README.md•17.6 kB
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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.
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## ✨ 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 <a href="https://github.com/topoteretes/cognee/blob/main/.env.template">template.</a>
To use different LLM providers / database configurations, and for more info check out our <a href="https://docs.cognee.ai">documentation</a>.
## 🐳 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
- **codify**: Analyse a code repository, build a code graph, stores it in memory
- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS
- **list_data**: List all datasets and their data items with IDs for deletion operations
- **delete**: Delete specific data from a dataset (supports soft/hard deletion modes)
- **prune**: Reset cognee for a fresh start (removes all data)
- **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/<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 <a href="https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md"><code>CODE_OF_CONDUCT</code></a> for more information.
## 💫 Contributors
<a href="https://github.com/topoteretes/cognee/graphs/contributors">
<img alt="contributors" src="https://contrib.rocks/image?repo=topoteretes/cognee"/>
</a>
## Star History
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