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Graphiti MCP Server

@mateicanavra

Repository

Basic information

Category

Other

Transports

stdio

Author

mateicanavra

About Graphiti MCP Server

Graphiti Model Context Protocol (MCP) Server - An MCP server for knowledge graph management via Graphiti

Graphiti MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-rawr-graphiti": {
      "command": "python3",
      "args": [
        "-m",
        "pip",
        "install",
        "--user",
        "pipx"
      ]
    }
  }
}

Graphiti MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Graphiti MCP Server?

The Graphiti MCP Server allows AI agents to interact with a knowledge graph for persistent memory, entity extraction, and relationship tracking using the Graphiti framework. It is designed for developers building agentic systems that need a structured, queryable memory layer.

How to use Graphiti MCP Server?

Install the graphiti CLI via pipx (recommended) or set up a local development environment with venv. Copy and edit the .env file with Neo4j credentials and an OpenAI API key, then use graphiti compose and graphiti up to start the Docker containers (Neo4j database and MCP server). The MCP server communicates over SSE or Stdio transport.

Key features of Graphiti MCP Server

  • Exposes Graphiti functionality via MCP (SSE or Stdio transport).
  • Tools for adding and searching episodes, nodes, and facts.
  • Supports custom entity type definitions for tailored extraction.
  • CLI for project initialization, entity management, and Docker control.
  • Uses Docker Compose for easy deployment of Neo4j and server.
  • Leverages uv for fast dependency management.

Use cases of Graphiti MCP Server

  • Persistent memory for AI agents across conversation turns.
  • Extracting entities and relationships from unstructured text.
  • Building knowledge graphs that evolve over time with new data.
  • Integrating a knowledge graph backend into MCP‑compatible AI clients.

Frequently asked questions

1

What are the prerequisites for running the server?

Python 3.10 or higher, Docker & Docker Compose, and the `uv` package manager.

2

How does the MCP server communicate with clients?

It supports both SSE (Server‑Sent Events) and Stdio transport, configurable via MCP.

3

What database does the server use?

It uses Neo4j as the graph database, run as a Docker container managed by Docker Compose.

4

How do I configure the server?

Edit the `.env` file in the cloned repository to set Neo4j credentials, OpenAI API key, and optional model settings.

5

What is the `graphiti` CLI used for?

The CLI handles project initialization, entity management, generating Docker Compose files, and starting/stopping services.

Comments

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