Graphiti MCP Server

MCP clients can use the server to add episodes, search the graph, manage graph data, and inspect the server status.

Supported providers

The server supports these database providers:

• FalkorDB, which is the default provider
• Neo4j

The server supports OpenAI, Azure OpenAI, Anthropic, Gemini, and Groq for LLM inference. It supports OpenAI, Azure OpenAI, Gemini, and Voyage for embeddings.

Quick Start

Prerequisites

Before you start, install or configure:

1. Python 3.10 or later.
2. uv.
3. A FalkorDB or Neo4j database.
4. Credentials for the selected LLM and embedding providers.

Installation

Clone the repository:

git clone https://github.com/getzep/graphiti.git
cd graphiti

Install the MCP server dependencies:

cd mcp_server
uv sync

Configuration

Command-line arguments override environment variables. Environment variables override values in config/config.yaml.

For the default FalkorDB configuration, set these variables in .env:

OPENAI_API_KEY=your_openai_api_key_here
MODEL_NAME=gpt-5.5
FALKORDB_URI=redis://localhost:6379
FALKORDB_PASSWORD=
FALKORDB_DATABASE=default_db

For Neo4j, set the connection variables and select the provider when you start the server:

OPENAI_API_KEY=your_openai_api_key_here
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_neo4j_password

Running the Server

The default transport is streamable HTTP. Start the server from the mcp_server directory:

uv run main.py

The MCP endpoint is http://localhost:8000/mcp/.

Select Neo4j with a command-line argument:

uv run main.py --database-provider neo4j

MCP Client Integration

The server supports streamable HTTP and stdio. The SSE transport remains available for compatibility, but it is deprecated.

Claude Desktop

Configure a stdio client with the main.py entry point:

{
  "mcpServers": {
    "graphiti-context": {
      "transport": "stdio",
      "command": "/path/to/uv",
      "args": [
        "run",
        "--directory",
        "/path/to/graphiti/mcp_server",
        "main.py",
        "--transport",
        "stdio",
        "--database-provider",
        "neo4j"
      ],
      "env": {
        "OPENAI_API_KEY": "your_api_key",
        "MODEL_NAME": "gpt-5.5",
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "your_password"
      }
    }
  }
}

Cursor IDE

Start the server with the default HTTP transport. Then configure the client to use the streamable HTTP endpoint:

{
  "mcpServers": {
    "graphiti-context": {
      "url": "http://localhost:8000/mcp/"
    }
  }
}

Available Tools

The server registers these tools:

• add_memory
• search_nodes
• search_memory_facts
• delete_entity_edge
• delete_episode
• get_entity_edge
• get_episodes
• summarize_saga
• build_communities
• add_triplet
• get_episode_entities
• clear_graph
• get_status

clear_graph clears the selected group IDs. If a request omits the group IDs, the tool uses the configured default group. The tool requires an effective group ID.

Docker Deployment

Run the provided Compose file from the mcp_server directory:

docker compose -f docker/docker-compose.yml up

The Compose file starts FalkorDB and the MCP server. The server uses streamable HTTP at http://localhost:8000/mcp/.

Concurrency and telemetry

The SEMAPHORE_LIMIT environment variable controls episode-processing concurrency. Decrease the value if an LLM provider returns rate-limit errors.

Graphiti sends anonymous initialization telemetry by default. Disable telemetry with:

GRAPHITI_TELEMETRY_ENABLED=false

Next Steps

The MCP Server README contains the complete configuration schema and provider examples.