F\u002Fgithub.com\u002F99x-incubator\u002Fremarkable-2-llm) - Integrating LLMs into reMarkable could unlock transformative features like auto-completion, grammar\u002Fstyle corrections, contextual suggest…\n- [Repo-To-Text](https:\u002F\u002Fgithub.com\u002Fkirill-markin\u002Frepo-to-text) - Convert a repository structure and its contents into a single text file, including the tree output and file contents in markdown code blo…\n- [Resume_Render_From_Job_Description](https:\u002F\u002Fgithub.com\u002FAIHawk-FOSS\u002Fresume_render_from_job_description) - Resume_Builder_AIHawk is a powerful Python tool that allows you to automatically customize your resume based on a job URL, ensuring it pe…\n- [Rome-Llm](https:\u002F\u002Fgithub.com\u002Fajn313\u002FROME-LLM) - Tools for Recurrent Optimization via Machine Editing and related benchmarks\n- [Selenium-Agent](https:\u002F\u002Fgithub.com\u002Fahmadrosid\u002Fselenium-agent) - LLM agent using selenium as a tool. Have fun!\n- [Simple-Llm-Exporter](https:\u002F\u002Fgithub.com\u002Frealityinspector\u002Fsimple-llm-exporter) - a tool to export entire scripts to a text file with a file tree and description, for exporting to llm's\n- [SkillLite](https:\u002F\u002Fgithub.com\u002FEXboys\u002Fskilllite) - A lightweight, zero-dependency runtime for the agentsskills protocol that enables AI agents to securely execute portable skills locally. Written in Rust with native OS sandboxing, millisecond cold starts, single binary deployment [github](https:\u002F\u002Fgithub.com\u002FEXboys\u002Fskilllite)\n- [Smart_Fault_Injector_Llm](https:\u002F\u002Fgithub.com\u002FJiaHuann\u002FSmart_Fault_Injector_LLM) - Intelligent kernel error injection\u002Ftesting tool based on large model and eBPF.(基于大模型和eBPF的智能化kernel错误注入、测试工具)\n- [Speech-To-Code](https:\u002F\u002Fgithub.com\u002Fdharllc\u002Fspeech-to-code) - llm assisted development tools\n- [Speechless](https:\u002F\u002Fgithub.com\u002Fuukuguy\u002Fspeechless) - LLM based agents with proactive interactions, long-term memory, external tool integration, and local deployment capabilities.\n- [Splaa](https:\u002F\u002Fgithub.com\u002Fcp3249\u002Fsplaa) - SPLAA is an AI assistant framework that utilizes voice recognition, text-to-speech, and tool-calling capabilities to provide a conversati…\n- [Stan](https:\u002F\u002Fgithub.com\u002Fkaifcoder\u002FStan) - Develop and deploy a Large Language Model (LLM) based tool for generating human like responses to natural language inputs for network not…\n- [Stock-Analysis-With-Llm](https:\u002F\u002Fgithub.com\u002Fbauer-jan\u002Fstock-analysis-with-llm) - This repository provides tools and workflows for stock analysis using large language models (LLMs). It combines financial data processing…\n- [Structgenius](https:\u002F\u002Fgithub.com\u002Fjaadbarg\u002FStructGenius) - Download boilerplate file structure of any tree diagram you give\n- [Tapir](https:\u002F\u002Fgithub.com\u002Fephes\u002Ftapir) - Some llm tools\n- [Textcloak](https:\u002F\u002Fgithub.com\u002Fumutcamliyurt\u002FTextCloak) - A tool for concealing writing style using LLM\n- [Thoughtloom](https:\u002F\u002Fgithub.com\u002Ftbiehn\u002Fthoughtloom) - ThoughtLoom is a powerful tool designed to foster creativity and enhance productivity through the use of LLMs directly from the command l…\n- [Tiger](https:\u002F\u002Fgithub.com\u002FUpsonic\u002FTiger) - No Crypto - Scam alarm - This project is not releated with any crypto currencies. | Neuralink for your AI Agents - LangChain - Autogen - …\n- [Toolcommander](https:\u002F\u002Fgithub.com\u002FNicerWang\u002FToolCommander) - Official implementation of \"From Allies to Adversaries - Manipulating LLM Tool Scheduling through Adversarial Injection\".\n- [Toolla](https:\u002F\u002Fgithub.com\u002Ffoomprep\u002Ftoolla) - High level tool use for LLMs\n- [Toolplanner](https:\u002F\u002Fgithub.com\u002FXiaoMi\u002Ftoolplanner) - ToolPlanner - A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback\n- [Toolqa](https:\u002F\u002Fgithub.com\u002Fnight-chen\u002FToolQA) - ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels …\n- [Tools](https:\u002F\u002Fgithub.com\u002Fbuildownai\u002Ftools) - Monorepository of LLM based t AI ools provided by BuildOwn.AI\n- [Tora](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FToRA) - ToRA is a series of Tool-integrated Reasoning LLM Agents designed to solve challenging mathematical reasoning problems by interacting with …\n- [Trafilatura](https:\u002F\u002Fgithub.com\u002Fadbar\u002Ftrafilatura) - Python & Command-line tool to gather text and metadata on the Web - Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML\n- [Unchained](https:\u002F\u002Fgithub.com\u002Faaronamelgar\u002Funchained) - A Django-based tool for prompt engineering and LLM system evaluation.\n- [Useful-Generativeai-Tools-Repo](https:\u002F\u002Fgithub.com\u002Fanishsingh20\u002FUseful-GenerativeAI-Tools-Repo) - This repository has useful prompts for LLM and Generative AI models like Bard and ChatGPT\n- [Visuallm](https:\u002F\u002Fgithub.com\u002Fgortibaldik\u002Fvisuallm) - Visualization tool for various generation tasks on Language Models.\n- [WFGY](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY) - An open-source framework for debugging and stress testing LLMs under long-horizon, high-tension text scenarios. Includes a TXT-based debugging app for structured sequences to identify where reasoning breaks and retrieval fails [github](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY)\n- [Whitebox-Code-Gpt](https:\u002F\u002Fgithub.com\u002FDecron\u002FWhitebox-Code-GPT) - Repository of instructions for Programming-specific GPT models\n- [Widemem-Ai](https:\u002F\u002Fgithub.com\u002Fremete618\u002Fwidemem-ai) - Lightweight Python memory layer for LLM agents with importance scoring, temporal decay, 3-tier hierarchical memory, YMYL prioritization, and batch conflict resolution. Local-first with SQLite + FAISS. [github](https:\u002F\u002Fgithub.com\u002Fremete618\u002Fwidemem-ai) | [website](https:\u002F\u002Fwidemem.ai) | [pypi](https:\u002F\u002Fpypi.org\u002Fproject\u002Fwidemem-ai\u002F)\n- [Wildguard](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fwildguard) - Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs\n- [Wxflows](https:\u002F\u002Fgithub.com\u002FIBM\u002Fwxflows) - Examples and tutorials for building AI applications with watsonx.ai Flows Engine\n- [Xemantic-Ai-Tool-Schema](https:\u002F\u002Fgithub.com\u002Fxemantic\u002Fxemantic-ai-tool-schema) - Kotlin multiplatform AI\u002FLLM tool use (function calling) JSON Schema generator\n- [Yahoo-Finance-Llm-Agent](https:\u002F\u002Fgithub.com\u002Fojasskapre\u002Fyahoo-finance-llm-agent) - The Yahoo Finance Agent is an application that combines OpenAI's LLMs, the Yahoo Finance Python library, and LangChain's tools to provide…\n- [Zahara-Litellm](https:\u002F\u002Fgithub.com\u002FLiquidAdTech\u002FZahara-LiteLLM) - Gen AI tools\n\n### 工作流\n- [Abc-Classroom](https:\u002F\u002Fgithub.com\u002Fearthlab\u002Fabc-classroom) - 用于自动化 GitHub Classroom 和自动评分工作流的工具\n- [Actions](https:\u002F\u002Fgithub.com\u002FAzure\u002Factions) - 使用 Azure Actions 编写和运行 GitHub 工作流\n- [Actions](https:\u002F\u002Fgithub.com\u002Fbackstage\u002Factions) - 用于自动化 Backstage 工作流的自定义操作\n- [Actions-Workflow-Samples](https:\u002F\u002Fgithub.com\u002FAzure\u002Factions-workflow-samples) - 帮助开发者轻松上手使用 GitHub Action 工作流部署到 Azure\n- [Activepieces](https:\u002F\u002Fgithub.com\u002Factivepieces\u002Factivepieces) - 最友好的开源 AI 自动化工具 ✨ 工作流自动化工具，支持 200 多种集成 \u002F 企业级自动化工具 \u002F Zapier 替代…\n- [Advanced-Gpts](https:\u002F\u002Fgithub.com\u002Fnerority\u002FAdvanced-GPTs) - 自定义 GPT 展示，具有高级工作流和操作逻辑。\n- [Airflow](https:\u002F\u002Fgithub.com\u002Fapache\u002Fairflow) - Apache Airflow - 一个用于以编程方式编写、调度和监控工作流的平台\n- [Airflow-Cookbook](https:\u002F\u002Fgithub.com\u002Fbahchis\u002Fairflow-cookbook) - Airflow 工作流管理平台食谱\n- [Alfred-Calculate-Anything](https:\u002F\u002Fgithub.com\u002Fbiati-digital\u002Falfred-calculate-anything) - 使用自然语言计算任何内容的 Alfred 工作流\n- [Alfred-Github-Workflow](https:\u002F\u002Fgithub.com\u002Fgharlan\u002Falfred-github-workflow) - Alfred 的 GitHub 工作流\n- [Alfred-Pinboard-Rs](https:\u002F\u002Fgithub.com\u002Fspamwax\u002Falfred-pinboard-rs) - Pinboard 的 Alfred 工作流（Rust）\n- [Alfred-Terminalfinder](https:\u002F\u002Fgithub.com\u002FLeEnno\u002Falfred-terminalfinder) - 将当前 Finder 窗口在 Terminal\u002FiTerm 中打开，反之亦然的 Alfred 工作流\n- [Alfred-Workflow](https:\u002F\u002Fgithub.com\u002Fjoetannenbaum\u002Falfred-workflow) - Alfred 工作流的 PHP 辅助库\n- [Alfred-Workflow-Todoist](https:\u002F\u002Fgithub.com\u002Fmoranje\u002Falfred-workflow-todoist) - 用于管理 Todoist 任务的 Alfred 工作流\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Fzenorocha\u002Falfred-workflows) - 🤘 一系列将震撼你世界的 Alfred 3 和 4 工作流\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Fvitorgalvao\u002Falfred-workflows) - Alfred 工作流集合\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Flearn-anything\u002Falfred-workflows) - 惊人的 Alfred 工作流\n- [Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Fwillfarrell\u002Falfred-workflows) - 针对开发者的 Alfred 工作流\n- [Alfred-Workflows-Scientific](https:\u002F\u002Fgithub.com\u002Fandrewning\u002Falfred-workflows-scientific) - 面向科学应用的 Alfred 工作流集合\n- [Alfred2-Ruby-Template](https:\u002F\u002Fgithub.com\u002Fzhaocai\u002Falfred2-ruby-template) - Alfred 2 工作流 Ruby 模板\n- [Alfredworkflow.Com](https:\u002F\u002Fgithub.com\u002Fhzlzh\u002FAlfredWorkflow.com) - 公开的 Alfred 工作流集合\n- [Alfy](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Falfy) - 轻松创建 Alfred 工作流\n- [Amazon-Mwaa-Examples](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Famazon-mwaa-examples) - Amazon Managed Workflows for Apache Airflow (MWAA) 示例仓库包含示例 DAG、requirements.txt、插件和 CloudFormati…\n- [Ambrose](https:\u002F\u002Fgithub.com\u002Ftwitter-archive\u002Fambrose) - 用于数据工作流可视化和实时监控的平台\n- [Argo-Python-Dsl](https:\u002F\u002Fgithub.com\u002Fargoproj-labs\u002Fargo-python-dsl) - Argo 工作流的 Python DSL\n- [Argo-Workflows](https:\u002F\u002Fgithub.com\u002Fargoproj\u002Fargo-workflows) - Kubernetes 的工作流引擎\n- [Argo-Workflows-Demo](https:\u002F\u002Fgithub.com\u002Fvfarcic\u002Fargo-workflows-demo) - Argo 工作流\n- [Arrow-User2022](https:\u002F\u002Fgithub.com\u002Fdjnavarro\u002Farrow-user2022) - 使用 Apache Arrow 处理超内存数据的工作流\n- [Astro-Sdk](https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fastro-sdk) - Astro SDK 允许使用 Python 和 SQL 快速且干净地开发 {提取、加载、转换} 工作流，基于 Apache Airflow。\n- [Atomate2](https:\u002F\u002Fgithub.com\u002Fmaterialsproject\u002Fatomate2) - atomate2 是一套计算材料科学工作流库\n- [Autopr](https:\u002F\u002Fgithub.com\u002Firgolic\u002FAutoPR) - 在你的代码库上运行由 AI 驱动的工作流\n- [Awesome-Alfred-Workflows](https:\u002F\u002Fgithub.com\u002Falfred-workflows\u002Fawesome-alfred-workflows) - 精选的优秀 Alfred 工作流列表\n- [Aws-Ddk](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Faws-ddk) - 一个开源开发框架，帮助你在 AWS 上构建数据工作流和现代数据架构\n- [Aws-Genomics-Workflows](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Faws-genomics-workflows) - AWS 上的基因组学工作流\n- [Aws-Lambda-Fsm-Workflows](https:\u002F\u002Fgithub.com\u002FWorkiva\u002Faws-lambda-fsm-workflows) - 一个用于在 AWS Lambda 上开发基于有限状态机的工作流的 Python 框架\n- [Aws-Mwaa-Local-Runner](https:\u002F\u002Fgithub.com\u002Faws\u002Faws-mwaa-local-runner) - 该仓库提供了一个命令行界面（CLI）工具，可复制亚马逊托管的 Apache Airflow 工作流环境…\n- [Aws-Swf-Flow-Library](https:\u002F\u002Fgithub.com\u002Faws\u002Faws-swf-flow-library) - AWS Simple Workflow 流框架库\n- [Azkaban](https:\u002F\u002Fgithub.com\u002Fazkaban\u002Fazkaban) - Azkaban 工作流管理器\n- [Backbone-Boilerplate](https:\u002F\u002Fgithub.com\u002Ftbranyen\u002Fbackbone-boilerplate) - 用于构建 Backbone 应用程序的工作流\n- [Biobakery_Workflows](https:\u002F\u002Fgithub.com\u002Fbiobakery\u002Fbiobakery_workflows) - bioBakery 工作流是一系列用于执行常见微生物群落分析的工作流和任务，采用标准化、验证过的…\n- [Bioinformatics](https:\u002F\u002Fgithub.com\u002Fjumphone\u002FBioinformatics) - 生物信息学工作流\n- [Bioinformatics](https:\u002F\u002Fgithub.com\u002Fstevekm\u002FBioinformatics) - 生物信息学分析脚本、工作流、通用代码示例\n- [Bktask](https:\u002F\u002Fgithub.com\u002FBackelite\u002FBkTask) - iOS 的异步工作流库\n- [Captain](https:\u002F\u002Fgithub.com\u002Fharbur\u002Fcaptain) - Captain - 将你的 Git 工作流转换为 Docker 🐳 容器\n- [Celery-Director](https:\u002F\u002Fgithub.com\u002Fovh\u002Fcelery-director) - 使用 Celery 构建工作流的简单快速框架\n- [ChatGPT API](https:\u002F\u002Fplatform.openai.com\u002F) OpenAI 提供的用于将 GPT 模型集成到应用程序中的 API\n- [Ci-Gha-Workflow](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fci-gha-workflow) - OpenCV 项目的 GitHub Actions 工作流\n- [Classifai](https:\u002F\u002Fgithub.com\u002F10up\u002Fclassifai) - 使用人工智能增强 WordPress 内容工作流和用户参与度\n- [Comfyui-Workflows](https:\u002F\u002Fgithub.com\u002Fxiwan\u002FcomfyUI-workflows) - 存储我的像素或其他有趣的 comfyui 工作流\n- [Comfyui-Workflows-Zho](https:\u002F\u002Fgithub.com\u002FZHO-ZHO-ZHO\u002FComfyUI-Workflows-ZHO) - 我的 ComfyUI 工作流合集 | My ComfyUI workflows collection\n- [Comfyui-Workspace-Manager](https:\u002F\u002Fgithub.com\u002F11cafe\u002Fcomfyui-workspace-manager) - 一个用于组织和管理所有工作流、模型的 ComfyUI 工作流和模型管理扩展。无缝切换…\n- [Comfyui-Yolain-Workflows](https:\u002F\u002Fgithub.com\u002Fyolain\u002FComfyUI-Yolain-Workflows) - 这里有一些很棒的 comfyui 工作流，它们是使用 comfyui-easy-use 节点包构建的\n- [Comfyui_Examples](https:\u002F\u002Fgithub.com\u002Fcomfyanonymous\u002FComfyUI_examples) - ComfyUI 工作流示例\n- [Comfyui_Workflows](https:\u002F\u002Fgithub.com\u002Fcubiq\u002FComfyUI_Workflows) - 一个包含良好文档、易于遵循的 ComfyUI 工作流的仓库\n- [Comfyuimini](https:\u002F\u002Fgithub.com\u002FImDarkTom\u002FComfyUIMini) - 一个适合移动设备的 WebUI，用于运行 ComfyUI 工作流\n- [Configs](https:\u002F\u002Fgithub.com\u002Fnf-core\u002Fconfigs) - 用于定义不同机构计算环境中特定参数的配置文件\n- [Confluent-Kubernetes-Examples](https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fconfluent-kubernetes-examples) - Confluent for Kubernetes 的示例场景工作流\n- [Corewf](https:\u002F\u002Fgithub.com\u002FUiPath\u002FCoreWF) - WF 运行时移植到 .NET 6 上运行\n- [Couler](https:\u002F\u002Fgithub.com\u002Fcouler-proj\u002Fcouler) - 用于在不同工作流引擎上构建和管理工作流的统一接口，例如 Argo Workflows、Tekton Pipelines 和 Ap…\n- [Create-Actionsprs](https:\u002F\u002Fgithub.com\u002Fjhutchings1\u002FCreate-ActionsPRs) - 此仓库会创建拉取请求，将 GitHub Actions 工作流推送到一组工作流中\n- [Crewai-Examples](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples) - 展示如何使用 CrewAI 框架自动化工作流的示例集合\n- [Cuda-Quantum](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcuda-quantum) - C++ 和 Python 对 CUDA Quantum 编程模型的支持，用于异构量子-经典混合工作流\n- [Cylc-Flow](https:\u002F\u002Fgithub.com\u002Fcylc\u002Fcylc-flow) - Cylc - 一种用于循环系统的工作流引擎\n- [Cylc-Ui](https:\u002F\u002Fgithub.com\u002Fcylc\u002Fcylc-ui) - 用于监控和控制 Cylc 工作流的 Web 应用程序\n- [Cypress-Realworld-App](https:\u002F\u002Fgithub.com\u002Fcypress-io\u002Fcypress-realworld-app) - 一款支付应用程序，用于演示 Cypress 测试方法、模式和工作流的实际应用\n- [D6Tflow](https:\u002F\u002Fgithub.com\u002Fd6t\u002Fd6tflow) - 用于构建高效数据科学工作流的 Python 库\n- [Darktable](https:\u002F\u002Fgithub.com\u002Fdarktable-org\u002Fdarktable) - darktable 是一款开源摄影工作流应用和 RAW 图像处理软件\n- [Data-Engineering](https:\u002F\u002Fgithub.com\u002FGokuMohandas\u002Fdata-engineering) - 构建现代数据栈并编排工作流，以创建高质量的数据用于分析和机器学习应用\n- [Datadog](https:\u002F\u002Fgithub.com\u002Fmasci\u002Fdatadog) - 从 GitHub 工作流发送 Datadog 指标和事件\n- [Deepl-Alfred-Workflow2](https:\u002F\u002Fgithub.com\u002FAlexanderWillner\u002Fdeepl-alfred-workflow2) - DeepL Alfred 工作流\n- [Deploy-Pages](https:\u002F\u002Fgithub.com\u002Factions\u002Fdeploy-pages) - GitHub Action 用于将构件发布到 GitHub Pages 以进行部署\n- [Design_Diagrams](https:\u002F\u002Fgithub.com\u002Fiam-veeramalla\u002Fdesign_diagrams) - 流程图、工作流和图表\n- [Docker-Compose-Laravel](https:\u002F\u002Fgithub.com\u002Faschmelyun\u002Fdocker-compose-laravel) - 用于本地 Laravel 开发的 docker-compose 工作流\n- [Docker-Compose-Wordpress](https:\u002F\u002Fgithub.com\u002Faschmelyun\u002Fdocker-compose-wordpress) - 用于本地 WordPress 开发的 docker-compose 工作流\n- [Docker-Wordpress](https:\u002F\u002Fgithub.com\u002Fpaulczar\u002Fdocker-wordpress) - 演示开发工作流 ... vagrant -> docker -> openstack\n- [Documentation](https:\u002F\u002Fgithub.com\u002Fow2-proactive\u002Fdocumentation) - ProActive 工作流与调度的文档\n- [Dolphinscheduler](https:\u002F\u002Fgithub.com\u002Fapache\u002Fdolphinscheduler) - Apache DolphinScheduler 是现代数据编排平台。能够以低代码方式快速创建高性能工作流\n- [Ds-Workflows-R](https:\u002F\u002Fgithub.com\u002Fposit-conf-2024\u002Fds-workflows-r) - posit::conf(2024) 研讨会 - 使用 Posit 工具进行数据科学工作流 — R 重点\n- [Earth2Studio](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fearth2studio) - 开源深度学习框架，用于探索、构建和部署 AI 天气\u002F气候工作流\n- [Elodie](https:\u002F\u002Fgithub.com\u002Fjmathai\u002Felodie) - 基于 EXIF 的照片助手、整理者和工作流自动化工具\n- [Elsa-Core](https:\u002F\u002Fgithub.com\u002Felsa-workflows\u002Felsa-core) - 一个 .NET 工作流库\n- [Ephemeris](https:\u002F\u002Fgithub.com\u002Fgalaxyproject\u002Fephemeris) - 用于管理 Galaxy 插件的库 - 工具、索引数据和工作流\n- [Ert-Runner.El](https:\u002F\u002Fgithub.com\u002Frejeep\u002Fert-runner.el) - 有观点的 Ert 测试工作流\n- [Examples](https:\u002F\u002Fgithub.com\u002Fconcourse\u002Fexamples) - Concourse 工作流示例\n- [Extension-Ci-Tools](https:\u002F\u002Fgithub.com\u002Fduckdb\u002Fextension-ci-tools) - 包含用于构建 DuckDB 扩展的可重用工作流\u002F动作的仓库\n- [Extractthinker](https:\u002F\u002Fgithub.com\u002Fenoch3712\u002FExtractThinker) - ExtractThinker 是一个面向 LLM 的文档智能库，提供 ORM 风格的交互，实现灵活而强大的文档工作流\n- [Fastshell](https:\u002F\u002Fgithub.com\u002FHosseinKarami\u002Ffastshell) - 极其快速的前端样板和工作流，HTML5、Gulp、Sass\n- [Fireshell](https:\u002F\u002Fgithub.com\u002Ftoddmotto\u002Ffireshell) - 极其快速的前端样板和工作流，HTML5、Grunt、Sass\n- [Fireworks](https:\u002F\u002Fgithub.com\u002Fmaterialsproject\u002Ffireworks) - Fireworks 工作流管理仓库\n- [Flask-App](https:\u002F\u002Fgithub.com\u002Fbrennv\u002Fflask-app) - 用于演示 CI\u002FCD 工作流的示例应用\n- [Floki](https:\u002F\u002Fgithub.com\u002FCyb3rWard0g\u002Ffloki) - 简单易懂的代理式工作流\n- [Flux-Core](https:\u002F\u002Fgithub.com\u002Fflux-framework\u002Fflux-core) - Flux 资源管理框架的核心服务\n- [Flyte](https:\u002F\u002Fgithub.com\u002FExpediaGroup\u002Fflyte) - Flyte 将你使用的工具整合成易于定义、自动化的工作流\n- [Fogworkflowsim](https:\u002F\u002Fgithub.com\u002FISEC-AHU\u002FFogWorkflowSim) - 用于雾计算中工作流模拟和性能评估的环境\n- [Funflow](https:\u002F\u002Fgithub.com\u002Ftweag\u002Ffunflow) - 函数式工作流\n- [Gatk4-Rnaseq-Germline-Snps-Indels](https:\u002F\u002Fgithub.com\u002Fgatk-workflows\u002Fgatk4-rnaseq-germline-snps-indels) - 用于处理 RNA 数据以发现胚系短变异的 GATK v4 及相关工具的工作流\n- [Gatk4-Somatic-Cnvs](https:\u002F\u002Fgithub.com\u002Fgatk-workflows\u002Fgatk4-somatic-cnvs) - 此仓库已归档，这些工作流将被放置在 GATK 仓库的 scripts 目录下。这些工作流也组织…\n- [Gdoc-Downloader](https:\u002F\u002Fgithub.com\u002Fuid\u002Fgdoc-downloader) - 将 Google 文档下载为文本文件，从而实现同时进行 LaTeX 编辑等工作流\n- [Generativeaiexamples](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FGenerativeAIExamples) - 面向加速基础设施和微服务架构的生成式 AI 参考工作流\n- [Ghat](https:\u002F\u002Fgithub.com\u002Ffregante\u002Fghat) - 🛕 在多个仓库之间复用 GitHub Actions 工作流\n- [Git-Collaboration](https:\u002F\u002Fgithub.com\u002Fjduckles\u002Fgit-collaboration) - 用于演示协作工作流的仓库\n- [Git-Guppy](https:\u002F\u002Fgithub.com\u002Ftherealklanni\u002Fgit-guppy) - 为你的 gulp 工作流添加简单的 git-hook 集成\n- [Git-Octopus](https:\u002F\u002Fgithub.com\u002Flesfurets\u002Fgit-octopus) - 持续合并的工作流\n- [Git-Practice](https:\u002F\u002Fgithub.com\u002Fawesome-academy\u002Fgit-practice) - 关于 Git 工作流的练习\n- [Git-Smart](https:\u002F\u002Fgithub.com\u002Fgeelen\u002Fgit-smart) - 为你的 Git 工作流增添一些智慧\n- [Github-Action-Gitflow-Release-Workflow](https:\u002F\u002Fgithub.com\u002Fthomaseizinger\u002Fgithub-action-gitflow-release-workflow) - 使用 GitHub actions 在 GitFlow 风格项目中实现自动化发布的示例工作流\n- [Github-Actions](https:\u002F\u002Fgithub.com\u002FFuelLabs\u002Fgithub-actions) - 可重用的动作工作流\n- [Github-Actions-Flutter-Workflows](https:\u002F\u002Fgithub.com\u002Fzgosalvez\u002Fgithub-actions-flutter-workflows) - 针对 Flutter 项目的意见性 GitHub Action 工作流\n- [Github-Script](https:\u002F\u002Fgithub.com\u002Factions\u002Fgithub-script) - 使用 JavaScript 编写调用 GitHub API 的工作流脚本\n- [Gitwash](https:\u002F\u002Fgithub.com\u002Fmatthew-brett\u002Fgitwash) - 使用 git 的工作流\n- [Global-Workflow](https:\u002F\u002Fgithub.com\u002FNOAA-EMC\u002Fglobal-workflow) - 支持全球预报系统（GFS）的全球超级结构\u002F工作流\n- [Graphql-Cli](https:\u002F\u002Fgithub.com\u002FUrigo\u002Fgraphql-cli) - 📟 用于常见 GraphQL 开发工作流的命令行工具\n- [Graphql-Playground](https:\u002F\u002Fgithub.com\u002Fgraphql\u002Fgraphql-playground) - 🎮 GraphQL IDE，用于改善开发工作流（GraphQL 订阅、交互式文档和协作）\n- [Gulp](https:\u002F\u002Fgithub.com\u002Fgulpjs\u002Fgulp) - 一个用于自动化和增强你的工作流的工具包\n- [Hawk-Projects](https:\u002F\u002Fgithub.com\u002Fferventdesert\u002FHawk-Projects) - Hawk 和 etlpy 的项目配置。xml 格式的工作流定义\n- [Helm-Secrets](https:\u002F\u002Fgithub.com\u002Fjkroepke\u002Fhelm-secrets) - 一个 Helm 插件，帮助你使用 Git 工作流管理秘密，并将它们存储在任何地方\n- [Idseq-Workflows](https:\u002F\u002Fgithub.com\u002Fchanzuckerberg\u002Fidseq-workflows) - 便携式 WDL 工作流，用于 IDseq 生产流水线\n- [Inker](https:\u002F\u002Fgithub.com\u002Fposabsolute\u002Finker) - 演进的电子邮件开发和交付工作流\n- [Isaac_Perceptor](https:\u002F\u002Fgithub.com\u002FNVIDIA-ISAAC-ROS\u002Fisaac_perceptor) - 感知工作流\n- [Jira-Connect-Orb](https:\u002F\u002Fgithub.com\u002FCircleCI-Public\u002Fjira-connect-orb) - 在 Jira 中显示 CircleCI 工作流和部署的状态！\n- [Keras-Cv](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fkeras-cv) - 使用 Keras 实现工业级计算机视觉工作流\n- [Ketrew](https:\u002F\u002Fgithub.com\u002Fhammerlab\u002Fketrew) - 跟踪实验性工作流\n- [Konfig](https:\u002F\u002Fgithub.com\u002FKusionStack\u002Fkonfig) - 应用程序模型和组件的共享仓库，以及用于 GitOps 工作流的 CI 套件\n- [Kuroko2](https:\u002F\u002Fgithub.com\u002Fcookpad\u002Fkuroko2) - Kuroko2 是一个基于 Web 的作业调度器\u002F工作流引擎\n- [Laraadmin-Crm](https:\u002F\u002Fgithub.com\u002Fdwijitsolutions\u002Flaraadmin-crm) - LaraAdmin 是一个开源 CRM，适用于快速启动基于 Admin 的应用程序，具有高级 CRUD 生成、模式管理器等功能…\n- [Laravel-4-Generators](https:\u002F\u002Fgithub.com\u002Fdahabit\u002FLaravel-4-Generators) - 使用生成器快速加速你的 Laravel 4 工作流\n- [Lexikworkflowbundle](https:\u002F\u002Fgithub.com\u002Flexik\u002FLexikWorkflowBundle) - Symfony2 的简单工作流捆绑包\n- [Libmolgrid](https:\u002F\u002Fgithub.com\u002Fgnina\u002Flibmolgrid) - 综合性库，用于快速、GPU 加速的分子网格化，适用于深度学习工作流\n- [Linker](https:\u002F\u002Fgithub.com\u002Fm-reda\u002Flinker) - 工作流编辑器库\n- [Livebook](https:\u002F\u002Fgithub.com\u002Flivebook-dev\u002Flivebook) - 使用交互式 Elixir 笔记本自动化代码和数据工作流\n- [Llmstack](https:\u002F\u002Fgithub.com\u002Ftrypromptly\u002FLLMStack) - 无代码多智能体框架，用于使用你的数据构建 LLM 智能体、工作流和应用程序\n- [Luigi](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fluigi) - Luigi 是一个 Python 模块，可以帮助你构建复杂的批处理作业管道。它处理依赖关系解析、工作流管理、可视…\n- [Machine-Learning-Examples](https:\u002F\u002Fgithub.com\u002Faaronkub\u002Fmachine-learning-examples) - 该仓库 содержит各种机器学习工作流示例。\n- [Maestro](https:\u002F\u002Fgithub.com\u002FNetflix\u002Fmaestro) - Maestro - Netflix 的工作流编排器\n- [Marmot](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fmarmot) - Marmot 工作流执行引擎\n- [Metropolis-Nim-Workflows](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fmetropolis-nim-workflows) - 一系列用于构建使用 NIM 的智能代理的参考工作流\n- [Modin](https:\u002F\u002Fgithub.com\u002Fmodin-project\u002Fmodin) - Modin - 通过更改一行代码即可扩展你的 Pandas 工作流\n- [Molecule-Action](https:\u002F\u002Fgithub.com\u002Fgofrolist\u002Fmolecule-action) - GitHub Action，用于在你的工作流中运行 molecule！\n- [N8N-Workflows](https:\u002F\u002Fgithub.com\u002Freorx\u002Fn8n-workflows) - 我的 n8n 自动化工作流\n- [Nactivity](https:\u002F\u002Fgithub.com\u002Fzhangzihan\u002Fnactivity) - 工作流引擎活动 activiti\n- [Ngx-I18Nsupport](https:\u002F\u002Fgithub.com\u002Fmartinroob\u002Fngx-i18nsupport) - 一些用于 Angular i18n 工作流的工具\n- [Nipype](https:\u002F\u002Fgithub.com\u002Fnipy\u002Fnipype) - 神经影像学软件包的工作流和接口\n- [Notion-Search-Alfred-Workflow](https:\u002F\u002Fgithub.com\u002Fwrjlewis\u002Fnotion-search-alfred-workflow) - 一个 Alfred 工作流，用于即时搜索 Notion\n- [Ollama.Nvim](https:\u002F\u002Fgithub.com\u002Fnomnivore\u002Follama.nvim) - 一个用于管理和集成 ollama 工作流的 neovim 插件\n- [Oozie-Examples](https:\u002F\u002Fgithub.com\u002Fdbist\u002Foozie-examples) - 示例 oozie 工作流\n- [Open-Workflows](https:\u002F\u002Fgithub.com\u002Famiaopensource\u002Fopen-workflows) - A\u002FV 归档的开放工作流和资源列表\n- [Optix-Toolkit](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Foptix-toolkit) - 一套支持 GPU 光线追踪应用中常见工作流的实用工具\n- [Organization-Workflows](https:\u002F\u002Fgithub.com\u002FSvanBoxel\u002Forganization-workflows) - 需要集中管理和运行跨多个仓库的 Actions 工作流吗？这款应用可以帮你完成。\n- [n8n](https:\u002F\u002Fgithub.com\u002Fn8n-io\u002Fn8n) - 公平代码的工作流自动化平台，具备原生 AI 功能。结合可视化构建与自定义代码，可自托管或使用云服务，支持 400 多种集成\n- [Perl-Workflow](https:\u002F\u002Fgithub.com\u002Fperl-workflow\u002Fperl-workflow) - 工作流 - 一个简单、灵活的系统，用于实施工作流\u002F状态机\n- [Pimcore-Workflow-Gui](https:\u002F\u002Fgithub.com\u002FYouweGit\u002Fpimcore-workflow-gui) - 为 Pimcore 工作流添加了一个漂亮的 GUI\n- [Pipeline](https:\u002F\u002Fgithub.com\u002Fmyntra\u002Fpipeline) - Pipeline 是一个用于构建多阶段并发工作流的包，具有集中式日志输出\n- [Pipenv](https:\u002F\u002Fgithub.com\u002Fpypa\u002Fpipenv) - 人类友好的 Python 开发工作流\n- [Pitagora-Cwl](https:\u002F\u002Fgithub.com\u002Fpitagora-network\u002Fpitagora-cwl) - Pitagora-Network 的 Common Workflow Language 工具和工作流\n- [Pr-Harmony](https:\u002F\u002Fgithub.com\u002Fmonitorjbl\u002Fpr-harmony) - Stash 的额外拉取请求工作流\n- [Prefabworkflows_Spiderrobots](https:\u002F\u002Fgithub.com\u002FUnityTechnologies\u002FPrefabWorkflows_SpiderRobots) - 演示新 Prefab 工作流的项目 - Prefab 模式、编辑和嵌套！\n- [Process-Engine.Js](https:\u002F\u002Fgithub.com\u002Foliverzy\u002Fprocess-engine.js) - Node.js 商业流程\u002F工作流引擎\n- [Publish-Packages](https:\u002F\u002Fgithub.com\u002Fskills\u002Fpublish-packages) - 使用 GitHub Actions 将你的项目发布到 Docker 镜像中\n- [Puppet-Git-Hooks](https:\u002F\u002Fgithub.com\u002Fadrienthebo\u002Fpuppet-git-hooks) - 各种各样的 git hooks 用于木偶工作流\n- [Purchase-Workflow](https:\u002F\u002Fgithub.com\u002FOCA\u002Fpurchase-workflow) - Odoo 采购、工作流和组织\n- [Pyspur](https:\u002F\u002Fgithub.com\u002FPySpur-Dev\u002Fpyspur) - 基于图的 LLM 工作流编辑器\n- [Pyzeebe](https:\u002F\u002Fgithub.com\u002Fcamunda-community-hub\u002Fpyzeebe) - Zeebe 工作流引擎的 Python 客户端\n- [Ramp-Workflow](https:\u002F\u002Fgithub.com\u002Fparis-saclay-cds\u002Framp-workflow) - 用于在 pydata（pandas、scikit-learn、pytorch、keras 等）基础上构建预测性工作流的工具包\n- [Rasflow](https:\u002F\u002Fgithub.com\u002Fzhxiaokang\u002FRASflow) - RNA-Seq 分析工作流\n- [Rayder](https:\u002F\u002Fgithub.com\u002Fdevanshbatham\u002Frayder) - 一个轻量级工具，用于编排和组织你的漏洞挖掘侦察\u002F渗透测试命令行工作流\n- [Reaflow](https:\u002F\u002Fgithub.com\u002Freaviz\u002Freaflow) - 🎯 React 库，用于构建工作流编辑器、流程图和图表。由 @goodcodeus 维护\n- [Redmine_Workflow_Enhancements](https:\u002F\u002Fgithub.com\u002Fdr-itz\u002Fredmine_workflow_enhancements) - Redmine 工作流增强。未维护\n- [Redux-User-Auth](https:\u002F\u002Fgithub.com\u002FChinwike1\u002Fredux-user-auth) - 使用 MERN 栈构建的用户身份验证工作流\n- [Repo-File-Sync-Action](https:\u002F\u002Fgithub.com\u002FBetaHuhn\u002Frepo-file-sync-action) - 🔄 GitHub Action，用于保持多个仓库之间的文件同步，例如 Action 工作流或整个目录\n- [Reusable-Workflows](https:\u002F\u002Fgithub.com\u002Factions\u002Freusable-workflows) - 用于开发动作的可重用工作流\n- [Rexpect](https:\u002F\u002Fgithub.com\u002Frust-cli\u002Frexpect) - .github\u002Fworkflows\u002Fci.yml\n- [Rhessysworkflows](https:\u002F\u002Fgithub.com\u002Fselimnairb\u002FRHESSysWorkflows) - RHESSysWorkflows 提供用于构建 RHESSys 模型的 Python 脚本\n- [Row-Oriented-Workflows](https:\u002F\u002Fgithub.com\u002Fjennybc\u002Frow-oriented-workflows) - R 中基于行的工作流，使用 tidyverse\n- [Ruote-Kit](https:\u002F\u002Fgithub.com\u002Fkennethkalmer\u002Fruote-kit) - RESTish 包装器，用于 ruote 工作流引擎\n- [Rust-Build](https:\u002F\u002Fgithub.com\u002Fesp-rs\u002Frust-build) - 用于部署\u002F构建 Rust 分支 esp-rs\u002Frust 的安装工具和工作流，支持 Xtensa 和 RISC-V\n- [Sage](https:\u002F\u002Fgithub.com\u002Froots\u002Fsage) - WordPress 初始主题，带有 Laravel Blade 组件和模板、Tailwind CSS 以及现代开发工作流\n- [Sale-Workflow](https:\u002F\u002Fgithub.com\u002FOCA\u002Fsale-workflow) - Odoo 销售、工作流和组织\n- [Search-Alfred-Workflows](https:\u002F\u002Fgithub.com\u002FAcidham\u002Fsearch-alfred-workflows) - 搜索 Alfred 工作流\n- [Seargesdxl](https:\u002F\u002Fgithub.com\u002FSeargeDP\u002FSeargeSDXL) - ComfyUI 中 SDXL 的自定义节点和工作流\n- [Serverless-Workflows](https:\u002F\u002Fgithub.com\u002Frhdhorchestrator\u002Fserverless-workflows) - 一组精选的无服务器工作流\n- [Setup-Jfrog-Cli](https:\u002F\u002Fgithub.com\u002Fjfrog\u002Fsetup-jfrog-cli) - 在你的 GitHub Actions 工作流中设置 JFrog CLI\n- [Shadcn-Next-Workflows](https:\u002F\u002Fgithub.com\u002Fnobruf\u002Fshadcn-next-workflows) - 使用 React Flows、Next.js 和 Shadcnui 构建交互式工作流。轻松创建、连接和验证自定义节点\n- [Shimmering-Obsidian](https:\u002F\u002Fgithub.com\u002Fchrisgrieser\u002Fshimmering-obsidian) - Alfred 工作流，具有数十种功能，可用于控制你的 Obsidian 保险库\n- [Skyvern](https:\u002F\u002Fgithub.com\u002FSkyvern-AI\u002Fskyvern) - 使用 LLM 和计算机视觉自动化基于浏览器的工作流\n- [Slashbase-Go](https:\u002F\u002Fgithub.com\u002Fslashbase\u002Fslashbase-go) - 现代数据库 IDE，适用于你的开发和数据工作流。支持 MySQL、PostgreSQL 和 MongoDB\n- [Slickflow](https:\u002F\u002Fgithub.com\u002Fbesley\u002FSlickflow) - .NET 开源工作流引擎, .NET 开源工作流\n- [Smriprep](https:\u002F\u002Fgithub.com\u002Fnipreps\u002Fsmriprep) - NIPreps（神经影像预处理工具）的结构磁共振 PREProcessing（sMRIPrep）工作流\n- [Sos](https:\u002F\u002Fgithub.com\u002Fvatlab\u002Fsos) - SOS 工作流系统，用于日常数据分析\n- [Spiffworkflow](https:\u002F\u002Fgithub.com\u002Fsartography\u002FSpiffWorkflow) - 一个用纯 Python 实现的强大工作流引擎\n- [Spreads](https:\u002F\u002Fgithub.com\u002FDIYBookScanner\u002Fspreads) - 用于书籍数字化的模块化工作流助手\n- [Starter-Workflows](https:\u002F\u002Fgithub.com\u002Factions\u002Fstarter-workflows) - 加速新的 GitHub Actions 工作流\n- [Step-Functions-Workflows-Collection](https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fstep-functions-workflows-collection) - Step Functions 工作流。更多信息请访问网站 - https:\u002F\u002Fserverlessland.com\u002Fworkflows\n- [Streamflow](https:\u002F\u002Fgithub.com\u002Flmco\u002Fstreamflow) - StreamFlow™ 是一种流处理工具，旨在帮助构建和监控处理工作流\n- [Super-Table](https:\u002F\u002Fgithub.com\u002Fverbb\u002Fsuper-table) - 使用 Super Table 加速你的 Craft 工作流\n- [Swiftcurrent](https:\u002F\u002Fgithub.com\u002Fwwt\u002FSwiftCurrent) - 一个用于管理 Swift 中复杂工作流的库\n- [T2T-Polish](https:\u002F\u002Fgithub.com\u002Farangrhie\u002FT2T-Polish) - T2T 基因组组装的评估和优化工作流\n- [Tactic](https:\u002F\u002Fgithub.com\u002FSouthpaw-TACTIC\u002FTACTIC) - 开源远程协作平台，用于配置和部署企业工作流解决方案\n- [Tangelo](https:\u002F\u002Fgithub.com\u002Fsandbox-quantum\u002FTangelo) - 一个用于探索量子计算机和模拟器上端到端化学工作流的 Python 包\n- [Terraform-With-Circleci-Example](https:\u002F\u002Fgithub.com\u002Ffedekau\u002Fterraform-with-circleci-example) - 这是一个使用 Terraform 和 CircleCI 2.0 工作流自动部署基础设施的示例\n- [Texera](https:\u002F\u002Fgithub.com\u002FTexera\u002Ftexera) - 使用工作流进行协作式以机器学习为中心的数据分析\n- [Tfc-Workflows-Tooling](https:\u002F\u002Fgithub.com\u002Fhashicorp\u002Ftfc-workflows-tooling) - 用于自动化 HCP Terraform API 运行的工具\n- [Tideflow](https:\u002F\u002Fgithub.com\u002Ftideflow-io\u002Ftideflow) - 构建可扩展的自动化。Tideflow 是一个实时、开源的工作流执行和监控 Web 应用程序\n- [Trailblazer-Activity](https:\u002F\u002Fgithub.com\u002Ftrailblazer\u002Ftrailblazer-activity) - 建模商业工作流并执行它们\n- [Txtai](https:\u002F\u002Fgithub.com\u002Fneuml\u002Ftxtai) - 💡 一体化开源嵌入数据库，用于语义搜索、LLM 协调和语言模型工作流\n- [User_Guide](https:\u002F\u002Fgithub.com\u002Fcommon-workflow-language\u002Fuser_guide) - CWL v1.0 - v1.2 用户指南\n- [Vanilla-Parcel-Boilerplate](https:\u002F\u002Fgithub.com\u002Fbradtraversy\u002Fvanilla-parcel-boilerplate) - 简单的初始工作流，用于使用 Parcel 构建 vanilla js 应用程序\n- [Vault-Config-Operator](https:\u002F\u002Fgithub.com\u002Fredhat-cop\u002Fvault-config-operator) - 一个运营商，用于在 Kubernetes 内部支持 Haschicorp Vault 配置工作流\n- [Vector-Vein](https:\u002F\u002Fgithub.com\u002FAndersonBY\u002Fvector-vein) - 无代码 AI 工作流。拖放工作流节点，用你的 AI 代理使用你的工作流\n- [Venture](https:\u002F\u002Fgithub.com\u002Fksassnowski\u002Fventure) - Venture 允许你在 Laravel 应用程序中创建和管理复杂的异步工作流\n- [Viewflow](https:\u002F\u002Fgithub.com\u002Fviewflow\u002Fviewflow) - Django 的可重用工作流库\n- [Vivliostyle-Cli](https:\u002F\u002Fgithub.com\u002Fvivliostyle\u002Fvivliostyle-cli) - ⚒ 加速命令行出版工作流\n- [Vscode-Tips-Tricks](https:\u002F\u002Fgithub.com\u002Fahmadawais\u002FVSCode-Tips-Tricks) - VSCode-Tips-Tricks 示例和工作流，帮助你成为 Visual Studio Code 高级用户 →\n- [Vue-Vscode-Snippets](https:\u002F\u002Fgithub.com\u002Fsdras\u002Fvue-vscode-snippets) - 这些片段是为了以最无缝的方式加速我的工作流而创建的\n- [Waveterm](https:\u002F\u002Fgithub.com\u002Fwavetermdev\u002Fwaveterm) - 一个开源、跨平台的终端，用于无缝工作流\n- [Windmill](https:\u002F\u002Fgithub.com\u002Fwindmill-labs\u002Fwindmill) - 开源开发者平台，用于为你的整个基础设施赋能，将脚本转化为 Webhook、工作流和 UI。最快的工作流引擎（13x…\n- [With](https:\u002F\u002Fgithub.com\u002Fmchav\u002Fwith) - 使用单一工具进行连续工作流的命令前缀\n- [Wordpressify](https:\u002F\u002Fgithub.com\u002Fluangjokaj\u002Fwordpressify) - 🎈 自动化你的 WordPress 开发工作流\n- [Workflow](https:\u002F\u002Fgithub.com\u002Finveniosoftware-contrib\u002Fworkflow) - 简单的 Python 式工作流\n- [Workflow-Dispatch](https:\u002F\u002Fgithub.com\u002Fbenc-uk\u002Fworkflow-dispatch) - 一个 GitHub Action，用于触发工作流，使用 `workflow_dispatch` 事件\n- [Workflow-Reactjs](https:\u002F\u002Fgithub.com\u002Ffdaciuk\u002Fworkflow-reactjs) - 我的使用 ReactJS + Webpack 3+ 的工作流\n- [Workflows](https:\u002F\u002Fgithub.com\u002Fwarpdotdev\u002Fworkflows) - Workflows 使浏览、搜索、执行和分享命令（或一系列命令）变得容易——无需离开终端\n- [Workflows](https:\u002F\u002Fgithub.com\u002F42coders\u002Fworkflows) - Workflows Package 为你的 Laravel 应用程序添加了拖放式工作流\n- [Workflows](https:\u002F\u002Fgithub.com\u002FAnswerDotAI\u002Fworkflows) - 用于 fastai 项目中的复合动作工作流\n- [Workflows](https:\u002F\u002Fgithub.com\u002Fasottile\u002Fworkflows) - 可重用的 github 工作流 \u002F 动作\n- [Workflows-Samples](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fworkflows-samples) - 该仓库包含 Cloud Workflows 的示例\n- [Workflows_And_Package_Management](https:\u002F\u002Fgithub.com\u002Feanbit-rt\u002FWorkflows_and_package_management) - 可重复性和包管理 - 工作流语言（CWL、Snakemake、Conda）。\n- [Yeoman](https:\u002F\u002Fgithub.com\u002Fyeoman\u002Fyeoman) - Yeoman - 一套用于自动化开发工作流的工具\n- [Youtrack-Workflows](https:\u002F\u002Fgithub.com\u002FJetBrains\u002Fyoutrack-workflows) - YouTrack 自定义工作流仓库\n- [Zapier](https:\u002F\u002Fgithub.com\u002Fzapier\u002Fzapier-platform) - 用于在 Zapier 上构建集成的 SDK\n- [Zorow](https:\u002F\u002Fgithub.com\u002Fopenmainframeproject\u002Fzorow) - z\u002FOS 开源工作流仓库（zorow），是一个致力于贡献和合作 z\u002FOSMF 工作流的开源社区。\n\n---\n\n\n\n## 贡献\n\n💡 欢迎大家贡献！  \n\n您可以随时提交拉取请求、建议新增资源，或[打开一个议题](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002Fnew)。  \n\n请确保您的提交符合以下指南：\n\n- 与 AI 代理相关（例如：使用代理、学习型代理、构建代理等类别）  \n- 文档清晰且易于访问（例如：名称 + 链接 + 一句话描述）。  \n\n### 贡献者  \n\n感谢所有帮助构建和改进此列表的优秀贡献者：  \n\n**拉取请求贡献者：**  \n- [@almogbot](https:\u002F\u002Fgithub.com\u002Falmogbot) — Wolfpack  \n- [@ankitdn](https:\u002F\u002Fgithub.com\u002Fankitdn) — Vulert  \n- [@arielshad](https:\u002F\u002Fgithub.com\u002Farielshad) — Shep  \n- [@b1rdmania](https:\u002F\u002Fgithub.com\u002Fb1rdmania) — GhostClaw  \n- [@Bartupso](https:\u002F\u002Fgithub.com\u002FBartupso)  \n- [@bmdhodl](https:\u002F\u002Fgithub.com\u002Fbmdhodl) — AgentGuard  \n- [@cafferychen777](https:\u002F\u002Fgithub.com\u002Fcafferychen777) — ChatSpatial  \n- [@connerlambden](https:\u002F\u002Fgithub.com\u002Fconnerlambden) — BGPT MCP  \n- [@dancourse](https:\u002F\u002Fgithub.com\u002Fdancourse) — OpenClaw  \n- [@daxaur](https:\u002F\u002Fgithub.com\u002Fdaxaur) — OpenPaw  \n- [@GitDakky](https:\u002F\u002Fgithub.com\u002FGitDakky) — Yoyo  \n- [@hanzili](https:\u002F\u002Fgithub.com\u002Fhanzili) — Hanzi  \n- [@hashimwarren](https:\u002F\u002Fgithub.com\u002Fhashimwarren) — Mastra AI  \n- [@hivemoot-forager](https:\u002F\u002Fgithub.com\u002Fhivemoot-forager) — Hivemoot  \n- [@igorcosta](https:\u002F\u002Fgithub.com\u002Figorcosta) — Autohand Code CLI  \n- [@In3tinct](https:\u002F\u002Fgithub.com\u002FIn3tinct) — Androidmeda 更新  \n- [@johnnyfish](https:\u002F\u002Fgithub.com\u002Fjohnnyfish) — OneCLI  \n- [@kantorcodes](https:\u002F\u002Fgithub.com\u002Fkantorcodes) — Registry Broker  \n- [@keon](https:\u002F\u002Fgithub.com\u002Fkeon) — Lumen  \n- [@kody-w](https:\u002F\u002Fgithub.com\u002Fkody-w) — Rappterbook  \n- [@L1AD](https:\u002F\u002Fgithub.com\u002FL1AD) — PolicyLayer  \n- [@m13v](https:\u002F\u002Fgithub.com\u002Fm13v) — Fazm  \n- [@mantrahq502](https:\u002F\u002Fgithub.com\u002Fmantrahq502) — Mantra  \n- [@minhoyoo-iotrust](https:\u002F\u002Fgithub.com\u002Fminhoyoo-iotrust) — WAIaaS  \n- [@murdore](https:\u002F\u002Fgithub.com\u002Fmurdore) — NeuroLink  \n- [@NikitaDmitrieff](https:\u002F\u002Fgithub.com\u002FNikitaDmitrieff) — auto-co  \n- [@pinchwork](https:\u002F\u002Fgithub.com\u002Fpinchwork) — Pinchwork  \n- [@PubliusAu](https:\u002F\u002Fgithub.com\u002FPubliusAu) — 工具\u002F基准测试补充  \n- [@Quinnod345](https:\u002F\u002Fgithub.com\u002FQuinnod345) — context-engine-ai  \n- [@remete618](https:\u002F\u002Fgithub.com\u002Fremete618) — widemem-ai  \n- [@RusDyn](https:\u002F\u002Fgithub.com\u002FRusDyn) — WritBase  \n- [@sagiyak-rgb](https:\u002F\u002Fgithub.com\u002Fsagiyak-rgb) — TeamHero  \n- [@SKULLFIRE07](https:\u002F\u002Fgithub.com\u002FSKULLFIRE07) — Cortex  \n- [@superlowburn](https:\u002F\u002Fgithub.com\u002Fsuperlowburn) — AgentHive  \n- [@The-Nexus-Guard](https:\u002F\u002Fgithub.com\u002FThe-Nexus-Guard) — AIP Identity Protocol  \n- [@tigranbs](https:\u002F\u002Fgithub.com\u002Ftigranbs) — Sayna.ai  \n- [@vishalveerareddy123](https:\u002F\u002Fgithub.com\u002Fvishalveerareddy123) — Lynkr  \n- [@yubing744](https:\u002F\u002Fgithub.com\u002Fyubing744) — Agent-Manager-Skill  \n\n**议题贡献者：**  \n- [@AClerbois](https:\u002F\u002Fgithub.com\u002FAClerbois) — Microsoft Agent Framework ([#10](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F10))  \n- [@aro-brez](https:\u002F\u002Fgithub.com\u002Faro-brez) — 8OWLS \u002F WeEvolve ([#41](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F41))  \n- [@atchl7](https:\u002F\u002Fgithub.com\u002Fatchl7) — AI 会议截止日期 ([#14](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F14))  \n- [@clawddar](https:\u002F\u002Fgithub.com\u002Fclawddar) — MoltBook ([#28](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F28))  \n- [@dbhurley](https:\u002F\u002Fgithub.com\u002Fdbhurley) — Plasmate ([#139](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F139))  \n- [@elliotllliu](https:\u002F\u002Fgithub.com\u002Felliotllliu) — AgentShield ([#94](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F94))  \n- [@EXboys](https:\u002F\u002Fgithub.com\u002FEXboys) — SkillLite ([#27](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F27))  \n- [@getmilodev](https:\u002F\u002Fgithub.com\u002Fgetmilodev) — Milo ([#83](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F83))  \n- [@IsaacGHX](https:\u002F\u002Fgithub.com\u002FIsaacGHX) — AgentFlow ([#7](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F7))  \n- [@jacobsd32-cpu](https:\u002F\u002Fgithub.com\u002Fjacobsd32-cpu) — DJD 代理评分 ([#60](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F60))  \n- [@johnxie](https:\u002F\u002Fgithub.com\u002Fjohnxie) — Taskade GitHub 链接 ([#47](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F47))  \n- [@khadinakbaronline](https:\u002F\u002Fgithub.com\u002Fkhadinakbaronline) — Humanizer PRO ([#110](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F110))  \n- [@nicofains1](https:\u002F\u002Fgithub.com\u002Fnicofains1) — AgentWatch ([#87](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F87))  \n- [@onestardao](https:\u002F\u002Fgithub.com\u002Fonestardao) — WFGY ([#33](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F33))  \n- [@ori-cofounder](https:\u002F\u002Fgithub.com\u002Fori-cofounder) — GNAP ([#99](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F99))  \n- [@Robocular](https:\u002F\u002Fgithub.com\u002FRobocular) — Clawdia 代理网关 ([#72](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F72))  \n- [@samirasadov28-code](https:\u002F\u002Fgithub.com\u002Fsamirasadov28-code) — StoryRoute ([#125](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F125))  \n- [@Sendersby](https:\u002F\u002Fgithub.com\u002FSendersby) — TiOLi AGENTIS ([#131](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F131))  \n- [@zomux](https:\u002F\u002Fgithub.com\u002Fzomux) — OpenAgents Network ([#52](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F52))  \n\n---\n\n## 宣传推广  \n\n📢 请帮忙向您的朋友圈分享这个仓库，共同壮大 **Awesome AI Agents** 社区！  \n\n您可以给仓库点个赞、订阅我们的新闻通讯，并在 LinkedIn 和\u002F或 Twitter 上分享。  \n\n以下是具体操作方法：  \n\n### ⭐ 给仓库点个赞 ⭐  \n⭐ **为什么要给这个仓库点赞？**  \n您的点赞可以帮助更多人发现这一宝贵资源！通过点赞，您可以：  \n- 支持 AI 代理社区的发展。  \n- 每天都能获取最新内容更新（每 24 小时一次）。  \n- 激励更多人探索智能系统的世界。  \n\n让我们一起塑造 AI 的未来吧！🌟  \n\n### 新闻通讯  \n📩 **订阅每日资讯**：每天获取 **100 多条更新**，不错过任何 ML 代理领域的突破进展。立即加入我们：**[https:\u002F\u002Fagents.blog](https:\u002F\u002Fagents.blog)**。  \n\n### LinkedIn  \n使用以下简单文案在 LinkedIn 上分享：  \n\n> 🚀 **Awesome AI Agents** - 精选的前沿工具、资源和鼓舞人心的项目，专注于 AI 代理领域。开源、社区驱动，助您探索 AI 的未来！🌐  \n> 🔗 [立即查看！](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents)  \n> #AI #机器学习 #自动化 #开源 #人工智能 #数据科学 #创新  \n\n[点击此处前往 LinkedIn 分享！](https:\u002F\u002Fwww.linkedin.com\u002Fsharing\u002Fshare-offsite\u002F?url=https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents)\n\n### Twitter\n转发这条推文，分享到 Twitter：\n\n> 🚀 发现 **Awesome AI Agents** 仓库！🤖 一个精心整理的 AI 代理集合，适用于自动化、自然语言处理等领域！开源且由社区驱动！🌟  \n> 🔗 [立即查看！](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents)  \n> #AI #机器学习 #自动化 #开源 #人工智能 #创新\n\n[点击这里直接发推！](https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?text=Discover%20the%20Awesome%20AI%20Agents%20repo%21%20A%20curated%20collection%20of%20AI%20agents%20for%20automation%2C%20NLP%2C%20and%20more%21%20Open-source%20%26%20community-driven%21%20%F0%9F%9A%80%20Check%20it%20out%20here%3A%20https%3A%2F%2Fgithub.com%2Fjim-schwoebel%2Fawesome_ai_agents%20%23AI%20%23MachineLearning%20%23Automation%20%23OpenSource%20%23ArtificialIntelligence%20%23Innovation)\n\n## 许可证\n\n📜 本仓库采用 [Apache 2.0](LICENSE) 许可证授权。  \n\n我们以开源为荣，旨在造福更广泛的社区，请随时 fork 并扩展！","# Awesome AI Agents 快速上手指南\n\n`awesome_ai_agents` 并非一个可直接安装的单一软件包或框架，而是一个**精选的资源集合仓库**。它汇集了当前最先进的 AI Agent 工具、框架、数据集、学习课程及开源项目。本指南将帮助你如何利用该仓库快速找到并启动适合你的 AI Agent 项目。\n\n## 环境准备\n\n由于本仓库包含多种不同类型的 Agent 项目（基于 Python、Node.js 等不同技术栈），具体的系统要求取决于你选择使用的具体工具。以下是通用的基础环境建议：\n\n*   **操作系统**: Windows, macOS, 或 Linux (推荐 Ubuntu 20.04+)\n*   **核心依赖**:\n    *   **Git**: 用于克隆仓库。\n    *   **Python 3.8+**: 大多数 AI Agent 框架（如 LangChain, AutoGen）基于 Python。\n    *   **Node.js 18+**: 部分基于 JavaScript\u002FTypeScript 的 Agent 工具需要此环境。\n    *   **API Keys**: 准备相关大模型服务商的 API Key（如 OpenAI, Anthropic, 或国内的大模型平台）。\n*   **国内加速方案**:\n    *   克隆仓库时若速度慢，可使用 Gitee 镜像（如有）或配置 Git 代理。\n    *   Python 包安装推荐配置清华源或阿里源：\n        ```bash\n        pip config set global.index-url https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n        ```\n\n## 获取资源与安装\n\n你不需要“安装”这个列表本身，而是需要克隆仓库以浏览资源，然后根据你的需求安装具体的 Agent 工具。\n\n### 1. 克隆仓库\n获取最新的资源列表：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents.git\ncd awesome_ai_agents\n```\n\n### 2. 选择并安装具体工具\n浏览仓库中的 `README.md` 或目录结构，找到你感兴趣的类别（例如 `Frameworks`, `Applications`, `Coding Agent`）。\n\n**示例：安装一个流行的 Agent 框架 (以 LangChain 为例)**\n如果在仓库的 \"Frameworks\" 部分找到了 LangChain，安装命令如下：\n\n```bash\npip install langchain langchain-openai\n```\n\n**示例：安装特定的开源 Agent 项目 (以 Open Interpreter 为例)**\n如果在 \"Applications\" 部分看到了 Open Interpreter：\n\n```bash\npip install open-interpreter\n```\n\n> **提示**: 每个具体项目都有其独立的 `requirements.txt` 或安装说明，请务必进入对应项目的官方文档或 GitHub 页面查看详细步骤。\n\n## 基本使用\n\n本仓库的核心用法是**检索**和**参考**。以下是典型的使用流程：\n\n### 场景一：寻找现成的 AI 应用\n如果你想直接使用现有的 AI Agent 来提高效率（如自动写代码、处理文档）：\n\n1.  打开克隆后的 `README.md` 文件。\n2.  定位到 **Using Agents -> Applications** 章节。\n3.  根据需求查找类别，例如 **Coding Assistant** 或 **Data Analysis**。\n4.  点击链接访问项目主页（如 Cursor, Sierra 等），按照该项目指引注册或使用。\n\n### 场景二：开发自己的 AI Agent\n如果你想构建一个自定义 Agent：\n\n1.  在 `README.md` 中定位到 **Building Agents** 章节。\n2.  **选择框架**: 查看 **Frameworks** 列表（如 AutoGen, CrewAI, LangGraph），选择一个适合的框架。\n3.  **参考示例**: 查看 **Repositories** 或 **Workflows** 部分，找到类似的开源代码库。\n4.  **运行示例代码**:\n    假设你选择了某个基于 Python 的简单 Agent 模板，通常的使用模式如下：\n\n    ```python\n    # 伪代码示例：基于常见框架的结构\n    from my_chosen_framework import Agent, LLM\n\n    # 初始化模型\n    llm = LLM(api_key=\"YOUR_API_KEY\")\n\n    # 定义 Agent\n    agent = Agent(\n        role=\"Research Assistant\",\n        goal=\"Summarize technical papers\",\n        llm=llm\n    )\n\n    # 执行任务\n    response = agent.run(\"Explain the latest advancements in LLM agents.\")\n    print(response)\n    ```\n\n### 场景三：学习与进阶\n*   **初学者**: 查看 **Learning Agents -> Courses** 部分，跟随推荐的课程入门。\n*   **研究者**: 查看 **Datasets** 和 **Benchmarks** 部分，获取评估数据和测试集。\n\n---\n*注：本仓库内容每日更新，建议定期 `git pull` 获取最新的工具和项目信息。*","某初创团队的技术负责人正计划开发一套基于多智能体协作的自动化客服系统，急需寻找合适的开源框架、预训练模型及数据集来加速原型验证。\n\n### 没有 awesome_ai_agents 时\n- **信息检索效率极低**：需要在 GitHub、Hugging Face 和技术博客间反复切换搜索，耗费数天时间才能零散找到几个相关项目，且难以判断其活跃度。\n- **技术选型盲目**：缺乏对现有 1500+ 资源的宏观视野，容易重复造轮子，甚至选用了已停止维护的过时框架，导致后期重构成本高昂。\n- **生态资源缺失**：难以一次性获取配套的提示词工程技巧、评测数据集及学习课程，团队需自行摸索最佳实践，拖慢了整体研发进度。\n- **错失合作机会**：完全不知道行业内即将举办的\"Agents Connect\"等关键会议，错过了与潜在投资者交流及观摩最新实时演示的宝贵窗口。\n\n### 使用 awesome_ai_agents 后\n- **一站式精准定位**：直接浏览分类清晰的清单，几分钟内即可锁定当前最热门的多智能体编排框架和专用数据集，将调研周期从数天缩短至数小时。\n- **决策依据充分**：依托每日更新的资源列表和社区星级反馈，快速甄别出高活跃度、文档完善的工具，确保技术栈的先进性与稳定性。\n- **全链路资源覆盖**：顺藤摸瓜直接获取从底层模型到上层提示词优化的完整工具链，并找到配套课程帮助团队成员快速上手，显著降低学习门槛。\n- **紧跟前沿动态**：通过仓库公告第一时间掌握行业会议资讯，成功报名参与线上演示会，直观了解竞品策略并建立了关键的行业人脉连接。\n\nawesome_ai_agents 将原本分散破碎的 AI 智能体生态资源整合为一张清晰的导航图，让开发者能从繁琐的搜寻工作中解脱，专注于核心价值的创造。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjim-schwoebel_awesome_ai_agents_cc7ff6df.png","jim-schwoebel","Jim Schwoebel","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fjim-schwoebel_a7a182ed.jpg","CEO @ Quome, building AI agents.",null,"Los Angeles, CA","jim_schwoebel","https:\u002F\u002Fschwoebel.me\u002Fjim\u002F","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel",1550,444,"2026-04-15T13:22:14","Apache-2.0",1,"","未说明",{"notes":89,"python":87,"dependencies":90},"该仓库（awesome_ai_agents）是一个 curated list（精选列表），用于汇总 AI Agent 相关的工具、资源、框架、数据集和项目链接，其本身不是一个可独立运行的软件或模型，因此没有特定的操作系统、GPU、内存、Python 版本或依赖库要求。具体的运行环境需求取决于用户从列表中选择的各个子项目或工具。",[],[15,13,52,14],[93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112],"agent-based-modeling","agentic","agentic-ai","agentic-workflow","agents","ai","ai-agent","ai-agent-framework","ai-agent-tools","ai-agents-cli","ai-agents-framework","aiagent","aiagents","aiworkflow","learning","multi-agent","multi-agent-system","multi-agent-systems","awesome-list","awesome-lists","2026-03-27T02:49:30.150509","2026-04-16T16:15:23.679036",[116,121,126,131,136,141,146,151,156,161],{"id":117,"question_zh":118,"answer_zh":119,"source_url":120},35962,"如何向该列表提交新的 AI 代理工具或框架建议？","您可以直接在 GitHub 上创建一个 Issue（如本例所示），提供项目的名称、URL、简短描述以及建议的分类部分。维护者通常会回复确认，并可能邀请您提交一个 Pull Request (PR) 以便正式合并到列表中。例如，维护者曾回复：'谢谢建议！介意提交一个 PR 吗？我可以合并它！'或者直接告知已添加到特定章节。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F14",{"id":122,"question_zh":123,"answer_zh":124,"source_url":125},35963,"WFGY 是什么，它在代理生态系统中起什么作用？","WFGY 不是一个代理框架本身，而是一个用于代理和 LLM 系统的可重用工作负载及调试框架。它专注于在长周期、高张力的文本场景下对大语言模型进行调试和压力测试。用户可以将任何代理栈插入其 TXT 包中，记录长时间会话的轨迹或指标，从而评估系统的推理断裂点、检索失败点以及即兴发挥的情况。它已被添加到列表的'Tools'（工具）部分，与 Langfuse 和 Guardrails 等调试评估平台归类在一起。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F33",{"id":127,"question_zh":128,"answer_zh":129,"source_url":130},35964,"AgentFlow 框架的主要特点和优势是什么？","AgentFlow 是一个可训练的多代理框架，通过'流内优化'（in-the-flow optimization）协调四个专用模块（规划器、执行器、验证器、生成器）。其核心优势在于使用 Flow-GRPO 强化学习直接在多轮任务循环中训练规划器，从而比单体方法获得显著的性能提升。该框架支持平滑集成各种工具（如数学、编码、搜索、金融等），已被收录在列表的'Frameworks'（框架）部分。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F7",{"id":132,"question_zh":133,"answer_zh":134,"source_url":135},35965,"StoryRoute 是如何实现自主 GPS 定位音频导览的？","StoryRoute 是一个完全自主的 LLM 驱动代理，无需用户在操作期间输入。其工作流程包括：1. 感知（Sense）：通过地理定位 API 监控 GPS 坐标；2. 推理（Reason）：基于位置和历史信息进行上下文感知的 LLM 提示；3. 行动（Act）：动态生成故事并进行文本转语音合成。它的目标是为用户提供个性化的全球导游服务，是一个生产部署的自主代理管道示例。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F125",{"id":137,"question_zh":138,"answer_zh":139,"source_url":140},35966,"SkillLite 是什么，它如何解决代理技能的安全执行问题？","SkillLite 是一个轻量级、零依赖的运行时环境，专为 `agentsskills` 协议设计，旨在让 AI 代理在本地安全地执行可移植技能。它使用 Rust 编写，利用原生操作系统沙箱（如 macOS 的 Seatbelt 或 Linux namespaces）来运行不受信任的技能代码。其特点包括毫秒级冷启动、单二进制文件部署，且不需要云、Docker 或 Python，实现了真正的本地优先架构。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F27",{"id":142,"question_zh":143,"answer_zh":144,"source_url":145},35967,"Microsoft Agent Framework 与 AutoGen 和 Semantic Kernel 有什么关系？","Microsoft Agent Framework 是微软最近宣布的产品，它将 AutoGen 和 Semantic Kernel 两个项目融合在了一起。如果您在寻找微软官方的代理开发框架，现在应关注这个统一后的框架。该项目已被添加到列表中。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F10",{"id":147,"question_zh":148,"answer_zh":149,"source_url":150},35968,"Plasmate 浏览器引擎为 AI 代理提供了哪些关键功能？","Plasmate 是专为 AI 代理构建的浏览器引擎，它能将 HTML 编译为语义对象模型（SOM），相比原始 HTML 可实现 10 倍的 Token 压缩。关键功能包括：V8 JS 渲染、CDP 兼容性、认证浏览支持、MCP 服务器集成，以及在 8GB RAM 下支持 500 个并发会话。它属于基础设施\u002FWeb 浏览类别。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F139",{"id":152,"question_zh":153,"answer_zh":154,"source_url":155},35969,"TiOLi AGENTIS 平台的主要功能是什么？","TiOLi AGENTIS 是一个面向 AI 代理的金融交易所基础设施。它允许代理注册、交易、相互雇佣并建立声誉系统。该平台拥有 23 个 MCP 工具和 400+ 个 REST 端点，所有交易均经过区块链验证。它是一个代理经济基础设施的典型例子。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F131",{"id":157,"question_zh":158,"answer_zh":159,"source_url":160},35970,"Humanizer PRO 工具主要用来做什么，有哪些模式？","Humanizer PRO 是一个 AI 文本人性化工具，旨在将 AI 生成的内容转换为自然的人类语言，帮助绕过 Turnitin、GPTZero 等检测工具，同时保留原意。它提供三种模式：Stealth（通用绕过）、Academic（针对 Turnitin 优化）和 SEO（搜索引擎优化）。它还包含 AI 检测扫描功能，并支持通过 MCP 集成到 ChatGPT 和 Claude 中作为工具使用。","https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fawesome_ai_agents\u002Fissues\u002F110",{"id":162,"question_zh":163,"answer_zh":164,"source_url":120},35971,"在哪里可以找到 AI\u002FML 会议的投稿截止日期？","可以使用 'AI Conference Deadline' (aiconferenceddl.com) 这一资源。它是一个简单的追踪器，帮助研究人员从单一列表中跟踪主要会议的征稿启事（CFP），访问官方网站，并规划投稿时间，无需注册账户或复杂设置。该资源已被添加到列表的'Tools'部分。",[166],{"id":167,"version":168,"summary_zh":169,"released_at":170},281288,"v0.01","awesome_ai_agents 仓库的第一个版本，包含1500多项资源。","2025-01-03T22:59:14"]