openclaw/docs/memory-cognee.md
2026-01-28 12:10:13 +01:00

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---
summary: "Cognee knowledge graph memory: setup, Docker config, and usage"
read_when:
- Setting up Cognee memory provider
- Configuring knowledge graph memory
- Running Cognee with Docker
---
# Cognee Memory Provider
Clawdbot supports **Cognee** as an optional memory provider. Unlike the default SQLite-based vector memory, Cognee builds a knowledge graph with entity extraction and semantic relationships, providing richer contextual memory for your AI agent.
## What is Cognee?
Cognee is an AI memory framework that:
- Extracts entities (people, places, concepts) from documents
- Builds a knowledge graph of relationships
- Enables semantic search with LLM-powered reasoning
- Supports multiple search modes (insights, chunks, summaries)
Learn more at [docs.cognee.ai](https://docs.cognee.ai/).
## Setup Options
### Option 1: Local Docker (Recommended)
Run Cognee locally using Docker Compose:
**Step 1: Create docker-compose.yml**
```yaml
version: '3.8'
services:
cognee:
image: topoteretes/cognee:latest
container_name: cognee
ports:
- "8000:8000"
environment:
# Optional: Set API key for authentication
- COGNEE_API_KEY=your-local-api-key
# Database configuration
- DATABASE_URL=postgresql://cognee:cognee@postgres:5432/cognee
volumes:
- cognee_data:/app/data
depends_on:
- postgres
restart: unless-stopped
postgres:
image: postgres:15-alpine
container_name: cognee-postgres
environment:
- POSTGRES_USER=cognee
- POSTGRES_PASSWORD=cognee
- POSTGRES_DB=cognee
volumes:
- postgres_data:/var/lib/postgresql/data
restart: unless-stopped
volumes:
cognee_data:
postgres_data:
```
**Step 2: Start Cognee**
```bash
docker-compose up -d
```
**Step 3: Verify**
```bash
curl http://localhost:8000/status
# Should return: {"status":"healthy"}
```
### Option 2: Cognee Cloud
Use the hosted Cognee service:
1. Sign up at [platform.cognee.ai](https://platform.cognee.ai/)
2. Get your API key from the dashboard
3. Use base URL: `https://cognee--cognee-saas-backend-serve.modal.run`
## Configuration
Add Cognee memory configuration to your `~/.clawdbot/config.yaml`:
### Basic Configuration (Docker Local)
```yaml
agents:
defaults:
memorySearch:
enabled: true
provider: cognee
sources: [memory] # or [memory, sessions]
cognee:
baseUrl: http://localhost:8000
datasetName: clawdbot
searchType: insights # or "chunks", "summaries"
maxResults: 6
autoCognify: true
```
### Cloud Configuration
```yaml
agents:
defaults:
memorySearch:
enabled: true
provider: cognee
sources: [memory, sessions]
cognee:
baseUrl: https://cognee--cognee-saas-backend-serve.modal.run
apiKey: your-api-key-here # Required for cloud
datasetName: clawdbot
searchType: insights
maxResults: 8
autoCognify: true
timeoutSeconds: 60
```
## Configuration Options
| Option | Type | Default | Description |
|--------|------|---------|-------------|
| `baseUrl` | string | `http://localhost:8000` | Cognee API endpoint |
| `apiKey` | string | - | API key (required for cloud, optional for local) |
| `datasetName` | string | `"clawdbot"` | Dataset name for organizing memories |
| `searchType` | string | `"insights"` | Search mode: `insights`, `chunks`, or `summaries` |
| `maxResults` | number | `6` | Maximum search results returned |
| `autoCognify` | boolean | `true` | Auto-process documents after adding |
| `cognifyBatchSize` | number | `100` | Batch size for processing |
| `timeoutSeconds` | number | `30` | Request timeout in seconds |
## Search Types
Cognee offers three search modes:
### Insights (Recommended)
Best for: **High-level understanding and reasoning**
- Returns AI-generated insights from knowledge graph
- Combines multiple related facts
- Good for: "What projects am I working on?" or "Summarize my notes about X"
### Chunks
Best for: **Specific text matching**
- Returns raw document chunks
- Similar to traditional vector search
- Good for: Finding exact quotes or specific information
### Summaries
Best for: **Document overviews**
- Returns condensed summaries
- Good for: Quick scanning of content
## Usage
### Memory Files
Cognee automatically syncs your memory files:
- `MEMORY.md` or `memory.md` in workspace root
- All `*.md` files in `memory/` directory
### Session Transcripts (Optional)
Enable session memory to index conversation history:
```yaml
agents:
defaults:
memorySearch:
provider: cognee
sources: [memory, sessions] # Include sessions
experimental:
sessionMemory: true
```
### Manual Sync
Force a memory sync:
```bash
# Not yet implemented - coming soon
clawdbot memory sync --provider cognee
```
### Check Status
```bash
# Not yet implemented - coming soon
clawdbot memory status --provider cognee
```
## How It Works
1. **Add**: Memory files are sent to Cognee with metadata
2. **Cognify**: Cognee processes documents:
- Extracts entities (people, places, concepts)
- Identifies relationships
- Builds knowledge graph
3. **Search**: Agent queries use semantic search:
- Searches knowledge graph
- Returns relevant insights/chunks/summaries
- Includes metadata and scores
## Comparison: Cognee vs SQLite Memory
| Feature | Cognee | SQLite (Default) |
|---------|--------|------------------|
| **Setup** | Requires Docker/cloud | Built-in, no setup |
| **Offline** | No (needs service) | Yes (fully local) |
| **Search** | Knowledge graph + LLM | Vector + BM25 hybrid |
| **Entities** | Extracted automatically | Not available |
| **Relationships** | Yes (graph-based) | No |
| **Speed** | Slower (API calls) | Faster (local DB) |
| **Memory** | Stored externally | SQLite file |
| **Best for** | Rich context, reasoning | Fast lookup, privacy |
## Troubleshooting
### Connection Failed
**Error**: `Failed to connect to Cognee at http://localhost:8000`
**Solutions**:
1. Verify Docker is running: `docker ps | grep cognee`
2. Check Cognee logs: `docker logs cognee`
3. Test manually: `curl http://localhost:8000/status`
4. Ensure port 8000 is not blocked
### Slow Performance
**Solutions**:
1. Reduce `maxResults` (try 3-5 instead of 10+)
2. Use `searchType: "chunks"` for faster results
3. Set `autoCognify: false` and cognify manually
4. Check Docker resource limits
### Out of Memory
**Solutions**:
1. Increase Docker memory limit (Docker Desktop settings)
2. Reduce `cognifyBatchSize` (try 50 instead of 100)
3. Process fewer files at once
4. Clear old datasets via Cognee API
## Advanced Configuration
### Per-Agent Override
```yaml
agents:
defaults:
memorySearch:
provider: openai # Default for all agents
agents:
research-bot:
memorySearch:
provider: cognee # Override for this agent
cognee:
searchType: insights
maxResults: 10
```
### Hybrid Setup (Not Yet Supported)
Future versions may support using both Cognee and SQLite:
- Cognee for semantic understanding
- SQLite for fast local lookup
## Docker Production Tips
### Health Checks
Add health checks to docker-compose.yml:
```yaml
services:
cognee:
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/status"]
interval: 30s
timeout: 10s
retries: 3
start_period: 40s
```
### Resource Limits
```yaml
services:
cognee:
deploy:
resources:
limits:
cpus: '2'
memory: 4G
reservations:
cpus: '1'
memory: 2G
```
### Persistent Storage
Mount volumes for persistence:
```yaml
volumes:
- ./cognee_data:/app/data
- ./cognee_logs:/app/logs
```
## Roadmap
Planned features:
- [ ] `clawdbot memory status --provider cognee` command
- [ ] `clawdbot memory sync --provider cognee` command
- [ ] Hybrid mode (Cognee + SQLite)
- [ ] Graph visualization export
- [ ] Manual entity management
## Resources
- [Cognee Documentation](https://docs.cognee.ai/)
- [Cognee GitHub](https://github.com/topoteretes/cognee)
- [Clawdbot Memory Guide](/memory)
- [Docker Setup Guide](/install/docker)
## Feedback
Cognee integration is new. Report issues at:
- Clawdbot: [github.com/clawdbot/clawdbot/issues](https://github.com/clawdbot/clawdbot/issues)
- Cognee: [github.com/topoteretes/cognee/issues](https://github.com/topoteretes/cognee/issues)