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Graphiti MCP Server

by gifflet

Graphiti MCP Server is a powerful knowledge graph server designed for AI agents. It is built with Neo4j and integrates with the Model Context Protocol (MCP).

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Graphiti MCP Server 🧠

License

License

Docker

Docker

🌟 A powerful knowledge graph server for AI agents, built with Neo4j and integrated with Model Context Protocol (MCP).

🚀 Features

  • 🔄 Dynamic knowledge graph management with Neo4j
  • 🤖 Seamless integration with OpenAI models
  • 🔌 MCP (Model Context Protocol) support
  • 🐳 Docker-ready deployment
  • 🎯 Custom entity extraction capabilities
  • 🔍 Advanced semantic search functionality

🛠️ Installation

Prerequisites

  • Docker and Docker Compose
  • Python 3.10 or higher
  • OpenAI API key

Quick Start 🚀

  1. Clone the repository:
git clone https://github.com/gifflet/graphiti-mcp-server.git
cd graphiti-mcp-server
  1. Set up environment variables:
cp .env.sample .env
  1. Edit .env with your configuration:
# Required for LLM operations
OPENAI_API_KEY=your_openai_api_key_here
MODEL_NAME=gpt-4o
  1. Start the services:
docker compose up

🔧 Configuration

Neo4j Settings 🗄️

Default configuration for Neo4j:

  • Username: neo4j
  • Password: demodemo
  • URI: bolt://neo4j:7687 (within Docker network)
  • Memory settings optimized for development

Docker Environment Variables 🐳

You can run with environment variables directly:

OPENAI_API_KEY=your_key MODEL_NAME=gpt-4o docker compose up

🔌 Integration

Cursor IDE Integration 🖥️

  1. Configure Cursor to connect to Graphiti:
{
  "mcpServers": {
    "Graphiti": {
      "url": "http://localhost:8000/sse"
    }
  }
}
  1. Add Graphiti rules to Cursor's User Rules (see graphiti_cursor_rules.md)
  2. Start an agent session in Cursor

🏗️ Architecture

The server consists of two main components:

  • Neo4j database for graph storage
  • Graphiti MCP server for API and LLM operations

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Neo4j team for the amazing graph database
  • OpenAI for their powerful LLM models
  • MCP community for the protocol specification