Graphiti MCP Server 🧠

🌟 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

2. Set up environment variables:

cp .env.sample .env

3. Edit .env with your configuration:

# Required for LLM operations
OPENAI_API_KEY=your_openai_api_key_here
MODEL_NAME=gpt-4o

4. 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"
    }
  }
}

2. Add Graphiti rules to Cursor's User Rules (see graphiti_cursor_rules.md)
3. 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