langgraph-ai

# LangGraph AI Repository A comprehensive collection of LangGraph implementations, tutorials, and advanced AI workflows covering Agentic RAG systems, MCP (Model Context Protocol) development, and practical AI application patterns. ## Overview This repository serves as a implementation guide for building sophisticated AI applications using LangGraph. It contains practical examples, tutorials, and production-ready implementations across multiple domains: - **Agentic RAG Systems**: Advanced retrieval-augmented generation with adaptive routing and self-correction mechanisms - **MCP Development**: Complete Model Context Protocol server and client implementations - **Workflow Patterns**: Orchestration patterns for complex AI workflows - **Human-in-the-Loop Systems**: Interactive AI systems with human oversight - **Advanced RAG Agents**: Sophisticated retrieval and generation systems ## Repository Structure ``` langgraph-ai/ ├── rag/ │ ├── rag-from-scratch/ │ │ └── 1_rag_overview.ipynb │ ├── rag-agents/ │ │ ├── Building an Advanced RAG Agent.ipynb │ │ └── rag-as-tool-in-langgraph-agents.ipynb │ ├── agentic-rag/ │ │ ├── agentic-rag-systems/ │ │ │ └── building-adaptive-rag/ │ │ └── agentic-workflow-pattern/ │ │ ├── 1-prompting_chaining.ipynb │ │ ├── 2-routing.ipynb │ │ ├── 3-parallelization.ipynb │ │ ├── 4-orchestrator-worker.ipynb │ │ └── 5-Evaluator-optimizer.ipynb ├── mcp/ │ ├── 01-build-your-own-server-client/ │ ├── 02-build-mcp-client-with-multiple-server-support/ │ ├── 03-build-mcp-server-client-using-sse/ │ └── 04-build-streammable-http-mcp-client/ ├── langgraph-cookbook/ │ ├── human-in-the-loop/ │ │ ├── 01-human-in-the-loop.ipynb │ │ ├── 02-human-in-the-loop.ipynb │ │ └── 03-human-in-the-loop.ipynb │ └── tool-calling -vs-react.ipynb ├── .gitignore ├── .gitmodules ├── README.md └── requirements.txt ``` ## Prerequisites Before setting up this repository, ensure you have the following installed: - Python 3.10 or higher (depends on the project) - UV package manager (recommended) or pip - Git ## Installation and Setup ### Step 1: Clone the Repository ```bash git clone https://github.com/piyushagni5/langgraph-ai.git cd langgraph-ai ``` ### Step 2: Install UV Package Manager If you haven't installed UV yet, install it using: ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` For Windows (PowerShell): ```powershell powershell -c "irm https://astral.sh/uv/install.ps1 | iex" ``` ### Step 3: Create Virtual Environment Navigate to the specific project directory you want to work with. For example, to work with the Adaptive RAG system: ```bash cd langgraph-cookbook/agentic-patterns ``` Create a virtual environment using UV: ```bash uv venv --python 3.10 ``` ### Step 4: Activate Virtual Environment **On macOS/Linux:** ```bash source .venv/bin/activate ``` **On Windows:** ```bash .venv\Scripts\activate ``` ### Step 5: Install Dependencies **Using UV (Recommended):** ```bash uv pip install -r requirements.txt ``` **Using pip (Alternative):** ```bash pip install -r requirements.txt ``` ### Step 6: Adding Virtual Environment to Jupyter Kernel To use your UV virtual environment with Jupyter notebooks, you need to install ipykernel and register the environment as a kernel: **Install ipykernel in the virtual environment**: ```bash uv pip install ipykernel ``` **Register the virtual environment as a Jupyter kernel**: ```bash python -m ipykernel install --user --name=langgraph-ai --display-name="LangGraph AI" ``` When you open a notebook, you can select the "LangGraph AI" kernel from the kernel menu. ### Step 7: Environment Configuration Create a `.env` file in your project directory with the necessary API keys: ```env ANTHROPIC_API_KEY="your-anthropic-api-key" # LANGCHAIN_API_KEY="your-langchain-api-key" # optional # LANGCHAIN_TRACING_V2=True # optional # LANGCHAIN_PROJECT="multi-agent-swarm" # optional ``` **Note**: The `LANGCHAIN_API_KEY` is required if you enable tracing with `LANGCHAIN_TRACING_V2=true`. ## Running Projects ### Adaptive RAG System ```bash cd agentic-rag/agentic-rag-systems/building-adaptive-rag uv run main.py ``` ### Running Tests ```bash uv run pytest . -s -v ``` ## Contributing Contributions are welcome! Please feel free to submit pull requests or open issues for: - Bug fixes and improvements - New tutorial implementations - Documentation enhancements - Performance optimizations ## License This project is open source and available under the MIT License. --- **Note**: This repository contains multiple independent projects. Each project has its own requirements and setup instructions. Please refer to individual project README files for specific details.