Graphiti MCP Server

Graphiti is a framework 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. That is what the graphiti mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.

The short version

• 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

The tools it exposes

The server publishes 12 tools. What each one is for:

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

Getting it running

The server ships on npm as mcp-remote, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

What it needs from you

Configuration is passed through the environment: NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD, OPENAI_API_KEY, MODEL_NAME, OPENAI_BASE_URL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.

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

How it compares

Among the knowledge and memory options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Graphiti's toolset — add_episode, search_nodes, search_facts and 9 more — is a fair guide to whether it matches your workflow. It is maintained by chen9z; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Graphiti's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Things to watch

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• With 12 tools registered it takes up a noticeable share of the context window; turn it off in projects that never touch Graphiti.
• Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
• Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.