Graphiti-Memory MCP Server

A Model Context Protocol (MCP) server that provides memory and knowledge graph operations using Neo4j and the Graphiti framework.

Features

• 📝 Add Memories: Store episodes and information in the knowledge graph with automatic entity extraction

• 🧠 Search Nodes: Query entities in your knowledge graph using natural language

• 🔗 Search Facts: Find relationships and connections between entities

• 📚 Retrieve Episodes: Get historical episodes and memories

• 🗑️ Management Tools: Delete episodes, edges, and clear the graph

• 🤖 AI-Powered: Optional OpenAI integration for enhanced entity extraction

• 📊 Real-time Data: Direct connection to your Neo4j database

• 🛠️ Built-in Diagnostics: Comprehensive error messages and troubleshooting

Installation

Prerequisites

1. Neo4j Database: You need a running Neo4j instance

   # Install Neo4j (via Homebrew on macOS)
   brew install neo4j
   
   # Start Neo4j
   neo4j start

2. Python 3.10+: Required for the MCP server

Install from PyPI

pip install graphiti-memory

Install from Source

git clone https://github.com/alankyshum/graphiti-memory.git
cd graphiti-memory
pip install -e .

Configuration

MCP Configuration

Add to your MCP client configuration file (e.g., Claude Desktop config):

{
  "mcpServers": {
    "graphiti-memory": {
      "command": "graphiti-mcp-server",
      "env": {
        "NEO4J_URI": "neo4j://127.0.0.1:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "your-password-here",
        "OPENAI_API_KEY": "your-openai-key-here",
        "GRAPHITI_GROUP_ID": "default"
      }
    }
  }
}

Neo4j Setup

1. Set Password (first-time setup):

   neo4j-admin dbms set-initial-password YOUR_PASSWORD

2. Test Connection:

   # HTTP interface
   curl http://127.0.0.1:7474
   
   # Bolt protocol
   nc -zv 127.0.0.1 7687

Available Tools

1. add_memory

Add an episode or memory to the knowledge graph. This is the primary way to add information.

Example:

{
  "name": "add_memory",
  "arguments": {
    "name": "Project Discussion",
    "episode_body": "We discussed the new AI feature roadmap for Q2. Focus on improving entity extraction.",
    "source": "text",
    "group_id": "project-alpha"
  }
}

Parameters:

• name: Name of the episode (required)

• episode_body: Content to store - text, message, or JSON (required)

• source: Type of content - "text", "message", or "json" (default: "text")

• group_id: Optional namespace for organizing data

• source_description: Optional description

2. search_memory_nodes

Search for nodes (entities) in the knowledge graph using natural language.

Example:

{
  "name": "search_memory_nodes",
  "arguments": {
    "query": "machine learning",
    "max_nodes": 10
  }
}

Returns: List of nodes with UUID, name, summary, labels, and timestamps.

3. search_memory_facts

Search for facts (relationships) between entities in the knowledge graph.

Example:

{
  "name": "search_memory_facts",
  "arguments": {
    "query": "what technologies are related to AI",
    "max_facts": 10
  }
}

Returns: List of fact triples with source, target, and relationship details.

4. get_episodes

Retrieve recent episodes for a specific group.

Example:

{
  "name": "get_episodes",
  "arguments": {
    "group_id": "project-alpha",
    "last_n": 10
  }
}

5. delete_episode

Delete an episode from the knowledge graph.

Example:

{
  "name": "delete_episode",
  "arguments": {
    "uuid": "episode-uuid-here"
  }
}

6. delete_entity_edge

Delete a fact (entity edge) from the knowledge graph.

Example:

{
  "name": "delete_entity_edge",
  "arguments": {
    "uuid": "edge-uuid-here"
  }
}

7. get_entity_edge

Retrieve a specific entity edge by UUID.

Example:

{
  "name": "get_entity_edge",
  "arguments": {
    "uuid": "edge-uuid-here"
  }
}

8. clear_graph

Clear all data from the knowledge graph (DESTRUCTIVE).

Example:

{
  "name": "clear_graph",
  "arguments": {}
}

Usage

With Claude Desktop

Configure in ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "graphiti-memory": {
      "command": "graphiti-mcp-server",
      "env": {
        "NEO4J_URI": "neo4j://127.0.0.1:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "your-password",
        "OPENAI_API_KEY": "your-openai-key-here",
        "GRAPHITI_GROUP_ID": "default"
      }
    }
  }
}

Note: OPENAI_API_KEY is optional. Without it, entity extraction will be limited but the server will still work.

Standalone Testing

Test the server directly from command line:

export NEO4J_URI="neo4j://127.0.0.1:7687"
export NEO4J_USER="neo4j"
export NEO4J_PASSWORD="your-password"

echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' | graphiti-mcp-server

Troubleshooting

Connection Failed

Error: Connection refused or ServiceUnavailable

Solutions:

1. Check Neo4j is running: neo4j status

2. Start Neo4j: neo4j start

3. Verify port 7687 is accessible: nc -zv 127.0.0.1 7687

Authentication Failed

Error: Unauthorized or authentication failure

Solutions:

1. Verify password is correct

2. Reset password: neo4j-admin dbms set-initial-password NEW_PASSWORD

3. Update password in MCP configuration

4. Use test tool to verify: test_neo4j_auth

Package Not Found

Error: neo4j package not installed

This package automatically installs the neo4j dependency. If you see this error:

pip install neo4j

Development

Setup Development Environment

git clone https://github.com/alankyshum/graphiti-memory.git
cd graphiti-memory
pip install -e ".[dev]"

Running Tests

# Test the server
python -m graphiti_memory.server << 'EOF'
{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}
EOF

Architecture

MCP Client (Claude Desktop / Cline / etc.)
    ↓
Graphiti-Memory Server
    ↓
Neo4j Database

The server:

• Listens on stdin for JSON-RPC messages

• Logs diagnostics to stderr

• Responds on stdout with JSON-RPC

• Maintains persistent Neo4j connection

Contributing

Contributions welcome! Please:

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Submit a pull request

License

MIT License - see LICENSE file for details.

Links

• GitHub: https://github.com/alankyshum/graphiti-memory

• PyPI: https://pypi.org/project/graphiti-memory/

• Issues: https://github.com/alankyshum/graphiti-memory/issues

• MCP Specification: https://modelcontextprotocol.io

Credits

Built for use with:

• Model Context Protocol (MCP)

• Neo4j

• Graphiti - Knowledge graph framework