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Start the Cognee MCP server using Docker to quickly test AI memory integration.

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Prerequisites

  • Docker installed and running
  • OpenAI API key

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Setup Steps

1

Set Your API Key

export LLM_API_KEY=your_api_key_here
2

Create Environment File

echo "LLM_API_KEY=your_api_key_here" > .env
3

Start the Server

docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
The server starts on port 8000 with HTTP transport mode.
4

Verify the Server

curl http://localhost:8000/health
You should see a healthy response from the server.
The container removes all data when stopped. Use volume mounts for persistent storage.

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API Mode (Shared Knowledge Graph)

To connect multiple clients to a shared knowledge graph, run MCP in API mode pointing to a centralized Cognee backend:
1

Start Cognee Backend

First, start a Cognee backend instance:
docker run -e LLM_API_KEY=your_api_key_here -p 8080:8000 --rm -it cognee/cognee:main
2

Start MCP in API Mode

Start the MCP server and point it to the backend:
docker run -e TRANSPORT_MODE=sse -e API_URL=http://localhost:8080 -p 8000:8000 --rm -it cognee/cognee-mcp:main
The container automatically converts localhost to host.docker.internal so the MCP container can reach your host machine. The MCP server now acts as an interface to the shared backend.
3

Connect Additional Clients (Optional)

If you need to support multiple clients, start additional MCP instances on different ports:
docker run -e TRANSPORT_MODE=sse -e API_URL=http://localhost:8080 -p 8001:8000 --rm -it cognee/cognee-mcp:main
Each client connects to its own MCP instance, but all share the same knowledge graph through the backend.
  • The API mode requires SSE or HTTP transport
  • The localhost in API_URL is automatically mapped to work from inside the container
  • Add -e API_TOKEN=your_token if your backend requires authentication

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Connect to AI Clients

After starting the server, connect it to your AI development tool:

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AI-powered code editor with native MCP support

Claude Code

Command-line AI assistant from Anthropic

Cline

VS Code extension for AI-assisted development

Continue

Open-source AI coding assistant

Roo Code

AI-powered development environment

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Next Steps

Tools Reference

See all available MCP tools and operations

Local Setup

Run from source for customization and development