LightRAG MCP Server

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

Description

LightRAG MCP Server is a bridge between LightRAG API and MCP-compatible clients. It allows using LightRAG (Retrieval-Augmented Generation) capabilities in various AI tools that support the MCP protocol.

Key Features

• Information Retrieval: Execute semantic and keyword queries to documents
• Document Management: Upload, index, and track document status
• Knowledge Graph Operations: Manage entities and relationships in the knowledge graph
• Monitoring: Check LightRAG API status and document processing

Installation

This server is designed to be used as an MCP server and should be installed in a virtual environment using uv, not as a system-wide package.

Development Installation

# Create a virtual environment
uv venv --python 3.11

# Install the package in development mode
uv pip install -e .

Requirements

• Python 3.11+
• Running LightRAG API server

Usage

LightRAG MCP server supports two MCP transport modes:

• stdio (default): run through an MCP client configuration file (mcp-config.json)
• streamable-http: run as a standalone HTTP server for remote MCP clients

Command Line Options

LightRAG API connection:

• --host: LightRAG API host (default: localhost)
• --port: LightRAG API port (default: 9621)
• --api-key: LightRAG API key (optional)

MCP transport:

• --mcp-transport: MCP transport (stdio or streamable-http, default: stdio)
• --mcp-host: MCP HTTP host (default: 127.0.0.1)
• --mcp-port: MCP HTTP port (default: 8000)
• --mcp-streamable-http-path: Streamable HTTP endpoint path (default: /mcp)
• --mcp-stateless-http: Enable stateless Streamable HTTP mode (new session per request)
• --mcp-json-response: Return JSON responses instead of SSE for Streamable HTTP

Integration with LightRAG API

The MCP server requires a running LightRAG API server. Start it as follows:

# Create virtual environment
uv venv --python 3.11

# Install dependencies
uv pip install -r LightRAG/lightrag/api/requirements.txt

# Start LightRAG API
uv run LightRAG/lightrag/api/lightrag_server.py --host localhost --port 9621 --working-dir ./rag_storage --input-dir ./input --llm-binding openai --embedding-binding openai --log-level DEBUG

Setting up as MCP server (stdio)

To set up LightRAG MCP as an MCP server, add the following configuration to your MCP client configuration file (e.g., mcp-config.json):

Using uvenv (uvx):

{
  "mcpServers": {
    "lightrag-mcp": {
      "command": "uvx",
      "args": [
        "lightrag_mcp",
        "--host",
        "localhost",
        "--port",
        "9621",
        "--api-key",
        "your_api_key"
      ]
    }
  }
}

Development

{
  "mcpServers": {
    "lightrag-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/lightrag_mcp",
        "run",
        "src/lightrag_mcp/main.py",
        "--host",
        "localhost",
        "--port",
        "9621",
        "--api-key",
        "your_api_key"
      ]
    }
  }
}

Replace /path/to/lightrag_mcp with the actual path to your lightrag-mcp directory.

Running over Streamable HTTP (standalone server)

Use this when you need remote access or want to host the MCP server behind HTTP infrastructure:

uv run src/lightrag_mcp/main.py \
  --mcp-transport streamable-http \
  --mcp-host 0.0.0.0 \
  --mcp-port 8000 \
  --mcp-streamable-http-path /mcp \
  --host localhost \
  --port 9621 \
  --api-key your_api_key

MCP clients should connect to: http://localhost:8000/mcp

Available MCP Tools

Document Queries

• query_document: Execute a query to documents through LightRAG API

Document Management

• insert_document: Add text directly to LightRAG storage
• upload_document: Upload document from file to the /input directory
• insert_file: Add document from file directly to storage
• insert_batch: Add batch of documents from directory
• scan_for_new_documents: Start scanning the /input directory for new documents
• get_documents: Get list of all uploaded documents
• get_pipeline_status: Get status of document processing in pipeline

Knowledge Graph Operations

• get_graph_labels: Get labels (node and relationship types) from knowledge graph
• create_entities: Create multiple entities in knowledge graph
• edit_entities: Edit multiple existing entities in knowledge graph
• delete_by_entities: Delete multiple entities from knowledge graph by name
• delete_by_doc_ids: Delete all entities and relationships associated with multiple documents
• create_relations: Create multiple relationships between entities in knowledge graph
• edit_relations: Edit multiple relationships between entities in knowledge graph
• merge_entities: Merge multiple entities into one with relationship migration

Monitoring

• check_lightrag_health: Check LightRAG API status

Development

Installing development dependencies

uv pip install -e ".[dev]"

Running linters

ruff check src/
mypy src/

License

MIT