Graphiti MCP Server

A Node.js implementation of the Graphiti temporal knowledge graph system as a Model Context Protocol (MCP) server.

Features

• Temporal Knowledge Graph: Build and query time-aware knowledge graphs
• Multiple Search Types: Semantic, keyword, and hybrid search capabilities
• MCP Integration: Full Model Context Protocol server implementation
• Multi-LLM Support: OpenAI-compatible API integration (supports OpenAI, Moonshot, etc.)
• Neo4j Database: High-performance graph database backend
• NPX Support: Easy installation and execution via NPX

Installation

Via NPX (Recommended)

npx @zhangzichao2008/mcp-graphiti

Via NPM

npm install -g @zhangzichao2008/mcp-graphiti
graphiti-mcp

From Source

git clone https://github.com/steven0lisa/mcp-graphiti.git
cd mcp-graphiti
npm install
npm run build
npm start

Configuration

Create a .env file in your project directory:

# Neo4j Database Configuration
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=neo4j_password
NEO4J_DATABASE=neo4j

# AI Model Configuration (统一配置)
# 支持任何OpenAI兼容的API服务，包括Moonshot、OpenAI、智谱AI等
# LLM和Embedding共享同一套配置
EMBEDDING_API_KEY=your_api_key_here
EMBEDDING_API_URL=https://open.bigmodel.cn/api/paas/v4/embeddings
EMBEDDING_MODEL=embedding-3

# Server Configuration
LOG_LEVEL=info
NODE_ENV=development

# Graphiti Configuration
GRAPHITI_DATABASE=neo4j
GRAPHITI_EMBEDDING_DIMENSION=1536

Usage with Claude Desktop

Add this to your Claude Desktop configuration file:

{
  "mcpServers": {
    "graphiti": {
      "command": "npx",
      "args": ["@zhangzichao2008/mcp-graphiti"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "neo4j_password",
        "NEO4J_DATABASE": "neo4j",
        "EMBEDDING_API_KEY": "your_api_key_here",
        "EMBEDDING_API_URL": "https://open.bigmodel.cn/api/paas/v4/embeddings",
        "EMBEDDING_MODEL": "embedding-3"
      }
    }
  }
}

Available Tools

1. add_episodes

Add text documents (episodes) to the knowledge graph.

Parameters:

• episodes: Array of episode objects with:
  • name: Episode title
  • content: Episode text content
  • source_description: Optional source description
  • source: Optional source identifier
  • reference_time: Optional reference time

2. search

Search the knowledge graph using semantic, keyword, or hybrid search.

Parameters:

• query: Search query string
• num_results: Number of results (1-100, default: 10)
• search_type: 'semantic', 'keyword', or 'hybrid' (default: hybrid)

3. get_entities

Get entities by name and optionally by type.

Parameters:

• name: Entity name to search for
• entity_type: Optional entity type filter

4. get_facts

Get facts/relationships between entities.

Parameters:

• source_node_name: Optional source entity name
• target_node_name: Optional target entity name
• fact_type: Optional relationship type

5. health_check

Check the health status of the Graphiti server.

Development

Setup

npm install

Development Mode

npm run dev

Build

npm run build

Testing

npm test
npm run test:coverage

Linting

npm run lint
npm run lint:fix

Architecture

The project follows a modular architecture:

• src/types/: TypeScript type definitions
• src/drivers/: Database drivers (Neo4j)
• src/llm/: LLM client implementations
• src/core/: Core Graphiti functionality
• src/utils/: Utility functions
• src/mcp-server.ts: MCP server implementation

API Reference

Graphiti Class

The main class that provides knowledge graph operations:

class Graphiti {
  async addEpisodes(episodes: Episode[]): Promise<void>;
  async search(query: string, numResults?: number, searchType?: string): Promise<SearchResult[]>;
  async getEntities(name: string, entityType?: string): Promise<Node[]>;
  async getFacts(sourceNodeName?: string, targetNodeName?: string, factType?: string): Promise<Edge[]>;
  async healthCheck(): Promise<{ database: boolean; llm: boolean }>;
}

Environment Variables

| Variable | Description | Default | |----------|-------------|---------| | NEO4J_URI | Neo4j database URI | bolt://localhost:7687 | | NEO4J_USER | Neo4j username | neo4j | | NEO4J_PASSWORD | Neo4j password | Required | | NEO4J_DATABASE | Neo4j database name | neo4j | | EMBEDDING_API_KEY | AI model API key (OpenAI-compatible) | Required | | EMBEDDING_API_URL | AI model API URL | https://api.openai.com/v1 | | EMBEDDING_MODEL | AI model name | gpt-3.5-turbo | | GRAPHITI_EMBEDDING_DIMENSION | Embedding vector dimension | 1536 | | LOG_LEVEL | Log level | info | | NODE_ENV | Node environment | development |

License

Apache 2.0 License

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run the test suite
6. Submit a pull request

Support

For issues and questions:

• GitHub Issues: https://github.com/steven0lisa/mcp-graphiti/issues
• Discord: [Join our Discord server]

Security

Important: Never commit API keys or sensitive information to the repository. Always use environment variables for configuration.