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53
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17
Release Date
4/30/2025
about 2 months ago
Detailed Description
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 storageupload_document
: Upload document from file to the /input directoryinsert_file
: Add document from file directly to storageinsert_batch
: Add batch of documents from directoryscan_for_new_documents
: Start scanning the /input directory for new documentsget_documents
: Get list of all uploaded documentsget_pipeline_status
: Get status of document processing in pipeline
Knowledge Graph Operations
get_graph_labels
: Get labels (node and relationship types) from knowledge graphcreate_entities
: Create multiple entities in knowledge graphedit_entities
: Edit multiple existing entities in knowledge graphdelete_by_entities
: Delete multiple entities from knowledge graph by namedelete_by_doc_ids
: Delete all entities and relationships associated with multiple documentscreate_relations
: Create multiple relationships between entities in knowledge graphedit_relations
: Edit multiple relationships between entities in knowledge graphmerge_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
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