What is Grafitimcp

GrafitiMcp is a knowledge graph memory system designed for AI agents, utilizing the Model Context Protocol (MCP) to facilitate the storage and retrieval of information with rich context.

Use cases

Use cases for GrafitiMcp include enhancing AI agents’ ability to remember and retrieve contextual information, improving conversational AI systems, and developing intelligent applications that require dynamic knowledge management.

How to use

To use GrafitiMcp, clone the repository, set up environment variables including your OpenAI API key, and start the services using Docker Compose. Connect to the MCP Server via the specified port (8001) in your applications or Roo Code.

Key features

Key features of GrafitiMcp include a temporal memory graph for AI agents, integration with Neo4j for knowledge storage, and a Model Context Protocol interface for seamless interaction.

Where to use

GrafitiMcp can be used in various fields such as AI development, natural language processing, and any application requiring contextual memory management for AI agents.

Graphiti MCP Server

A knowledge graph memory system for AI agents using the Model Context Protocol (MCP).

Based on Graphiti, an open-source knowledge graph for AI agents.

Overview

Graphiti MCP Server provides a temporal memory graph for AI agents, allowing them to store and retrieve information with rich context. It uses:

• Graphiti: Core graph functionality for managing entities and relationships
• Neo4j: Graph database for storing knowledge
• Graphiti: Core graph functionality for managing entities and relationships
• MCP Server: Model Context Protocol interface for AI agents to interact with the graph

Prerequisites

• Docker and Docker Compose
• OpenAI API key

Setup Instructions

1. Clone the repository

   git clone <repository-url>
   cd GrafitiMcp
   

2. Set up environment variables

   cp .env.template .env
   

   Edit the .env file and add your OpenAI API key and other configuration:

   NEO4J_PASSWORD=your_secure_password_here
   OPENAI_API_KEY=your_openai_api_key_here
   MODEL_NAME=gpt-4o  # or another OpenAI model
   

3. Start the services

   docker-compose up
   

   This will start:

   • Neo4j database on port 8044
   • Graphiti API on port 8080
   • Graphiti MCP Server on port 8001

Connecting to the MCP Server

The MCP server runs on port 8001 and uses Server-Sent Events (SSE) transport by default.

In Roo Code

To connect to the MCP server in Roo Code:

1. Open Roo Code
2. Go to Settings > MCP Servers
3. Add a new MCP server with:
   • Name: graphiti-mcp
   • URL: http://localhost:8001
   • Transport: sse

In Custom Applications

To connect to the MCP server in your own applications, use the MCP client library:

from mcp.client import MCPClient

# Create an MCP client
client = MCPClient("http://localhost:8001", transport="sse")

# Connect to the server
await client.connect()

# Use the tools provided by the server
result = await client.use_tool("add_episode", {
    "name": "Test Episode",
    "episode_body": "This is a test episode",
    "source": "text"
})

Basic Usage Examples

Adding Information to the Graph

# Add a text episode
await client.use_tool("add_episode", {
    "name": "Company News",
    "episode_body": "Acme Corp announced a new product line today.",
    "source": "text",
    "source_description": "news article"
})

# Add structured JSON data
await client.use_tool("add_episode", {
    "name": "Customer Profile",
    "episode_body": "{\"company\": {\"name\": \"Acme Technologies\"}, \"products\": [{\"id\": \"P001\", \"name\": \"CloudSync\"}, {\"id\": \"P002\", \"name\": \"DataMiner\"}]}",
    "source": "json",
    "source_description": "CRM data"
})

Searching the Graph

# Search for nodes
nodes = await client.use_tool("search_nodes", {
    "query": "What products does Acme Technologies offer?",
    "max_nodes": 5
})

# Search for facts
facts = await client.use_tool("search_facts", {
    "query": "Tell me about Acme Corp's product announcements",
    "max_facts": 5
})

Troubleshooting

Connection Issues

If you can’t connect to the MCP server:

1. Ensure all services are running: docker-compose ps
2. Check the logs: docker-compose logs graphiti-mcp
3. Verify the server is accessible: curl http://localhost:8001/status

Neo4j Connection Errors

If the MCP server can’t connect to Neo4j:

1. Check Neo4j is running: docker-compose logs neo4j
2. Verify Neo4j credentials in the .env file
3. Ensure Neo4j is healthy: docker-compose exec neo4j cypher-shell -u neo4j -p <your-password> "RETURN 1;"

License

[Specify your license here]