LangGraph RAG

A Retrieval-Augmented Generation (RAG) system that serves LangGraph documentation through the Model Context Protocol (MCP).

Overview

This project builds a documentation retrieval system that:

1. Collects and processes LangGraph documentation from the official website
2. Creates a vector database from this documentation for semantic search
3. Exposes this knowledge through the Model Context Protocol (MCP)
4. Integrates with MCP-compatible hosts like VS Code, Cursor, Claude Desktop, or Windsurf

How It Works

1. Documentation Collection and Processing (Context)

• Recursively scrapes and cleans LangGraph documentation from multiple website URLs using RecursiveUrlLoader and BeautifulSoup.
• Splits text into manageable chunks using RecursiveCharacterTextSplitter with tiktoken for accurate token counting.
• Embeds chunks into vector representations using BAAI/bge-large-en-v1.5 embeddings.
• Stores vectors in an SKLearnVectorStore for efficient retrieval.

2. Retrieval System (Tool)

• Implements a retrieval function that finds the most relevant documentation chunks for a given query.
• Integrates this function with language models like Claude to provide context-aware responses.
• Returns formatted responses that include source attribution and relevant context.

3. MCP Server Integration

• Wraps the retrieval tool in an MCP server using the fastmcp library.
• Exposes the retrieval function as a tool that MCP-compatible hosts can use.
• Provides access to both the retrieval system and additional resources (like the full documentation file).

Requirements

• Python 3.10+
• Docker and Docker Compose (recommended)
• Anthropic API key (for Claude models)

Installation and Setup

You can run this project either with Docker (recommended) or in a local Python environment.

Using Docker (Recommended)

1. Clone this repository:

   git clone https://github.com/yourusername/langraph-rag-mcp.git
   cd langraph-rag-mcp
   

2. Set up your API keys in a .env file: Create a .env file in the project root and add your Anthropic API key:

   echo "ANTHROPIC_API_KEY=your_api_key_here" > .env
   

   The docker-compose.yml file will automatically load this environment variable.

Local Environment (without Docker)

1. Clone this repository:

   git clone https://github.com/yourusername/langraph-rag-mcp.git
   cd langraph-rag-mcp
   

2. Create and activate a virtual environment:

   conda create -n mcp python=3.13
   conda activate mcp
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Set up your API keys in a .env file:

   echo "ANTHROPIC_API_KEY=your_api_key_here" > .env
   

Usage

The process involves two main steps: first, generating the vector store, and second, running the MCP server.

Step 1: Generate the Vector Store

You only need to do this once, or whenever you want to update the documentation.

1. Open and run the rag-tool.ipynb notebook in a Jupyter environment.
2. This will:
   • Download the latest LangGraph documentation.
   • Save the full documentation to llms_full.txt.
   • Split the documents into chunks.
   • Create and persist a vector store at sklearn_vectorstore.parquet.

Step 2: Run the MCP Server

With Docker

The easiest way to run the server is using the provided shell script, which wraps Docker Compose.

bash run-mcp-docker.sh

This script will build the Docker image if it doesn't exist, start the container, and then execute the MCP server inside it, correctly handling standard I/O for MCP communication.

Without Docker

If you are not using Docker, you can run the MCP server directly in dev mode through the command:

mcp dev langgraph-mcp.py

Configuring MCP Hosts

To use this MCP server with a compatible editor, you need to configure it.

VS Code

1. Open your VS Code settings.json file. (You can find it via the command palette: Preferences: Open User Settings (JSON)).
2. Add the following configuration to the file. Make sure to replace <path-to-your-project> with the absolute path to the langraph-rag-mcp directory on your machine.

"mcp.servers": [
    {
        "name": "langgraph-mcp",
        "command": [
            "/bin/bash",
            "<path-to-your-project>/run-mcp-docker.sh"
        ],
        "env": {
            "ANTHROPIC_API_KEY": "<your-anthropic-api-key>"
        }
    }
]

If you are not using Docker, change the command to:

"command": [
    "<path-to-your-conda-env>/bin/python",
    "<path-to-your-project>/langgraph-mcp.py"
]

System Architecture

(Phase 1: Data Ingestion - Performed once on Host Machine)
┌─────────────┐      ┌──────────────────┐      ┌───────────────────────────────┐
│ LangGraph   │      │ Jupyter Notebook │      │ Vector Store & Full Docs      │
│ Docs (Web)  │───▶  │ (rag-tool.ipynb) │───▶  │ (.parquet & .txt files)       │
└─────────────┘      └──────────────────┘      └───────────────────────────────┘


(Phase 2: Live RAG System - Request/Response Flow)
┌──────────────┐                             ┌─────────────┐
│   VS Code    │                             │  .env file  │
│(User Interface)│                             │ (API KEY)   │
└──────┬───────┘                             └──────┬──────┘
       │ 1. User Query                             │ (provides)
       ▼                                           │
┌──────┴───────────────────────────────────────────┴───────────────────────────────┐
│ Host Machine Boundary                                                            │
│                                                                                  │
│      ┌────────────────────┐          ┌─────────────────────────────────────────┐ │
│      │ run-mcp-docker.sh  │ 2. Execs │  🐳 Docker Container                      │ │
│      │ (Entrypoint Script)│────▶     │                                         │ │
│      └────────────────────┘          │  ┌───────────────────────────────────┐  │ │
│                                      │  │   🐍 Python MCP Server              │  │ │
│      ▲                             ◀─┼──│   (langgraph-mcp.py)              │  │ │
│      │ 7. Final Response             │  └───────────────┬───────────────────┘  │ │
│      │                               │                  │ 3. Reads Data From  │ │
│      └───────────────────────────────│──────────────────│─────────────────────┘ │
│                                      │                  │                       │
│                                      │                  ▼                       │
│                                      │  ┌───────────────────────────────────┐  │
│ (files mounted from Host) ············  │   Mounted Vector Store & Docs     │  │
│                                      │  └───────────────────────────────────┘  │
│                                      └─────────────────────────────────────────┘ │
│                                                                                  │
└──────────────────────────────────────────────────────────────────────────────────┘
                                                        │ 4. API Call
                                                        │    (sends augmented prompt)
                                                        ▼
                                             ┌────────────────────┐
                                             │ ☁️ Anthropic API   │
                                             │   (Claude LLM)     │
                                             └──────────┬─────────┘
                                                        │ 5. Generation
                                                        │
                                                        ◀························
                                                          6. Returns Response
                                                          (to MCP Server)

Resources

MCP From Scratch