Graphiti MCP But Working

🚀 Graphiti MCP Server - Enhanced Fork

Graphiti is a framework designed for building and querying temporally - aware knowledge graphs, specifically tailored for AI agents operating in dynamic environments. Unlike traditional retrieval - augmented generation (RAG) methods, Graphiti continuously integrates user interactions, structured and unstructured enterprise data, and external information into a coherent, queryable graph. It supports incremental data updates, efficient retrieval, and precise historical queries without the need for complete graph recomputation, making it suitable for developing interactive, context - aware AI applications.

This is an enhanced Model Context Protocol (MCP) server implementation for Graphiti. The MCP server exposes Graphiti's key functionality through the MCP protocol, enabling AI assistants to interact with Graphiti's knowledge graph capabilities.

✨ Features

Key Enhancements in This Fork

This enhanced version comes with several significant improvements over the original implementation:

1. 🚀 Latest Graphiti Core Compatibility: Utilizes the current version of graphiti - core, incorporating all the latest features and improvements.
2. 🤖 GPT - 5, O1, O3 Model Support: Properly handles OpenAI's reasoning models with automatic parameter adjustment (disables temperature, reasoning, and verbosity parameters).
3. 🔒 Token - Based Authentication: A production - ready nonce token authentication system that enables secure public deployment via SSE transport.
4. 📊 New list_group_ids Tool: Allows you to discover and manage all group IDs across nodes and relationships in your knowledge graph.
5. 🛡️ Enhanced Security: Pure ASGI middleware - based authentication with constant - time token comparison to prevent timing attacks.
6. 🔇 Telemetry Control: Automatically disables telemetry for privacy - focused deployments (set before graphiti_core imports).
7. ⚡ Simplified Dependencies: Removes Azure OpenAI dependencies for easier setup and deployment.

About Azure Support

Note on Azure OpenAI: Azure OpenAI support was removed during refactoring due to implementation conflicts with the new authentication middleware. If you need Azure OpenAI support in this enhanced MCP server, pull requests are welcome! The original implementation can be found in the upstream Graphiti repository.

About This Fork

This fork maintains compatibility with the latest Graphiti core while adding production - ready features for secure public deployment. It focuses on OpenAI API compatibility and enhanced security features.

General Features

The Graphiti MCP server exposes the following key high - level functions of Graphiti:

• Episode Management: Add, retrieve, and delete episodes (text, messages, or JSON data).
• Entity Management: Search and manage entity nodes and relationships in the knowledge graph.
• Search Capabilities: Search for facts (edges) and node summaries using semantic and hybrid search.
• Group Management: Organize and manage groups of related data with group_id filtering.
• Graph Maintenance: Clear the graph and rebuild indices.

🚀 Quick Start

Clone this enhanced fork

git clone https://github.com/michabbb/graphiti - mcp - but - working.git
cd graphiti - mcp - but - working

or

gh repo clone michabbb/graphiti - mcp - but - working
cd graphiti - mcp - but - working

For Claude Desktop and other stdio only clients

1. Note the full path to this directory.

pwd

2. Install the Graphiti prerequisites.
3. Configure Claude, Cursor, or other MCP client to use [Graphiti with a stdio transport](#integrating - with - mcp - clients). See the client documentation on where to find their MCP configuration files.

For Cursor and other sse - enabled clients (Recommended)

1. Configure your environment variables (copy .env.example to .env and set your OPENAI_API_KEY).
2. Start the service using Docker Compose

docker compose up

3. Point your MCP client to http://localhost:8000/sse

For secure public deployment, see the Authentication Guide for setting up nonce token authentication.

📦 Installation

Prerequisites

1. Ensure you have Python 3.10 or higher installed.
2. A running Neo4j database (version 5.26 or later required).
3. OpenAI API key for LLM operations.

Setup

1. Clone the repository and navigate to the mcp_server directory.
2. Use uv to create a virtual environment and install dependencies:

# Install uv if you don't have it already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create a virtual environment and install dependencies in one step
uv sync

📚 Documentation

Configuration

The server uses the following environment variables:

You can set these variables in a .env file in the project directory.

Running the Server

To run the Graphiti MCP server directly using uv:

uv run graphiti_mcp_server.py

With options:

uv run graphiti_mcp_server.py --model gpt - 4.1 - mini --transport sse

Available arguments:

• --model: Overrides the MODEL_NAME environment variable.
• --small - model: Overrides the SMALL_MODEL_NAME environment variable.
• --temperature: Overrides the LLM_TEMPERATURE environment variable.
• --transport: Choose the transport method (sse or stdio, default: sse).
• --group - id: Set a namespace for the graph (optional). If not provided, defaults to "default".
• --destroy - graph: If set, destroys all Graphiti graphs on startup.
• --use - custom - entities: Enable entity extraction using the predefined ENTITY_TYPES

Concurrency and LLM Provider 429 Rate Limit Errors

Graphiti's ingestion pipelines are designed for high concurrency, controlled by the SEMAPHORE_LIMIT environment variable. By default, SEMAPHORE_LIMIT is set to 10 concurrent operations to help prevent 429 rate limit errors from your LLM provider. If you encounter such errors, try lowering this value. If your LLM provider allows higher throughput, you can increase SEMAPHORE_LIMIT to boost episode ingestion performance.

Docker Deployment

The Graphiti MCP server can be deployed using Docker. The Dockerfile uses uv for package management, ensuring consistent dependency installation.

Environment Configuration

Before running the Docker Compose setup, you need to configure the environment variables. You have two options:

1. Using a .env file (recommended):
   • Copy the provided .env.example file to create a .env file:

     cp .env.example .env
     

   • Edit the .env file to set your OpenAI API key and other configuration options:

     # Required for LLM operations
     OPENAI_API_KEY=your_openai_api_key_here
     MODEL_NAME=gpt - 4.1 - mini
     # Optional: OPENAI_BASE_URL only needed for non - standard OpenAI endpoints
     # OPENAI_BASE_URL=https://api.openai.com/v1
     

   • The Docker Compose setup is configured to use this file if it exists (it's optional).
2. Using environment variables directly:
   • You can also set the environment variables when running the Docker Compose command:

     OPENAI_API_KEY=your_key MODEL_NAME=gpt - 4.1 - mini docker compose up
     

Neo4j Configuration

The Docker Compose setup includes a Neo4j container with the following default configuration:

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

Running with Docker Compose

A Graphiti MCP container is available at: zepai/knowledge - graph - mcp. The latest build of this container is used by the Compose setup below. Start the services using Docker Compose:

docker compose up

Or if you're using an older version of Docker Compose:

docker - compose up

This will start both the Neo4j database and the Graphiti MCP server. The Docker setup:

• Uses uv for package management and running the server.
• Installs dependencies from the pyproject.toml file.
• Connects to the Neo4j container using the environment variables.
• Exposes the server on port 8000 for HTTP - based SSE transport.
• Includes a healthcheck for Neo4j to ensure it's fully operational before starting the MCP server.

Integrating with MCP Clients

Configuration

To use the Graphiti MCP server with an MCP - compatible client, configure it to connect to the server:

⚠️ Important Note

You will need the Python package manager, uv installed. Please refer to the [uv install instructions](https://docs.astral.sh/uv/getting - started/installation/).

Ensure that you set the full path to the uv binary and your Graphiti project folder.

{
  "mcpServers": {
    "graphiti - memory": {
      "transport": "stdio",
      "command": "/Users/<user>/.local/bin/uv",
      "args": [
        "run",
        "--isolated",
        "--directory",
        "/Users/<user>>/dev/zep/graphiti/mcp_server",
        "--project",
        ".",
        "graphiti_mcp_server.py",
        "--transport",
        "stdio"
      ],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "password",
        "OPENAI_API_KEY": "sk - XXXXXXXX",
        "MODEL_NAME": "gpt - 4.1 - mini"
      }
    }
  }
}

For SSE transport (HTTP - based), you can use this configuration:

{
  "mcpServers": {
    "graphiti - memory": {
      "transport": "sse",
      "url": "http://localhost:8000/sse"
    }
  }
}

Available Tools

The Graphiti MCP server exposes the following tools:

• add_episode: Add an episode to the knowledge graph (supports text, JSON, and message formats).
• search_nodes: Search the knowledge graph for relevant node summaries.
• search_facts: Search the knowledge graph for relevant facts (edges between entities).
• delete_entity_edge: Delete an entity edge from the knowledge graph.
• delete_episode: Delete an episode from the knowledge graph.
• get_entity_edge: Get an entity edge by its UUID.
• get_episodes: Get the most recent episodes for a specific group.
• clear_graph: Clear all data from the knowledge graph and rebuild indices.
• get_status: Get the status of the Graphiti MCP server and Neo4j connection

Working with JSON Data

The Graphiti MCP server can process structured JSON data through the add_episode tool with source="json". This allows you to automatically extract entities and relationships from structured data:

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"
)

Integrating with the Cursor IDE

To integrate the Graphiti MCP Server with the Cursor IDE, follow these steps:

1. Run the Graphiti MCP server using the SSE transport:

python graphiti_mcp_server.py --transport sse --use - custom - entities --group - id <your_group_id>

Hint: specify a group_id to namespace graph data. If you do not specify a group_id, the server will use "default" as the group_id. or

docker compose up

2. Configure Cursor to connect to the Graphiti MCP server.

{
  "mcpServers": {
    "graphiti - memory": {
      "url": "http://localhost:8000/sse"
    }
  }
}

3. Add the Graphiti rules to Cursor's User Rules. See cursor_rules.md for details.
4. Kick off an agent session in Cursor. The integration enables AI assistants in Cursor to maintain persistent memory through Graphiti's knowledge graph capabilities.

Integrating with Claude Desktop (Docker MCP Server)

The Graphiti MCP Server container uses the SSE MCP transport. Claude Desktop does not natively support SSE, so you'll need to use a gateway like mcp - remote.

1. Run the Graphiti MCP server using SSE transport:

   docker compose up
   

2. (Optional) Install mcp - remote globally: If you prefer to have mcp - remote installed globally, or if you encounter issues with npx fetching the package, you can install it globally. Otherwise, npx (used in the next step) will handle it for you.

   npm install -g mcp - remote
   

3. Configure Claude Desktop: Open your Claude Desktop configuration file (usually claude_desktop_config.json) and add or modify the mcpServers section as follows:

   {
     "mcpServers": {
       "graphiti - memory": {
         // You can choose a different name if you prefer
         "command": "npx", // Or the full path to mcp - remote if npx is not in your PATH
         "args": [
           "mcp - remote",
           "http://localhost:8000/sse" // Ensure this matches your Graphiti server's SSE endpoint
         ]
       }
     }
   }
   

   If you already have an mcpServers entry, add graphiti - memory (or your chosen name) as a new key within it.
4. Restart Claude Desktop for the changes to take effect.

Requirements

• Python 3.10 or higher
• Neo4j database (version 5.26 or later required)
• OpenAI API key (for LLM operations and embeddings)
• MCP - compatible client

Telemetry

The Graphiti MCP server uses the Graphiti core library, which includes anonymous telemetry collection. When you initialize the Graphiti MCP server, anonymous usage statistics are collected to help improve the framework.

What's Collected

• Anonymous identifier and system information (OS, Python version).
• Graphiti version and configuration choices (LLM provider, database backend, embedder type).
• No personal data, API keys, or actual graph content is ever collected

How to Disable

To disable telemetry in the MCP server, set the environment variable:

export GRAPHITI_TELEMETRY_ENABLED=false

Or add it to your .env file:

GRAPHITI_TELEMETRY_ENABLED=false

For complete details about what's collected and why, see the Telemetry section in the main Graphiti README.

📄 License

This project is licensed under the same license as the parent Graphiti project.