base on # LangChain- Develop AI Agents with LangChain & LangGraph 🦜🔗 **Learn LangChain and LangGraph by building real world AI Agents (Python, Latest Version 0.3.0+)** This course is designed to teach you how to QUICKLY harness the power of the LangChain library for LLM applications. Build 3 end-to-end working LangChain based generative AI applications with no fluff, no toy examples - just real projects using real APIs and real-world skills.   [](https://twitter.com/EdenMarco177) [](LICENSE) [](https://www.udemy.com/course/langchain/?couponCode=SEP-2025) ## 💡 What You'll Build This course takes you through building 7 real-world AI agent projects, from simple hello-world applications to advanced agentic systems: | Project | Type | Description | |---------|------|-------------| | 👋 Hello World Agent | Branch (`project/hello-world`) | Your first AI agent - basic structure and LLM integration | | 💻 Code Interpreter | Branch (`project/code-interpreter`) | AI-powered code execution and analysis | | 🧠 ReAct Under the Hood | Branch (`project/react-under-hood`) | Understanding reasoning and acting patterns in AI agents | | 🔍 [Ice Breaker](https://github.com/emarco177/IceBreaker) | External Repo | Social media profile analyzer | | 📝 Medium Analyzer | External Repo | Content analysis and insights generator | | 📚 [Documentation Helper](https://github.com/emarco177/documentation-helper) | External Repo | Intelligent documentation assistant | | 🪞 [Reflection Agent](https://github.com/emarco177/langgraph-course/tree/project/reflection-agent) | External Repo | Self-improving agent with reflection and critique capabilities | | 🔄 [Reflexion Agent](https://github.com/emarco177/langgraph-course/tree/project/reflexion-agent) | External Repo | Advanced self-correcting agent using reflexion techniques | | 🤖 [Agentic RAG](https://github.com/emarco177/langgraph-course/tree/project/agentic-rag) | External Repo | Advanced retrieval-augmented generation system | ## 📚 Course Highlights - **7 Complete Projects** - From beginner to advanced implementations including Ice Breaker, Documentation Helper, and Code Interpreter - **Real-World Applications** - Build agents that solve actual problems with live APIs - **Modern Tech Stack** - LangChain v0.3+, LangGraph, Pinecone, FAISS, Streamlit - **Practical Skills** - Learn RAG, vector databases, prompt engineering, and agent workflows - **Interactive Learning** - Follow commits chronologically for step-by-step learning ## 🤔 Learning Path ### Phase 1: Foundations 1. **Hello World Chain** - Basic agent structure and LLM integration 2. **Code Interpreter** - Tool calling and code execution capabilities ### Phase 2: Real-World Applications 3. **Ice Breaker** - Data collection and social media integration 4. **Documentation Helper** - RAG implementation and knowledge management ### Phase 3: Advanced Concepts 5. **Blog Analyzer** - Multi-step reasoning and content analysis 6. **Agentic RAG** - Self-correcting agents with memory and planning ## ▶️ Getting Started ### 🛠️ Prerequisites - **This is not a beginner course** - Basic software engineering concepts needed - Familiarity with: git, Python, environment variables, classes, testing and debugging - Python 3.10+ - Any Python package manager (uv, poetry, pipenv) - but NOT conda! - Access to an LLM (can be open source via Ollama, or cloud providers like OpenAI, Anthropic, Gemini) - No Machine Learning experience needed ### ⚙️ Setup Instructions 1. **Clone the repository** ```bash git clone https://github.com/emarco177/langchain-course cd langchain-course ``` 2. **Choose your learning path** **For branch-based projects:** ```bash # Start with Hello World git checkout project/hello-world uv sync uv run python main.py # Progress to Code Interpreter git checkout project/code-interpreter uv sync uv run python main.py ``` **For external repository projects:** ```bash # Clone specific project repositories git clone https://github.com/emarco177/ice_breaker cd ice_breaker # Follow project-specific setup instructions ``` 3. **Follow the commits** - Each commit represents a lesson or feature implementation - Use `git log --oneline` to see the learning progression - Checkout previous commits to understand the development process ## 📁 Branches Structure ``` langchain-course/ ├── project/hello-world/ # Basic Chain ├── project/code-interpreter/ # Slim Code execution └── project/react-under-hood/ # ReAct Algorithm Deep Dive ``` **External Projects:** - [Ice Breaker](https://github.com/emarco177/ice_breaker) - Social media profile analyzer - [Medium Analyzer](https://github.com/emarco177/blog-analyzer) - Content analysis and insights generator - [Documentation Helper](https://github.com/emarco177/documentation-helper) - AI documentation assistant - [Reflection Agent](https://github.com/emarco177/langgraph-course/tree/project/reflection-agent) - Self-improving agent with reflection and critique capabilities - [Reflexion Agent](https://github.com/emarco177/langgraph-course/tree/project/reflexion-agent) - Advanced self-correcting agent using reflexion techniques - [Agentic RAG](https://github.com/emarco177/langgraph-course/tree/project/agentic-rag) - Advanced retrieval-augmented generation system ## 📚 Learning Objectives By the end of this course, you'll be able to: - Build AI agents from scratch using modern frameworks - Implement tool calling and external API integrations - Create RAG systems with vector databases - Design multi-step reasoning workflows - Deploy agents to production environments - Handle error correction and self-improvement in agents - Optimize agent performance and cost efficiency ## 🙏 Acknowledgements Big thanks to the **LangChain / LangGraph** team and their excellent [documentation and tutorials](https://langchain-ai.github.io/langgraph/tutorials/introduction/) that make this course possible. ## 🌟 Support If you find this project helpful, please consider: - ⭐ Starring the repository - 🐛 Reporting issues - 💡 Contributing improvements - 📢 Sharing with others --- <div align="center"> ### 🔗 Connect with Me [](https://www.udemy.com/course/langchain/?referralCode=D981B8213164A3EA91AC) [](https://www.linkedin.com/in/eden-marco/) [](https://twitter.com/EdenEmarco177) **Built with ❤️ by Eden Marco** </div> ", Assign "at most 3 tags" to the expected json: {"id":"14393","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"