cognee/cognee-mcp

cognee‑mcp - Run cognee’s memory engine as a Model Context Protocol server

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Build memory for Agents and query from any client that speaks MCP – in your terminal or IDE.

✨ Features

• SSE & stdio transports – choose real‑time streaming --transport sse or the classic stdio pipe
• Integrated logging – all actions written to a rotating file (see get_log_file_location()) and mirrored to console in dev
• Local file ingestion – feed .md, source files, Cursor rule‑sets, etc. straight from disk
• Background pipelines – long‑running cognify & codify jobs spawn off‑thread; check progress with status tools
• Developer rules bootstrap – one call indexes .cursorrules, .cursor/rules, AGENT.md, and friends into the developer_rules nodeset
• Prune & reset – wipe memory clean with a single prune call when you want to start fresh

Please refer to our documentation here for further information.

🚀 Quick Start

1. Clone cognee repo

   git clone https://github.com/topoteretes/cognee.git
   

2. Navigate to cognee-mcp subdirectory

   cd cognee/cognee-mcp
   

3. Install uv if you don't have one

   pip install uv
   

4. Install all the dependencies you need for cognee mcp server with uv

   uv sync --dev --all-extras --reinstall
   

5. Activate the virtual environment in cognee mcp directory

   source .venv/bin/activate
   

6. Set up your OpenAI API key in .env for a quick setup with the default cognee configurations

   LLM_API_KEY="YOUR_OPENAI_API_KEY"
   

7. Run cognee mcp server with stdio (default)

   python src/server.py
   

   or stream responses over SSE

   python src/server.py --transport sse
   

You can do more advanced configurations by creating .env file using our template. To use different LLM providers / database configurations, and for more info check out our documentation.

🐳 Docker Usage

If you’d rather run cognee-mcp in a container, you have two options:

1. Build locally
   1. Make sure you are in /cognee root directory and have a fresh .env containing only your LLM_API_KEY (and your chosen settings).
   2. Remove any old image and rebuild:

      docker rmi cognee/cognee-mcp:main || true
      docker build --no-cache -f cognee-mcp/Dockerfile -t cognee/cognee-mcp:main .
      

   3. Run it:

      docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
      

2. Pull from Docker Hub (no build required):

   # With your .env file
   docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
   
   
   

💻 Basic Usage

The MCP server exposes its functionality through tools. Call them from any MCP client (Cursor, Claude Desktop, Cline, Roo and more).

Available Tools

• cognify: Turns your data into a structured knowledge graph and stores it in memory

• codify: Analyse a code repository, build a code graph, stores it in memory

• search: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, INSIGHTS

• prune: Reset cognee for a fresh start

• cognify_status / codify_status: Track pipeline progress

Remember – use the CODE search type to query your code graph. For huge repos, run codify on modules incrementally and cache results.

IDE Example: Cursor

1. After you run the server as described in the Quick Start, create a run script for cognee. Here is a simple example:

   #!/bin/bash
   export ENV=local
   export TOKENIZERS_PARALLELISM=false
   export EMBEDDING_PROVIDER="fastembed"
   export EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"
   export EMBEDDING_DIMENSIONS=384
   export EMBEDDING_MAX_TOKENS=256
   export LLM_API_KEY=your-OpenAI-API-key
   uv --directory /{cognee_root_path}/cognee-mcp run cognee
   

   Remember to replace your-OpenAI-API-key and {cognee_root_path} with correct values.

2. Install Cursor and navigate to Settings → MCP Tools → New MCP Server

3. Cursor will open mcp.json file in a new tab. Configure your cognee MCP server by copy-pasting the following:

   {
     "mcpServers": {
       "cognee": {
         "command": "sh",
         "args": [
           "/{path-to-your-script}/run-cognee.sh"
         ]
       }
     }
   }
   

   Remember to replace {path-to-your-script} with the correct value of the path of the script you created in the first step.

That's it! You can refresh the server from the toggle next to your new cognee server. Check the green dot and the available tools to verify your server is running.

Now you can open your Cursor Agent and start using cognee tools from it via prompting.

Development and Debugging

Debugging

To use debugger, run: bash mcp dev src/server.py

Open inspector with timeout passed: http://localhost:5173?timeout=120000

To apply new changes while developing cognee you need to do:

1. poetry lock in cognee folder
2. uv sync --dev --all-extras --reinstall
3. mcp dev src/server.py

Development

In order to use local cognee:

1. Uncomment the following line in the cognee-mcp pyproject.toml file and set the cognee root path.

   #"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/<username>/Desktop/cognee"
   

   Remember to replace file:/Users/<username>/Desktop/cognee with your actual cognee root path.

2. Install dependencies with uv in the mcp folder

   uv sync --reinstall
   

Code of Conduct

We are committed to making open source an enjoyable and respectful experience for our community. See CODE_OF_CONDUCT for more information.

💫 Contributors

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