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

Demo . Learn more · Join Discord · Join r/AIMemory

[![GitHub forks](https://img.shields.io/github/forks/topoteretes/cognee.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/network/) [![GitHub stars](https://img.shields.io/github/stars/topoteretes/cognee.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/stargazers/) [![GitHub commits](https://badgen.net/github/commits/topoteretes/cognee)](https://GitHub.com/topoteretes/cognee/commit/) [![Github tag](https://badgen.net/github/tag/topoteretes/cognee)](https://github.com/topoteretes/cognee/tags/) [![Downloads](https://static.pepy.tech/badge/cognee)](https://pepy.tech/project/cognee) [![License](https://img.shields.io/github/license/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/blob/main/LICENSE) [![Contributors](https://img.shields.io/github/contributors/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/graphs/contributors) cognee - Memory for AI Agents in 5 lines of code | Product Hunt topoteretes%2Fcognee | Trendshift Build memory for Agents and query from any client that speaks MCP – in your terminal or IDE.
## ✨ Features - Multiple transports – choose Streamable HTTP --transport http (recommended for web deployments), SSE --transport sse (real‑time streaming), or stdio (classic pipe, default) - **API Mode** – connect to an already running Cognee FastAPI server instead of using cognee directly (see [API Mode](#-api-mode) below) - 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](https://docs.cognee.ai/how-to-guides/deployment/mcp) 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 ``` or run with Streamable HTTP transport (recommended for web deployments) ``` python src/server.py --transport http --host 127.0.0.1 --port 8000 --path /mcp ``` 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: ```bash docker rmi cognee/cognee-mcp:main || true docker build --no-cache -f cognee-mcp/Dockerfile -t cognee/cognee-mcp:main . ``` 3. Run it: ```bash # For HTTP transport (recommended for web deployments) docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main # For SSE transport docker run -e TRANSPORT_MODE=sse --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main # For stdio transport (default) docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main ``` **Installing optional dependencies at runtime:** You can install optional dependencies when running the container by setting the `EXTRAS` environment variable: ```bash # Install a single optional dependency group at runtime docker run \ -e TRANSPORT_MODE=http \ -e EXTRAS=aws \ --env-file ./.env \ -p 8000:8000 \ --rm -it cognee/cognee-mcp:main # Install multiple optional dependency groups at runtime (comma-separated) docker run \ -e TRANSPORT_MODE=sse \ -e EXTRAS=aws,postgres,neo4j \ --env-file ./.env \ -p 8000:8000 \ --rm -it cognee/cognee-mcp:main ``` **Available optional dependency groups:** - `aws` - S3 storage support - `postgres` / `postgres-binary` - PostgreSQL database support - `neo4j` - Neo4j graph database support - `neptune` - AWS Neptune support - `chromadb` - ChromaDB vector store support - `scraping` - Web scraping capabilities - `distributed` - Modal distributed execution - `langchain` - LangChain integration - `llama-index` - LlamaIndex integration - `anthropic` - Anthropic models - `groq` - Groq models - `mistral` - Mistral models - `ollama` / `huggingface` - Local model support - `docs` - Document processing - `codegraph` - Code analysis - `monitoring` - Sentry & Langfuse monitoring - `redis` - Redis support - And more (see [pyproject.toml](https://github.com/topoteretes/cognee/blob/main/pyproject.toml) for full list) 2. **Pull from Docker Hub** (no build required): ```bash # With HTTP transport (recommended for web deployments) docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main # With SSE transport docker run -e TRANSPORT_MODE=sse --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main # With stdio transport (default) docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main ``` **With runtime installation of optional dependencies:** ```bash # Install optional dependencies from Docker Hub image docker run \ -e TRANSPORT_MODE=http \ -e EXTRAS=aws,postgres \ --env-file ./.env \ -p 8000:8000 \ --rm -it cognee/cognee-mcp:main ``` ### **Important: Docker vs Direct Usage** **Docker uses environment variables**, not command line arguments: - ✅ Docker: `-e TRANSPORT_MODE=http` - ❌ Docker: `--transport http` (won't work) **Direct Python usage** uses command line arguments: - ✅ Direct: `python src/server.py --transport http` - ❌ Direct: `-e TRANSPORT_MODE=http` (won't work) ### **Docker API Mode** To connect the MCP Docker container to a Cognee API server running on your host machine: #### **Simple Usage (Automatic localhost handling):** ```bash # Start your Cognee API server on the host python -m cognee.api.client # Run MCP container in API mode - localhost is automatically converted! docker run \ -e TRANSPORT_MODE=sse \ -e API_URL=http://localhost:8000 \ -e API_TOKEN=your_auth_token \ -p 8001:8000 \ --rm -it cognee/cognee-mcp:main ``` **Note:** The container will automatically convert `localhost` to `host.docker.internal` on Mac/Windows/Docker Desktop. You'll see a message in the logs showing the conversion. #### **Explicit host.docker.internal (Mac/Windows):** ```bash # Or explicitly use host.docker.internal docker run \ -e TRANSPORT_MODE=sse \ -e API_URL=http://host.docker.internal:8000 \ -e API_TOKEN=your_auth_token \ -p 8001:8000 \ --rm -it cognee/cognee-mcp:main ``` #### **On Linux (use host network or container IP):** ```bash # Option 1: Use host network (simplest) docker run \ --network host \ -e TRANSPORT_MODE=sse \ -e API_URL=http://localhost:8000 \ -e API_TOKEN=your_auth_token \ --rm -it cognee/cognee-mcp:main # Option 2: Use host IP address # First, get your host IP: ip addr show docker0 docker run \ -e TRANSPORT_MODE=sse \ -e API_URL=http://172.17.0.1:8000 \ -e API_TOKEN=your_auth_token \ -p 8001:8000 \ --rm -it cognee/cognee-mcp:main ``` **Environment variables for API mode:** - `API_URL`: URL of the running Cognee API server - `API_TOKEN`: Authentication token (optional, required if API has authentication enabled) **Note:** When running in API mode: - Database migrations are automatically skipped (API server handles its own DB) - Some features are limited (see [API Mode Limitations](#-api-mode)) ## 🔗 MCP Client Configuration After starting your Cognee MCP server with Docker, you need to configure your MCP client to connect to it. ### **SSE Transport Configuration** (Recommended) **Start the server with SSE transport:** ```bash docker run -e TRANSPORT_MODE=sse --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main ``` **Configure your MCP client:** #### **Claude CLI (Easiest)** ```bash claude mcp add cognee-sse -t sse http://localhost:8000/sse ``` **Verify the connection:** ```bash claude mcp list ``` You should see your server connected: ``` Checking MCP server health... cognee-sse: http://localhost:8000/sse (SSE) - ✓ Connected ``` #### **Manual Configuration** **Claude (`~/.claude.json`)** ```json { "mcpServers": { "cognee": { "type": "sse", "url": "http://localhost:8000/sse" } } } ``` **Cursor (`~/.cursor/mcp.json`)** ```json { "mcpServers": { "cognee-sse": { "url": "http://localhost:8000/sse" } } } ``` ### **HTTP Transport Configuration** (Alternative) **Start the server with HTTP transport:** ```bash docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main ``` **Configure your MCP client:** #### **Claude CLI (Easiest)** ```bash claude mcp add cognee-http -t http http://localhost:8000/mcp ``` **Verify the connection:** ```bash claude mcp list ``` You should see your server connected: ``` Checking MCP server health... cognee-http: http://localhost:8000/mcp (HTTP) - ✓ Connected ``` #### **Manual Configuration** **Claude (`~/.claude.json`)** ```json { "mcpServers": { "cognee": { "type": "http", "url": "http://localhost:8000/mcp" } } } ``` **Cursor (`~/.cursor/mcp.json`)** ```json { "mcpServers": { "cognee-http": { "url": "http://localhost:8000/mcp" } } } ``` ### **Dual Configuration Example** You can configure both transports simultaneously for testing: ```json { "mcpServers": { "cognee-sse": { "type": "sse", "url": "http://localhost:8000/sse" }, "cognee-http": { "type": "http", "url": "http://localhost:8000/mcp" } } } ``` **Note:** Only enable the server you're actually running to avoid connection errors. ## 🌐 API Mode The MCP server can operate in two modes: ### **Direct Mode** (Default) The MCP server directly imports and uses the cognee library. This is the default mode with full feature support. ### **API Mode** The MCP server connects to an already running Cognee FastAPI server via HTTP requests. This is useful when: - You have a centralized Cognee API server running - You want to separate the MCP server from the knowledge graph backend - You need multiple MCP servers to share the same knowledge graph **Starting the MCP server in API mode:** ```bash # Start your Cognee FastAPI server first (default port 8000) cd /path/to/cognee python -m cognee.api.client # Then start the MCP server in API mode cd cognee-mcp python src/server.py --api-url http://localhost:8000 --api-token YOUR_AUTH_TOKEN ``` **API Mode with different transports:** ```bash # With SSE transport python src/server.py --transport sse --api-url http://localhost:8000 --api-token YOUR_TOKEN # With HTTP transport python src/server.py --transport http --api-url http://localhost:8000 --api-token YOUR_TOKEN ``` **API Mode with Docker:** ```bash # On Mac/Windows (use host.docker.internal to access host) docker run \ -e TRANSPORT_MODE=sse \ -e API_URL=http://host.docker.internal:8000 \ -e API_TOKEN=YOUR_TOKEN \ -p 8001:8000 \ --rm -it cognee/cognee-mcp:main # On Linux (use host network) docker run \ --network host \ -e TRANSPORT_MODE=sse \ -e API_URL=http://localhost:8000 \ -e API_TOKEN=YOUR_TOKEN \ --rm -it cognee/cognee-mcp:main ``` **Command-line arguments for API mode:** - `--api-url`: Base URL of the running Cognee FastAPI server (e.g., `http://localhost:8000`) - `--api-token`: Authentication token for the API (optional, required if API has authentication enabled) **Docker environment variables for API mode:** - `API_URL`: Base URL of the running Cognee FastAPI server - `API_TOKEN`: Authentication token (optional, required if API has authentication enabled) **API Mode limitations:** Some features are only available in direct mode: - `codify` (code graph pipeline) - `cognify_status` / `codify_status` (pipeline status tracking) - `prune` (data reset) - `get_developer_rules` (developer rules retrieval) - `list_data` with specific dataset_id (detailed data listing) Basic operations like `cognify`, `search`, `delete`, and `list_data` (all datasets) work in both modes. ## 💻 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 - **cognee_add_developer_rules**: Ingest core developer rule files into memory - **codify**: Analyse a code repository, build a code graph, stores it in memory - **delete**: Delete specific data from a dataset (supports soft/hard deletion modes) - **get_developer_rules**: Retrieve all developer rules that were generated based on previous interactions - **list_data**: List all datasets and their data items with IDs for deletion operations - **save_interaction**: Logs user-agent interactions and query-answer pairs - **prune**: Reset cognee for a fresh start (removes all data) - **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, SUMMARIES, CYPHER, and FEELING_LUCKY - **cognify_status / codify_status**: Track pipeline progress **Data Management Examples:** ```bash # List all available datasets and data items list_data() # List data items in a specific dataset list_data(dataset_id="your-dataset-id-here") # Delete specific data (soft deletion - safer, preserves shared entities) delete(data_id="data-uuid", dataset_id="dataset-uuid", mode="soft") # Delete specific data (hard deletion - removes orphaned entities) delete(data_id="data-uuid", dataset_id="dataset-uuid", mode="hard") ``` ## 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. Update dependencies in cognee folder if needed 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`](pyproject.toml) file and set the cognee root path. ``` #"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users//Desktop/cognee" ``` Remember to replace `file:/Users//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 contributors ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=topoteretes/cognee&type=Date)](https://star-history.com/#topoteretes/cognee&Date)