[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-ashishpatel26--500-AI-Agents-Projects":3,"tool-ashishpatel26--500-AI-Agents-Projects":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",150037,2,"2026-04-10T23:33:47",[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":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"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":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":79,"stars":82,"forks":83,"last_commit_at":84,"license":85,"difficulty_score":32,"env_os":86,"env_gpu":87,"env_ram":87,"env_deps":88,"category_tags":91,"github_topics":92,"view_count":32,"oss_zip_url":79,"oss_zip_packed_at":79,"status":17,"created_at":95,"updated_at":96,"faqs":97,"releases":113},4958,"ashishpatel26\u002F500-AI-Agents-Projects","500-AI-Agents-Projects","The 500 AI Agents Projects is a curated collection of AI agent use cases across various industries. It showcases practical applications and provides links to open-source projects for implementation, illustrating how AI agents are transforming sectors such as healthcare, finance, education, retail, and more.","500-AI-Agents-Projects 是一个精心策划的开源项目合集，汇集了跨越医疗、金融、教育、零售等多个行业的 500 多个 AI 智能体（AI Agent）实际应用案例。它旨在解决开发者在寻找高质量、可落地的 AI 智能体参考方案时面临的痛点，通过提供具体的应用场景描述及对应的开源代码链接，直观展示 AI 技术如何赋能各行各业。\n\n无论是希望快速上手的开发者、寻求灵感的研究人员，还是对 AI 落地感兴趣的企业技术人员，都能从中获益。该项目不仅按行业分类整理了用例，还独特地依据主流开发框架（如 CrewAI、AutoGen、LangGraph 等）进行了归纳，方便用户根据技术栈直接查找相关实现。从自动化的股票交易机器人到个性化的虚拟助教，再到全天候客户支持助手，这里提供了丰富的实战代码资源。通过参考这些经过筛选的项目，用户可以大幅降低探索成本，加速从概念验证到实际部署的过程，是学习和构建 AI 智能体应用的宝贵资源库。","# 🌟 500+ AI Agent Projects \u002F UseCases\n\n[![500-AI-Agents-Projects - UseCase](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F500--AI--Agents--Projects-UseCase-2ea44f?logo=https%3A%2F%2Fstatic-00.iconduck.com%2Fassets.00%2Frobot-emoji-2048x2044-kay057lt.png&logoColor=2ea44f)](https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects)\n\n![img](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_27f3aa10fbd7.jpg)\n\nA curated collection of AI agent use cases across industries, showcasing practical applications and linking to open-source projects for implementation. Explore how AI agents are transforming industries like healthcare, finance, education, and more! 🤖✨\n\n---\n\n## 📋 Table of Contents\n\n- [Introduction](#introduction)\n- [Industry Usecase](#-industry-usecase-mindmap)\n- [Use Case Table](#use-case-table)\n- [Framework Wise UseCase](#framework-wise-usecases)\n - [CrewAI UseCase](#framework-name-crewai)\n - [AutoGen UseCase](#framework-name-autogen)\n - [Agno UseCase](#framework-name-agno)\n - [Langgraph UseCase](#framework-name-langgraph)\n- [Contributing](#contributing)\n- [License](#license)\n\n---\n\n## 🧠 Introduction\n\nArtificial Intelligence (AI) agents are revolutionizing the way industries operate. From personalized learning to financial trading bots, AI agents bring efficiency, innovation, and scalability. This repository provides:\n\n- A categorized list of industries where AI agents are making an impact.\n- Detailed use cases with links to open-source projects for implementation.\n\nWhether you're a developer, researcher, or business enthusiast, this repository is your go-to resource for AI agent inspiration and learning.\n\n---\n\n## 🏭 Industry UseCase MindMap\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_d47e974063d5.png)\n\n---\n\n## 🧩 Use Case Table\n\n| Use Case | Industry | Description | Code Github |\n| ------------------------------------------- | ---------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| **HIA (Health Insights Agent)** | Healthcare | analyses medical reports and provide health insights. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fharshhh28\u002Fhia.git) |\n| **AI Health Assistant** | Healthcare | Diagnoses and monitors diseases using patient data. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fahmadvh\u002FAI-Agents-for-Medical-Diagnostics.git) |\n| **Automated Trading Bot** | Finance | Automates stock trading with real-time market analysis. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FMingyuJ666\u002FStockagent.git) |\n| **Virtual AI Tutor** | Education | Provides personalized education tailored to users. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fhqanhh\u002FEduGPT.git) |\n| **24\u002F7 AI Chatbot** | Customer Service | Handles customer queries around the clock. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcustomer_support_agent_langgraph.ipynb) |\n| **Product Recommendation Agent** | Retail | Suggests products based on user preferences and history. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FRecAI) |\n| **Self-Driving Delivery Agent** | Transportation | Optimizes routes and autonomously delivers packages. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fsled-group\u002FdriVLMe) |\n| **Factory Process Monitoring Agent** | Manufacturing | Monitors production lines and ensures quality control. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fyuchenxia\u002Fllm4ias) |\n| **Property Pricing Agent** | Real Estate | Analyzes market trends to determine property prices. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FAleksNeStu\u002Fai-real-estate-assistant) |\n| **Smart Farming Assistant** | Agriculture | Provides insights on crop health and yield predictions. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fmohammed97ashraf\u002FLLM_Agri_Bot) |\n| **Energy Demand Forecasting Agent** | Energy | Predicts energy usage to optimize grid management. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fyecchen\u002FMIRAI) |\n| **Content Personalization Agent** | Entertainment | Recommends personalized media based on preferences. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fcrosleythomas\u002FMirrorGPT) |\n| **Legal Document Review Assistant** | Legal | Automates document review and highlights key clauses. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Ffirica\u002Flegalai) |\n| **Recruitment Recommendation Agent** | Human Resources | Suggests best-fit candidates for job openings. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fsentient-engineering\u002Fjobber) |\n| **Virtual Travel Assistant** | Hospitality | Plans travel itineraries based on preferences. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fnirbar1985\u002Fai-travel-agent) |\n| **AI Game Companion Agent** | Gaming | Enhances player experience with real-time assistance. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fonjas-buidl\u002FLLM-agent-game) |\n| **Real-Time Threat Detection Agent** | Cybersecurity | Identifies potential threats and mitigates attacks. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FNVISOsecurity\u002Fcyber-security-llm-agents) |\n| **E-commerce Personal Shopper Agent** | E-commerce | Helps customers find products they’ll love. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FHoanganhvu123\u002FShoppingGPT) |\n| **Logistics Optimization Agent** | Supply Chain | Plans efficient delivery routes and manages inventory. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FOptiGuide) |\n| **Vibe Hacking Agent** | Cybersecurity | Autonomous Multi-Agent Based Red Team Testing Service. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FPurpleAILAB\u002FDecepticon) |\n| **MediSuite-Ai-Agent** | Health insurance | A medical ai agent that helps automating the process of hospitals \u002F insurance claiming workflow. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FMahmoudRabea13\u002FMediSuite-Ai-Agent) | \n| **Lina-Egyptian-Medical-Chatbot** | Health insurance | A medical ai agent that helps automating the process of hospitals \u002F insurance claiming workflow. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FMahmoudRabea13\u002FMediSuite-Ai-Agent) |\n\n## Framework wise Usecases\n\n---\n\n### **Framework Name**: **CrewAI**\n\n| Use Case | Industry | Description | GitHub |\n| -------------------------------- | ----------------------- | -------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📧 Email Auto Responder Flow | 🗣️ Communication | Automates email responses based on predefined criteria to enhance communication efficiency. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Femail_auto_responder_flow) |\n| 📝 Meeting Assistant Flow | 🛠️ Productivity | Assists in organizing and managing meetings, including scheduling and agenda preparation. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Fmeeting_assistant_flow) |\n| 🔄 Self Evaluation Loop Flow | 👥 Human Resources | Facilitates self-assessment processes within an organization, aiding in performance reviews. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Fself_evaluation_loop_flow) |\n| 📈 Lead Score Flow | 💼 Sales | Evaluates and scores potential leads to prioritize outreach in sales strategies. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Flead-score-flow) |\n| 📊 Marketing Strategy Generator | 📢 Marketing | Develops marketing strategies by analyzing market trends and audience data. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmarketing_strategy) |\n| 📝 Job Posting Generator | 🧑‍💼 Recruitment | Creates job postings by analyzing job requirements, aiding in recruitment processes. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fjob-posting) |\n| 🔄 Recruitment Workflow | 🧑‍💼 Recruitment | Streamlines the recruitment process by automating various tasks involved in hiring. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Frecruitment) |\n| 🔍 Match Profile to Positions | 🧑‍💼 Recruitment | Matches candidate profiles to suitable job positions to enhance recruitment efficiency. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmatch_profile_to_positions) |\n| 📸 Instagram Post Generator | 📱 Social Media | Generates and schedules Instagram posts automatically, streamlining social media management. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Finstagram_post) |\n| 🌐 Landing Page Generator | 💻 Web Development | Automates the creation of landing pages for websites, facilitating web development tasks. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Flanding_page_generator) |\n| 🎮 Game Builder Crew | 🎮 Game Development | Assists in the development of games by automating certain aspects of game creation. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fgame-builder-crew) |\n| 💹 Stock Analysis Tool | 💰 Finance | Provides tools for analyzing stock market data to assist in financial decision-making. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fstock_analysis) |\n| 🗺️ Trip Planner | ✈️ Travel | Assists in planning trips by organizing itineraries and managing travel details. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Ftrip_planner) |\n| 🎁 Surprise Trip Planner | ✈️ Travel | Plans surprise trips by selecting destinations and activities based on user preferences. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fsurprise_trip) |\n| 📚 Write a Book with Flows | ✍️ Creative Writing | Assists authors in writing books by providing structured workflows and writing assistance. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Fwrite_a_book_with_flows) |\n| 🎬 Screenplay Writer | ✍️ Creative Writing | Aids in writing screenplays by offering templates and guidance for script development. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fscreenplay_writer) |\n| ✅ Markdown Validator | 📄 Documentation | Validates Markdown files to ensure proper formatting and adherence to standards. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmarkdown_validator) |\n| 🧠 Meta Quest Knowledge | 📚 Knowledge Management | Manages and organizes knowledge related to Meta Quest, facilitating information retrieval. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmeta_quest_knowledge) |\n| 🤖 NVIDIA Models Integration | 🤖 AI Integration | Integrates NVIDIA AI models into workflows to enhance computational capabilities. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fintegrations\u002Fnvidia_models) |\n| 🗂️ Prep for a Meeting | 🛠️ Productivity | Assists in preparing for meetings by organizing materials and setting agendas. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fprep-for-a-meeting) |\n| 🛠️Starter Template | 🛠️ Development | Provides a starter template for new projects to streamline the setup process. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fstarter_template) |\n| 🔗CrewAI + LangGraph Integration | 🤖 AI Integration | Demonstrates integration between CrewAI and LangGraph for enhanced workflow automation. | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fintegrations\u002FCrewAI-LangGraph) |\n\n\n### **Framework Name**: **Autogen**\n\n> **Code Generation, Execution, and Debugging**\n\n| Use Case | Industry | Description | Notebook |\n| --------------------------------------------------------------------------------------- | ----------------------- | --------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🤖 Automated Task Solving with Code Generation, Execution & Debugging | 💻 Software Development | Demonstrates automated task-solving by generating, executing, and debugging code. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_auto_feedback_from_code_execution) |\n| 🧑‍💻 Automated Code Generation and Question Answering with Retrieval Augmented Agents | 💻 Software Development | Generates code and answers questions using retrieval-augmented methods. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_RetrieveChat) |\n| 🧠 Automated Code Generation and Question Answering with Qdrant-based Retrieval | 💻 Software Development | Utilizes Qdrant for enhanced retrieval-augmented agent performance. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_RetrieveChat_qdrant) |\n\n> **Multi-Agent Collaboration (>3 Agents)**\n\n| Use Case | Industry | Description | Notebook |\n| :----------------------------------------------------------------------- | :-------------------------- | :------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 🤝 Automated Task Solving by Group Chat (3 members, 1 manager) | 🤝 Collaboration | Demonstrates group task-solving via multi-agent collaboration. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat) |\n| 📊 Automated Data Visualization by Group Chat (3 members, 1 manager) | 📊 Data Analysis | Uses multi-agent collaboration to create data visualizations. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_vis) |\n| 🧩 Automated Complex Task Solving by Group Chat (6 members, 1 manager) | 🤝 Collaboration | Solves complex tasks collaboratively with a larger group of agents. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_research) |\n| 🧑‍💻 Automated Task Solving with Coding & Planning Agents | 🛠️ Planning & Development | Combines coding and planning agents for solving tasks effectively. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_planning.ipynb) |\n| 📐 Automated Task Solving with Transition Paths Specified in a Graph | 🤝 Collaboration | Uses predefined transition paths in a graph for solving tasks. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_finite_state_machine) |\n| 🧠 Running a Group Chat as an Inner-Monologue via the SocietyOfMindAgent | 🧠 Cognitive Sciences | Simulates inner-monologue for problem-solving using group chats. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_society_of_mind) |\n| 🔧 Running a Group Chat with Custom Speaker Selection Function | 🤝 Collaboration | Implements a custom function for speaker selection in group chats. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_customized) |\n\n> **Sequential Multi-Agent Chats**\n\n| Use Case | Industry | Description | Notebook |\n| :--------------------------------------------------------------------------------- | :--------------------- | :------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🔄 Solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent | 🔄 Workflow Automation | Automates sequential task-solving with a single initiating agent. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_multi_task_chats) |\n| ⏳ Async-solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent | 🔄 Workflow Automation | Handles asynchronous task-solving in a sequence of chats initiated by one agent. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_multi_task_async_chats) |\n| 🤝 Solving Multiple Tasks in a Sequence of Chats Initiated by Different Agents | 🔄 Workflow Automation | Facilitates sequential task-solving with different agents initiating each chat. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchats_sequential_chats) |\n\n> **Nested Chats**\n\n| Use Case | Industry | Description | Notebook |\n| :----------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🧠 Solving Complex Tasks with Nested Chats | 🧠 Problem Solving | Uses nested chats to solve hierarchical and complex problems. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nestedchat) |\n| 🔄 Solving Complex Tasks with A Sequence of Nested Chats | 🧠 Problem Solving | Demonstrates sequential task-solving using nested chats. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nested_sequential_chats) |\n| 🏭 OptiGuide for Solving a Supply Chain Optimization Problem with Nested Chats | 🏭 Supply Chain Optimization | Showcases how to solve supply chain optimization problems using nested chats, a coding agent, and a safeguard agent. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nestedchat_optiguide) |\n| ♟️ Conversational Chess with Nested Chats and Tool Use | 🎮 Gaming | Explores the use of nested chats for playing conversational chess with integrated tools. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nested_chats_chess) |\n\n> **Application**\n\n| Use Case | Industry | Description | Notebook |\n| :------------------------------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 🔄 Automated Continual Learning from New Data | 📊 Machine Learning | Continuously learns from new data inputs for adaptive AI. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_stream.ipynb) |\n| 🏭 OptiGuide - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization | 🏭 Supply Chain Optimization | Highlights a solution combining coding, tool use, and safeguarding for supply chain optimization. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nestedchat_optiguide) |\n| 🤖 AutoAnny - A Discord bot built using AutoGen | 💬 Communication Tools | Showcases the development of a Discord bot using AutoGen for enhanced interaction. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Ftree\u002Fmain\u002Fsamples\u002Fapps\u002Fauto-anny) |\n\n> **Tools**\n\n| Use Case | Industry | Description | Notebook |\n| :--------------------------------------------------------------------- | :----------------------------- | :------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🌐 Web Search: Solve Tasks Requiring Web Info | 🔍 Information Retrieval | Searches the web to gather information required for completing tasks. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_web_info.ipynb) |\n| 🔧 Use Provided Tools as Functions | 🛠️ Tool Integration | Demonstrates how to use pre-provided tools as callable functions in AutoGen. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_function_call_currency_calculator) |\n| 🔗 Use Tools via Sync and Async Function Calling | 🛠️ Tool Integration | Illustrates synchronous and asynchronous tool usage within AutoGen workflows. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_function_call_async) |\n| 🧩 Task Solving with Langchain Provided Tools as Functions | 🔍 Language Processing | Leverages Langchain tools for task-solving within AutoGen. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_langchain.ipynb) |\n| 📚 RAG: Group Chat with Retrieval Augmented Generation | 🤝 Collaboration | Enables group chat with Retrieval Augmented Generation (RAG) to support information sharing. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_RAG) |\n| ⚙️ Function Inception: Update\u002FRemove Functions During Conversations | 🔧 Development Tools | Allows AutoGen agents to modify their functions dynamically during conversations. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_inception_function.ipynb) |\n| 🔊 Agent Chat with Whisper | 🎙️ Audio Processing | Demonstrates AI agent capabilities for transcription and translation using Whisper. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_video_transcript_translate_with_whisper) |\n| 📏 Constrained Responses via Guidance | 💡 Natural Language Processing | Shows how to use guidance to constrain responses generated by agents. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_guidance.ipynb) |\n| 🌍 Browse the Web with Agents | 🌐 Information Retrieval | Explains how to configure agents to browse and retrieve information from the web. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_surfer.ipynb) |\n| 📊 SQL: Natural Language Text to SQL Query Using Spider Benchmark | 💾 Database Management | Converts natural language inputs into SQL queries using the Spider benchmark. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_sql_spider.ipynb) |\n| 🕸️ Web Scraping with Apify | 🌐 Data Gathering | Illustrates web scraping techniques with Apify using AutoGen. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_webscraping_with_apify) |\n| 🕷️ Web Crawling: Crawl Entire Domain with Spider API | 🌐 Data Gathering | Explains how to crawl entire domains using the Spider API. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_webcrawling_with_spider) |\n| 💻 Write a Software App Task by Task with Specially Designed Functions | 💻 Software Development | Builds a software application step-by-step using designed functions. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_function_call_code_writing.ipynb) |\n\n> **Human Development**\n\n| Use Case | Industry | Description | Notebook |\n| :--------------------------------------------------------------- | :---------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 💬 Simple Example in ChatGPT Style | 🧠 Conversational AI | Demonstrates a simple conversational example in the style of ChatGPT. | [![Example](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Example-blue?logo=openai)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fsamples\u002Fsimple_chat.py) |\n| 🤖 Auto Code Generation, Execution, Debugging and Human Feedback | 💻 Software Development | Showcases code generation, execution, debugging with human feedback integrated into the workflow. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_human_feedback.ipynb) |\n| 👥 Automated Task Solving with GPT-4 + Multiple Human Users | 🤝 Collaboration | Enables task solving with multiple human users collaborating with GPT-4. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_two_users.ipynb) |\n| 🔄 Agent Chat with Async Human Inputs | 🧠 Conversational AI | Supports asynchronous human input during agent conversations. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002FAsync_human_input.ipynb) |\n\n> **Agent Teaching and Learning**\n\n| Use Case | Industry | Description | Notebook |\n| :------------------------------------------------------------------- | :-------------------------- | :--------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📘 Teach Agents New Skills & Reuse via Automated Chat | 🎓 Education & Training | Demonstrates teaching new skills to agents and enabling their reuse in automated chats. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_teaching) |\n| 🧠 Teach Agents New Facts, User Preferences and Skills Beyond Coding | 🎓 Education & Training | Shows how to teach agents new facts, user preferences, and non-coding skills. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_teachability) |\n| 🤖 Teach OpenAI Assistants Through GPTAssistantAgent | 💻 AI Assistant Development | Illustrates how to enhance OpenAI assistants' capabilities using GPTAssistantAgent. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_teachable_oai_assistants.ipynb) |\n| 🔄 Agent Optimizer: Train Agents in an Agentic Way | 🛠️ Optimization | Explains how to train agents effectively in an agentic manner using the Agent Optimizer. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_agentoptimizer.ipynb) |\n\n> **Multi-Agent Chat with OpenAI Assistants in the loop**\n\n| Use Case | Industry | Description | Notebook |\n| :-------------------------------------------------------- | :----------------------- | :---------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🌟 Hello-World Chat with OpenAI Assistant in AutoGen | 🤖 Conversational AI | A basic example of chatting with OpenAI Assistant using AutoGen. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_twoagents_basic.ipynb) |\n| 🔧 Chat with OpenAI Assistant using Function Call | 🔧 Development Tools | Illustrates how to use function calls with OpenAI Assistant in chats. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_function_call.ipynb) |\n| 🧠 Chat with OpenAI Assistant with Code Interpreter | 💻 Software Development | Demonstrates the use of OpenAI Assistant as a code interpreter in chats. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_code_interpreter.ipynb) |\n| 🔍 Chat with OpenAI Assistant with Retrieval Augmentation | 📚 Information Retrieval | Enables retrieval-augmented conversations with OpenAI Assistant. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_retrieval.ipynb) |\n| 🤝 OpenAI Assistant in a Group Chat | 🤝 Collaboration | Shows how OpenAI Assistant can collaborate with other agents in a group chat. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_groupchat.ipynb) |\n| 🛠️ GPTAssistantAgent based Multi-Agent Tool Use | 🔧 Development Tools | Explains how to use GPTAssistantAgent for multi-agent tool usage. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fgpt_assistant_agent_function_call.ipynb) |\n\n> **Non-OpenAI Models**\n\n| Use Case | Industry | Description | Notebook |\n| :------------------------------------------------ | :-------- | :---------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| ♟️ Conversational Chess using Non-OpenAI Models | 🎮 Gaming | Explores conversational chess implemented with non-OpenAI models. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nested_chats_chess_altmodels) |\n\n> **Multimodal Agent**\n\n| Use Case | Industry | Description | Notebook |\n| :--------------------------------------------- | :------------------ | :-------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🎨 Multimodal Agent Chat with DALLE and GPT-4V | 🖼️ Multimedia AI | Combines DALLE and GPT-4V for multimodal agent communication. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_dalle_and_gpt4v.ipynb) |\n| 🖌️ Multimodal Agent Chat with Llava | 📷 Image Processing | Uses Llava for enabling multimodal agent conversations with image processing. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_lmm_llava.ipynb) |\n| 🖼️ Multimodal Agent Chat with GPT-4V | 🖼️ Multimedia AI | Leverages GPT-4V for visual and conversational interactions in multimodal agents. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_lmm_gpt-4v.ipynb) |\n\n> **Long Context Handling**\n\n| Use Case | Industry | Description | Notebook |\n| :--------------------------------------- | :--------------- | :--------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📜 Long Context Handling as A Capability | 🧠 AI Capability | Demonstrates techniques for handling long context effectively within AI workflows. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_transform_messages) |\n\n> **Evaluation and Assessment**\n\n| Use Case | Industry | Description | Notebook |\n| :----------------------------------------------------------------------------------- | :------------------------ | :------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📊 AgentEval: A Multi-Agent System for Assessing Utility of LLM-Powered Applications | 📈 Performance Evaluation | Introduces AgentEval for evaluating and assessing the performance of LLM-based applications. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagenteval_cq_math.ipynb) |\n\n> **Automatic Agent Building**\n\n| Use Case | Industry | Description | Notebook |\n| :------------------------------------------------------------ | :---------------- | :------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🏗️ Automatically Build Multi-agent System with AgentBuilder | 🤖 AI Development | Explains how to automatically build multi-agent systems using the AgentBuilder tool. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fautobuild_basic.ipynb) |\n| 📚 Automatically Build Multi-agent System from Agent Library | 🤖 AI Development | Shows how to construct multi-agent systems by leveraging a pre-defined agent library. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fautobuild_agent_library.ipynb) |\n\n> **Observability**\n\n| Use Case | Industry | Description | Notebook |\n| :---------------------------------------------------------------- | :------------------------ | :----------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 📊 Track LLM Calls, Tool Usage, Actions and Errors using AgentOps | 📈 Monitoring & Analytics | Demonstrates how to monitor LLM interactions, tool usage, and errors using AgentOps. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_agentops.ipynb) |\n\n> **Enhanced Inferences**\n\n| Use Case | Industry | Description | Notebook |\n| :--------------------------------------------------------------------- | :----------------- | :----------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🔗 API Unification | 🔧 API Management | Explains how to unify API usage with documentation and code examples. | [![Documentation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Documentation-blue?logo=readthedocs)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002Fdocs\u002FUse-Cases\u002Fenhanced_inference\u002F#api-unification) |\n| ⚙️ Utility Functions to Help Managing API Configurations Effectively | 🔧 API Management | Demonstrates utility functions to manage API configurations more effectively. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Ftopics\u002Fllm_configuration) |\n| 💰 Cost Calculation | 📈 Cost Management | Introduces methods for tracking token usage and estimating costs for LLM interactions. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_cost_token_tracking.ipynb) |\n| ⚡ Optimize for Code Generation | 📊 Optimization | Highlights cost-effective optimization techniques for improving code generation with LLMs. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Foai_completion.ipynb) |\n| 📐 Optimize for Math | 📊 Optimization | Explains techniques to optimize LLM performance for solving mathematical problems. | [![Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView-Notebook-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Foai_chatgpt_gpt4.ipynb) |\n\n### **Framework Name**: **Agno**\n\n> **UseCase**\n\n| Use Case | Industry | Description | Notebook |\n| :--------------------------------- | :----------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🤖 Support Agent | 💻 Software Development \u002F AI \u002F Framework Support | The Agno Support Agent helps developers with the Agno framework by providing real-time answers, explanations, and code examples. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fagno_support_agent.py) |\n| 🎥 YouTube Agent | 📺 Media & Content | An intelligent agent that analyzes YouTube videos by generating detailed summaries, timestamps, themes, and content breakdowns using AI tools. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fyoutube_agent.py) |\n| 📊 Finance Agent | 💼 Finance | An advanced AI-powered market analyst that delivers real-time stock market insights, analyst recommendations, financial deep-dives, and sector-specific trends. Supports prompts for detailed analysis of companies like AAPL, TSLA, NVDA, etc. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fthinking_finance_agent.py) |\n| 📚 Study Partner | 🎓 Education | Assists users in learning by finding resources, answering questions, and creating study plans. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fstudy_partner.py) |\n| 🛍️ Shopping Partner Agent | 🏬 E-commerce | A product recommender agent that helps users find matching products based on preferences from trusted platforms like Amazon, Flipkart, etc. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fshopping_partner.py) |\n| 🎓 Research Scholar Agent | 🧠 Education \u002F Research | An AI-powered academic assistant that performs advanced academic searches, analyzes recent publications, synthesizes findings across disciplines, and writes well-structured academic reports with proper citations. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fresearch_agent_exa.py) |\n| 🧠 Research Agent | 🗞️ Media & Journalism | A research agent that combines web search and professional journalistic writing. It performs deep investigations and produces NYT-style reports. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fresearch_agent.py) |\n| 🍳 Recipe Creator | 🍽️ Food & Culinary | An AI-powered recipe recommendation agent that provides personalized recipes based on ingredients, preferences, and time constraints. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Frecipe_creator.py) |\n| 🗞️ Finance Agent | 💼 Finance | A powerful financial analyst agent combining real-time stock data, analyst insights, company fundamentals, and market news. Ideal for analyzing companies like Apple, Tesla, NVIDIA, and sectors like semiconductors or automotive. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Ffinance_agent.py) |\n| 🧠 Financial Reasoning Agent | 📈 Finance | Uses a Claude-3.5 Sonnet-based agent to analyze stocks like NVDA using tools for reasoning and Yahoo Finance data. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Freasoning_finance_agent.py) |\n| 🤖 Readme Generator Agent | 💻 Software Dev | Agent generates high-quality READMEs for GitHub repositories using repo metadata. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Freadme_generator.py) |\n| 🎬 Movie Recommendation Agent | 🎥 Entertainment | An intelligent agent that gives personalized movie recommendations using Exa and GPT-4o, analyzing genres, themes, and latest ratings. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fmovie_recommedation.py) |\n| 🔍 Media Trend Analysis Agent | 📰 Media & News | Analyzes emerging trends, patterns, and influencers from digital platforms using AI-powered agents and scraping. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fmedia_trend_analysis_agent.py) |\n| ⚖️ Legal Document Analysis Agent | 🏛️ Legal Tech | An AI agent that analyzes legal documents from PDF URLs and provides legal insights based on a knowledge base using vector embeddings and GPT-4o. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Flegal_consultant.py) |\n| 🤔 DeepKnowledge | 🧠 Research | This agent performs iterative searches through its knowledge base, breaking down complex queries into sub-questions and synthesizing comprehensive answers. It uses Agno docs for demonstration and is designed for deep reasoning and exploration. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fdeep_knowledge.py) |\n| 📚 Book Recommendation Agent | 🧠 Publishing & Media | An intelligent agent that provides personalized book suggestions using literary data, reader preferences, reviews, and release info. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fbook_recommendation.py) |\n| 🏠 MCP Airbnb Agent | 🛎️ Hospitality | Create an AI Agent using MCP and Llama 4 to search Airbnb listings with filters like workspace & transport proximity. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fairbnb_mcp.py) |\n| 🤖 Assist\t Agent | 🧠 AI Framework | An AI agent using GPT-4o to answer questions about the Agno framework with hybrid search and embedded knowledge. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fagno_assist.py) |\n\n### **Framework Name**: **Langgraph**\n\n> **UseCase**\n\n| Use Case | Industry | Description | Notebook |\n| :------------------------------------ | :---------------------------- | :----------------------------------------------------------- | :----------------------------------------------------------- |\n| 🤖 Chatbot Simulation Evaluation | 💻 💬 AI \u002F Quality Assurance | Simulate user interactions to evaluate chatbot performance, ensuring robustness and reliability in real-world scenarios. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fchatbot-simulation-evaluation\u002Fagent-simulation-evaluation.ipynb) |\n| 🧠 Information Gathering via Prompting | 🧠 AI \u002F Research & Development | This tutorial demonstrates how to design a LangGraph workflow that utilizes prompting techniques to gather information effectively. It showcases how to structure prompts and manage the flow of information to build intelligent agents. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fchatbots\u002Finformation-gather-prompting.ipynb) |\n| 🧠 Code Assistant with LangGraph | 💻 Software Development | This tutorial demonstrates how to build a resilient code assistant using LangGraph. It guides you through creating a graph-based agent that can handle code generation, error checking, and iterative refinement, ensuring robust and accurate coding assistance. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fcode_assistant\u002Flanggraph_code_assistant.ipynb) |\n| 🧑‍💼 Customer Support Agent | 🧑‍💼 Customer Support Agent | This tutorial demonstrates how to build a customer support agent using LangGraph. It guides you through creating a graph-based agent that can handle customer inquiries, providing automated support and enhancing user experience. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fcustomer-support\u002Fcustomer-support.ipynb) |\n| 🔁 Extraction with Retries | 🧠 AI \u002F Data Extraction | This tutorial demonstrates how to implement retry mechanisms in LangGraph workflows, ensuring robust data extraction processes that can handle transient errors and improve reliability. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fextraction\u002Fretries.ipynb) |\n| 🧠 Multi-Agent Workflow | 🧠 AI \u002F Workflow Orchestration | This tutorial demonstrates how to build a multi-agent system using LangGraph's agent supervisor. It guides you through creating a supervisor agent that orchestrates multiple specialized agents, managing task delegation and communication flow. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fmulti_agent\u002Fagent_supervisor.ipynb) |\n| 🧠 Hierarchical Agent Teams | 🧠 AI \u002F Workflow Orchestration | This tutorial demonstrates how to build a hierarchical agent system using LangGraph. It guides you through creating a top-level supervisor agent that delegates tasks to specialized sub-agents, enabling complex workflows with clear task delegation and communication. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fmulti_agent\u002Fhierarchical_agent_teams.ipynb) |\n| 🤝 Multi-Agent Collaboration | 🧠 AI \u002F Workflow Orchestration | This tutorial demonstrates how to implement multi-agent collaboration using LangGraph. It guides you through creating multiple specialized agents that work together to accomplish a complex task, showcasing the power of agent collaboration in AI workflows. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fmulti_agent\u002Fmulti-agent-collaboration.ipynb) |\n| 🧠 Plan-and-Execute Agent | 🧠 AI \u002F Workflow Orchestration | This tutorial demonstrates how to build a \"Plan-and-Execute\" style agent using LangGraph. It guides you through creating an agent that first generates a multi-step plan and then executes each step sequentially, revisiting and modifying the plan as necessary. This approach is inspired by the Plan-and-Solve paper and the Baby-AGI project, aiming to enhance long-term planning and task execution in AI workflows. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fplan-and-execute\u002Fplan-and-execute.ipynb) |\n| 🧠 SQL Agent | 🧠 AI \u002F Database Interaction | This tutorial demonstrates how to build an agent that can answer questions about a SQL database. The agent fetches available tables, determines relevance to the question, retrieves schemas, generates a query, checks for errors, executes it, and formulates a response. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fsql-agent.ipynb) |\n| 🧠 Reflection Agent | 🧠 AI \u002F Workflow Orchestration | This tutorial demonstrates how to build a reflection agent using LangGraph. It guides you through creating an agent that can critique and revise its own outputs, enhancing the quality and reliability of generated content. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Freflection\u002Freflection.ipynb)|\n| 🧠 Reflexion Agent | 🧠 AI \u002F Workflow Orchestration | This tutorial demonstrates how to build a reflexion agent using LangGraph. It guides you through creating an agent that can reflect on its actions and outcomes, enabling iterative improvement and more accurate decision-making in complex workflows. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Freflexion\u002Freflexion.ipynb)|\n| **LangGraph Agentic RAG** | | | |\n| 🧠 **Adaptive RAG** | 🧠 AI \u002F Information Retrieval | This tutorial demonstrates how to build an Adaptive RAG system using LangGraph. It guides you through creating a dynamic retrieval process that adjusts based on query complexity, enhancing the efficiency and accuracy of information retrieval. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_adaptive_rag.ipynb) |\n| 🧠 **Adaptive RAG (Local)** | 🧠 AI \u002F Information Retrieval | This tutorial focuses on implementing Adaptive RAG with local models, allowing for offline retrieval and generation, which is crucial for environments with limited internet access or privacy concerns. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_adaptive_rag_local.ipynb) |\n| 🤖 **Agentic RAG** | 🤖 AI \u002F Intelligent Agents | Learn to build an Agentic RAG system where an agent determines the best retrieval strategy before generating a response, improving the relevance and accuracy of answers. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_agentic_rag.ipynb) |\n| 🤖 **Agentic RAG (Local)** | 🤖 AI \u002F Intelligent Agents | This tutorial extends Agentic RAG to local environments, enabling the use of local models and data sources for retrieval and generation tasks. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_agentic_rag_local.ipynb) |\n| 🧠 **Corrective RAG (CRAG)** | 🧠 AI \u002F Information Retrieval | Implement a Corrective RAG system that evaluates and refines retrieved documents before passing them to the generator, ensuring higher-quality outputs. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_crag.ipynb) |\n| 🧠 **Corrective RAG (Local)** | 🧠 AI \u002F Information Retrieval | This tutorial focuses on building a Corrective RAG system using local resources, allowing for offline document evaluation and refinement processes. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_crag_local.ipynb) |\n| 🧠 **Self-RAG** | 🧠 AI \u002F Information Retrieval | Learn to implement Self-RAG, where the system reflects on its responses and retrieves additional information if necessary, enhancing the accuracy and relevance of generated content. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_self_rag.ipynb) |\n| 🧠 **Self-RAG (Local)** | 🧠 AI \u002F Information Retrieval | This tutorial demonstrates how to implement Self-RAG using local models and data sources, enabling offline reflection and retrieval processes. | [![AI Agent Code - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_self_rag_local.ipynb) |\n\n\n\n\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! 🎉 Here's how you can help:\n\n1. Fork the repository.\n2. Add a new use case or improve an existing one.\n3. Submit a pull request with your changes.\n\nPlease follow our [Contributing Guidelines](CONTRIBUTING.md) for more details.\n\n---\n\n## Star History\n\n\u003Cpicture>\n \u003Csource\n media=\"(prefers-color-scheme: dark)\"\n srcset=\"\n https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_4ee898a645fd.png\n \"\n \u002F>\n \u003Csource\n media=\"(prefers-color-scheme: light)\"\n srcset=\"\n https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_4ee898a645fd.png\n \"\n \u002F>\n \u003Cimg\n alt=\"Star History Chart\"\n src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_4ee898a645fd.png\"\n \u002F>\n\u003C\u002Fpicture>\n\n---\n\n## 📜 License\n\nThis repository is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.\n\n---\n\n## 🚀 Let's Build Together!\n\nFeel free to share this repository with your network and star ⭐ it if you find it useful. Let’s collaborate to create the ultimate resource for AI agent use cases!\n","# 🌟 500+ 人工智能代理项目\u002F用例\n\n[![500-AI-Agents-Projects - UseCase](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F500--AI--Agents--Projects-UseCase-2ea44f?logo=https%3A%2F%2Fstatic-00.iconduck.com%2Fassets.00%2Frobot-emoji-2048x2044-kay057lt.png&logoColor=2ea44f)](https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects)\n\n![img](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_27f3aa10fbd7.jpg)\n\n一个精心整理的人工智能代理跨行业用例合集，展示了实际应用场景，并链接到可用于实施的开源项目。探索人工智能代理如何变革医疗、金融、教育等行业！🤖✨\n\n---\n\n## 📋 目录\n\n- [简介](#introduction)\n- [行业用例](#-industry-usecase-mindmap)\n- [用例表格](#use-case-table)\n- [框架分类用例](#framework-wise-usecases)\n - [CrewAI 用例](#framework-name-crewai)\n - [AutoGen 用例](#framework-name-autogen)\n - [Agno 用例](#framework-name-agno)\n - [Langgraph 用例](#framework-name-langgraph)\n- [贡献](#contributing)\n- [许可证](#license)\n\n---\n\n## 🧠 简介\n\n人工智能（AI）代理正在彻底改变各行业的运作方式。从个性化学习到金融交易机器人，AI代理带来了效率、创新和可扩展性。本仓库提供：\n\n- 按行业分类的列表，展示AI代理正在产生影响的领域。\n- 详细的用例，附有用于实现的开源项目链接。\n\n无论您是开发者、研究人员还是商业爱好者，这个仓库都是您获取AI代理灵感和学习知识的一站式资源。\n\n---\n\n## 🏭 行业用例思维导图\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_d47e974063d5.png)\n\n---\n\n## 🧩 用例表格\n\n| 用例 | 行业 | 描述 | GitHub 代码库 |\n| ------------------------------------------- | ---------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| **HIA（健康洞察代理）** | 医疗保健 | 分析医疗报告并提供健康洞察。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fharshhh28\u002Fhia.git) |\n| **AI 健康助手** | 医疗保健 | 利用患者数据诊断和监测疾病。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fahmadvh\u002FAI-Agents-for-Medical-Diagnostics.git) |\n| **自动化交易机器人** | 金融 | 通过实时市场分析自动进行股票交易。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FMingyuJ666\u002FStockagent.git) |\n| **虚拟 AI 辅导老师** | 教育 | 提供针对用户的个性化教育。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fhqanhh\u002FEduGPT.git) |\n| **7x24 小时 AI 聊天机器人** | 客户服务 | 全天候处理客户咨询。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcustomer_support_agent_langgraph.ipynb) |\n| **产品推荐代理** | 零售 | 根据用户偏好和历史记录推荐产品。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FRecAI) |\n| **自动驾驶配送代理** | 交通运输 | 优化路线并自主递送包裹。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fsled-group\u002FdriVLMe) |\n| **工厂流程监控代理** | 制造业 | 监控生产线并确保质量控制。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fyuchenxia\u002Fllm4ias) |\n| **房产定价代理** | 房地产 | 分析市场趋势以确定房产价格。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FAleksNeStu\u002Fai-real-estate-assistant) |\n| **智能农业助手** | 农业 | 提供作物健康状况的洞察及产量预测。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fmohammed97ashraf\u002FLLM_Agri_Bot) |\n| **能源需求预测代理** | 能源 | 预测能源使用情况以优化电网管理。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fyecchen\u002FMIRAI) |\n| **内容个性化代理** | 娱乐 | 根据偏好推荐个性化媒体。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fcrosleythomas\u002FMirrorGPT) |\n| **法律文件审查助手** | 法律 | 自动化审查文件并突出显示关键条款。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Ffirica\u002Flegalai) |\n| **招聘推荐代理** | 人力资源 | 为职位空缺推荐最合适的候选人。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fsentient-engineering\u002Fjobber) |\n| **虚拟旅行助手** | 酒店餐饮 | 根据偏好规划旅行行程。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fnirbar1985\u002Fai-travel-agent) |\n| **AI 游戏伴侣代理** | 游戏 | 通过实时协助提升玩家体验。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fonjas-buidl\u002FLLM-agent-game) |\n| **实时威胁检测代理** | 网络安全 | 识别潜在威胁并缓解攻击。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FNVISOsecurity\u002Fcyber-security-llm-agents) |\n| **电商个人购物代理** | 电子商务 | 帮助客户找到他们喜欢的产品。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FHoanganhvu123\u002FShoppingGPT) |\n| **物流优化代理** | 供应链 | 规划高效配送路线并管理库存。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FOptiGuide) |\n| **Vibe Hacking 代理** | 网络安全 | 基于多智能体的自主红队测试服务。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FPurpleAILAB\u002FDecepticon) |\n| **MediSuite-Ai-Agent** | 健康保险 | 一款医疗人工智能代理，用于自动化医院\u002F保险理赔流程。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FMahmoudRabea13\u002FMediSuite-Ai-Agent) | \n| **Lina-埃及-医疗聊天机器人** | 健康保险 | 一款医疗人工智能代理，用于自动化医院\u002F保险理赔流程。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCode-GitHub-black?logo=github)](https:\u002F\u002Fgithub.com\u002FMahmoudRabea13\u002FMediSuite-Ai-Agent) |\n\n## 框架层面的用例\n\n---\n\n### **框架名称**：**CrewAI**\n\n| 用例 | 行业 | 描述 | GitHub |\n| ---------------------------- | ------------------- | ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📧 邮件自动回复流程 | 🗣️ 沟通 | 根据预设条件自动化邮件回复，提升沟通效率。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Femail_auto_responder_flow) |\n| 📝 会议助理流程 | 🛠️ 生产力 | 协助组织和管理会议，包括日程安排和议程准备。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Fmeeting_assistant_flow) |\n| 🔄 自我评估循环流程 | 👥 人力资源 | 促进组织内部的自我评估流程，助力绩效考核。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Fself_evaluation_loop_flow) |\n| 📈 潜在客户评分流程 | 💼 销售 | 评估并为潜在客户打分，以优先安排销售策略中的跟进工作。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Flead-score-flow) |\n| 📊 市场营销策略生成器 | 📢 营销 | 通过分析市场趋势和受众数据制定市场营销策略。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmarketing_strategy) |\n| 📝 招聘信息发布生成器 | 🧑‍💼 招聘 | 通过分析职位需求创建招聘公告，辅助招聘流程。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fjob-posting) |\n| 🔄 招聘工作流 | 🧑‍💼 招聘 | 通过自动化招聘过程中的各项任务，简化招聘流程。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Frecruitment) |\n| 🔍 简历与职位匹配 | 🧑‍💼 招聘 | 将候选人简历与合适的职位进行匹配，提高招聘效率。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmatch_profile_to_positions) |\n| 📸 Instagram帖子生成器 | 📱 社交媒体 | 自动化生成并安排Instagram帖子，简化社交媒体管理。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Finstagram_post) |\n| 🌐 落地页生成器 | 💻 网站开发 | 自动化创建网站落地页，简化网页开发任务。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Flanding_page_generator) |\n| 🎮 游戏开发团队 | 🎮 游戏开发 | 通过自动化游戏制作的某些环节，协助游戏开发。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fgame-builder-crew) |\n| 💹 股票分析工具 | 💰 金融 | 提供股票市场数据分析工具，辅助财务决策。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fstock_analysis) |\n| 🗺️ 旅行计划工具 | ✈️ 旅行 | 通过整理行程和管理旅行细节，协助规划旅行。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Ftrip_planner) |\n| 🎁 惊喜旅行计划工具 | ✈️ 旅行 | 根据用户偏好选择目的地和活动，规划惊喜之旅。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fsurprise_trip) |\n| 📚 使用流程撰写书籍 | ✍️ 创意写作 | 通过提供结构化的流程和写作辅助，帮助作者完成书籍创作。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fflows\u002Fwrite_a_book_with_flows) |\n| 🎬 剧本编写工具 | ✍️ 创意写作 | 提供剧本模板和指导，协助编写电影或电视剧本。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fscreenplay_writer) |\n| ✅ Markdown格式校验工具 | 📄 文档 | 校验Markdown文件，确保格式正确并符合标准。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmarkdown_validator) |\n| 🧠 Meta Quest知识库 | 📚 知识管理 | 管理和整理与Meta Quest相关知识，便于信息检索。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fmeta_quest_knowledge) |\n| 🤖 NVIDIA模型集成 | 🤖 AI集成 | 将NVIDIA的AI模型集成到工作流中，提升计算能力。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fintegrations\u002Fnvidia_models) |\n| 🗂️ 会议准备工作 | 🛠️ 生产力 | 通过整理材料和制定议程，协助会议准备工作。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fprep-for-a-meeting) |\n| 🛠️ 初始项目模板 | 🛠️ 开发 | 提供新项目的初始模板，简化项目搭建流程。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fcrews\u002Fstarter_template) |\n| 🔗 CrewAI + LangGraph集成 | 🤖 AI集成 | 展示CrewAI与LangGraph的集成，实现更高效的工作流自动化。 | [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Repository-blue)](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples\u002Ftree\u002Fmain\u002Fintegrations\u002FCrewAI-LangGraph) |\n\n### **框架名称**：**Autogen**\n\n> **代码生成、执行与调试**\n\n| 使用场景 | 行业 | 描述 | 笔记本 |\n| --------------------------------------------------------------------------------------- | ----------------------- | -------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🤖 自动化任务解决：代码生成、执行与调试 | 💻 软件开发 | 展示通过生成、执行和调试代码实现自动化任务解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_auto_feedback_from_code_execution) |\n| 🧑‍💻 基于检索增强的代理自动代码生成与问答 | 💻 软件开发 | 利用检索增强方法生成代码并回答问题。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_RetrieveChat) |\n| 🧠 基于Qdrant的检索增强，自动代码生成与问答 | 💻 软件开发 | 采用Qdrant提升检索增强型代理性能。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_RetrieveChat_qdrant) |\n\n> **多智能体协作（超过3个智能体）**\n\n| 使用场景 | 行业 | 描述 | 笔记本 |\n| :----------------------------------------------------------------------- | :-------------------------- | :----------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 🤝 团队聊天实现自动化任务解决（3名成员，1名管理者） | 🤝 协作 | 展示通过多智能体协作完成团队任务解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat) |\n| 📊 团队聊天实现自动化数据可视化（3名成员，1名管理者） | 📊 数据分析 | 利用多智能体协作创建数据可视化。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_vis) |\n| 🧩 团队聊天实现复杂任务自动化解决（6名成员，1名管理者） | 🤝 协作 | 通过更大规模的智能体协作解决复杂任务。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_research) |\n| 🧑‍💻 编码与规划智能体协同实现自动化任务解决 | 🛠️ 规划与开发 | 结合编码和规划智能体高效解决问题。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_planning.ipynb) |\n| 📐 基于图中预定义转换路径的自动化任务解决 | 🤝 协作 | 利用图中的预设转换路径解决任务。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_finite_state_machine) |\n| 🧠 通过SocietyOfMindAgent将团队聊天模拟为内心独白 | 🧠 认知科学 | 利用团队聊天模拟内心独白进行问题解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_society_of_mind) |\n| 🔧 具有自定义发言者选择功能的团队聊天 | 🤝 协作 | 实现团队聊天中的自定义发言者选择功能。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_customized) |\n\n> **顺序式多智能体聊天**\n\n| 使用场景 | 行业 | 描述 | 笔记本 |\n| :--------------------------------------------------------------------------------- | :--------------------- | :---------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🔄 由单个智能体发起的一系列聊天中依次解决多个任务 | 🔄 工作流自动化 | 通过单一发起智能体实现顺序式任务自动化解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_multi_task_chats) |\n| ⏳ 由单个智能体发起的一系列聊天中异步解决多个任务 | 🔄 工作流自动化 | 处理由一个智能体发起的系列聊天中的异步任务解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_multi_task_async_chats) |\n| 🤝 由不同智能体分别发起的一系列聊天中依次解决多个任务 | 🔄 工作流自动化 | 通过不同智能体依次发起聊天来完成顺序式任务解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchats_sequential_chats) |\n\n> **嵌套聊天**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :----------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🧠 使用嵌套对话解决复杂任务 | 🧠 问题解决 | 利用嵌套对话解决分层且复杂的问题。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nestedchat) |\n| 🔄 使用一系列嵌套对话解决复杂任务 | 🧠 问题解决 | 展示如何通过嵌套对话进行顺序式任务解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nested_sequential_chats) |\n| 🏭 嵌套对话下的 OptiGuide 解决供应链优化问题 | 🏭 供应链优化 | 展示如何利用嵌套对话、编码智能体和安全保护智能体来解决供应链优化问题。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nestedchat_optiguide) |\n| ♟️ 使用嵌套对话与工具的会话式国际象棋 | 🎮 游戏 | 探索使用嵌套对话结合集成工具进行会话式国际象棋对弈。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nested_chats_chess) |\n\n> **应用**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :------------------------------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 🔄 基于新数据的自动化持续学习 | 📊 机器学习 | 持续从新的数据输入中学习，实现自适应人工智能。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_stream.ipynb) |\n| 🏭 OptiGuide - 用于供应链优化的编码、工具使用、安全保障及问答 | 🏭 供应链优化 | 突出展示一种结合编码、工具使用和安全保障的供应链优化解决方案。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nestedchat_optiguide) |\n| 🤖 AutoAnny - 使用 AutoGen 构建的 Discord 机器人 | 💬 通信工具 | 展示如何使用 AutoGen 开发一个增强交互功能的 Discord 机器人。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Ftree\u002Fmain\u002Fsamples\u002Fapps\u002Fauto-anny) |\n\n> **工具**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :--------------------------------------------------------------------- | :----------------------------- | :------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🌐 网络搜索：解决需要网络信息的任务 | 🔍 信息检索 | 搜索网络以收集完成任务所需的信息。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_web_info.ipynb) |\n| 🔧 将提供的工具作为函数使用 | 🛠️ 工具集成 | 展示如何在 AutoGen 中将预先提供的工具作为可调用函数使用。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_function_call_currency_calculator) |\n| 🔗 通过同步和异步函数调用使用工具 | 🛠️ 工具集成 | 展示在 AutoGen 工作流中同步和异步使用工具的方法。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_function_call_async) |\n| 🧩 使用 Langchain 提供的工具作为函数解决问题 | 🔍 语言处理 | 利用 Langchain 工具在 AutoGen 中解决问题。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_langchain.ipynb) |\n| 📚 RAG：带有检索增强生成的群聊 | 🤝 协作 | 启用带有检索增强生成（RAG）的群聊，以支持信息共享。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_groupchat_RAG) |\n| ⚙️ 函数内嵌：在对话过程中更新或移除函数 | 🔧 开发工具 | 允许 AutoGen 代理在对话过程中动态修改其函数。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_inception_function.ipynb) |\n| 🔊 带有 Whisper 的代理聊天 | 🎙️ 音频处理 | 展示使用 Whisper 进行转录和翻译的 AI 代理能力。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_video_transcript_translate_with_whisper) |\n| 📏 通过引导实现受限响应 | 💡 自然语言处理 | 展示如何使用引导来限制代理生成的响应。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_guidance.ipynb) |\n| 🌍 使用代理浏览网页 | 🌐 信息检索 | 解释如何配置代理以浏览和从网上获取信息。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_surfer.ipynb) |\n| 📊 SQL：使用 Spider 基准将自然语言文本转换为 SQL 查询 | 💾 数据库管理 | 使用 Spider 基准将自然语言输入转换为 SQL 查询。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_sql_spider.ipynb) |\n| 🕸️ 使用 Apify 进行网页抓取 | 🌐 数据采集 | 展示使用 AutoGen 和 Apify 进行网页抓取的技术。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_webscraping_with_apify) |\n| 🕷️ 网页爬取：使用 Spider API 爬取整个域名 | 🌐 数据采集 | 解释如何使用 Spider API 爬取整个域名。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_webcrawling_with_spider) |\n| 💻 使用专门设计的函数逐步编写软件应用任务 | 💻 软件开发 | 使用设计好的函数逐步构建一个软件应用程序。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_function_call_code_writing.ipynb) |\n\n> **人类发展**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :--------------------------------------------------------------- | :---------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 💬 类似 ChatGPT 的简单对话示例 | 🧠 对话式 AI | 展示一个类似 ChatGPT 风格的简单对话示例。 | [![示例](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-示例-blue?logo=openai)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fsamples\u002Fsimple_chat.py) |\n| 🤖 自动代码生成、执行、调试及人工反馈 | 💻 软件开发 | 展示代码生成、执行、调试，并将人工反馈融入工作流。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_human_feedback.ipynb) |\n| 👥 使用 GPT-4 和多名人类用户进行自动化任务解决 | 🤝 协作 | 实现由多名人类用户与 GPT-4 协同完成任务解决。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_two_users.ipynb) |\n| 🔄 带有异步人类输入的智能体聊天 | 🧠 对话式 AI | 支持在智能体对话过程中进行异步的人类输入。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002FAsync_human_input.ipynb) |\n\n> **智能体的教学与学习**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :------------------------------------------------------------------- | :-------------------------- | :--------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📘 通过自动化聊天教授智能体新技能并实现复用 | 🎓 教育与培训 | 演示如何向智能体教授新技能，并使其能够在自动化聊天中被复用。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_teaching) |\n| 🧠 向智能体教授新事实、用户偏好及非编码技能 | 🎓 教育与培训 | 展示如何向智能体传授新事实、用户偏好以及非编码技能。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_teachability) |\n| 🤖 通过 GPTAssistantAgent 教授 OpenAI 助手 | 💻 AI 助手开发 | 说明如何利用 GPTAssistantAgent 提升 OpenAI 助手的能力。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_teachable_oai_assistants.ipynb) |\n| 🔄 智能体优化器：以智能体方式训练智能体 | 🛠️ 优化 | 解释如何使用智能体优化器，以智能体化的方式高效训练智能体。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_agentoptimizer.ipynb) |\n\n> **包含 OpenAI 助手的多智能体聊天**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :-------------------------------------------------------- | :----------------------- | :---------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🌟 使用 AutoGen 与 OpenAI 助手进行“Hello-World”聊天 | 🤖 对话式 AI | 使用 AutoGen 与 OpenAI 助手进行基本对话的示例。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_twoagents_basic.ipynb) |\n| 🔧 使用函数调用与 OpenAI 助手聊天 | 🔧 开发工具 | 展示如何在聊天中使用 OpenAI 助手的函数调用功能。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_function_call.ipynb) |\n| 🧠 使用代码解释器与 OpenAI 助手聊天 | 💻 软件开发 | 演示如何将 OpenAI 助手用作聊天中的代码解释器。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_code_interpreter.ipynb) |\n| 🔍 带有检索增强的 OpenAI 助手聊天 | 📚 信息检索 | 实现带有检索增强的 OpenAI 助手对话。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_retrieval.ipynb) |\n| 🤝 OpenAI 助手参与群聊 | 🤝 协作 | 展示 OpenAI 助手如何与其他智能体在群聊中协作。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_oai_assistant_groupchat.ipynb) |\n| 🛠️ 基于 GPTAssistantAgent 的多智能体工具使用 | 🔧 开发工具 | 解释如何使用 GPTAssistantAgent 进行多智能体工具操作。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fgpt_assistant_agent_function_call.ipynb) |\n\n> **非 OpenAI 模型**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :------------------------------------------------ | :-------- | :---------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| ♟️ 使用非 OpenAI 模型进行对话式国际象棋 | 🎮 游戏 | 探索使用非 OpenAI 模型实现的对话式国际象棋。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_nested_chats_chess_altmodels) |\n\n> **多模态智能体**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :--------------------------------------------- | :------------------ | :-------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🎨 多模态智能体与 DALL·E 和 GPT-4V 对话 | 🖼️ 多媒体 AI | 结合 DALL·E 和 GPT-4V 实现多模态智能体的交流。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_dalle_and_gpt4v.ipynb) |\n| 🖌️ 多模态智能体与 LLaVA 对话 | 📷 图像处理 | 使用 LLaVA 实现多模态智能体的图像处理对话。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_lmm_llava.ipynb) |\n| 🖼️ 多模态智能体与 GPT-4V 对话 | 🖼️ 多媒体 AI | 利用 GPT-4V 实现多模态智能体的视觉和对话交互。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_lmm_gpt-4v.ipynb) |\n\n> **长上下文处理**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :--------------------------------------- | :--------------- | :--------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📜 长上下文处理作为一种能力 | 🧠 AI 能力 | 展示在 AI 工作流中有效处理长上下文的技术。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Fnotebooks\u002Fagentchat_transform_messages) |\n\n> **评估与测评**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :----------------------------------------------------------------------------------- | :------------------------ | :------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 📊 AgentEval：用于评估大语言模型驱动应用实用性的多智能体系统 | 📈 性能评估 | 介绍AgentEval，用于评估和衡量基于大语言模型的应用程序性能。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagenteval_cq_math.ipynb) |\n\n> **自动构建智能体**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :------------------------------------------------------------ | :---------------- | :------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🏗️ 使用AgentBuilder自动构建多智能体系统 | 🤖 AI开发 | 解释如何使用AgentBuilder工具自动构建多智能体系统。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fautobuild_basic.ipynb) |\n| 📚 从智能体库中自动构建多智能体系统 | 🤖 AI开发 | 展示如何利用预定义的智能体库来构建多智能体系统。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fautobuild_agent_library.ipynb) |\n\n> **可观测性**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :---------------------------------------------------------------- | :------------------------ | :----------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| 📊 使用AgentOps跟踪LLM调用、工具使用、行动及错误 | 📈 监控与分析 | 演示如何使用AgentOps监控LLM交互、工具使用情况以及错误信息。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_agentops.ipynb) |\n\n> **增强推理能力**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :--------------------------------------------------------------------- | :----------------- | :----------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🔗 API统一 | 🔧 API管理 | 解释如何将API的使用与文档和代码示例统一起来。 | [![文档](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-文档-blue?logo=readthedocs)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002Fdocs\u002FUse-Cases\u002Fenhanced_inference\u002F#api-unification) |\n| ⚙️ 帮助有效管理API配置的实用函数 | 🔧 API管理 | 演示用于更有效地管理API配置的实用函数。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F0.2\u002Fdocs\u002Ftopics\u002Fllm_configuration) |\n| 💰 成本计算 | 📈 成本管理 | 介绍用于追踪Token使用量并估算LLM交互成本的方法。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Fagentchat_cost_token_tracking.ipynb) |\n| ⚡ 针对代码生成进行优化 | 📊 优化 | 重点介绍通过LLM提升代码生成效率的成本效益优化技巧。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Foai_completion.ipynb) |\n| 📐 针对数学问题进行优化 | 📊 优化 | 解释针对解决数学问题优化LLM性能的技术。 | [![笔记本](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F查看-笔记本-blue?logo=jupyter)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen\u002Fblob\u002F0.2\u002Fnotebook\u002Foai_chatgpt_gpt4.ipynb) |\n\n\n\n### **框架名称**: **Agno**\n\n> **用例**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :--------------------------------- | :----------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🤖 支持代理 | 💻 软件开发 \u002F AI \u002F 框架支持 | Agno 支持代理通过提供实时解答、解释和代码示例，帮助开发者使用 Agno 框架。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fagno_support_agent.py) |\n| 🎥 YouTube 代理 | 📺 媒体与内容 | 一个智能代理，利用 AI 工具生成详细的视频摘要、时间戳、主题和内容拆解，从而分析 YouTube 视频。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fyoutube_agent.py) |\n| 📊 金融代理 | 💼 金融 | 一个先进的 AI 驱动的市场分析师，提供实时股市洞察、分析师建议、财务深度解析以及特定行业的趋势。支持对 AAPL、TSLA、NVDA 等公司的详细分析请求。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fthinking_finance_agent.py) |\n| 📚 学习伙伴 | 🎓 教育 | 通过查找资源、解答问题和制定学习计划来帮助用户学习。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fstudy_partner.py) |\n| 🛍️ 购物伙伴代理 | 🏬 电子商务 | 一个产品推荐代理，根据用户的偏好，从亚马逊、Flipkart 等可信平台中帮助用户找到匹配的产品。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fshopping_partner.py) |\n| 🎓 研究学者代理 | 🧠 教育 \u002F 研究 | 一个 AI 驱动的学术助手，能够进行高级学术搜索、分析最新出版物、跨学科综合研究成果，并撰写结构合理、引用规范的学术报告。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fresearch_agent_exa.py) |\n| 🧠 研究代理 | 🗞️ 媒体与新闻 | 一个结合网络搜索和专业新闻写作的研究代理。它能够进行深度调查并产出类似《纽约时报》风格的报道。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fresearch_agent.py) |\n| 🍳 食谱生成器 | 🍽️ 食品与烹饪 | 一个 AI 驱动的食谱推荐代理，根据食材、偏好和时间限制提供个性化食谱。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Frecipe_creator.py) |\n| 🗞️ 金融代理 | 💼 金融 | 一个强大的金融分析师代理，结合实时股票数据、分析师见解、公司基本面和市场新闻。非常适合分析苹果、特斯拉、英伟达等公司以及半导体或汽车等行业。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Ffinance_agent.py) |\n| 🧠 金融推理代理 | 📈 金融 | 使用基于 Claude-3.5 Sonnet 的代理，借助推理工具和 Yahoo Finance 数据分析 NVDA 等股票。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Freasoning_finance_agent.py) |\n| 🤖 Readme 生成器代理 | 💻 软件开发 | 该代理利用仓库元数据为 GitHub 仓库生成高质量的 README 文件。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Freadme_generator.py) |\n| 🎬 电影推荐代理 | 🎥 娱乐 | 一个智能代理，利用 Exa 和 GPT-4o 分析类型、主题和最新评分，提供个性化的电影推荐。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fmovie_recommedation.py) |\n| 🔍 媒体趋势分析代理 | 📰 媒体与新闻 | 利用 AI 驱动的代理和网页抓取技术，分析数字平台上的新兴趋势、模式和意见领袖。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fmedia_trend_analysis_agent.py) |\n| ⚖️ 法律文件分析代理 | 🏛️ 法律科技 | 一个 AI 代理，通过 PDF 链接分析法律文件，并基于知识库和向量嵌入以及 GPT-4o 提供法律见解。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Flegal_consultant.py) |\n| 🤔 DeepKnowledge | 🧠 研究 | 该代理在其知识库中进行迭代搜索，将复杂问题分解为子问题，并综合出全面的答案。它使用 Agno 文档进行演示，专为深度推理和探索而设计。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fdeep_knowledge.py) |\n| 📚 图书推荐代理 | 🧠 出版与媒体 | 一个智能代理，利用文学数据、读者偏好、评论和新书发布信息，提供个性化的图书建议。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fbook_recommendation.py) |\n| 🏠 MCP Airbnb 代理 | 🛎️ 酒店业 | 使用 MCP 和 Llama 4 创建一个 AI 代理，以工作空间和交通便利性等过滤条件搜索 Airbnb 房源。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fairbnb_mcp.py) |\n| 🤖 Assist 代理 | 🧠 AI 框架 | 一个使用 GPT-4o 的 AI 代理，结合混合搜索和嵌入式知识回答关于 Agno 框架的问题。 | [![AI 代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Fagno-agi\u002Fagno\u002Fblob\u002Fmain\u002Fcookbook\u002Fexamples\u002Fagents\u002Fagno_assist.py) |\n\n### **框架名称**: **Langgraph**\n\n> **用例**\n\n| 用例 | 行业 | 描述 | 笔记本 |\n| :------------------------------------ | :---------------------------- | :----------------------------------------------------------- | :----------------------------------------------------------- |\n| 🤖 聊天机器人仿真评估 | 💻 💬 人工智能 \u002F 质量保证 | 模拟用户交互以评估聊天机器人的性能，确保其在真实场景中的健壮性和可靠性。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fchatbot-simulation-evaluation\u002Fagent-simulation-evaluation.ipynb) |\n| 🧠 通过提示信息收集 | 🧠 人工智能 \u002F 研发 | 本教程演示如何设计一个利用提示技术有效收集信息的LangGraph工作流。它展示了如何构建提示结构并管理信息流动，从而打造智能代理。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fchatbots\u002Finformation-gather-prompting.ipynb) |\n| 🧠 使用LangGraph的代码助手 | 💻 软件开发 | 本教程演示如何使用LangGraph构建一个稳健的代码助手。它引导你创建一个基于图的代理，能够处理代码生成、错误检查和迭代优化，从而提供强大且准确的编码辅助。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fcode_assistant\u002Flanggraph_code_assistant.ipynb) |\n| 🧑‍💼 客户支持代理 | 🧑‍💼 客户支持代理 | 本教程演示如何使用LangGraph构建客户支持代理。它指导你创建一个基于图的代理，能够处理客户咨询，提供自动化支持并提升用户体验。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fcustomer-support\u002Fcustomer-support.ipynb) |\n| 🔁 带重试的数据提取 | 🧠 人工智能 \u002F 数据提取 | 本教程演示如何在LangGraph工作流中实现重试机制，确保数据提取过程能够应对临时性错误，从而提高可靠性。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fextraction\u002Fretries.ipynb) |\n| 🧠 多智能体工作流 | 🧠 人工智能 \u002F 工作流编排 | 本教程演示如何使用LangGraph的代理主管构建多智能体系统。它引导你创建一个负责协调多个专业智能体的主管代理，管理任务分配和通信流程。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fmulti_agent\u002Fagent_supervisor.ipynb) |\n| 🧠 分层智能体团队 | 🧠 人工智能 \u002F 工作流编排 | 本教程演示如何使用LangGraph构建分层智能体系统。它指导你创建一个顶层主管代理，将任务委派给专门的子代理，从而实现复杂的流程，并明确任务分工与沟通。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fmulti_agent\u002Fhierarchical_agent_teams.ipynb) |\n| 🤝 多智能体协作 | 🧠 人工智能 \u002F 工作流编排 | 本教程演示如何使用LangGraph实现多智能体协作。它引导你创建多个专业智能体协同完成复杂任务，展示智能体协作在AI工作流中的强大能力。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fmulti_agent\u002Fmulti-agent-collaboration.ipynb) |\n| 🧠 计划执行型代理 | 🧠 人工智能 \u002F 工作流编排 | 本教程演示如何使用LangGraph构建“计划执行”型代理。它引导你创建一个先生成多步计划，再按顺序执行每一步，并根据需要 revisitar 和修改计划的代理。这种方法受到Plan-and-Solve论文和Baby-AGI项目启发，旨在增强AI工作流中的长期规划和任务执行能力。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fplan-and-execute\u002Fplan-and-execute.ipynb) |\n| 🧠 SQL代理 | 🧠 人工智能 \u002F 数据库交互 | 本教程演示如何构建一个能够回答SQL数据库相关问题的代理。该代理会获取可用表，判断与问题的相关性，检索模式，生成查询，检查错误，执行查询并形成响应。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Fsql-agent.ipynb) |\n| 🧠 反思型代理 | 🧠 人工智能 \u002F 工作流编排 | 本教程演示如何使用LangGraph构建反思型代理。它引导你创建一个能够批判和修订自身输出的代理，从而提升生成内容的质量和可靠性。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Freflection\u002Freflection.ipynb)|\n| 🧠 反思型代理 | 🧠 人工智能 \u002F 工作流编排 | 本教程演示如何使用LangGraph构建反思型代理。它引导你创建一个能够反思自身行动及结果的代理，从而实现迭代改进，并在复杂的工作流中做出更准确的决策。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Freflexion\u002Freflexion.ipynb)|\n| **LangGraph代理式RAG** | | | |\n| 🧠 **自适应RAG** | 🧠 人工智能 \u002F 信息检索 | 本教程演示如何使用LangGraph构建自适应RAG系统。它引导你创建一个可根据查询复杂度动态调整的检索流程，从而提升信息检索的效率和准确性。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_adaptive_rag.ipynb) |\n| 🧠 **自适应RAG（本地）** | 🧠 人工智能 \u002F 信息检索 | 本教程专注于使用本地模型实现自适应RAG，允许在离线环境下进行检索和生成，这对于网络受限或存在隐私顾虑的环境至关重要。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_adaptive_rag_local.ipynb) |\n| 🤖 **代理式RAG** | 🤖 人工智能 \u002F 智能代理 | 学习构建代理式RAG系统，其中代理会在生成响应前确定最佳的检索策略，从而提高答案的相关性和准确性。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_agentic_rag.ipynb) |\n| 🤖 **代理式RAG（本地）** | 🤖 人工智能 \u002F 智能代理 | 本教程将代理式RAG扩展到本地环境，允许使用本地模型和数据源进行检索和生成任务。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_agentic_rag_local.ipynb) |\n| 🧠 **纠正式RAG（CRAG）** | 🧠 人工智能 \u002F 信息检索 | 实现纠正式RAG系统，在文档传递给生成器之前对其进行评估和精炼，以确保更高品质的输出。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_crag.ipynb) |\n| 🧠 **纠正式RAG（本地）** | 🧠 人工智能 \u002F 信息检索 | 本教程专注于使用本地资源构建纠正式RAG系统，允许在离线状态下进行文档评估和精炼。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_crag_local.ipynb) |\n| 🧠 **自我RAG** | 🧠 人工智能 \u002F 信息检索 | 学习实施自我RAG，系统会反思自身的响应，并在必要时检索更多信息，从而提升生成内容的准确性和相关性。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_self_rag.ipynb) |\n| 🧠 **自我RAG（本地）** | 🧠 人工智能 \u002F 信息检索 | 本教程演示如何使用本地模型和数据源实施自我RAG，从而实现离线反思和检索过程。 | [![AI代理代码 - Python](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=AI+Agent+Code&message=Python&color=%23244cd1)](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\u002Fblob\u002Fmain\u002Fdocs\u002Fdocs\u002Ftutorials\u002Frag\u002Flanggraph_self_rag_local.ipynb) |\n\n---\n\n\n\n## 🤝 贡献\n\n欢迎贡献！🎉 以下是你可以提供帮助的方式：\n\n1. 复刻仓库。\n2. 添加一个新的用例或改进现有的用例。\n3. 提交包含你更改的拉取请求。\n\n请遵循我们的[贡献指南](CONTRIBUTING.md)，以获取更多详细信息。\n\n---\n\n## 星标历史\n\n\u003Cpicture>\n \u003Csource\n media=\"(prefers-color-scheme: dark)\"\n srcset=\"\n https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_4ee898a645fd.png\n \"\n \u002F>\n \u003Csource\n media=\"(prefers-color-scheme: light)\"\n srcset=\"\n https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_4ee898a645fd.png\n \"\n \u002F>\n \u003Cimg\n alt=\"星标历史图表\"\n src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_readme_4ee898a645fd.png\"\n \u002F>\n\u003C\u002Fpicture>\n\n---\n\n## 📜 许可证\n\n本仓库采用 MIT 许可证。更多信息请参阅 [LICENSE](LICENSE) 文件。\n\n---\n\n## 🚀 让我们一起构建！\n\n如果你觉得这个仓库有用，欢迎与你的朋友分享并给它点个赞 ⭐。让我们携手合作，打造最全面的 AI 代理用例资源吧！","# 500+ AI Agent 项目快速上手指南\n\n本项目是一个精选的 AI Agent 用例集合，涵盖医疗、金融、教育等多个行业，并提供了基于 CrewAI、AutoGen、LangGraph 等主流框架的开源实现代码。本指南将帮助你快速定位所需场景并运行示例。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下要求：\n\n* **操作系统**: Linux, macOS, 或 Windows (推荐 WSL2)\n* **Python**: 版本 3.10 或更高\n* **Git**: 用于克隆仓库\n* **API Keys**: 根据具体项目需求，可能需要准备 OpenAI API Key、Anthropic API Key 或其他大模型服务商的密钥。\n\n**前置依赖安装建议：**\n建议使用 `venv` 或 `conda` 创建独立的虚拟环境，以避免依赖冲突。\n\n```bash\npython -m venv ai-agents-env\nsource ai-agents-env\u002Fbin\u002Factivate # Windows 用户请使用: ai-agents-env\\Scripts\\activate\n```\n\n> 💡 **国内加速提示**：在安装 Python 依赖时，推荐使用清华或阿里镜像源以提升下载速度。\n> ```bash\n> pip config set global.index-url https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n> ```\n\n## 安装步骤\n\n由于本项目是多个独立案例的集合，而非单一安装包，因此需要针对你感兴趣的具体案例进行克隆和安装。\n\n### 1. 克隆仓库\n首先克隆主仓库以浏览目录结构：\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects.git\ncd 500-AI-Agents-Projects\n```\n\n### 2. 选择并安装具体案例\n根据下方的“用例速查表”找到你感兴趣的项目链接，克隆对应的子仓库。\n\n**示例：以 CrewAI 的“邮件自动回复”为例**\n```bash\n# 克隆具体的案例仓库\ngit clone https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI-examples.git\ncd crewAI-examples\u002Fflows\u002Femail_auto_responder_flow\n\n# 安装该案例所需的依赖\npip install -r requirements.txt\n# 如果该案例使用 poetry\n# pip install poetry && poetry install\n```\n\n## 基本使用\n\n每个项目的具体运行方式略有不同，但通常遵循以下通用流程：配置环境变量 -> 运行脚本。\n\n### 通用运行示例\n\n假设你正在运行一个基于 **CrewAI** 的简单 Agent 任务（如上述邮件回复案例）：\n\n1. **配置 API Key**\n 在项目根目录下创建 `.env` 文件，或在终端中导出变量：\n ```bash\n export OPENAI_API_KEY=\"sk-your-api-key-here\"\n ```\n\n2. **运行主程序**\n 查看项目中的 `main.py` 或 `run.py` 并执行：\n ```bash\n python main.py\n ```\n\n### 行业用例速查与代码入口\n\n你可以直接访问以下热门领域的开源项目代码进行尝试：\n\n| 应用场景 | 行业 | 描述 | 代码仓库指令 |\n| :--- | :--- | :--- | :--- |\n| **HIA (健康洞察)** | 医疗健康 | 分析医疗报告并提供健康建议 | `git clone https:\u002F\u002Fgithub.com\u002Fharshhh28\u002Fhia.git` |\n| **自动交易机器人** | 金融 | 实时市场分析并自动执行股票交易 | `git clone https:\u002F\u002Fgithub.com\u002FMingyuJ666\u002FStockagent.git` |\n| **虚拟 AI 导师** | 教育 | 提供个性化的学习辅导 | `git clone https:\u002F\u002Fgithub.com\u002Fhqanhh\u002FEduGPT.git` |\n| **产品推荐助手** | 零售 | 基于用户历史推荐商品 | `git clone https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FRecAI` |\n| **法律文档审查** | 法律 | 自动审查文档并高亮关键条款 | `git clone https:\u002F\u002Fgithub.com\u002Ffirica\u002Flegalai` |\n\n### 按框架查找用例\n\n如果你偏好特定的开发框架，可以参考以下分类直接寻找示例：\n\n* **CrewAI**: 适合多角色协作任务（如：招聘工作流、营销策略生成）。\n * 入口：`crewAI-examples` 仓库中的 `crews` 或 `flows` 目录。\n* **AutoGen**: 适合复杂的对话和多代理交互场景。\n* **LangGraph**: 适合需要精细控制状态和循环的工作流（如：24\u002F7 客服聊天机器人）。\n * 示例：`customer_support_agent_langgraph.ipynb`\n\n**运行 Jupyter Notebook 示例（如 LangGraph 案例）：**\n```bash\npip install jupyter\njupyter notebook customer_support_agent_langgraph.ipynb\n```\n\n> 注意：具体运行命令请以各子项目仓库中的 `README.md` 说明为准。","一家中型电商公司的技术团队正急需开发一套能根据用户历史行为实时推荐商品的智能系统，以应对即将到来的促销大促。\n\n### 没有 500-AI-Agents-Projects 时\n- **选型迷茫耗时久**：团队在 CrewAI、AutoGen 和 LangGraph 等多个框架间犹豫不决，花费数周调研各框架在零售场景的适用性，严重拖慢项目启动进度。\n- **重复造轮子**：开发人员需从零编写用户画像分析、商品匹配算法及对话逻辑，缺乏可参考的成熟代码结构，导致基础功能开发周期长达一个月。\n- **场景落地风险高**：由于缺乏同行业的成功案例参考，团队难以预判推荐逻辑在实际高并发下的表现，担心上线后出现推荐不准或响应延迟等问题。\n\n### 使用 500-AI-Agents-Projects 后\n- **精准定位框架**：通过查阅\"Framework Wise UseCase\"章节，团队迅速锁定 LangGraph 在零售推荐领域的成熟应用案例，当天即完成技术栈决策。\n- **代码复用加速**：直接复用仓库中\"Product Recommendation Agent\"的开源代码链接，基于现成的用户偏好分析逻辑进行微调，将核心功能开发时间压缩至 3 天。\n- **实战经验借鉴**：参考医疗和金融等其他行业的高复杂度案例，团队优化了系统的异常处理机制，显著提升了推荐引擎在促销高峰期的稳定性与准确率。\n\n500-AI-Agents-Projects 通过提供跨行业的实战代码库与清晰的技术路线图，将企业从繁琐的探索性开发中解放出来，实现了 AI 智能体应用的快速落地与价值变现。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fashishpatel26_500-AI-Agents-Projects_27f3aa10.jpg","ashishpatel26","Ashish Patel","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fashishpatel26_9e4e7549.jpg","AI Researcher & Principal Architect AI\u002FML & Data Science at Oracle\r\n| xIBMers | Rank 3 Kaggle Kernel Master","Oracle | xIBMers","Ahmedabad","shriganesh.patel@gmail.com",null,"https:\u002F\u002Fmedium.com\u002Fml-research-lab","https:\u002F\u002Fgithub.com\u002Fashishpatel26",27992,4888,"2026-04-07T03:49:16","MIT","","未说明",{"notes":89,"python":87,"dependencies":90},"该仓库是一个 AI Agent 应用案例的 curated 集合（目录），本身不是一个单一的可执行工具，因此没有统一的运行环境要求。仓库中列出的 500+ 个项目分别基于不同的框架（如 CrewAI, AutoGen, Langgraph, Agno 等）和具体的应用场景。用户需要点击表格中的链接进入各个独立的子项目仓库，查看其具体的 requirements.txt 或安装文档以获取对应的操作系统、GPU、内存、Python 版本及依赖库信息。",[],[13],[93,94],"ai-agents","genai","2026-03-27T02:49:30.150509","2026-04-11T16:59:58.223442",[98,103,108],{"id":99,"question_zh":100,"answer_zh":101,"source_url":102},22522,"仓库中的框架链接（如 CrewAI）无法打开或失效怎么办？","维护者已确认部分链接曾出现失效问题，但目前已修复并正常工作。如果遇到链接打不开的情况，请稍后重试或检查是否已被更新修复。","https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects\u002Fissues\u002F24",{"id":104,"question_zh":105,"answer_zh":106,"source_url":107},22523,"项目的许可证条款是什么？是否有正式的 LICENSE 文件？","项目采用 MIT 许可证。维护者已补全了缺失的文件，现在可以在仓库根目录找到正式的 LICENSE 文件以及贡献指南（CONTRIBUTION.md）。","https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects\u002Fissues\u002F10",{"id":109,"question_zh":110,"answer_zh":111,"source_url":112},22521,"如何为该项目贡献代码或参与项目？","在贡献之前，请务必阅读项目的贡献指南。维护者指出，所有贡献者应参考 CONTRIBUTION.md 文件以了解具体流程和规则。","https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects\u002Fissues\u002F12",[]]