[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-NirDiamant--GenAI_Agents":3,"tool-NirDiamant--GenAI_Agents":62},[4,18,28,36,45,54],{"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":24,"last_commit_at":25,"category_tags":26,"status":17},9989,"n8n","n8n-io\u002Fn8n","n8n 是一款面向技术团队的公平代码（fair-code）工作流自动化平台，旨在让用户在享受低代码快速构建便利的同时，保留编写自定义代码的灵活性。它主要解决了传统自动化工具要么过于封闭难以扩展、要么完全依赖手写代码效率低下的痛点，帮助用户轻松连接 400 多种应用与服务，实现复杂业务流程的自动化。\n\nn8n 特别适合开发者、工程师以及具备一定技术背景的业务人员使用。其核心亮点在于“按需编码”：既可以通过直观的可视化界面拖拽节点搭建流程，也能随时插入 JavaScript 或 Python 代码、调用 npm 包来处理复杂逻辑。此外，n8n 原生集成了基于 LangChain 的 AI 能力，支持用户利用自有数据和模型构建智能体工作流。在部署方面，n8n 提供极高的自由度，支持完全自托管以保障数据隐私和控制权，也提供云端服务选项。凭借活跃的社区生态和数百个现成模板，n8n 让构建强大且可控的自动化系统变得简单高效。",184740,2,"2026-04-19T23:22:26",[16,14,13,15,27],"插件",{"id":29,"name":30,"github_repo":31,"description_zh":32,"stars":33,"difficulty_score":10,"last_commit_at":34,"category_tags":35,"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":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":24,"last_commit_at":42,"category_tags":43,"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",161147,"2026-04-19T23:31:47",[14,13,44],"语言模型",{"id":46,"name":47,"github_repo":48,"description_zh":49,"stars":50,"difficulty_score":51,"last_commit_at":52,"category_tags":53,"status":17},8272,"opencode","anomalyco\u002Fopencode","OpenCode 是一款开源的 AI 编程助手（Coding Agent），旨在像一位智能搭档一样融入您的开发流程。它不仅仅是一个代码补全插件，而是一个能够理解项目上下文、自主规划任务并执行复杂编码操作的智能体。无论是生成全新功能、重构现有代码，还是排查难以定位的 Bug，OpenCode 都能通过自然语言交互高效完成，显著减少开发者在重复性劳动和上下文切换上的时间消耗。\n\n这款工具专为软件开发者、工程师及技术研究人员设计，特别适合希望利用大模型能力来提升编码效率、加速原型开发或处理遗留代码维护的专业人群。其核心亮点在于完全开源的架构，这意味着用户可以审查代码逻辑、自定义行为策略，甚至私有化部署以保障数据安全，彻底打破了传统闭源 AI 助手的“黑盒”限制。\n\n在技术体验上，OpenCode 提供了灵活的终端界面（Terminal UI）和正在测试中的桌面应用程序，支持 macOS、Windows 及 Linux 全平台。它兼容多种包管理工具，安装便捷，并能无缝集成到现有的开发环境中。无论您是追求极致控制权的资深极客，还是渴望提升产出的独立开发者，OpenCode 都提供了一个透明、可信",144296,1,"2026-04-16T14:50:03",[13,27],{"id":55,"name":56,"github_repo":57,"description_zh":58,"stars":59,"difficulty_score":24,"last_commit_at":60,"category_tags":61,"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。",109154,"2026-04-18T11:18:24",[14,15,13],{"id":63,"github_repo":64,"name":65,"description_en":66,"description_zh":67,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":77,"owner_location":78,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":97,"forks":98,"last_commit_at":99,"license":100,"difficulty_score":24,"env_os":101,"env_gpu":101,"env_ram":101,"env_deps":102,"category_tags":109,"github_topics":110,"view_count":24,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":124,"updated_at":125,"faqs":126,"releases":156},9937,"NirDiamant\u002FGenAI_Agents","GenAI_Agents","50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.","GenAI_Agents 是一个专注于生成式 AI 智能体开发的开源资源库，旨在帮助开发者从零开始构建并部署各类 AI 应用。它收录了超过 50 个详细的教程与代码实现案例，内容覆盖范围极广：从基础的对话机器人，到复杂的协同多智能体系统（Multi-Agent Systems），甚至包括 HR 助手、艺术导览及数据分析助理等具体场景应用。\n\n该项目主要解决了学习者在探索 AI 智能体技术时面临的“入门难”和“缺乏实战参考”的痛点。通过将抽象的理论转化为可运行的代码示例，它让用户能够直观地理解智能体的工作原理、交互逻辑及架构设计，从而大幅降低开发门槛。\n\nGenAI_Agents 特别适合 AI 开发者、技术研究人员以及希望深入掌握大模型应用的学生使用。对于想要快速验证想法的原型开发者，这里提供了丰富的现成模板；对于进阶用户，其中关于多智能体协作和高级检索增强生成（RAG）结合的实践案例，则提供了极具价值的技术参考。无论是想迈出构建第一个智能体的第一步，还是致力于研发前沿的复杂系统，这个不断更新的社区资源库都能提供坚实的支持。","[![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](http:\u002F\u002Fmakeapullrequest.com)\n[![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-Connect-blue)](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnir-diamant-759323134\u002F)\n[![Reddit](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FReddit-Join%20our%20subreddit-FF4500?style=flat-square&logo=reddit&logoColor=white)](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FEducationalAI\u002F)\n[![Twitter](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002FNirDiamantAI?label=Follow%20@NirDiamantAI&style=social)](https:\u002F\u002Ftwitter.com\u002FNirDiamantAI)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDiscord-Join%20our%20community-7289da?style=flat-square&logo=discord&logoColor=white)](https:\u002F\u002Fdiscord.gg\u002FcA6Aa4uyDX)\n\n\n> 🌟 **Support This Project:** Your sponsorship fuels innovation in GenAI agent development. **[Become a sponsor](https:\u002F\u002Fgithub.com\u002Fsponsors\u002FNirDiamant)** to help maintain and expand this valuable resource!\n\n# GenAI Agents: Comprehensive Repository for Development and Implementation 🚀\n\nWelcome to one of the most extensive and dynamic collections of Generative AI (GenAI) agent tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing GenAI agents, ranging from simple conversational bots to complex, multi-agent systems.\n\n\u003Cdiv align=\"center\">\n\n## 📖 From the Same Author\n\n\u003Ca href=\"https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme&click=book-buy-amazon-image&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=Best%20Seller%20Image\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_d5acda037ddb.png\" alt=\"#1 Best Seller in Generative AI on Amazon - Click to buy\" width=\"500\">\u003C\u002Fa>\n\n**[RAG Made Simple](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme&click=book-buy-amazon-title&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=RAG%20Made%20Simple)** — **#1 Best Seller on Amazon in Generative AI.**\n22 RAG techniques with intuition, comparisons, and illustrations. **Free with Kindle Unlimited** or **$0.99** launch price (goes up soon).\n\n### 👉 [**Get the book on Amazon**](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme&click=book-buy-amazon-cta&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=Get%20the%20book%20on%20Amazon)\n\n\u003C\u002Fdiv>\n\n## 🏆 Sponsors\n\n\u003Cdiv align=\"center\">\n\n\u003Ca href=\"https:\u002F\u002Fcoderabbit.link\u002Fnir\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_fdef4d816bf0.png\" height=\"80\" alt=\"CodeRabbit\" \u002F>\u003C\u002Fa>\n\n\u003C\u002Fdiv>\n\n\n\n> **Recently added:** HR AI Assistant, Art Tourguide with LightRAG, Contextual Quoting System, ML\u002FDS Assistant, Gutenberg Sage | **52 tutorials** and growing\n\n## 📫 Stay Updated!\n\n\u003Cdiv align=\"center\">\n\u003Ctable>\n\u003Ctr>\n\u003Ctd align=\"center\">🚀\u003Cbr>\u003Cb>Cutting-edge\u003Cbr>Updates\u003C\u002Fb>\u003C\u002Ftd>\n\u003Ctd align=\"center\">💡\u003Cbr>\u003Cb>Expert\u003Cbr>Insights\u003C\u002Fb>\u003C\u002Ftd>\n\u003Ctd align=\"center\">🎯\u003Cbr>\u003Cb>Top 0.1%\u003Cbr>Content\u003C\u002Fb>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n[![Subscribe to DiamantAI Newsletter](images\u002Fsubscribe-button.svg)](https:\u002F\u002Fdiamantai.substack.com\u002F?r=336pe4&utm_campaign=pub-share-checklist)\n\n*Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials!* ***Plus, subscribers get exclusive early access and special 33% discounts to my book and the upcoming RAG Techniques course!***\n\u003C\u002Fdiv>\n\n[![DiamantAI's newsletter](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_bd4960312f91.png)](https:\u002F\u002Fdiamantai.substack.com\u002F?r=336pe4&utm_campaign=pub-share-checklist)\n\n\n## Introduction\n\nGenerative AI agents are at the forefront of artificial intelligence, revolutionizing the way we interact with and leverage AI technologies. This repository is designed to guide you through the development journey, from basic agent implementations to advanced, cutting-edge systems.\n\n\u003Cdiv align=\"center\">\n\u003Ctable>\n\u003Ctr>\n\u003Ctd>\n\u003Ch3>📚 Learn to Build Your First AI Agent\u003C\u002Fh3>\n\u003Cp>\u003Cstrong>\u003Ca href=\"https:\u002F\u002Fdiamantai.substack.com\u002Fp\u002Fyour-first-ai-agent-simpler-than\">Your First AI Agent: Simpler Than You Think\u003C\u002Fa>\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>This detailed blog post complements the repository by providing a complete A-Z walkthrough with in-depth explanations of core concepts, step-by-step implementation, and the theory behind AI agents. It's designed to be incredibly simple to follow while covering everything you need to know to build your first working agent from scratch.\u003C\u002Fp>\n\u003Cp>\u003Cem>💡 Plus: Subscribe to the newsletter for exclusive early access to tutorials and special discounts on upcoming courses and books!\u003C\u002Fem>\u003C\u002Fp>\n\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\u003C\u002Fdiv>\n\n\nOur goal is to provide a valuable resource for everyone - from beginners taking their first steps in AI to seasoned practitioners pushing the boundaries of what's possible. By offering a range of examples from foundational to complex, we aim to facilitate learning, experimentation, and innovation in the rapidly evolving field of GenAI agents.\n\nFurthermore, this repository serves as a platform for showcasing innovative agent creations. Whether you've developed a novel agent architecture or found an innovative application for existing techniques, we encourage you to share your work with the community.\n\n## Related Projects\n\n🚀 Level up with my **[Agents Towards Production](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production)** repository. It delivers horizontal, code-first tutorials that cover every tool and step in the lifecycle of building production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches, making it the smartest place to start if you're serious about shipping agents to production.\n\n📚 Dive into my **[comprehensive guide on RAG techniques](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FRAG_Techniques)** to learn about integrating external knowledge into AI systems, enhancing their capabilities with up-to-date and relevant information retrieval.\n\n🖋️ Explore my **[Prompt Engineering Techniques guide](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FPrompt_Engineering)** for an extensive collection of prompting strategies, from fundamental concepts to advanced methods, improving your ability to communicate effectively with AI language models.\n\n## A Community-Driven Knowledge Hub\n\n**This repository grows stronger with your contributions!** Join our vibrant communities - the central hubs for shaping and advancing this project together 🤝\n\n**[Educational AI Subreddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FEducationalAI\u002F)**\n\n**[GenAI Agents Discord Community](https:\u002F\u002Fdiscord.gg\u002FcA6Aa4uyDX)**\n\nWhether you're a novice eager to learn or an expert ready to share your knowledge, your insights can shape the future of GenAI agents. Join us to propose ideas, get feedback, and collaborate on innovative implementations. For contribution guidelines, please refer to our **[CONTRIBUTING.md](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002FCONTRIBUTING.md)** file. Let's advance GenAI agent technology together!\n\n🔗 For discussions on GenAI, agents, or to explore knowledge-sharing opportunities, feel free to **[connect on LinkedIn](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnir-diamant-759323134\u002F)**.\n\n## Key Features\n\n- 🎓 Learn to build GenAI agents from beginner to advanced levels\n- 🧠 Explore a wide range of agent architectures and applications\n- 📚 Step-by-step tutorials and comprehensive documentation\n- 🛠️ Practical, ready-to-use agent implementations\n- 🌟 Regular updates with the latest advancements in GenAI\n- 🤝 Share your own agent creations with the community\n\n## GenAI Agent Implementations\n\nBelow is a comprehensive overview of our GenAI agent implementations, organized by category and functionality. Each implementation is designed to showcase different aspects of AI agent development, from basic conversational agents to complex multi-agent systems.\n\n| # | Category | Agent Name | Framework | Key Features |\n|----|-------------------|-------------------------------|-------------------|------------------------------------------------------------------------------|\n| 1 | 🌱 **Beginner** | [Simple Conversational Agent](all_agents_tutorials\u002Fsimple_conversational_agent.ipynb) | LangChain\u002FPydanticAI | Context-aware conversations, history management |\n| 2 | 🌱 **Beginner** | [Simple Question Answering](all_agents_tutorials\u002Fsimple_question_answering_agent.ipynb) | LangChain | Query understanding, concise answers |\n| 3 | 🌱 **Beginner** | [Simple Data Analysis](all_agents_tutorials\u002Fsimple_data_analysis_agent_notebook.ipynb) | LangChain\u002FPydanticAI | Dataset interpretation, natural language queries |\n| 4 | 🔧 **Framework** | [Introduction to LangGraph](all_agents_tutorials\u002Flanggraph-tutorial.ipynb) | LangGraph | Modular AI workflows, state management |\n| 5 | 🔧 **Framework** | [Model Context Protocol (MCP)](all_agents_tutorials\u002Fmcp-tutorial.ipynb) | MCP | AI-external resource integration |\n| 6 | 🎓 **Educational**| [ATLAS: Academic Task System](all_agents_tutorials\u002FAcademic_Task_Learning_Agent_LangGraph.ipynb) | LangGraph | Multi-agent academic planning, note-taking |\n| 7 | 🎓 **Educational**| [Scientific Paper Agent](all_agents_tutorials\u002Fscientific_paper_agent_langgraph.ipynb) | LangGraph | Literature review automation |\n| 8 | 🎓 **Educational**| [Chiron - Feynman Learning](all_agents_tutorials\u002Fchiron_learning_agent_langgraph.ipynb) | LangGraph | Adaptive learning, checkpoint system |\n| 9 | 💼 **Business** | [Customer Support Agent](all_agents_tutorials\u002Fcustomer_support_agent_langgraph.ipynb) | LangGraph | Query categorization, sentiment analysis |\n| 10 | 💼 **Business** | [Essay Grading Agent](all_agents_tutorials\u002Fessay_grading_system_langgraph.ipynb) | LangGraph | Automated grading, multiple criteria |\n| 11 | 💼 **Business** | [Travel Planning Agent](all_agents_tutorials\u002Fsimple_travel_planner_langgraph.ipynb) | LangGraph | Personalized itineraries |\n| 12 | 💼 **Business** | [GenAI Career Assistant](all_agents_tutorials\u002Fagent_hackathon_genAI_career_assistant.ipynb) | LangGraph | Career guidance, learning paths |\n| 13 | 💼 **Business** | [Project Manager Assistant](all_agents_tutorials\u002Fproject_manager_assistant_agent.ipynb) | LangGraph | Task generation, risk assessment |\n| 14 | 💼 **Business** | [Contract Analysis Assistant](all_agents_tutorials\u002FClauseAI.ipynb) | LangGraph | Clause analysis, compliance checking |\n| 15 | 💼 **Business** | [E2E Testing Agent](all_agents_tutorials\u002Fe2e_testing_agent.ipynb) | LangGraph | Test automation, browser control |\n| 16 | 🎨 **Creative** | [GIF Animation Generator](all_agents_tutorials\u002Fgif_animation_generator_langgraph.ipynb) | LangGraph | Text-to-animation pipeline |\n| 17 | 🎨 **Creative** | [TTS Poem Generator](all_agents_tutorials\u002Ftts_poem_generator_agent_langgraph.ipynb) | LangGraph | Text classification, speech synthesis |\n| 18 | 🎨 **Creative** | [Music Compositor](all_agents_tutorials\u002Fmusic_compositor_agent_langgraph.ipynb) | LangGraph | AI music composition |\n| 19 | 🎨 **Creative** | [Content Intelligence](all_agents_tutorials\u002FContentIntelligence.ipynb) | LangGraph | Multi-platform content generation |\n| 20 | 🎨 **Creative** | [Business Meme Generator](all_agents_tutorials\u002Fbusiness_meme_generator.ipynb) | LangGraph | Brand-aligned meme creation |\n| 21 | 🎨 **Creative** | [Murder Mystery Game](all_agents_tutorials\u002Fmurder_mystery_agent_langgraph.ipynb) | LangGraph | Procedural story generation |\n| 22 | 📊 **Analysis** | [Memory-Enhanced Conversational](all_agents_tutorials\u002Fmemory_enhanced_conversational_agent.ipynb)| LangChain | Short\u002Flong-term memory integration |\n| 23 | 📊 **Analysis** | [Multi-Agent Collaboration](all_agents_tutorials\u002Fmulti_agent_collaboration_system.ipynb) | LangChain | Historical research, data analysis |\n| 24 | 📊 **Analysis** | [Self-Improving Agent](all_agents_tutorials\u002Fself_improving_agent.ipynb) | LangChain | Learning from interactions |\n| 25 | 📊 **Analysis** | [Task-Oriented Agent](all_agents_tutorials\u002Ftask_oriented_agent.ipynb) | LangChain | Text summarization, translation |\n| 26 | 📊 **Analysis** | [Internet Search Agent](all_agents_tutorials\u002Fsearch_the_internet_and_summarize.ipynb) | LangChain | Web research, summarization |\n| 27 | 📊 **Analysis** | [Research Team - Autogen](all_agents_tutorials\u002Fresearch_team_autogen.ipynb) | AutoGen | Multi-agent research collaboration |\n| 28 | 📊 **Analysis** | [Sales Call Analyzer](all_agents_tutorials\u002Fsales_call_analyzer_agent.ipynb) | LangGraph | Audio transcription, NLP analysis |\n| 29 | 📊 **Analysis** | [Weather Emergency System](all_agents_tutorials\u002FWeather_Disaster_Management_AI_AGENT.ipynb) | LangGraph | Real-time data processing |\n| 30 | 📊 **Analysis** | [Self-Healing Codebase](all_agents_tutorials\u002Fself_healing_code.ipynb) | LangGraph | Error detection, automated fixes |\n| 31 | 📊 **Analysis** | [DataScribe](all_agents_tutorials\u002Fdatabase_discovery_fleet.ipynb) | LangGraph | Database exploration, query planning |\n| 32 | 📊 **Analysis** | [Memory-Enhanced Email](all_agents_tutorials\u002Fmemory-agent-tutorial.ipynb) | LangGraph | Email triage, response generation |\n| 33 | 📰 **News** | [News TL;DR](all_agents_tutorials\u002Fnews_tldr_langgraph.ipynb) | LangGraph | News summarization, API integration |\n| 34 | 📰 **News** | [AInsight](all_agents_tutorials\u002Fainsight_langgraph.ipynb) | LangGraph | AI\u002FML news aggregation |\n| 35 | 📰 **News** | [Journalism Assistant](all_agents_tutorials\u002Fjournalism_focused_ai_assistant_langgraph.ipynb) | LangGraph | Fact-checking, bias detection |\n| 36 | 📰 **News** | [Blog Writer](all_agents_tutorials\u002Fblog_writer_swarm.ipynb) | OpenAI Swarm | Collaborative content creation |\n| 37 | 📰 **News** | [Podcast Generator](all_agents_tutorials\u002Fgenerate_podcast_agent_langgraph.ipynb) | LangGraph | Content search, audio generation |\n| 38 | 🛍️ **Shopping** | [ShopGenie](all_agents_tutorials\u002FShopGenie.ipynb) | LangGraph | Product comparison, recommendations |\n| 39 | 🛍️ **Shopping** | [Car Buyer Agent](all_agents_tutorials\u002Fcar_buyer_agent_langgraph.ipynb) | LangGraph | Web scraping, decision support |\n| 40 | 🎯 **Task Management** | [Taskifier](all_agents_tutorials\u002Ftaskifier.ipynb) | LangGraph | Work style analysis, task breakdown |\n| 41 | 🎯 **Task Management** | [Grocery Management](all_agents_tutorials\u002Fgrocery_management_agents_system.ipynb) | CrewAI | Inventory tracking, recipe suggestions |\n| 42 | 🔍 **QA** | [LangGraph Inspector](all_agents_tutorials\u002Fgraph_inspector_system_langgraph.ipynb) | LangGraph | System testing, vulnerability detection |\n| 43 | 🔍 **QA** | [EU Green Deal Bot](all_agents_tutorials\u002FEU_Green_Compliance_FAQ_Bot.ipynb) | LangGraph | Regulatory compliance, FAQ system |\n| 44 | 🔍 **QA** | [Systematic Review](all_agents_tutorials\u002Fsystematic_review_of_scientific_articles.ipynb) | LangGraph | Academic paper processing, draft generation |\n| 45 | 🌟 **Advanced** | [Controllable RAG Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FControllable-RAG-Agent) | Custom | Complex question answering, deterministic graph |\n\nExplore our extensive list of GenAI agent implementations, sorted by categories:\n\n### 🌱 Beginner-Friendly Agents\n\n1. **Simple Conversational Agent**\n\n - **[LangChain](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_conversational_agent.ipynb)**\n - **[PydanticAI](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_conversational_agent-pydanticai.ipynb)**\n \n #### Overview 🔎\n A context-aware conversational AI maintains information across interactions, enabling more natural dialogues.\n\n #### Implementation 🛠️\n Integrates a language model, prompt template, and history manager to generate contextual responses and track conversation sessions.\n\n2. **[Simple Question Answering Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_question_answering_agent.ipynb)**\n \n #### Overview 🔎\n Answering (QA) agent using LangChain and OpenAI's language model understands user queries and provides relevant, concise answers.\n #### Implementation 🛠️\n Combines OpenAI's GPT model, a prompt template, and an LLMChain to process user questions and generate AI-driven responses in a streamlined manner.\n\n3. **Simple Data Analysis Agent**\n\n - **[LangChain](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_data_analysis_agent_notebook.ipynb)**\n - **[PydanticAI](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_data_analysis_agent_notebook-pydanticai.ipynb)**\n\n #### Overview 🔎\n An AI-powered data analysis agent interprets and answers questions about datasets using natural language, combining language models with data manipulation tools for intuitive data exploration.\n #### Implementation 🛠️\n Integrates a language model, data manipulation framework, and agent framework to process natural language queries and perform data analysis on a synthetic dataset, enabling accessible insights for non-technical users.\n\n### 🔧 Framework Tutorial\n\n4. **[Introduction to LangGraph: Building Modular AI Workflows](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Flanggraph-tutorial.ipynb)**\n \n #### Overview 🔎\n This tutorial introduces LangGraph, a powerful framework for creating modular, graph-based AI workflows. Learn how to leverage LangGraph to build more complex and flexible AI agents that can handle multi-step processes efficiently.\n\n #### Implementation 🛠️\n Step-by-step guide on using LangGraph to create a StateGraph workflow. The tutorial covers key concepts such as state management, node creation, and graph compilation. It demonstrates these principles by constructing a simple text analysis pipeline, serving as a foundation for more advanced agent architectures.\n \n #### Additional Resources 📚\n - **[Blog Post](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fyour-first-ai-agent-simpler-than?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n5. **[Model Context Protocol (MCP): Seamless Integration of AI and External Resources](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmcp-tutorial.ipynb)**\n \n #### Overview 🔎\n This tutorial introduces the Model Context Protocol (MCP), an open standard for connecting AI models with external data sources and tools. Learn how MCP serves as a universal bridge between GenAI agents and the wider digital ecosystem, enabling more capable and context-aware AI applications.\n\n #### Implementation 🛠️\n Provides a hands-on guide to implementing MCP servers and clients, demonstrating how to connect language models with external tools and data sources. The tutorial covers server setup, tool definition, and integration with AI clients, with practical examples of building useful agent capabilities through the protocol.\n\n #### Additional Resources 📚\n - **[Blog Post](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fmodel-context-protocol-mcp-explained?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n - **[Official MCP Documentation](https:\u002F\u002Fmodelcontextprotocol.io\u002Fintroduction)**\n - **[MCP GitHub Repository](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol)**\n\n### 🎓 Educational and Research Agents\n\n6. **[ATLAS: Academic Task and Learning Agent System](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FAcademic_Task_Learning_Agent_LangGraph.ipynb)**\n \n #### Overview 🔎\n ATLAS demonstrates how to build an intelligent multi-agent system that transforms academic support through AI-powered assistance. The system leverages LangGraph's workflow framework to coordinate multiple specialized agents that provide personalized academic planning, note-taking, and advisory support.\n\n #### Implementation 🛠️\n Implements a state-managed multi-agent architecture using four specialized agents (Coordinator, Planner, Notewriter, and Advisor) working in concert through LangGraph's workflow framework. The system features sophisticated workflows for profile analysis and academic support, with continuous adaptation based on student performance and feedback.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yxowMLL2dDI)** \n - **[Blog Post](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fatlas-when-artificial-intelligence?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n7. **[Scientific Paper Agent - Literature Review](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fscientific_paper_agent_langgraph.ipynb)**\n \n #### Overview 🔎\n An intelligent research assistant that helps users navigate, understand, and analyze scientific literature through an orchestrated workflow. The system combines academic APIs with sophisticated paper processing techniques to automate literature review tasks, enabling researchers to efficiently extract insights from academic papers while maintaining research rigor and quality control.\n\n #### Implementation 🛠️\n Leverages LangGraph to create a five-node workflow system including decision making, planning, tool execution, and quality validation nodes. The system integrates the CORE API for paper access, PDFplumber for document processing, and advanced language models for analysis. Key features include a retry mechanism for robust paper downloads, structured data handling through Pydantic models, and quality-focused improvement cycles with human-in-the-loop validation options.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fyoutu.be\u002FBc4YtpHY6Ws)** \n - **[Blog Post](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fnexus-ai-the-revolutionary-research?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n8. **[Chiron - A Feynman-Enhanced Learning Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fchiron_learning_agent_langgraph.ipynb)**\n \n #### Overview 🔎\n An adaptive learning agent that guides users through educational content using a structured checkpoint system and Feynman-style teaching. The system processes learning materials (either user-provided or web-retrieved), verifies understanding through interactive checkpoints, and provides simplified explanations when needed, creating a personalized learning experience that mimics one-on-one tutoring.\n\n #### Implementation 🛠️\n Uses LangGraph to orchestrate a learning workflow that includes checkpoint definition, context building, understanding verification, and Feynman teaching nodes. The system integrates web search for dynamic content retrieval, employs semantic chunking for context processing, and manages embeddings for relevant information retrieval. Key features include a 70% understanding threshold for progression, interactive human-in-the-loop validation, and structured output through Pydantic models for consistent data handling.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qsdiTGkB8mk)** \n\n### 💼 Business and Professional Agents\n\n9. **[Customer Support Agent (LangGraph)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcustomer_support_agent_langgraph.ipynb)**\n \n #### Overview 🔎\n An intelligent customer support agent using LangGraph categorizes queries, analyzes sentiment, and provides appropriate responses or escalates issues.\n\n #### Implementation 🛠️\n Utilizes LangGraph to create a workflow combining state management, query categorization, sentiment analysis, and response generation.\n\n10. **[Essay Grading Agent (LangGraph)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fessay_grading_system_langgraph.ipynb)**\n \n #### Overview 🔎\n An automated essay grading system using LangGraph and an LLM model evaluates essays based on relevance, grammar, structure, and depth of analysis.\n\n #### Implementation 🛠️\n Utilizes a state graph to define the grading workflow, incorporating separate grading functions for each criterion.\n\n11. **[Travel Planning Agent (LangGraph)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_travel_planner_langgraph.ipynb)**\n \n #### Overview 🔎\n A Travel Planner using LangGraph demonstrates how to build a stateful, multi-step conversational AI application that collects user input and generates personalized travel itineraries.\n\n #### Implementation 🛠️\n Utilizes StateGraph to define the application flow, incorporates custom PlannerState for process management.\n\n12. **[GenAI Career Assistant Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fagent_hackathon_genAI_career_assistant.ipynb)**\n\n #### Overview 🔎\n The GenAI Career Assistant demonstrates how to create a multi-agent system that provides personalized guidance for careers in Generative AI. Using LangGraph and Gemini LLM, the system delivers customized learning paths, resume assistance, interview preparation, and job search support.\n\n #### Implementation 🛠️\n Leverages a multi-agent architecture using LangGraph to coordinate specialized agents (Learning, Resume, Interview, Job Search) through TypedDict-based state management. The system employs sophisticated query categorization and routing while integrating with external tools like DuckDuckGo for job searches and dynamic content generation.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IcKh0ltXO_8)** \n \n13. **[Project Manager Assistant Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fproject_manager_assistant_agent.ipynb)**\n\n #### Overview 🔎\n An AI agent designed to assist in project management tasks by automating the process of creating actionable tasks from project descriptions, identifying dependencies, scheduling work, and assigning tasks to team members based on expertise. The system includes risk assessment and self-reflection capabilities to optimize project plans through multiple iterations, aiming to minimize overall project risk.\n\n #### Implementation 🛠️\n Leverages LangGraph to orchestrate a workflow of specialized nodes including task generation, dependency mapping, scheduling, allocation, and risk assessment. Each node uses GPT-4o-mini for structured outputs following Pydantic models. The system implements a feedback loop for self-improvement, where risk scores trigger reflection cycles that generate insights to optimize the project plan. Visualization tools display Gantt charts of the generated schedules across iterations.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=R7YWjzg3LpI)** \n\n14. **[Contract Analysis Assistant (ClauseAI)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FClauseAI.ipynb)**\n \n #### Overview 🔎\n ClauseAI demonstrates how to build an AI-powered contract analysis system using a multi-agent approach. The system employs specialized AI agents for different aspects of contract review, from clause analysis to compliance checking, and leverages LangGraph for workflow orchestration and Pinecone for efficient clause retrieval and comparison.\n\n #### Implementation 🛠️\n Implements a sophisticated state-based workflow using LangGraph to coordinate multiple AI agents through contract analysis stages. The system features Pydantic models for data validation, vector storage with Pinecone for clause comparison, and LLM-based analysis for generating comprehensive contract reports. The implementation includes parallel processing capabilities and customizable report generation based on user requirements.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rP8uv_tXuSI)** \n\n15. **[E2E Testing Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fe2e_testing_agent.ipynb)**\n \n #### Overview 🔎\n The E2E Testing Agent demonstrates how to build an AI-powered system that converts natural language test instructions into executable end-to-end web tests. Using LangGraph for workflow orchestration and Playwright for browser automation, the system enables users to specify test cases in plain English while handling the complexity of test generation and execution.\n\n #### Implementation 🛠️\n Implements a structured workflow using LangGraph to coordinate test generation, validation, and execution. The system features TypedDict state management, integration with Playwright for browser automation, and LLM-based code generation for converting natural language instructions into executable test scripts. The implementation includes DOM state analysis, error handling, and comprehensive test reporting.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jPXtpzcCtyA)** \n\n### 🎨 Creative and Content Generation Agents\n\n16. **[GIF Animation Generator Agent (LangGraph)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgif_animation_generator_langgraph.ipynb)**\n \n #### Overview 🔎\n A GIF animation generator that integrates LangGraph for workflow management, GPT-4 for text generation, and DALL-E for image creation, producing custom animations from user prompts.\n\n #### Implementation 🛠️\n Utilizes LangGraph to orchestrate a workflow that generates character descriptions, plots, and image prompts using GPT-4, creates images with DALL-E 3, and assembles them into GIFs using PIL. Employs asynchronous programming for efficient parallel processing.\n\n17. **[TTS Poem Generator Agent (LangGraph)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Ftts_poem_generator_agent_langgraph.ipynb)**\n \n #### Overview 🔎\n An advanced text-to-speech (TTS) agent using LangGraph and OpenAI's APIs classifies input text, processes it based on content type, and generates corresponding speech output.\n\n #### Implementation 🛠️\n Utilizes LangGraph to orchestrate a workflow that classifies input text using GPT models, applies content-specific processing, and converts the processed text to speech using OpenAI's TTS API. The system adapts its output based on the identified content type (general, poem, news, or joke).\n\n18. **[Music Compositor Agent (LangGraph)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmusic_compositor_agent_langgraph.ipynb)**\n \n #### Overview 🔎\n An AI Music Compositor using LangGraph and OpenAI's language models generates custom musical compositions based on user input. The system processes the input through specialized components, each contributing to the final musical piece, which is then converted to a playable MIDI file.\n\n #### Implementation 🛠️\n LangGraph orchestrates a workflow that transforms user input into a musical composition, using ChatOpenAI (GPT-4) to generate melody, harmony, and rhythm, which are then style-adapted. The final AI-generated composition is converted to a MIDI file using music21 and can be played back using pygame.\n\n19. **[Content Intelligence: Multi-Platform Content Generation Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FContentIntelligence.ipynb)**\n\n #### Overview 🔎\n Content Intelligence demonstrates how to build an advanced content generation system that transforms input text into platform-optimized content across multiple social media channels. The system employs LangGraph for workflow orchestration to analyze content, conduct research, and generate tailored content while maintaining brand consistency across different platforms.\n\n #### Implementation 🛠️\n Implements a sophisticated workflow using LangGraph to coordinate multiple specialized nodes (Summary, Research, Platform-Specific) through the content generation process. The system features TypedDict and Pydantic models for state management, integration with Tavily Search for research enhancement, and platform-specific content generation using GPT-4. The implementation includes parallel processing for multiple platforms and customizable content templates.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DPMtPbKmWnU)** \n\n20. **[Business Meme Generator Using LangGraph and Memegen.link](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fbusiness_meme_generator.ipynb)**\n \n #### Overview 🔎\n The Business Meme Generator demonstrates how to create an AI-powered system that generates contextually relevant memes based on company website analysis. Using LangGraph for workflow orchestration, the system combines Groq's Llama model for text analysis and the Memegen.link API to automatically produce brand-aligned memes for digital marketing.\n\n #### Implementation 🛠️\n Implements a state-managed workflow using LangGraph to coordinate website content analysis, meme concept generation, and image creation. The system features Pydantic models for data validation, asynchronous processing with aiohttp, and integration with external APIs (Groq, Memegen.link) to create a complete meme generation pipeline with customizable templates.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fyoutu.be\u002FlsdDaGmkSCw?si=oF3CGfhbRqz1_Vm8)** \n\n21. **[Murder Mystery Game with LLM Agents](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmurder_mystery_agent_langgraph.ipynb)**\n\n #### Overview 🔎 \n A text-based detective game that utilizes autonomous LLM agents as interactive characters in a procedurally generated murder mystery. Drawing inspiration from the UNBOUNDED paper, the system creates unique scenarios each time, with players taking on the role of Sherlock Holmes to solve the case through character interviews and deductive reasoning.\n\n #### Implementation 🛠️\n Leverages two LangGraph workflows - a main game loop for story\u002Fcharacter generation and game progression, and a conversation sub-graph for character interactions. The system uses a combination of LLM-powered narrative generation, character AI, and structured game mechanics to create an immersive investigative experience with replayable storylines.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_3cJYlk2EmA)**\n\n\n### 📊 Analysis and Information Processing Agents\n\n22. **[Memory-Enhanced Conversational Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmemory_enhanced_conversational_agent.ipynb)**\n \n #### Overview 🔎\n A memory-enhanced conversational AI agent incorporates short-term and long-term memory systems to maintain context within conversations and across multiple sessions, improving interaction quality and personalization.\n\n #### Implementation 🛠️\n Integrates a language model with separate short-term and long-term memory stores, utilizes a prompt template incorporating both memory types, and employs a memory manager for storage and retrieval. The system includes an interaction loop that updates and utilizes memories for each response.\n\n23. **[Multi-Agent Collaboration System](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmulti_agent_collaboration_system.ipynb)**\n \n #### Overview 🔎\n A multi-agent collaboration system combining historical research with data analysis, leveraging large language models to simulate specialized agents working together to answer complex historical questions.\n\n #### Implementation 🛠️\n Utilizes a base Agent class to create specialized HistoryResearchAgent and DataAnalysisAgent, orchestrated by a HistoryDataCollaborationSystem. The system follows a five-step process: historical context provision, data needs identification, historical data provision, data analysis, and final synthesis.\n\n24. **[Self-Improving Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fself_improving_agent.ipynb)**\n \n #### Overview 🔎\n A Self-Improving Agent using LangChain engages in conversations, learns from interactions, and continuously improves its performance over time through reflection and adaptation.\n\n #### Implementation 🛠️\n Integrates a language model with chat history management, response generation, and a reflection mechanism. The system employs a learning system that incorporates insights from reflection to enhance future performance, creating a continuous improvement loop.\n\n25. **[Task-Oriented Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Ftask_oriented_agent.ipynb)**\n \n #### Overview 🔎\n A language model application using LangChain that summarizes text and translates the summary to Spanish, combining custom functions, structured tools, and an agent for efficient text processing.\n\n #### Implementation 🛠️\n Utilizes custom functions for summarization and translation, wrapped as structured tools. Employs a prompt template to guide the agent, which orchestrates the use of tools. An agent executor manages the process, taking input text and producing both an English summary and its Spanish translation.\n\n26. **[Internet Search and Summarize Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsearch_the_internet_and_summarize.ipynb)**\n \n #### Overview 🔎\n An intelligent web research assistant that combines web search capabilities with AI-powered summarization, automating the process of gathering information from the internet and distilling it into concise, relevant summaries.\n\n #### Implementation 🛠️\n Integrates a web search module using DuckDuckGo's API, a result parser, and a text summarization engine leveraging OpenAI's language models. The system performs site-specific or general searches, extracts relevant content, generates concise summaries, and compiles attributed results for efficient information retrieval and synthesis.\n\n\n27. **[Multi agent research team - Autogen](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fresearch_team_autogen.ipynb)**\n\n #### Overview 🔎\n This technique explores a multi-agent system for collaborative research using the AutoGen library. It employs agents to solve tasks collaboratively, focusing on efficient execution and quality assurance. The system enhances research by distributing tasks among specialized agents.\n\n #### Implementation 🛠️\n Agents are configured with specific roles using the GPT-4 model, including admin, developer, planner, executor, and quality assurance. Interaction management ensures orderly communication with defined transitions. Task execution involves collaborative planning, coding, execution, and quality checking, demonstrating a scalable framework for various domains.\n\n #### Additional Resources 📚\n - **[comprehensive solution with UI](https:\u002F\u002Fgithub.com\u002Fyanivvak\u002Fdream-team)** \n - **[Blogpost](https:\u002F\u002Ftechcommunity.microsoft.com\u002Ft5\u002Fai-azure-ai-services-blog\u002Fbuild-your-dream-team-with-autogen\u002Fba-p\u002F4157961)**\n\n\n28. **[Sales Call Analyzer](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsales_call_analyzer_agent.ipynb)**\n \n #### Overview 🔎\n An intelligent system that automates the analysis of sales call recordings by combining audio transcription with advanced natural language processing. The analyzer transcribes audio using OpenAI's Whisper, processes the text using NLP techniques, and generates comprehensive reports including sentiment analysis, key phrases, pain points, and actionable recommendations to improve sales performance.\n\n #### Implementation 🛠️\n Utilizes multiple components in a structured workflow: OpenAI Whisper for audio transcription, CrewAI for task automation and agent management, and LangChain for orchestrating the analysis pipeline. The system processes audio through a series of steps from transcription to detailed analysis, leveraging custom agents and tasks to generate structured JSON reports containing insights about customer sentiment, sales opportunities, and recommended improvements.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SKAt_PvznDw)**\n\n29. **[Weather Emergency & Response System](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FWeather_Disaster_Management_AI_AGENT.ipynb)**\n\n #### Overview 🔎\n A comprehensive system demonstrating two agent graph implementations for weather emergency response: a real-time graph processing live weather data, and a hybrid graph combining real and simulated data for testing high-severity scenarios. The system handles complete workflow from data gathering through emergency plan generation, with automated notifications and human verification steps.\n\n #### Implementation 🛠️\n Utilizes LangGraph for orchestrating complex workflows with state management, integrating OpenWeatherMap API for real-time data, and Gemini for analysis and response generation. The system incorporates email notifications, social media monitoring simulation, and severity-based routing with configurable human verification for low\u002Fmedium severity events.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=AgiOAJl_apw)**\n\n30. **[Self-Healing Codebase System](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fself_healing_code.ipynb)**\n\n #### Overview 🔎\n An intelligent system that automatically detects, diagnoses, and fixes runtime code errors using LangGraph workflow orchestration and ChromaDB vector storage. The system maintains a memory of encountered bugs and their fixes through vector embeddings, enabling pattern recognition for similar errors across the codebase.\n\n #### Implementation 🛠️\n Utilizes a state-based graph workflow that processes function definitions and runtime arguments through specialized nodes for error detection, code analysis, and fix generation. Incorporates ChromaDB for vector-based storage of bug patterns and fixes, with automated search and retrieval capabilities for similar error patterns, while maintaining code execution safety through structured validation steps.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ga7ShvIXOvE)**\n\n31. **[DataScribe: AI-Powered Schema Explorer](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fdatabase_discovery_fleet.ipynb)**\n \n #### Overview 🔎\n An intelligent agent system that enables intuitive exploration and querying of relational databases through natural language interactions. The system utilizes a fleet of specialized agents, coordinated by a stateful Supervisor, to handle schema discovery, query planning, and data analysis tasks while maintaining contextual understanding through vector-based relationship graphs.\n \n #### Implementation 🛠️\n Leverages LangGraph for orchestrating a multi-agent workflow including discovery, inference, and planning agents, with NetworkX for relationship graph visualization and management. The system incorporates dynamic state management through TypedDict classes, maintains database context between sessions using a db_graph attribute, and includes safety measures to prevent unauthorized database modifications.\n\n32. **[Memory-Enhanced Email Agent (LangGraph & LangMem)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmemory-agent-tutorial.ipynb)**\n \n #### Overview 🔎\n An intelligent email assistant that combines three types of memory (semantic, episodic, and procedural) to create a system that improves over time. The agent can triage incoming emails, draft contextually appropriate responses using stored knowledge, and enhance its performance based on user feedback.\n \n #### Implementation 🛠️\n Leverages LangGraph for workflow orchestration and LangMem for sophisticated memory management across multiple memory types. The system implements a triage workflow with memory-enhanced decision making, specialized tools for email composition and calendar management, and a self-improvement mechanism that updates its own prompts based on feedback and past performance.\n\n #### Additional Resources 📚\n - **[Blog Post](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fbuilding-an-ai-agent-with-memory?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)\n\n### 📰 News and Information Agents\n\n33. **[News TL;DR using LangGraph](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fnews_tldr_langgraph.ipynb)**\n \n #### Overview 🔎\n A news summarization system that generates concise TL;DR summaries of current events based on user queries. The system leverages large language models for decision making and summarization while integrating with news APIs to access up-to-date content, allowing users to quickly catch up on topics of interest through generated bullet-point summaries.\n\n #### Implementation 🛠️\n Utilizes LangGraph to orchestrate a workflow combining multiple components: GPT-4o-mini for generating search terms and article summaries, NewsAPI for retrieving article metadata, BeautifulSoup for web scraping article content, and Asyncio for concurrent processing. The system follows a structured pipeline from query processing through article selection and summarization, managing the flow between components to produce relevant TL;DRs of current news articles.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0fRxW6miybI)**\n - **[Blog Post](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fstop-reading-start-understanding?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n34. **[AInsight: AI\u002FML Weekly News Reporter](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fainsight_langgraph.ipynb)**\n\n #### Overview 🔎\n AInsight demonstrates how to build an intelligent news aggregation and summarization system using a multi-agent architecture. The system employs three specialized agents (NewsSearcher, Summarizer, Publisher) to automatically collect, process and summarize AI\u002FML news for general audiences through LangGraph-based workflow orchestration.\n\n #### Implementation 🛠️\n Implements a state-managed multi-agent system using LangGraph to coordinate the news collection (Tavily API), technical content summarization (GPT-4), and report generation processes. The system features modular architecture with TypedDict-based state management, external API integration, and markdown report generation with customizable templates.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kH5S1is2D_0)**\n\n35. **[Journalism-Focused AI Assistant](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fjournalism_focused_ai_assistant_langgraph.ipynb)**\n\n #### Overview 🔎\n A specialized AI assistant that helps journalists tackle modern journalistic challenges like misinformation, bias, and information overload. The system integrates fact-checking, tone analysis, summarization, and grammar review tools to enhance the accuracy and efficiency of journalistic work while maintaining ethical reporting standards.\n\n #### Implementation 🛠️\n Leverages LangGraph to orchestrate a workflow of specialized components including language models for analysis and generation, web search integration via DuckDuckGo's API, document parsing tools like PyMuPDFLoader and WebBaseLoader, text splitting with RecursiveCharacterTextSplitter, and structured JSON outputs. Each component works together through a unified workflow to analyze content, verify facts, detect bias, extract quotes, and generate comprehensive reports.\n\n\n36. **[Blog Writer (Open AI Swarm)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fblog_writer_swarm.ipynb)**\n\n #### Overview 🔎\n A multi-agent system for collaborative blog post creation using OpenAI's Swarm package. It leverages specialized agents to perform research, planning, writing, and editing tasks efficiently.\n\n #### Implementation 🛠️\n Utilizes OpenAI's Swarm Package to manage agent interactions. Includes an admin, researcher, planner, writer, and editor, each with specific roles. The system follows a structured workflow: topic setting, outlining, research, drafting, and editing. This approach enhances content creation through task distribution, specialization, and collaborative problem-solving.\n\n #### Additional Resources 📚\n - **[Swarm Repo](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm)**\n\n37. **[Podcast Internet Search and Generate Agent 🎙️](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgenerate_podcast_agent_langgraph.ipynb)**\n\n #### Overview 🔎\n A two step agent that first searches the internet for a given topic and then generates a podcast on the topic found. The search step uses a search agent and search function to find the most relevant information. The second step uses a podcast generation agent and generation function to create a podcast on the topic found.\n\n #### Implementation 🛠️\n Utilizes LangGraph to orchestrate a two-step workflow. The first step involves a search agent and function to gather information from the internet. The second step uses a podcast generation agent and function to create a podcast based on the gathered information.\n\n\n### 🛍️ Shopping and Product Analysis Agents\n\n38. **[ShopGenie - Redefining Online Shopping Customer Experience](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FShopGenie.ipynb)**\n\n #### Overview 🔎\n An AI-powered shopping assistant that helps customers make informed purchasing decisions even without domain expertise. The system analyzes product information from multiple sources, compares specifications and reviews, identifies the best option based on user needs, and delivers recommendations through email with supporting video reviews, creating a comprehensive shopping experience.\n\n #### Implementation 🛠️\n Uses LangGraph to orchestrate a workflow combining Tavily for web search, Llama-3.1-70B for structured data analysis and product comparison, and YouTube API for review video retrieval. The system processes search results through multiple nodes including schema mapping, product comparison, review identification, and email generation. Key features include structured Pydantic models for consistent data handling, retry mechanisms for robust API interactions, and email delivery through SMTP for sharing recommendations.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Js0sK0u53dQ)**\n\n39. **[Car Buyer AI Agent](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcar_buyer_agent_langgraph.ipynb)**\n\n #### Overview 🔎\n The Smart Product Buyer AI Agent demonstrates how to build an intelligent system that assists users in making informed purchasing decisions. Using LangGraph and LLM-based intelligence, the system processes user requirements, scrapes product listings from websites like AutoTrader, and provides detailed analysis and recommendations for car purchases.\n\n #### Implementation 🛠️\n Implements a state-based workflow using LangGraph to coordinate user interaction, web scraping, and decision support. The system features TypedDict state management, async web scraping with Playwright, and integrates with external APIs for comprehensive product analysis. The implementation includes a Gradio interface for real-time chat interaction and modular scraper architecture for easy extension to additional product categories.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=I61I1fp0qys)**\n\n### 🎯 Task Management and Productivity Agents\n\n40. **[Taskifier - Intelligent Task Allocation & Management](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Ftaskifier.ipynb)**\n\n #### Overview 🔎\n An intelligent task management system that analyzes user work styles and creates personalized task breakdown strategies, born from the observation that procrastination often stems from task ambiguity among students and early-career professionals. The system evaluates historical work patterns, gathers relevant task information through web search, and generates customized step-by-step approaches to optimize productivity and reduce workflow paralysis.\n\n #### Implementation 🛠️\n Leverages LangGraph for orchestrating a multi-step workflow including work style analysis, information gathering via Tavily API, and customized plan generation. The system maintains state through the process, integrating historical work pattern data with fresh task research to output detailed, personalized task execution plans aligned with the user's natural working style.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1W_p_RVi9KE&t=25s)**\n \n41. **[Grocery Management Agents System](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgrocery_management_agents_system.ipynb)**\n\n #### Overview 🔎\n A multi-agent system built with CrewAI that automates grocery management tasks including receipt interpretation, expiration date tracking, inventory management, and recipe recommendations. The system uses specialized agents to extract data from receipts, estimate product shelf life, track consumption, and suggest recipes to minimize food waste.\n\n #### Implementation 🛠️\n Implements four specialized agents using CrewAI - a Receipt Interpreter that extracts item details from receipts, an Expiration Date Estimator that determines shelf life using online sources, a Grocery Tracker that maintains inventory based on consumption, and a Recipe Recommender that suggests meals using available ingredients. Each agent has specific tools and tasks orchestrated through a crew workflow.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=FlMu5pKSaHI)**\n\n### 🔍 Quality Assurance and Testing Agents\n\n42. **[LangGraph-Based Systems Inspector](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgraph_inspector_system_langgraph.ipynb)**\n \n #### Overview 🔎\n A comprehensive testing and validation tool for LangGraph-based applications that automatically analyzes system architecture, generates test cases, and identifies potential vulnerabilities through multi-agent inspection. The inspector employs specialized AI testers to evaluate different aspects of the system, from basic functionality to security concerns and edge cases.\n\n #### Implementation 🛠️\n Integrates LangGraph for workflow orchestration, multiple LLM-powered testing agents, and a structured evaluation pipeline that includes static analysis, test case generation, and results verification. The system uses Pydantic for data validation, NetworkX for graph representation, and implements a modular architecture that allows for parallel test execution and comprehensive result analysis.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fQd6lXc-Y9A)**\n - **[Blog Post](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Flanggraph-systems-inspector-an-ai?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n43. **[EU Green Deal FAQ Bot](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FEU_Green_Compliance_FAQ_Bot.ipynb)**\n\n #### Overview 🔎 \n The EU Green Deal FAQ Bot demonstrates how to build a RAG-based AI agent that helps businesses understand EU green deal policies. The system processes complex regulatory documents into manageable chunks and provides instant, accurate answers to common questions about environmental compliance, emissions reporting, and waste management requirements.\n\n #### Implementation 🛠️ \n Implements a sophisticated RAG pipeline using FAISS vectorstore for document storage, semantic chunking for preprocessing, and multiple specialized agents (Retriever, Summarizer, Evaluator) for query processing. The system features query rephrasing for improved accuracy, cross-reference with gold Q&A datasets for answer validation, and comprehensive evaluation metrics to ensure response quality and relevance.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Av0kBQjwU-Y)**\n\n44. **[Systematic Review Automation System + Paper Draft Creation](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsystematic_review_of_scientific_articles.ipynb)**\n \n #### Overview 🔎\n A comprehensive system for automating academic systematic reviews using a directed graph architecture and LangChain components. The system generates complete, publication-ready systematic review papers, automatically processing everything from literature search through final draft generation with multiple revision cycles.\n\n #### Implementation 🛠️\n Utilizes a state-based graph workflow that handles paper search and selection (up to 3 papers), PDF processing, and generates a complete academic paper with all standard sections (abstract, introduction, methods, results, conclusions, references). The system incorporates multiple revision cycles with automated critique and improvement phases, all orchestrated through LangGraph state management.\n\n #### Additional Resources 📚\n - **[YouTube Explanation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qi35mGGkCtg)**\n\n\n46. **[HR AI Assistant](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FHR_AI-Assistant.ipynb)**\n\n #### Overview 🔎\n An AI-powered recruitment assistant using LangGraph-based workflow for requirements gathering, job description generation, LinkedIn candidate search, and CV analysis.\n\n #### Implementation 🛠️\n Leverages LangChain and LangGraph to orchestrate a multi-step recruitment pipeline with structured state management, OpenAI for language generation, and automated candidate evaluation workflows.\n\n47. **[AI-Driven ML and Data Science Assistant](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fai_driven_ml_and_datascience_assistant.ipynb)**\n\n #### Overview 🔎\n A comprehensive ML assistant using LangGraph + OpenAI that loads datasets, performs preprocessing, feature engineering, model training, evaluation, and visualization through an agentic workflow.\n\n #### Implementation 🛠️\n Utilizes LangGraph for orchestrating ML pipeline tools including data preprocessing, model selection, hyperparameter tuning, and results visualization. Demonstrates end-to-end agentic ML workflows with Kaggle dataset integration.\n\n48. **[Art Tourguide with LightRAG](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fart_agent.ipynb)**\n\n #### Overview 🔎\n An interactive art tour guide using LightRAG (knowledge-graph RAG) and LangGraph for conversational exploration of art collections with structured data retrieval.\n\n #### Implementation 🛠️\n Combines LightRAG for knowledge-graph-based retrieval with LangGraph agent chains, interactive widget UI, and custom art data preparation. Demonstrates a novel application of graph-based RAG in a creative domain.\n\n49. **[Project Gutenberg Conversational Helper](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FGutenbergs_Sage.ipynb)**\n\n #### Overview 🔎\n A conversational agent for exploring Project Gutenberg texts using local LLMs via Ollama, with vector store RAG through Chroma\u002FPinecone and named entity recognition with spaCy.\n\n #### Implementation 🛠️\n Leverges LangGraph + Ollama for fully local LLM inference, multi-user support with session management, NER-enhanced retrieval, and dual vector store integration (Chroma for local, Pinecone for cloud).\n\n50. **[Contextual Quoting Agentic System](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcontextual_quoting_agentic_system.ipynb)**\n\n #### Overview 🔎\n A sophisticated multi-agent system for insurance\u002Fbusiness quoting using LangGraph with RAG, specialized agents for retrieval, reasoning, classification, and quote generation.\n\n #### Implementation 🛠️\n Features ChromaDB for RAG, SQLite for structured data, Pydantic schemas for validation, and a coordinated workflow of specialized agents (retriever, reasoning, classification, quote generation) using OpenAI + Groq. One of the most production-relevant multi-agent implementations in this collection.\n\n> 📖 **Want to understand the RAG techniques powering these agents?** [RAG Made Simple](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme-book-cta&click=book-buy-amazon-readme-cta&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=RAG%20Made%20Simple) covers 22 RAG techniques visually. Free with Kindle Unlimited.\n\n### 🌟 Special Advanced Technique 🌟\n\n45. **[Sophisticated Controllable Agent for Complex RAG Tasks 🤖](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FControllable-RAG-Agent)**\n\n #### Overview 🔎\n An advanced RAG solution designed to tackle complex questions that simple semantic similarity-based retrieval cannot solve. This approach uses a sophisticated deterministic graph as the \"brain\" 🧠 of a highly controllable autonomous agent, capable of answering non-trivial questions from your own data.\n\n #### Implementation 🛠️\n • Implement a multi-step process involving question anonymization, high-level planning, task breakdown, adaptive information retrieval and question answering, continuous re-planning, and rigorous answer verification to ensure grounded and accurate responses.\n\n## Prerequisites\n- Python 3.9+\n- Docker installed and running (required for some agents and setup)\n\n\n## Getting Started\n\nTo begin exploring and building GenAI agents:\n\n1. Clone this repository:\n ```\n git clone https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents.git\n ```\n2. Navigate to the technique you're interested in:\n ```\n cd all_agents_tutorials\u002Ftechnique-name\n ```\n3. Follow the detailed implementation guide in each technique's notebook.\n\n## 📚 Recommended reading\n\n*This list contains Amazon affiliate links. As an Amazon Associate I earn from qualifying purchases. Every book below is one I've read and genuinely recommend to engineers working in this space. The companion book to this repo is featured separately at the top of this README.*\n\n- [Build a Large Language Model (From Scratch)](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1633437167?tag=diamantai-genai-20) by Sebastian Raschka. Build a GPT-style model end to end in PyTorch.\n- [AI Engineering: Building Applications with Foundation Models](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098166302?tag=diamantai-genai-20) by Chip Huyen. Canonical reference for productionizing foundation-model apps.\n- [Hands-On Large Language Models](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098150961?tag=diamantai-genai-20) by Jay Alammar and Maarten Grootendorst. Visual, practical LLM walkthroughs.\n- [Natural Language Processing with Transformers](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098136799?tag=diamantai-genai-20) by Lewis Tunstall, Leandro von Werra, and Thomas Wolf. From the Hugging Face team.\n- [Designing Machine Learning Systems](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098107969?tag=diamantai-genai-20) by Chip Huyen. ML systems in production, still the standard reference.\n\n## Contributing\n\nWe welcome contributions from the community! If you have a new technique or improvement to suggest:\n\n1. Fork the repository\n2. Create your feature branch: `git checkout -b feature\u002FAmazingFeature`\n3. Commit your changes: `git commit -m 'Add some AmazingFeature'`\n4. Push to the branch: `git push origin feature\u002FAmazingFeature`\n5. Open a pull request\n\n\n## Contributors\n\n[![Contributors](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_9e11f25ae543.png)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fgraphs\u002Fcontributors)\n\n## License\n\nThis project is licensed under a custom non-commercial license - see the [LICENSE](LICENSE) file for details.\n\n---\n\n⭐️ If you find this repository helpful, please consider giving it a star!\n\nKeywords: GenAI, Generative AI, Agents, NLP, AI, Machine Learning, Natural Language Processing, LLM, Conversational AI, Task-Oriented AI\n","[![欢迎提交PR](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](http:\u002F\u002Fmakeapullrequest.com)\n[![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-Connect-blue)](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnir-diamant-759323134\u002F)\n[![Reddit](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FReddit-Join%20our%20subreddit-FF4500?style=flat-square&logo=reddit&logoColor=white)](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FEducationalAI\u002F)\n[![Twitter](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002FNirDiamantAI?label=Follow%20@NirDiamantAI&style=social)](https:\u002F\u002Ftwitter.com\u002FNirDiamantAI)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDiscord-Join%20our%20community-7289da?style=flat-square&logo=discord&logoColor=white)](https:\u002F\u002Fdiscord.gg\u002FcA6Aa4uyDX)\n\n\n> 🌟 **支持本项目：** 您的赞助将推动GenAI智能体开发领域的创新。**[成为赞助者](https:\u002F\u002Fgithub.com\u002Fsponsors\u002FNirDiamant)**，帮助维护并扩展这一宝贵资源！\n\n# GenAI智能体：全面的开发与实现资源库 🚀\n\n欢迎来到当今最全面、最具活力的生成式AI（GenAI）智能体教程与实现集合之一。本仓库旨在为学习、构建和分享GenAI智能体提供全方位的支持，涵盖从简单的对话机器人到复杂的多智能体系统。\n\n\u003Cdiv align=\"center\">\n\n## 📖 同一作者的其他作品\n\n\u003Ca href=\"https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme&click=book-buy-amazon-image&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=Best%20Seller%20Image\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_d5acda037ddb.png\" alt=\"#1 Best Seller in Generative AI on Amazon - Click to buy\" width=\"500\">\u003C\u002Fa>\n\n**[RAG轻松入门](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme&click=book-buy-amazon-title&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=RAG%20Made%20Simple)** — **亚马逊生成式AI类畅销书第一名。**\n22种RAG技术，配有直观解释、对比分析及图示。**Kindle Unlimited会员可免费阅读**，或以首发价**$0.99**购买（价格即将上调）。\n\n### 👉 [**在亚马逊购买本书**](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme&click=book-buy-amazon-cta&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=Get%20the%20book%20on%20Amazon)\n\n\u003C\u002Fdiv>\n\n## 🏆 赞助商\n\n\u003Cdiv align=\"center\">\n\n\u003Ca href=\"https:\u002F\u002Fcoderabbit.link\u002Fnir\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_fdef4d816bf0.png\" height=\"80\" alt=\"CodeRabbit\" \u002F>\u003C\u002Fa>\n\n\u003C\u002Fdiv>\n\n\n\n> **近期新增：** HR人工智能助手、结合LightRAG的艺术导览员、上下文引用系统、ML\u002FDS助手、古腾堡智者 | **52个教程** 并持续更新中\n\n## 📫 保持关注！\n\n\u003Cdiv align=\"center\">\n\u003Ctable>\n\u003Ctr>\n\u003Ctd align=\"center\">🚀\u003Cbr>\u003Cb>前沿\u003Cbr>资讯\u003C\u002Fb>\u003C\u002Ftd>\n\u003Ctd align=\"center\">💡\u003Cbr>\u003Cb>专家\u003Cbr>见解\u003C\u002Fb>\u003C\u002Ftd>\n\u003Ctd align=\"center\">🎯\u003Cbr>\u003Cb>顶尖0.1%\u003Cbr>内容\u003C\u002Fb>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n[![订阅DiamantAI通讯](images\u002Fsubscribe-button.svg)](https:\u002F\u002Fdiamantai.substack.com\u002F?r=336pe4&utm_campaign=pub-share-checklist)\n\n*加入超过5万名AI爱好者，获取独一无二的前沿洞察与免费教程！* ***此外，订阅用户还可享受独家抢先体验以及我书籍和即将推出的RAG技术课程的33%特别折扣！***\n\u003C\u002Fdiv>\n\n[![DiamantAI通讯](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_bd4960312f91.png)](https:\u002F\u002Fdiamantai.substack.com\u002F?r=336pe4&utm_campaign=pub-share-checklist)\n\n\n## 简介\n\n生成式AI智能体正处于人工智能领域的最前沿，正彻底改变我们与AI技术互动及应用的方式。本仓库旨在引导您完成从基础智能体实现到先进尖端系统的开发之旅。\n\n\u003Cdiv align=\"center\">\n\u003Ctable>\n\u003Ctr>\n\u003Ctd>\n\u003Ch3>📚 学习构建您的第一个AI智能体\u003C\u002Fh3>\n\u003Cp>\u003Cstrong>\u003Ca href=\"https:\u002F\u002Fdiamantai.substack.com\u002Fp\u002Fyour-first-ai-agent-simpler-than\">您的第一个AI智能体：比您想象的更简单\u003C\u002Fa>\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>这篇详尽的博客文章与本仓库相辅相成，提供了从A到Z的完整指南，深入解析核心概念、分步实施流程以及AI智能体背后的理论基础。文章设计得极其易于理解，同时涵盖了从零开始构建首个可用智能体所需的一切知识。\u003C\u002Fp>\n\u003Cp>\u003Cem>💡 小贴士：订阅通讯即可提前获取教程，并享受未来课程和书籍的特别折扣！\u003C\u002Fem>\u003C\u002Fp>\n\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\u003C\u002Fdiv>\n\n\n我们的目标是为所有人提供有价值的资源——无论是刚刚踏入AI领域的新手，还是不断突破技术边界的老练从业者。通过提供从基础到复杂的多种示例，我们希望促进快速发展的GenAI智能体领域的学习、实验与创新。\n\n此外，本仓库也是一个展示创新智能体成果的平台。无论您是开发了全新的智能体架构，还是为现有技术找到了创新的应用方式，我们都鼓励您与社区分享自己的成果。\n\n## 相关项目\n\n🚀 提升技能，请查看我的 **[Agents Towards Production](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production)** 仓库。它提供以代码为核心的横向教程，覆盖构建生产级GenAI智能体生命周期中的每一个工具和步骤，通过成熟的模式与可重用蓝图，指导您从萌芽阶段逐步走向规模化落地，是真正致力于将智能体投入生产的最佳起点。\n\n📚 深入了解我的 **[RAG技术综合指南](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FRAG_Techniques)**，学习如何将外部知识整合到AI系统中，从而借助最新、最相关的信息检索来增强其能力。\n\n🖋️ 探索我的 **[提示工程技巧指南](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FPrompt_Engineering)**，其中收录了从基础概念到高级方法的大量提示策略，帮助您提升与AI语言模型有效沟通的能力。\n\n## 社区驱动的知识中心\n\n**这个仓库因你的贡献而愈发强大！** 加入我们的活跃社区——这里是共同塑造和推动本项目发展的核心枢纽 🤝\n\n**[教育AI Reddit子版块](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FEducationalAI\u002F)**\n\n**[GenAI代理 Discord 社区](https:\u002F\u002Fdiscord.gg\u002FcA6Aa4uyDX)**\n\n无论你是渴望学习的新手，还是准备分享知识的专家，你的见解都能影响GenAI代理的未来发展方向。欢迎加入我们，提出想法、获取反馈，并协作实现创新应用。有关贡献指南，请参阅我们的 **[CONTRIBUTING.md](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002FCONTRIBUTING.md)** 文件。让我们携手推进GenAI代理技术的发展吧！\n\n🔗 如需讨论GenAI、代理相关话题，或探索知识共享机会，欢迎随时在 **[LinkedIn](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnir-diamant-759323134\u002F)** 上与我们联系。\n\n## 核心特性\n\n- 🎓 从入门到进阶，系统学习如何构建GenAI代理\n- 🧠 探索多样化的代理架构与应用场景\n- 📚 逐步教程与全面文档\n- 🛠️ 实用且开箱即用的代理实现\n- 🌟 定期更新，紧跟GenAI领域的最新进展\n- 🤝 与社区分享你自己的代理作品\n\n## GenAI代理实现\n\n以下是我们的GenAI代理实现的全面概览，按类别和功能划分。每个实现都旨在展示AI代理开发的不同方面，从基础对话代理到复杂的多代理系统。\n\n| # | 类别 | 代理名称 | 框架 | 主要特点 |\n|----|-------------------|-------------------------------|-------------------|------------------------------------------------------------------------------|\n| 1 | 🌱 **初学者** | [简单对话代理](all_agents_tutorials\u002Fsimple_conversational_agent.ipynb) | LangChain\u002FPydanticAI | 上下文感知对话，历史管理 |\n| 2 | 🌱 **初学者** | [简单问答代理](all_agents_tutorials\u002Fsimple_question_answering_agent.ipynb) | LangChain | 理解查询，简洁回答 |\n| 3 | 🌱 **初学者** | [简单数据分析代理](all_agents_tutorials\u002Fsimple_data_analysis_agent_notebook.ipynb) | LangChain\u002FPydanticAI | 数据集解读，自然语言查询 |\n| 4 | 🔧 **框架** | [LangGraph入门](all_agents_tutorials\u002Flanggraph-tutorial.ipynb) | LangGraph | 模块化AI工作流，状态管理 |\n| 5 | 🔧 **框架** | [模型上下文协议(MCP)](all_agents_tutorials\u002Fmcp-tutorial.ipynb) | MCP | AI与外部资源集成 |\n| 6 | 🎓 **教育**| [ATLAS：学术任务系统](all_agents_tutorials\u002FAcademic_Task_Learning_Agent_LangGraph.ipynb) | LangGraph | 多智能体学术规划，笔记记录 |\n| 7 | 🎓 **教育**| [科学论文代理](all_agents_tutorials\u002Fscientific_paper_agent_langgraph.ipynb) | LangGraph | 文献综述自动化 |\n| 8 | 🎓 **教育**| [Chiron - 费曼学习](all_agents_tutorials\u002Fchiron_learning_agent_langgraph.ipynb) | LangGraph | 自适应学习，检查点系统 |\n| 9 | 💼 **商业** | [客户支持代理](all_agents_tutorials\u002Fcustomer_support_agent_langgraph.ipynb) | LangGraph | 查询分类，情感分析 |\n| 10 | 💼 **商业** | [作文评分代理](all_agents_tutorials\u002Fessay_grading_system_langgraph.ipynb) | LangGraph | 自动评分，多维度标准 |\n| 11 | 💼 **商业** | [旅行规划代理](all_agents_tutorials\u002Fsimple_travel_planner_langgraph.ipynb) | LangGraph | 个性化行程 |\n| 12 | 💼 **商业** | [GenAI职业助手](all_agents_tutorials\u002Fagent_hackathon_genAI_career_assistant.ipynb) | LangGraph | 职业指导，学习路径 |\n| 13 | 💼 **商业** | [项目经理助手](all_agents_tutorials\u002Fproject_manager_assistant_agent.ipynb) | LangGraph | 任务生成，风险评估 |\n| 14 | 💼 **商业** | [合同分析助手](all_agents_tutorials\u002FClauseAI.ipynb) | LangGraph | 条款分析，合规性检查 |\n| 15 | 💼 **商业** | [端到端测试代理](all_agents_tutorials\u002Fe2e_testing_agent.ipynb) | LangGraph | 测试自动化，浏览器控制 |\n| 16 | 🎨 **创意** | [GIF动画生成器](all_agents_tutorials\u002Fgif_animation_generator_langgraph.ipynb) | LangGraph | 文本转动画流程 |\n| 17 | 🎨 **创意** | [TTS诗歌生成器](all_agents_tutorials\u002Ftts_poem_generator_agent_langgraph.ipynb) | LangGraph | 文本分类，语音合成 |\n| 18 | 🎨 **创意** | [音乐作曲家](all_agents_tutorials\u002Fmusic_compositor_agent_langgraph.ipynb) | LangGraph | AI音乐创作 |\n| 19 | 🎨 **创意** | [内容智能](all_agents_tutorials\u002FContentIntelligence.ipynb) | LangGraph | 多平台内容生成 |\n| 20 | 🎨 **创意** | [商业表情包生成器](all_agents_tutorials\u002Fbusiness_meme_generator.ipynb) | LangGraph | 品牌契合的表情包创作 |\n| 21 | 🎨 **创意** | [谋杀谜案游戏](all_agents_tutorials\u002Fmurder_mystery_agent_langgraph.ipynb) | LangGraph | 过程化故事生成 |\n| 22 | 📊 **分析** | [增强记忆的对话代理](all_agents_tutorials\u002Fmemory_enhanced_conversational_agent.ipynb)| LangChain | 短期\u002F长期记忆整合 |\n| 23 | 📊 **分析** | [多智能体协作](all_agents_tutorials\u002Fmulti_agent_collaboration_system.ipynb) | LangChain | 历史研究，数据分析 |\n| 24 | 📊 **分析** | [自我改进代理](all_agents_tutorials\u002Fself_improving_agent.ipynb) | LangChain | 从交互中学习 |\n| 25 | 📊 **分析** | [任务导向型代理](all_agents_tutorials\u002Ftask_oriented_agent.ipynb) | LangChain | 文本摘要，翻译 |\n| 26 | 📊 **分析** | [互联网搜索代理](all_agents_tutorials\u002Fsearch_the_internet_and_summarize.ipynb) | LangChain | 网络调研，总结 |\n| 27 | 📊 **分析** | [研究团队 - Autogen](all_agents_tutorials\u002Fresearch_team_autogen.ipynb) | AutoGen | 多智能体研究协作 |\n| 28 | 📊 **分析** | [销售电话分析器](all_agents_tutorials\u002Fsales_call_analyzer_agent.ipynb) | LangGraph | 音频转录，NLP分析 |\n| 29 | 📊 **分析** | [天气应急系统](all_agents_tutorials\u002FWeather_Disaster_Management_AI_AGENT.ipynb) | LangGraph | 实时数据处理 |\n| 30 | 📊 **分析** | [自愈代码库](all_agents_tutorials\u002Fself_healing_code.ipynb) | LangGraph | 错误检测，自动修复 |\n| 31 | 📊 **分析** | [DataScribe](all_agents_tutorials\u002Fdatabase_discovery_fleet.ipynb) | LangGraph | 数据库探索，查询计划 |\n| 32 | 📊 **分析** | [增强记忆的邮件代理](all_agents_tutorials\u002Fmemory-agent-tutorial.ipynb) | LangGraph | 邮件分类，回复生成 |\n| 33 | 📰 **新闻** | [新闻TL;DR](all_agents_tutorials\u002Fnews_tldr_langgraph.ipynb) | LangGraph | 新闻摘要，API集成 |\n| 34 | 📰 **新闻** | [AInsight](all_agents_tutorials\u002Fainsight_langgraph.ipynb) | LangGraph | AI\u002FML新闻聚合 |\n| 35 | 📰 **新闻** | [新闻助理](all_agents_tutorials\u002Fjournalism_focused_ai_assistant_langgraph.ipynb) | LangGraph | 事实核查，偏见检测 |\n| 36 | 📰 **新闻** | [博客写手](all_agents_tutorials\u002Fblog_writer_swarm.ipynb) | OpenAI Swarm | 协作式内容创作 |\n| 37 | 📰 **新闻** | [播客生成器](all_agents_tutorials\u002Fgenerate_podcast_agent_langgraph.ipynb) | LangGraph | 内容搜索，音频生成 |\n| 38 | 🛍️ **购物** | [ShopGenie](all_agents_tutorials\u002FShopGenie.ipynb) | LangGraph | 产品比较，推荐 |\n| 39 | 🛍️ **购物** | [购车代理](all_agents_tutorials\u002Fcar_buyer_agent_langgraph.ipynb) | LangGraph | 网页抓取，决策支持 |\n| 40 | 🎯 **任务管理** | [Taskifier](all_agents_tutorials\u002Ftaskifier.ipynb) | LangGraph | 工作方式分析，任务分解 |\n| 41 | 🎯 **任务管理** | [杂货管理](all_agents_tutorials\u002Fgrocery_management_agents_system.ipynb) | CrewAI | 库存跟踪，食谱建议 |\n| 42 | 🔍 **QA** | [LangGraph检查员](all_agents_tutorials\u002Fgraph_inspector_system_langgraph.ipynb) | LangGraph | 系统测试，漏洞检测 |\n| 43 | 🔍 **QA** | [欧盟绿色协议机器人](all_agents_tutorials\u002FEU_Green_Compliance_FAQ_Bot.ipynb) | LangGraph | 法规合规，FAQ系统 |\n| 44 | 🔍 **QA** | [系统综述](all_agents_tutorials\u002Fsystematic_review_of_scientific_articles.ipynb) | LangGraph | 学术论文处理，草稿生成 |\n| 45 | 🌟 **高级** | [可控RAG代理](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FControllable-RAG-Agent) | 自定义 | 复杂问题回答，确定性图 |\n\n探索我们按类别排序的广泛 GenAI 代理实现列表：\n\n\n\n### 🌱 适合初学者的代理\n\n1. **简单对话代理**\n\n - **[LangChain](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_conversational_agent.ipynb)**\n - **[PydanticAI](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_conversational_agent-pydanticai.ipynb)**\n \n #### 概述 🔎\n 上下文感知型对话 AI 能够在多次交互中保持信息连贯，从而实现更自然的对话。\n\n #### 实现 🛠️\n 集成语言模型、提示模板和历史记录管理器，以生成具有上下文的响应并跟踪对话会话。\n\n2. **[简单问答代理](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_question_answering_agent.ipynb)**\n \n #### 概述 🔎\n 基于 LangChain 和 OpenAI 语言模型的问答（QA）代理能够理解用户查询，并提供相关且简洁的答案。\n #### 实现 🛠️\n 结合 OpenAI 的 GPT 模型、提示模板和 LLMChain，以简化的方式处理用户问题并生成由 AI 驱动的响应。\n\n3. **简单数据分析代理**\n\n - **[LangChain](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_data_analysis_agent_notebook.ipynb)**\n - **[PydanticAI](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_data_analysis_agent_notebook-pydanticai.ipynb)**\n\n #### 概述 🔎\n 人工智能驱动的数据分析代理能够使用自然语言解释和回答关于数据集的问题，将语言模型与数据处理工具相结合，实现直观的数据探索。\n #### 实现 🛠️\n 集成语言模型、数据处理框架和代理框架，以处理自然语言查询并对合成数据集进行数据分析，从而为非技术人员提供易于理解的洞察。\n\n### 🔧 框架教程\n\n4. **[LangGraph 入门：构建模块化 AI 工作流](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Flanggraph-tutorial.ipynb)**\n \n #### 概述 🔎\n 本教程介绍 LangGraph，这是一个用于创建基于图的模块化 AI 工作流的强大框架。学习如何利用 LangGraph 构建更复杂、更灵活的 AI 代理，以高效地处理多步骤流程。\n\n #### 实现 🛠️\n 分步指南，展示如何使用 LangGraph 创建 StateGraph 工作流。教程涵盖状态管理、节点创建和图编译等关键概念，并通过构建一个简单的文本分析管道来演示这些原则，为更高级的代理架构奠定基础。\n \n #### 补充资源 📚\n - **[博客文章](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fyour-first-ai-agent-simpler-than?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n5. **[模型上下文协议 (MCP)：AI 与外部资源的无缝集成](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmcp-tutorial.ipynb)**\n \n #### 概述 🔎\n 本教程介绍模型上下文协议 (MCP)，这是一种用于连接 AI 模型与外部数据源和工具的开放标准。了解 MCP 如何作为 GenAI 代理与更广泛的数字生态系统之间的通用桥梁，从而实现功能更强大、更具上下文感知能力的 AI 应用程序。\n\n #### 实现 🛠️\n 提供 MCP 服务器和客户端的实践指南，演示如何将语言模型与外部工具和数据源连接起来。教程涵盖服务器设置、工具定义以及与 AI 客户端的集成，并通过实际案例展示了如何通过该协议构建有用的代理功能。\n\n #### 补充资源 📚\n - **[博客文章](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fmodel-context-protocol-mcp-explained?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n - **[MCP 官方文档](https:\u002F\u002Fmodelcontextprotocol.io\u002Fintroduction)**\n - **[MCP GitHub 仓库](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol)**\n\n### 🎓 教育与科研智能体\n\n6. **[ATLAS：学术任务与学习智能体系统](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FAcademic_Task_Learning_Agent_LangGraph.ipynb)**\n\n #### 概述 🔎\n ATLAS 展示了如何构建一个智能多智能体系统，通过人工智能驱动的辅助功能来革新学术支持服务。该系统利用 LangGraph 的工作流框架，协调多个专业智能体，为用户提供个性化的学业规划、笔记记录和咨询支持。\n\n #### 实现 🛠️\n 采用状态管理的多智能体架构，由四个专业智能体（协调员、规划师、笔记撰写者和顾问）协同工作，借助 LangGraph 的工作流框架完成任务。系统具备复杂的用户画像分析与学术支持流程，并能根据学生的学习表现和反馈持续调整优化。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yxowMLL2dDI)** \n - **[博客文章](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fatlas-when-artificial-intelligence?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n7. **[科学论文智能体——文献综述](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fscientific_paper_agent_langgraph.ipynb)**\n\n #### 概述 🔎\n 这是一个智能科研助手，通过精心设计的工作流帮助用户浏览、理解并分析科学文献。系统结合了学术 API 和先进的论文处理技术，实现文献综述任务的自动化，使研究人员能够高效地从学术论文中提取洞见，同时确保研究的严谨性和质量控制。\n\n #### 实现 🛠️\n 利用 LangGraph 构建了一个包含决策、规划、工具执行和质量验证节点的五节点工作流系统。系统集成了 CORE API 用于获取论文、PDFplumber 用于文档处理，以及先进的语言模型来进行分析。其关键特性包括用于稳健下载论文的重试机制、通过 Pydantic 模型进行结构化数据处理，以及结合人工审核选项的质量改进循环。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fyoutu.be\u002FBc4YtpHY6Ws)** \n - **[博客文章](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fnexus-ai-the-revolutionary-research?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n8. **[Chiron——费曼式增强型学习智能体](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fchiron_learning_agent_langgraph.ipynb)**\n\n #### 概述 🔎\n 这是一个自适应学习智能体，它通过结构化的检查点系统和费曼式教学法引导用户学习教育内容。系统会处理学习材料（可以是用户提供的，也可以是从网络上获取的），并通过交互式检查点验证理解程度，并在必要时提供简化的解释，从而打造一种模拟一对一辅导的个性化学习体验。\n\n #### 实现 🛠️\n 使用 LangGraph 协调一个包含检查点定义、情境构建、理解验证和费曼式教学等节点的学习工作流。系统集成了网络搜索功能以动态获取内容，采用语义分块技术处理上下文，并利用嵌入向量检索相关信息。其主要特点包括：需达到 70% 的理解阈值才能进入下一阶段、交互式的“人机协作”验证环节，以及通过 Pydantic 模型输出结构化结果以保证数据的一致性。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qsdiTGkB8mk)**\n\n### 💼 商业与专业代理\n\n9. **[客户支持代理（LangGraph）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcustomer_support_agent_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个使用 LangGraph 的智能客户支持代理，能够对用户问题进行分类、情感分析，并提供相应的回复或升级问题。\n\n #### 实现 🛠️\n 利用 LangGraph 创建了一个结合状态管理、查询分类、情感分析和回复生成的工作流。\n\n10. **[论文评分代理（LangGraph）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fessay_grading_system_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个基于 LangGraph 和 LLM 模型的自动化论文评分系统，可以根据相关性、语法、结构和分析深度来评估论文。\n\n #### 实现 🛠️\n 使用状态图定义评分流程，为每个评分标准集成独立的评分函数。\n\n11. **[旅行规划代理（LangGraph）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsimple_travel_planner_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个使用 LangGraph 的旅行规划助手，展示了如何构建一个有状态的多步骤对话式 AI 应用程序，收集用户输入并生成个性化的旅行行程。\n\n #### 实现 🛠️\n 使用 StateGraph 定义应用流程，集成自定义的 PlannerState 进行流程管理。\n\n12. **[GenAI 职业助理代理](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fagent_hackathon_genAI_career_assistant.ipynb)**\n\n #### 概述 🔎\n GenAI 职业助理演示了如何创建一个多智能体系统，为生成式 AI 领域的职业发展提供个性化指导。该系统利用 LangGraph 和 Gemini LLM，提供定制化的学习路径、简历协助、面试准备以及求职支持。\n\n #### 实现 🛠️\n 借助 LangGraph 的多智能体架构，通过基于 TypedDict 的状态管理协调专门的智能体（学习、简历、面试、求职）。系统采用复杂的查询分类和路由机制，并与 DuckDuckGo 等外部工具集成，用于求职搜索和动态内容生成。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IcKh0ltXO_8)** \n\n13. **[项目经理助理代理](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fproject_manager_assistant_agent.ipynb)**\n\n #### 概述 🔎\n 一个旨在协助项目管理任务的 AI 代理，能够自动从项目描述中生成可执行的任务、识别依赖关系、安排工作进度，并根据团队成员的专业技能分配任务。该系统还具备风险评估和自我反思功能，可通过多次迭代优化项目计划，以最大限度地降低整体项目风险。\n\n #### 实现 🛠️\n 利用 LangGraph 协调一系列专用节点，包括任务生成、依赖关系映射、进度安排、任务分配和风险评估。每个节点都使用 GPT-4o-mini 根据 Pydantic 模型输出结构化结果。系统实现了反馈循环以不断改进，风险评分会触发反思周期，从而生成优化项目计划的见解。可视化工具会展示各次迭代生成的甘特图。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=R7YWjzg3LpI)** \n\n14. **[合同分析助理（ClauseAI）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FClauseAI.ipynb)**\n\n #### 概述 🔎\n ClauseAI 展示了如何使用多智能体方法构建一个基于 AI 的合同分析系统。该系统针对合同审查的不同方面配备了专门的智能体，从条款分析到合规性检查，并利用 LangGraph 进行工作流编排，同时借助 Pinecone 实现高效的条款检索与比较。\n\n #### 实现 🛠️\n 使用 LangGraph 实现了一个复杂的状态驱动的工作流，以协调多个 AI 智能体完成合同分析的各个阶段。系统采用了 Pydantic 模型进行数据验证，利用 Pinecone 存储向量以实现条款对比，并通过 LLM 分析生成全面的合同报告。实现还包括并行处理能力以及可根据用户需求自定义生成报告的功能。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rP8uv_tXuSI)** \n\n15. **[端到端测试代理](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fe2e_testing_agent.ipynb)**\n\n #### 概述 🔎\n 端到端测试代理演示了如何构建一个基于 AI 的系统，将自然语言测试指令转换为可执行的网页端到端测试。该系统使用 LangGraph 进行工作流编排，结合 Playwright 实现浏览器自动化，使用户能够用通俗易懂的英语指定测试用例，同时处理测试生成和执行的复杂性。\n\n #### 实现 🛠️\n 使用 LangGraph 实现了一个结构化的工作流，以协调测试的生成、验证和执行。系统采用 TypedDict 状态管理，与 Playwright 集成实现浏览器自动化，并利用 LLM 代码生成技术将自然语言指令转化为可执行的测试脚本。实现还包括 DOM 状态分析、错误处理和全面的测试报告。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jPXtpzcCtyA)**\n\n### 🎨 创意与内容生成智能体\n\n16. **[GIF 动画生成智能体（LangGraph）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgif_animation_generator_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个结合 LangGraph 进行工作流管理、GPT-4 用于文本生成以及 DALL-E 用于图像创作的 GIF 动画生成器，能够根据用户提示生成定制化的动画。\n\n #### 实现 🛠️\n 使用 LangGraph 编排工作流，通过 GPT-4 生成角色描述、情节和图像提示，再利用 DALL-E 3 创建图像，并借助 PIL 将这些图像拼接成 GIF。整个流程采用异步编程以实现高效的并行处理。\n\n17. **[TTS 诗歌生成智能体（LangGraph）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Ftts_poem_generator_agent_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个基于 LangGraph 和 OpenAI API 的高级文本转语音（TTS）智能体，能够对输入文本进行分类，并根据内容类型进行相应处理，最终生成对应的语音输出。\n\n #### 实现 🛠️\n 利用 LangGraph 编排工作流，先使用 GPT 模型对输入文本进行分类，再应用特定于内容类型的处理方法，最后通过 OpenAI 的 TTS API 将处理后的文本转换为语音。系统会根据识别出的内容类型（通用文本、诗歌、新闻或笑话）调整输出方式。\n\n18. **[音乐作曲智能体（LangGraph）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmusic_compositor_agent_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个基于 LangGraph 和 OpenAI 语言模型的 AI 音乐作曲智能体，可以根据用户输入生成定制化的音乐作品。系统将输入分解为多个环节，每个环节负责音乐创作的不同部分，最终合成一首完整的 MIDI 格式可播放的音乐。\n\n #### 实现 🛠️\n LangGraph 负责编排工作流，将用户输入转化为音乐作品：使用 ChatOpenAI（GPT-4）生成旋律、和声及节奏，并对其进行风格化处理。最终生成的 AI 作曲会被 music21 转换为 MIDI 文件，随后可通过 pygame 播放。\n\n19. **[内容智能：多平台内容生成智能体](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FContentIntelligence.ipynb)**\n\n #### 概述 🔎\n 内容智能展示了如何构建一个先进的内容生成系统，能够将输入文本转化为适用于多个社交媒体平台的优化内容。该系统采用 LangGraph 进行工作流编排，分析内容、开展调研并生成符合各平台特性的定制化内容，同时确保品牌一致性。\n\n #### 实现 🛠️\n 使用 LangGraph 实现复杂的工作流，协调多个专业节点（摘要、调研、平台专属）完成内容生成过程。系统采用 TypedDict 和 Pydantic 模型进行状态管理，集成 Tavily Search 增强研究能力，并利用 GPT-4 生成各平台专属内容。此外，还支持多平台并行处理及自定义内容模板。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DPMtPbKmWnU)** \n\n20. **[基于 LangGraph 和 Memegen.link 的商业表情包生成器](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fbusiness_meme_generator.ipynb)**\n\n #### 概述 🔎\n 商业表情包生成器演示了如何构建一个基于 AI 的系统，能够根据公司网站分析生成上下文相关的表情包。该系统利用 LangGraph 编排工作流，结合 Groq 的 Llama 模型进行文本分析，并通过 Memegen.link API 自动生成与品牌一致的数字营销用表情包。\n\n #### 实现 🛠️\n 使用 LangGraph 实现状态管理的工作流，协调网站内容分析、表情包创意生成和图像制作。系统采用 Pydantic 模型进行数据验证，借助 aiohttp 实现异步处理，并集成外部 API（Groq、Memegen.link），形成完整的表情包生成流水线，支持自定义模板。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fyoutu.be\u002FlsdDaGmkSCw?si=oF3CGfhbRqz1_Vm8)** \n\n21. **[LLM 智能体参与的谋杀悬疑游戏](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmurder_mystery_agent_langgraph.ipynb)**\n\n #### 概述 🔎 \n 一款基于文本的侦探游戏，利用自主的 LLM 智能体作为互动角色，构建程序化生成的谋杀悬疑场景。受 UNBOUNDED 论文启发，每次都会生成独特的剧情，玩家扮演夏洛克·福尔摩斯，通过与角色对话和演绎推理来破解案件。\n\n #### 实现 🛠️\n 系统采用了两个 LangGraph 工作流——主游戏循环用于故事和角色的生成及游戏推进，以及用于角色交互的子图。系统结合 LLM 驱动的叙事生成、角色 AI 和结构化的游戏机制，打造出沉浸式的调查体验，并提供可重复游玩的故事情节。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_3cJYlk2EmA)**\n\n\n### 📊 分析与信息处理智能体\n\n22. **[记忆增强型对话智能体](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmemory_enhanced_conversational_agent.ipynb)**\n\n #### 概述 🔎\n 一种记忆增强型对话 AI 智能体，集成了短期和长期记忆系统，能够在单次对话及跨会话中保持上下文连贯性，从而提升交互质量和个性化程度。\n\n #### 实现 🛠️\n 将语言模型与独立的短期和长期记忆存储相结合，使用包含两种记忆类型的提示模板，并配备记忆管理器用于存储和检索。系统还包括一个交互循环，可在每次响应时更新和调用记忆。\n\n23. **[多智能体协作系统](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmulti_agent_collaboration_system.ipynb)**\n\n #### 概述 🔎\n 一个结合历史研究与数据分析的多智能体协作系统，利用大型语言模型模拟不同领域的专业智能体协同工作，共同解答复杂的历史问题。\n\n #### 实现 🛠️\n 通过基类 Agent 创建专门的历史研究智能体和数据分析智能体，并由历史数据协作系统进行协调。系统遵循五步流程：提供历史背景、确定数据需求、获取历史数据、进行数据分析，最后完成综合总结。\n\n24. **[自我改进型智能体](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fself_improving_agent.ipynb)**\n \n #### 概述 🔎\n 基于 LangChain 的自我改进型智能体能够进行对话交流，从交互中学习，并通过反思与适应不断优化自身性能。\n\n #### 实现 🛠️\n 将语言模型与聊天记录管理、响应生成以及反思机制相结合。系统配备一套学习机制，能够吸收反思所得的洞见以提升后续表现，从而形成持续改进的闭环。\n\n25. **[任务导向型智能体](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Ftask_oriented_agent.ipynb)**\n \n #### 概述 🔎\n 一款基于 LangChain 的语言模型应用，可对文本进行摘要并将其翻译成西班牙语，融合了自定义函数、结构化工具和智能体，实现高效的文本处理流程。\n\n #### 实现 🛠️\n 使用自定义函数分别完成摘要和翻译任务，并将其封装为结构化工具。借助提示模板引导智能体工作，由智能体协调工具的使用。最终由智能体执行器负责整个流程，接收输入文本后生成英文摘要及其西班牙语译文。\n\n26. **[互联网搜索与摘要智能体](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsearch_the_internet_and_summarize.ipynb)**\n \n #### 概述 🔎\n 一款智能化的网络研究助手，将网页搜索能力与 AI 驱动的摘要生成技术相结合，自动从互联网上收集信息并提炼出简洁、相关性强的摘要。\n\n #### 实现 🛠️\n 集成了基于 DuckDuckGo API 的网页搜索模块、结果解析器以及利用 OpenAI 语言模型的文本摘要引擎。系统可执行特定站点或通用搜索，提取相关内容，生成精炼摘要，并汇总带来源标注的结果，从而实现高效的信息检索与整合。\n\n\n27. **[多智能体研究团队 - Autogen](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fresearch_team_autogen.ipynb)**\n\n #### 概述 🔎\n 本方案探索了一种基于 AutoGen 库的多智能体协作研究系统。该系统采用多个智能体协同完成任务，注重执行效率与质量保障，通过将任务分配给不同专业领域的智能体来提升研究效能。\n\n #### 实现 🛠️\n 利用 GPT-4 模型为各智能体赋予特定角色，包括管理员、开发者、规划者、执行者和质量保证人员。通过交互管理确保沟通有序，并设定明确的流转规则。任务执行过程涵盖协作式规划、代码编写、实际执行及质量检查，展现出适用于多种领域的可扩展框架。\n\n #### 补充资源 📚\n - **[带有 UI 的完整解决方案](https:\u002F\u002Fgithub.com\u002Fyanivvak\u002Fdream-team)** \n - **[博客文章](https:\u002F\u002Ftechcommunity.microsoft.com\u002Ft5\u002Fai-azure-ai-services-blog\u002Fbuild-your-dream-team-with-autogen\u002Fba-p\u002F4157961)**\n\n\n28. **[销售通话分析器](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsales_call_analyzer_agent.ipynb)**\n \n #### 概述 🔎\n 一种智能化系统，通过结合音频转录与先进自然语言处理技术，自动分析销售通话录音。该分析器使用 OpenAI 的 Whisper 将音频转录为文本，再运用 NLP 技术对文本进行处理，最终生成包含情感分析、关键短语、痛点及改进建议在内的综合报告，以提升销售业绩。\n\n #### 实现 🛠️\n 系统采用结构化的流程，集成多个组件：OpenAI Whisper 负责音频转录，CrewAI 用于任务自动化和智能体管理，LangChain 则负责编排整个分析流水线。系统从音频转录开始，经过一系列步骤完成详细分析，借助自定义智能体和任务生成结构化的 JSON 报告，其中包含客户情绪洞察、销售机会以及改进建议等信息。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SKAt_PvznDw)**\n\n29. **[天气应急响应系统](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FWeather_Disaster_Management_AI_AGENT.ipynb)**\n\n #### 概述 🔎\n 一个综合性系统，展示了两种用于天气应急响应的智能体图实现：一种是实时图，用于处理实时气象数据；另一种是混合图，结合真实与模拟数据以测试高危场景。该系统覆盖从数据采集到应急计划生成的完整流程，并包含自动化通知及人工复核环节。\n\n #### 实现 🛠️\n 系统采用 LangGraph 进行复杂工作流的编排与状态管理，集成 OpenWeatherMap API 获取实时数据，同时利用 Gemini 进行分析并生成应对方案。此外，系统还具备邮件通知、社交媒体监控模拟功能，以及针对低\u002F中度事件配置的人工复核机制，以按严重程度进行分流。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=AgiOAJl_apw)**\n\n30. **[自愈型代码库系统](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fself_healing_code.ipynb)**\n\n #### 概述 🔎\n 一个智能化系统，利用 LangGraph 工作流编排和 ChromaDB 向量存储，自动检测、诊断并修复运行时代码错误。该系统通过向量嵌入保存已出现的 bug 及其修复方案，从而实现对代码库中类似问题的模式识别。\n\n #### 实现 🛠️\n 系统采用基于状态的图式工作流，将函数定义和运行时参数传递至专门的节点，用于错误检测、代码分析和修复生成。ChromaDB 被用作存储 bug 模式及修复方案的向量数据库，支持对相似错误模式的自动搜索与检索，同时通过结构化的验证步骤确保代码执行的安全性。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ga7ShvIXOvE)**\n\n31. **[DataScribe：人工智能驱动的模式探索器](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fdatabase_discovery_fleet.ipynb)**\n \n #### 概述 🔎\n 一个智能代理系统，通过自然语言交互实现对关系型数据库的直观探索与查询。该系统利用由状态感知的监督者协调的一组专业代理，负责模式发现、查询规划和数据分析任务，并借助基于向量的关系图来保持上下文理解。\n \n #### 实现 🛠️\n 使用 LangGraph 协调包含发现、推理和规划代理在内的多代理工作流，并结合 NetworkX 进行关系图的可视化与管理。系统采用 TypedDict 类进行动态状态管理，通过 db_graph 属性在会话间维持数据库上下文，并配备安全措施以防止未经授权的数据库修改。\n\n32. **[增强记忆功能的电子邮件代理（LangGraph 和 LangMem）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fmemory-agent-tutorial.ipynb)**\n \n #### 概述 🔎\n 一款智能电子邮件助手，融合语义记忆、情景记忆和程序性记忆三种类型的记忆机制，构建了一个能够不断自我提升的系统。该代理可以对收件箱中的邮件进行分类处理，利用存储的知识起草符合上下文的回复，并根据用户反馈持续优化自身性能。\n \n #### 实现 🛠️\n 利用 LangGraph 进行工作流编排，LangMem 则用于跨多种记忆类型的复杂记忆管理。系统实现了基于记忆增强决策的分类处理流程，配备了专门用于邮件撰写和日历管理的工具，并设计了一种自我改进机制，可根据反馈及过往表现更新自身的提示模板。\n\n #### 补充资源 📚\n - **[博客文章](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fbuilding-an-ai-agent-with-memory?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)\n\n### 📰 新闻与信息代理\n\n33. **[使用 LangGraph 的新闻 TL;DR](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fnews_tldr_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个基于用户查询生成时事简明 TL;DR 摘要的新闻摘要系统。该系统利用大型语言模型进行决策和摘要生成，同时集成新闻 API 以获取最新内容，使用户能够通过生成的要点式摘要快速了解感兴趣的主题。\n\n #### 实现 🛠️\n 使用 LangGraph 协调由多个组件组成的流程：GPT-4o-mini 用于生成搜索词和文章摘要，NewsAPI 用于检索文章元数据，BeautifulSoup 用于抓取文章内容，Asyncio 用于并发处理。系统遵循从查询处理到文章选择和摘要生成的结构化流程，在各组件之间管理流程流转，以生成当前新闻文章的相关 TL;DR 摘要。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0fRxW6miybI)**\n - **[博客文章](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Fstop-reading-start-understanding?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n34. **[AInsight：AI\u002FML 周刊新闻播报员](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fainsight_langgraph.ipynb)**\n\n #### 概述 🔎\n AInsight 展示了如何使用多智能体架构构建一个智能新闻聚合与摘要系统。该系统采用三个专业代理（新闻搜索者、摘要生成者、发布者），通过基于 LangGraph 的工作流编排，自动收集、处理并为普通受众总结 AI\u002FML 方面的新闻。\n\n #### 实现 🛠️\n 使用 LangGraph 实现了一个状态管理的多智能体系统，协调新闻采集（Tavily API）、技术内容摘要（GPT-4）以及报告生成等流程。系统具有模块化架构，采用 TypedDict 基于的状态管理、外部 API 集成，并支持使用可定制模板生成 Markdown 格式的报告。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kH5S1is2D_0)**\n\n35. **[面向新闻业的 AI 助手](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fjournalism_focused_ai_assistant_langgraph.ipynb)**\n\n #### 概述 🔎\n 一款专门帮助记者应对虚假信息、偏见和信息过载等现代新闻挑战的 AI 助手。该系统集成了事实核查、语气分析、摘要生成和语法检查工具，以提高新闻工作的准确性和效率，同时保持符合伦理的报道标准。\n\n #### 实现 🛠️\n 利用 LangGraph 协调包括用于分析和生成的语言模型、通过 DuckDuckGo API 集成的网络搜索、PyMuPDFLoader 和 WebBaseLoader 等文档解析工具、RecursiveCharacterTextSplitter 文本分割组件，以及结构化 JSON 输出在内的各个专业组件的工作流。各组件通过统一的工作流协同工作，对内容进行分析、核实事实、检测偏见、提取引文，并生成全面的报告。\n\n\n36. **[博客写手（OpenAI Swarm）](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fblog_writer_swarm.ipynb)**\n\n #### 概述 🔎\n 一个使用 OpenAI 的 Swarm 包协作创建博客文章的多智能体系统。它利用专业代理高效地完成研究、规划、写作和编辑任务。\n\n #### 实现 🛠️\n 使用 OpenAI 的 Swarm 包来管理智能体之间的交互。系统包含管理员、研究员、规划师、写手和编辑，每个角色都有明确的职责。系统遵循结构化的流程：确定主题、拟定提纲、进行研究、撰写初稿和编辑润色。这种方法通过任务分配、专业化和协作解决问题，提升了内容创作的效率。\n\n #### 补充资源 📚\n - **[Swarm 仓库](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm)**\n\n37. **[播客互联网搜索与生成代理 🎙️](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgenerate_podcast_agent_langgraph.ipynb)**\n\n #### 概述 🔎\n 一个两步代理，首先根据给定主题在互联网上搜索相关信息，然后基于搜索结果生成一档关于该主题的播客节目。第一步由搜索代理和搜索函数负责查找最相关的信息；第二步则由播客生成代理和生成函数根据所搜到的信息制作播客。 \n\n #### 实现 🛠️\n 使用 LangGraph 编排一个两步工作流。第一步由搜索代理和搜索函数从互联网上搜集信息；第二步则由播客生成代理和生成函数基于搜集到的信息制作播客。\n\n### 🛍️ 购物与产品分析智能体\n\n38. **[ShopGenie - 重新定义在线购物客户体验](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FShopGenie.ipynb)**\n\n #### 概述 🔎\n 这是一个基于人工智能的购物助手，即使用户缺乏相关领域的专业知识，也能帮助其做出明智的购买决策。该系统会从多个来源分析商品信息，比较规格和用户评价，根据用户需求找出最佳选项，并通过电子邮件附带视频评测的方式提供推荐，从而打造全方位的购物体验。\n\n #### 实现方式 🛠️\n 使用 LangGraph 编排工作流，结合 Tavily 进行网络搜索、Llama-3.1-70B 进行结构化数据分析和产品对比，以及 YouTube API 获取评测视频。系统通过多个节点处理搜索结果，包括模式映射、产品比较、评价筛选和邮件生成等环节。其关键特性包括使用 Pydantic 模型实现数据的一致性管理、为确保 API 调用的稳定性而设计的重试机制，以及通过 SMTP 协议发送邮件以分享推荐内容。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Js0sK0u53dQ)**\n\n39. **[汽车买家 AI 智能体](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcar_buyer_agent_langgraph.ipynb)**\n\n #### 概述 🔎\n 智能产品购买者 AI 智能体展示了如何构建一个能够协助用户做出明智购买决策的智能系统。该系统利用 LangGraph 和基于大语言模型的智能技术，处理用户需求、抓取 AutoTrader 等网站上的商品信息，并提供详细的汽车选购分析与建议。\n\n #### 实现方式 🛠️\n 基于状态的工作流由 LangGraph 协调用户交互、网页抓取和决策支持等功能。系统采用 TypedDict 进行状态管理，借助 Playwright 实现异步网页抓取，并集成外部 API 以完成全面的产品分析。此外，还配备了 Gradio 界面用于实时聊天交互，以及模块化的爬虫架构，便于扩展到其他商品类别。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=I61I1fp0qys)**\n\n### 🎯 任务管理与 productivity 智能体\n\n40. **[Taskifier - 智能任务分配与管理](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Ftaskifier.ipynb)**\n\n #### 概述 🔎\n 这是一个智能任务管理系统，能够分析用户的工作风格并制定个性化的任务分解策略。其设计灵感来源于观察：拖延症往往源于学生和职场新人对任务目标的模糊不清。该系统会评估用户的历史工作模式，通过网络搜索收集相关任务信息，并生成定制化的分步执行方案，以优化工作效率、缓解工作流程中的停滞感。\n\n #### 实现方式 🛠️\n 利用 LangGraph 编排一个多步骤的工作流，包含工作风格分析、通过 Tavily API 收集信息以及生成个性化计划等环节。系统在整个过程中维护状态，将历史工作模式数据与最新的任务研究相结合，最终输出符合用户自然工作习惯的详细、个性化的任务执行计划。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1W_p_RVi9KE&t=25s)**\n\n41. **[杂货管理多智能体系统](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgrocery_management_agents_system.ipynb)**\n\n #### 概述 🔎\n 这是一个基于 CrewAI 构建的多智能体系统，可自动化处理杂货管理相关的各项任务，包括收据解析、保质期追踪、库存管理以及食谱推荐等。该系统利用专门的智能体从收据中提取商品信息、借助网络资源估算商品保质期、根据消费情况跟踪库存，并基于现有食材推荐菜谱，从而最大限度地减少食物浪费。\n\n #### 实现方式 🛠️\n 使用 CrewAI 实现四个专用智能体：收据解析器负责从收据中提取商品明细；保质期估算器则利用线上资源判断商品的保质期；杂货追踪器根据消费记录维护库存；食谱推荐器则根据现有食材提出烹饪建议。每个智能体都配备特定工具和任务，由 Crew 工作流统一协调运行。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=FlMu5pKSaHI)**\n\n### 🔍 质量保证与测试代理\n\n42. **[基于 LangGraph 的系统检查器](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fgraph_inspector_system_langgraph.ipynb)**\n\n #### 概述 🔎\n 这是一个针对基于 LangGraph 的应用的全面测试与验证工具，能够自动分析系统架构、生成测试用例，并通过多智能体检查识别潜在漏洞。该检查器采用专门的 AI 测试代理来评估系统的各个方面，从基本功能到安全问题以及边缘情况。\n\n #### 实现 🛠️\n 集成了 LangGraph 用于工作流编排、多个由 LLM 驱动的测试代理，以及包含静态分析、测试用例生成和结果验证的结构化评估流程。系统使用 Pydantic 进行数据验证，NetworkX 用于图表示，并实现了模块化架构，支持并行测试执行和全面的结果分析。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fQd6lXc-Y9A)**\n - **[博客文章](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fdiamantai\u002Fp\u002Flanggraph-systems-inspector-an-ai?r=336pe4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false)**\n\n43. **[欧盟绿色协议 FAQ 机器人](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FEU_Green_Compliance_FAQ_Bot.ipynb)**\n\n #### 概述 🔎 \n 欧盟绿色协议 FAQ 机器人展示了如何构建一个基于 RAG 的 AI 代理，帮助企业管理者理解欧盟绿色协议的相关政策。该系统将复杂的法规文件分解为易于管理的片段，并针对环境合规、排放报告及废物管理要求等常见问题提供即时、准确的答案。\n\n #### 实现 🛠️ \n 使用 FAISS 向量存储实现了一套复杂的 RAG 流程，结合语义分块进行预处理，并配备多个专业代理（检索器、摘要生成器、评估器）来处理查询。系统具备查询改写功能以提高准确性，可与黄金问答数据集进行交叉引用以验证答案，并采用全面的评估指标确保响应的质量和相关性。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Av0kBQjwU-Y)**\n\n44. **[系统综述自动化系统 + 论文草稿生成](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fsystematic_review_of_scientific_articles.ipynb)**\n\n #### 概述 🔎\n 这是一个利用有向图架构和 LangChain 组件来自动化学术系统综述的综合系统。该系统能够生成完整的、可直接发表的系统综述论文，从文献检索到最终草稿生成，全程自动完成，并经过多次修订循环。\n\n #### 实现 🛠️\n 采用基于状态的图工作流，负责论文的搜索与筛选（最多 3 篇）、PDF 处理，并生成包含所有标准部分（摘要、引言、方法、结果、结论、参考文献）的完整学术论文。系统内置多轮修订机制，包含自动批评与改进阶段，全部由 LangGraph 的状态管理进行协调。\n\n #### 补充资源 📚\n - **[YouTube 解说视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qi35mGGkCtg)**\n\n\n46. **[HR AI 助手](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FHR_AI-Assistant.ipynb)**\n\n #### 概述 🔎\n 这是一款基于 LangGraph 的工作流驱动的人工智能招聘助手，用于需求收集、职位描述生成、LinkedIn 候选人搜索以及简历分析。\n\n #### 实现 🛠️\n 利用 LangChain 和 LangGraph 编排一个多步骤的招聘流程，实现结构化的状态管理；同时借助 OpenAI 进行语言生成，并自动化候选人评估工作流。\n\n47. **[AI 驱动的机器学习与数据科学助手](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fai_driven_ml_and_datascience_assistant.ipynb)**\n\n #### 概述 🔎\n 这是一个综合性的机器学习助手，结合 LangGraph 和 OpenAI，能够加载数据集、执行预处理、特征工程、模型训练、评估和可视化，整个过程通过智能体式的工作流完成。\n\n #### 实现 🛠️\n 使用 LangGraph 来编排机器学习流水线工具，包括数据预处理、模型选择、超参数调优和结果可视化。展示了端到端的智能体式机器学习工作流，并与 Kaggle 数据集集成。\n\n48. **[艺术导览助手，结合 LightRAG](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fart_agent.ipynb)**\n\n #### 概述 🔎\n 这是一款交互式艺术导览助手，采用 LightRAG（知识图谱 RAG）和 LangGraph 技术，通过对话式探索艺术收藏，并结合结构化数据检索。\n\n #### 实现 🛠️\n 将 LightRAG 的知识图谱检索与 LangGraph 智能体链相结合，辅以交互式小部件 UI 和自定义的艺术数据准备。展示了图-based RAG 在创意领域中的新颖应用。\n\n49. **[古腾堡计划对话助手](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002FGutenbergs_Sage.ipynb)**\n\n #### 概述 🔎\n 这是一个用于探索古腾堡计划文本的对话式智能体，通过 Ollama 使用本地 LLM，并结合 Chroma\u002FPinecone 的向量存储 RAG 以及 spaCy 的命名实体识别技术。\n\n #### 实现 🛠️\n 利用 LangGraph + Ollama 实现完全本地化的 LLM 推理，支持多用户会话管理，增强 NER 的检索能力，并集成双层向量存储（Chroma 用于本地，Pinecone 用于云端）。\n\n50. **[上下文报价智能体系统](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fblob\u002Fmain\u002Fall_agents_tutorials\u002Fcontextual_quoting_agentic_system.ipynb)**\n\n #### 概述 🔎\n 这是一个复杂的多智能体保险\u002F商业报价系统，基于 LangGraph 和 RAG 技术，配备专门的检索、推理、分类和报价生成智能体。\n\n #### 实现 🛠️\n 使用 ChromaDB 进行 RAG 检索，SQLite 存储结构化数据，Pydantic 模式用于验证，并通过 OpenAI + Groq 协调一系列专业智能体（检索器、推理器、分类器、报价生成器）的工作流程。这是本系列中最具生产实用价值的多智能体实现之一。\n\n> 📖 **想了解这些智能体背后所使用的 RAG 技术吗？** [RAG 简易指南](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=genai-agents--readme-book-cta&click=book-buy-amazon-readme-cta&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-genai-20&text=RAG%20Made%20Simple) 以可视化方式介绍了 22 种 RAG 技术。Kindle Unlimited 用户可免费阅读。\n\n### 🌟 特别进阶技术 🌟\n\n45. **[用于复杂 RAG 任务的高级可控智能体 🤖](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FControllable-RAG-Agent)**\n\n #### 概述 🔎\n 这是一种先进的 RAG 解决方案，旨在应对简单基于语义相似度的检索无法解决的复杂问题。该方法使用一个复杂的确定性图作为高度可控的自主智能体的“大脑” 🧠，能够根据您自己的数据回答非平凡的问题。\n\n #### 实现 🛠️\n • 实现一个多步骤流程，包括问题匿名化、高层次规划、任务分解、自适应信息检索与问答、持续重规划以及严格的答案验证，以确保生成的答案具有事实依据且准确可靠。\n\n## 前置条件\n- Python 3.9+\n- 已安装并运行 Docker（部分智能体和设置需要）\n\n\n## 开始使用\n\n要开始探索和构建 GenAI 智能体：\n\n1. 克隆本仓库：\n ```\n git clone https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents.git\n ```\n2. 导航到您感兴趣的技术目录：\n ```\n cd all_agents_tutorials\u002Ftechnique-name\n ```\n3. 按照每项技术笔记本中的详细实现指南进行操作。\n\n## 📚 推荐阅读\n\n*本列表包含亚马逊联盟链接。作为亚马逊联盟会员，我将从符合条件的购买中获得收入。以下每一本书都是我亲自阅读过，并真诚推荐给从事该领域工作的工程师们。本仓库的配套书籍已单独列在 README 的顶部。*\n\n- [从零构建大型语言模型](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1633437167?tag=diamantai-genai-20) 作者：塞巴斯蒂安·拉斯奇卡。使用 PyTorch 从头到尾构建一个类似 GPT 的模型。\n- [AI 工程：使用基础模型构建应用](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098166302?tag=diamantai-genai-20) 作者：奇普·休恩。生产级基础模型应用开发的经典参考书。\n- [动手实践大型语言模型](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098150961?tag=diamantai-genai-20) 作者：杰伊·阿拉马尔和马尔滕·赫鲁滕多斯特。图文并茂、注重实战的 LLM 全景解析。\n- [使用 Transformer 的自然语言处理](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098136799?tag=diamantai-genai-20) 作者：刘易斯·坦斯托尔、莱昂德罗·冯·韦拉和托马斯·沃尔夫。由 Hugging Face 团队编写。\n- [机器学习系统设计](https:\u002F\u002Fwww.amazon.com\u002Fdp\u002F1098107969?tag=diamantai-genai-20) 作者：奇普·休恩。关于生产环境中机器学习系统的经典参考书。\n\n## 贡献\n我们欢迎社区的贡献！如果您有新的技术或改进建议：\n\n1. 分支本仓库\n2. 创建您的功能分支：`git checkout -b feature\u002FAmazingFeature`\n3. 提交您的更改：`git commit -m '添加一些 AmazingFeature'`\n4. 推送到分支：`git push origin feature\u002FAmazingFeature`\n5. 打开一个拉取请求\n\n\n## 贡献者\n\n[![贡献者](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_readme_9e11f25ae543.png)](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fgraphs\u002Fcontributors)\n\n## 许可证\n本项目采用自定义的非商业许可——详情请参阅 [LICENSE](LICENSE) 文件。\n\n---\n\n⭐️ 如果您觉得本仓库有所帮助，请考虑给它点个赞！\n\n关键词：GenAI，生成式 AI，智能体，NLP，AI，机器学习，自然语言处理，LLM，对话式 AI，面向任务的 AI","# GenAI_Agents 快速上手指南\n\nGenAI_Agents 是一个全面的生成式 AI 智能体教程与实现仓库，涵盖从基础对话机器人到复杂多智能体系统的各种案例。本指南将帮助你快速搭建环境并运行第一个智能体示例。\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**: Windows, macOS 或 Linux\n* **Python 版本**: Python 3.9 或更高版本 (推荐 3.10+)\n* **包管理器**: pip 或 conda\n* **API 密钥**: 大多数示例需要大模型 API 密钥（如 OpenAI, Anthropic 等），请提前准备。\n\n**前置依赖检查：**\n```bash\npython --version\npip --version\n```\n\n## 安装步骤\n\n### 1. 克隆项目仓库\n首先，将项目代码克隆到本地：\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents.git\ncd GenAI_Agents\n```\n\n### 2. 创建虚拟环境\n建议为项目创建独立的虚拟环境以避免依赖冲突：\n```bash\npython -m venv venv\n```\n\n激活虚拟环境：\n* **Windows:**\n ```cmd\n venv\\Scripts\\activate\n ```\n* **macOS \u002F Linux:**\n ```bash\n source venv\u002Fbin\u002Factivate\n ```\n\n### 3. 安装核心依赖\n该项目包含多个不同框架（如 LangChain, LangGraph, PydanticAI）的示例。你可以选择安装通用依赖，或根据具体教程安装特定库。\n\n**安装基础通用依赖（推荐）：**\n```bash\npip install -r requirements.txt\n```\n*(注：如果根目录没有统一的 requirements.txt，请参考具体教程文件夹内的依赖说明，通常涉及以下核心库)*\n\n**手动安装核心库示例：**\n```bash\npip install langchain langchain-openai langgraph pydantic jupyter notebook python-dotenv\n```\n\n> **💡 国内加速提示**\n> 如果下载速度较慢，建议使用国内镜像源安装：\n> ```bash\n> pip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n> ```\n\n### 4. 配置环境变量\n大多数智能体需要访问 LLM API。在项目根目录创建 `.env` 文件并填入你的密钥：\n```bash\ntouch .env\n```\n编辑 `.env` 文件，添加以下内容（以 OpenAI 为例）：\n```env\nOPENAI_API_KEY=your_sk_here\n# 如果使用其他模型提供商，请参考具体教程添加相应变量\n```\n\n## 基本使用\n\n本项目以 Jupyter Notebook (`.ipynb`) 形式提供教程。以下是运行最简单的“基础对话智能体”的步骤。\n\n### 1. 启动 Jupyter Notebook\n在项目根目录下运行：\n```bash\njupyter notebook\n```\n浏览器将自动打开并显示文件列表。\n\n### 2. 运行入门示例\n导航至 `all_agents_tutorials` 文件夹，选择并打开 **Beginner** 类别的示例，例如：\n* `simple_conversational_agent.ipynb` (基础对话智能体)\n* `simple_question_answering_agent.ipynb` (基础问答智能体)\n\n### 3. 执行代码\n在 Notebook 界面中：\n1. 确保右上角内核已选择为你刚才激活的虚拟环境 (`venv`)。\n2. 点击菜单栏的 **Cell** -> **Run All**，或按 `Shift + Enter` 逐个单元格执行代码。\n3. 观察输出结果，智能体将根据预设逻辑处理你的输入并生成回复。\n\n### 4. 尝试修改\n试着修改 Notebook 中的 `user_input` 变量或系统提示词（System Prompt），重新运行以观察智能体行为的变化。\n\n---\n**下一步探索**：\n熟悉基础后，可尝试运行 `LangGraph` 相关教程（如 `langgraph-tutorial.ipynb`）以构建具有状态记忆和多步工作流的高级智能体。","某电商公司的技术团队正致力于构建一个能自动处理客户投诉、查询订单并协调物流的智能客服系统，以替代传统的人工流转模式。\n\n### 没有 GenAI_Agents 时\n- **开发门槛高且重复造轮子**：工程师需从零研究多智能体协作架构，缺乏现成的多 Agent 通信与任务拆解模板，导致基础代码编写耗时数周。\n- **场景覆盖单一**：受限于技术复杂度，初期只能实现简单的问答机器人，无法处理涉及“查询库存 + 协调物流 + 生成赔偿方案”的复杂连环任务。\n- **调试与维护困难**：缺乏标准化的实施案例参考，当智能体出现死循环或决策错误时，团队难以快速定位问题根源，系统稳定性差。\n- **知识更新滞后**：团队难以及时获取最新的代理技术（如轻量级 RAG 集成），导致构建的系统在上下文理解能力上落后于行业水平。\n\n### 使用 GenAI_Agents 后\n- **快速落地复杂架构**：直接复用仓库中 50+ 个成熟教程，基于现有的多智能体系统模板，仅用几天便搭建起包含“接待员”、“查询员”和“决策员”的协作网络。\n- **实现全流程自动化**：利用库中复杂的交互模式，系统能自主拆解用户诉求，串联多个专用智能体完成从订单检索到物流协调的闭环处理，无需人工干预。\n- **标准化开发与排错**：参照官方提供的最佳实践代码，团队迅速规范了智能体间的消息传递机制，显著降低了调试难度，系统运行稳定性大幅提升。\n- **即时集成前沿技术**：直接引入仓库最新更新的\"LightRAG 艺术向导”或“上下文引用系统”等案例，让客服系统具备了精准引用内部知识库的高级能力。\n\nGenAI_Agents 通过将抽象的多智能体理论转化为可执行的代码模板，帮助团队将原本需要数月研发的智能系统缩短至数周上线，极大降低了企业级 AI 应用的落地成本。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FNirDiamant_GenAI_Agents_107e71a9.png","NirDiamant","","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FNirDiamant_846222c2.jpg","GenAI Consultant | Open Source Hub | Best-selling book author ","DiamantAI",null,"NirDiamantAI","https:\u002F\u002Fdiamantai.substack.com","https:\u002F\u002Fgithub.com\u002FNirDiamant",[83,87,91,94],{"name":84,"color":85,"percentage":86},"Jupyter Notebook","#DA5B0B",99.9,{"name":88,"color":89,"percentage":90},"HTML","#e34c26",0,{"name":92,"color":93,"percentage":90},"Python","#3572A5",{"name":95,"color":96,"percentage":90},"JavaScript","#f1e05a",21382,3568,"2026-04-19T21:17:36","NOASSERTION","未说明",{"notes":103,"python":101,"dependencies":104},"README 主要提供教程和代码示例列表，未包含具体的安装指南或系统环境需求。该项目基于多种框架（如 LangChain, LangGraph）构建不同功能的 Agent，具体依赖和硬件需求可能因运行的特定教程而异。建议查看各个教程笔记本（.ipynb）文件以获取具体的环境配置要求。",[105,106,107,108],"LangChain","LangGraph","PydanticAI","MCP (Model Context Protocol)",[15,44,13,14],[111,112,113,114,115,116,117,118,119,120,121,122,123],"agents","genai","tutorials","langchain","langgraph","ai","llms","openai","llm","ai-agents","multi-agent","python","rag","2026-03-27T02:49:30.150509","2026-04-20T10:35:03.554834",[127,132,137,142,147,152],{"id":128,"question_zh":129,"answer_zh":130,"source_url":131},44616,"如何为项目贡献新的教程（例如 MCP 或 N8N 相关）？","欢迎社区贡献！如果您想添加新教程（如 MCP 架构、N8N 工作流等），请直接提交 Pull Request (PR)。教程应包含详细的文本解释和可运行的 Notebook 代码，最好提供多个示例以展示从基础到高级的用法。如果是外部工具集成，需确保其包含具体的代理模式教学而非仅仅是工具引用。","https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fissues\u002F87",{"id":133,"question_zh":134,"answer_zh":135,"source_url":136},44617,"运行 'docker compose up --build' 时遇到错误怎么办？","该项目依赖 Docker 环境。如果遇到启动错误，请首先确认您的系统已正确安装 Docker。维护者已将 Docker 列为 README 中的必要前置条件。若问题依旧，请提供完整的错误日志、堆栈跟踪、正在运行的 Notebook 名称、Python 版本以及操作系统信息，以便进一步诊断。","https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fissues\u002F92",{"id":138,"question_zh":139,"answer_zh":140,"source_url":141},44618,"N8N 是免费使用的吗？","是的，N8N 提供免费版本供任何人使用。您可以参考其社区版功能文档 (https:\u002F\u002Fdocs.n8n.io\u002Fhosting\u002Fcommunity-edition-features\u002F) 了解详情。用户可以通过 Docker 自行托管 N8N，从而连接 300-400+ 种服务来构建复杂的代理工作流。","https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fissues\u002F77",{"id":143,"question_zh":144,"answer_zh":145,"source_url":146},44619,"项目是否接受外部工具平台（如 AgentHive）的简单引用或推荐？","项目主要聚焦于动手实践的教程（Hands-on tutorials），而不是单纯的外部工具引用或列表推荐。如果您希望引入类似 AgentHive 这样的社交层或经济层概念，必须提交一个包含具体 Notebook 的 PR，详细教导用户如何实现特定的代理交互模式，而不仅仅是介绍该工具。","https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fissues\u002F107",{"id":148,"question_zh":149,"answer_zh":150,"source_url":151},44620,"如何为项目添加对新模型（如 Deepseek）的支持？","社区成员可以通过实现代码并提交 Pull Request (PR) 来添加对新模型的支持。维护者鼓励用户自行尝试实现相关功能，并通过 PR 将代码合并到主分支或新分支中，以丰富项目的模型兼容性。","https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents\u002Fissues\u002F88",{"id":153,"question_zh":154,"answer_zh":155,"source_url":131},44621,"MCP 教程的具体实施计划包含哪些内容？","规划的 MCP 教程将涵盖四个主要部分：1. 基础 MCP 服务器与客户端（学习架构原理）；2. 构建自定义工具的 MCP 服务器；3. 创建能通过 MCP 使用外部工具的代理；4. 高级集成（结合 LangGraph 构建复杂的工作流）。教程将以 Notebook 形式呈现，包含从 A 到 Z 的详细步骤。",[]]