Graphiti MCP Server Details
Graphiti MCP Server is an experimental implementation that exposes Graphiti's real-time, temporally-aware knowledge graph capabilities through the MCP (Model Context Protocol) interface. It enables AI agents and MCP clients to interact with Graphiti's knowledge graph for structured extraction, reasoning, and memory across conversations, documents, and enterprise data. The server supports multiple backends (FalkorDB by default and Neo4j), a variety of LLM providers (OpenAI, Anthropic, Gemini, Groq, Azure OpenAI), and multiple embedder options, all accessible via an HTTP MCP endpoint at /mcp/ for broad client compatibility. It also includes queue-based asynchronous episode processing, rich entity types for structured data, and flexible configuration through config.yaml, environment variables, or CLI arguments.
Use Case
The MCP Server provides a modular, extensible gateway to Graphiti's knowledge graph capabilities via the MCP protocol, enabling AI assistants to ingest, query, and reason over episodes, entities, and relationships in a scalable way. It supports adding episodes (text, JSON, or message formats), searching nodes and facts, managing edges and groups, and maintaining the graph through clear and rebuild operations. The server can be deployed with FalkorDB (default) or Neo4j, and it supports multiple LLMs and embedding providers to fit different deployment needs. Example usage includes ingesting structured JSON data as an episode, configuring MCP clients (e.g., Claude Desktop or VS Code) to connect over HTTP or stdio transports, and running with Docker Compose for combined containers or direct execution with an existing database.
Available Tools (9)
Examples & Tutorials
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"
){
"mcpServers": {
"graphiti": {
"uri": "http://localhost:8000/mcp/",
"transport": {
"type": "http"
}
}
}
}{
"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/mcp/" // The Graphiti server's HTTP endpoint\
]
}
}
}{
"mcpServers": {
"graphiti-memory": {
"transport": "http",
"url": "http://localhost:8000/mcp/"
}
}
}Installation Guide
<h1 class="text-2xl font-semibold mt-5 mb-3">Install uv if you don't have it already</h1>
curl -LsSf https://astral.sh/uv/install.sh | sh<h1 class="text-2xl font-semibold mt-5 mb-3">Create a virtual environment and install dependencies in one step</h1>
uv sync
<h1 class="text-2xl font-semibold mt-5 mb-3">Optional: Install additional LLM providers (anthropic, gemini, groq, voyage, sentence-transformers)</h1>
uv sync --extra providers
Integration Guides
Frequently Asked Questions
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Notes and warnings from the documentation:
Docker and Docker Compose (for the default FalkorDB setup); OpenAI API key for LLM operations (or API keys for other supported providers); (Optional) Python 3.10+ if running the MCP server standalone with an external FalkorDB instance
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