What is Lightrag Mcp

LightRAG MCP Server is a bridge that integrates the LightRAG API with AI tools using the MCP protocol, allowing users to leverage Retrieval-Augmented Generation capabilities.

Use cases

Use cases include integrating AI tools for enhanced document search, managing large datasets with knowledge graphs, and building applications that require real-time document processing and retrieval.

How to use

To use LightRAG MCP Server, set it up in a virtual environment and configure it via an MCP client configuration file (mcp-config.json). Ensure that the LightRAG API server is running before starting the MCP server.

Key features

Key features include information retrieval through semantic and keyword queries, document management for uploading and indexing documents, knowledge graph operations for managing entities and relationships, and monitoring capabilities for checking API status and document processing.

Where to use

LightRAG MCP Server can be used in various fields such as data analysis, AI research, document management systems, and any applications that require enhanced information retrieval and processing capabilities.

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

Important: LightRAG MCP server should only be run as an MCP server through an MCP client configuration file (mcp-config.json).

Command Line Options

The following arguments are available when configuring the server in mcp-config.json:

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

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

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.

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