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Tutorial : cognee-mcp (#3025)
Co-authored-by: kevco <kevin.cojean@agysoft.fr> Co-authored-by: Rizel Scarlett <rizel@squareup.com>
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Kevin Cojean
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documentation/docs/tutorials/advanced-cognee-usage.md
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title: Advanced Cognee Usage with Goose
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description: Advanced patterns for using Cognee knowledge graph with Goose for enhanced memory and automation
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Advanced Cognee Usage with Goose
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This tutorial covers advanced usage patterns for the Cognee extension with Goose, including automated memory management, knowledge graph optimization, and various integration strategies.
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## Overview
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While the basic [Cognee MCP setup](../mcp/cognee-mcp.md) gets you started, this tutorial explores how to make Goose autonomously use the knowledge graph and optimize your workflow.
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## Key Concepts
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### Knowledge Graph Memory
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Cognee creates a structured knowledge graph that:
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- Interconnects conversations, documents, images, and audio transcriptions
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- Supports over 30 data sources
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- Replaces traditional RAG systems with dynamic relationship mapping
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- Enables complex multi-hop reasoning
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### Search Types
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Understanding Cognee's search types is crucial for effective usage:
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| Search Type | Use Case | Description |
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|-------------|----------|-------------|
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| `SUMMARIES` | Summary requests | High-level overviews |
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| `INSIGHTS` | Relationship queries | Connections between entities |
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| `CHUNKS` | Specific facts | Raw text segments |
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| `COMPLETION` | Explanations | LLM-generated responses |
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| `GRAPH_COMPLETION` | Complex relations | Multi-hop reasoning |
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| `GRAPH_SUMMARY` | Concise answers | Brief, focused responses |
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| `GRAPH_COMPLETION_COT` | Multi-hop Q&A | Chain-of-thought reasoning |
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| `GRAPH_CONTEXT_EXT` | Context extension | Expanded context |
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| `CODE` | Code examples | Programming-related queries |
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## Automation Strategies
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<Tabs>
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<TabItem value="method1" label="Method 1 (Slow)" default>
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### Instruction Files
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Use instruction files for consistent behavior across sessions. This method uses fewer tokens but has slower startup.
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Create `~/.config/goose/cognee-instructions.md`:
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```markdown
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You are an LLM agent with access to a Cognee knowledge graph for memory.
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**IMPORTANT RULES:**
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- Never call the `prune` command
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- Always search memory before responding to user queries
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- Automatically cognify new information you learn about the user
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**Memory Workflow:**
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1. **Before each response**: Search the knowledge graph
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- Map user request to appropriate search type:
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- Summary → SUMMARIES
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- Relationships → INSIGHTS
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- Specific facts → CHUNKS
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- Explanations → COMPLETION
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- Complex relations → GRAPH_COMPLETION
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- Code examples → CODE
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2. **Search command**:
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```text
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cognee-mcp__search(\{
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search_query: "user prompt",
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search_type: "mapped type"
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\})
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```
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3. **Incorporate results** into your response
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**Memory Updates:**
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- When you learn new facts, preferences, or relationships about the user
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- Call: `cognee-mcp__cognify(\{ data: "information" \})`
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- Monitor with: `cognee-mcp__cognify_status()`
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**Code Analysis:**
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- When asked to analyze code repositories
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- Use: `cognee-mcp__codify(\{ repo_path: "path" \})`
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- Only process files returned by `rg --files`
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```
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Start Goose with instructions:
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```bash
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goose run -i ~/.config/goose/cognee-instructions.md -s
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```
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</TabItem>
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<TabItem value="method2" label="Method 2">
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### Goosehints File
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For faster startup with higher token usage, add to your `.goosehints` file:
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```text
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COGNEE_MEMORY_SYSTEM:
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You have access to a Cognee knowledge graph for persistent memory.
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MEMORY_RETRIEVAL_PROTOCOL:
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- Before responding, determine request type and map to search type
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- Search types: SUMMARIES, INSIGHTS, CHUNKS, COMPLETION, GRAPH_COMPLETION, CODE
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- Always call: cognee-mcp__search with search_query and search_type parameters
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- Incorporate memory results into responses
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MEMORY_STORAGE_PROTOCOL:
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- Auto-cognify new user facts, preferences, relationships
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- Call: cognee-mcp__cognify with data parameter
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- Never use prune command
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CODE_ANALYSIS_PROTOCOL:
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- For repositories: cognee-mcp__codify with repo_path parameter
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- Only process files from rg --files output
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```
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</TabItem>
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</Tabs>
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### Strategy 3: Memory MCP Integration
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Combine with the [Memory MCP extension](../mcp/memory-mcp.md) for hybrid approach:
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1. Store Cognee usage patterns as memories
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2. Use Memory MCP to trigger Cognee searches
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3. Lower token usage than goosehints
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4. More reliable than pure instruction files
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## Advanced Workflows
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### Developer Workflow
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For software development projects:
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```bash
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# Start Goose with Cognee
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goose session
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# In Goose, analyze your codebase
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> Goose, please codify this repository and then help me understand the architecture
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```
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Goose will:
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1. Run `cognee-mcp__codify` on your repository
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2. Build a code knowledge graph
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3. Answer architecture questions using the graph
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### Research Workflow
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For research and documentation:
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```bash
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# Cognify research documents
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> Goose, please cognify the contents of these research papers: paper1.pdf, paper2.pdf, paper3.pdf
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# Later, query relationships
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> What are the connections between the methodologies in these papers?
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```
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### Personal Assistant Workflow
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For personal productivity:
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```bash
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# Store preferences
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> Remember that I prefer morning meetings, work best with 2-hour focused blocks, and need 15-minute breaks between calls
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# Query later
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> Based on my preferences, how should I structure tomorrow's schedule?
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```
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## Performance Optimization
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### Server Configuration
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For optimal performance, run Cognee as a separate server:
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```bash
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# Create optimized startup script
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cat > start-cognee-optimized.sh << 'EOF'
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#!/bin/bash
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set -e
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# Performance settings
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export DEBUG=false
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export LOG_LEVEL=WARNING
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export RATE_LIMIT_INTERVAL=30
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# Model configuration
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export LLM_API_KEY=${OPENAI_API_KEY}
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export LLM_MODEL=openai/gpt-4o-mini # Faster, cheaper model
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export EMBEDDING_API_KEY=${OPENAI_API_KEY}
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export EMBEDDING_MODEL=openai/text-embedding-3-small # Faster embedding
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# Server settings
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export HOST=0.0.0.0
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export PORT=8000
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cd /path/to/cognee-mcp
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uv run python src/server.py --transport sse
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EOF
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chmod +x start-cognee-optimized.sh
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```
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### Memory Management
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Monitor and manage your knowledge graph:
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```bash
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# Check status
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> Goose, what's the status of the cognify pipeline?
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# Selective pruning (if needed)
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> Goose, can you help me identify outdated information in the knowledge graph?
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```
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## Troubleshooting
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### Common Issues
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1. **Slow startup**: Use Method 2 (separate server) configuration
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2. **Memory not persisting**: Check file permissions and paths
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3. **Search returning empty results**: Ensure data was properly cognified
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4. **High token usage**: Use instruction files instead of goosehints
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### Debug Commands
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```bash
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# Check Cognee logs
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tail -f ~/.local/share/cognee/logs/cognee.log
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# Test server connection
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curl http://localhost:8000/health
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# Verify knowledge graph status
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# In Goose session:
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> Goose, run cognify_status and codify_status
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```
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## Best Practices
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### Data Organization
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1. **Use nodesets** for organizing different types of information:
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```bash
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# Developer rules
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> Goose, add these coding standards to the 'developer_rules' nodeset
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# Project-specific info
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> Goose, cognify this project documentation with nodeset 'project_alpha'
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```
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2. **Regular maintenance**:
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- Review and update stored information monthly
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- Remove outdated preferences and facts
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- Optimize search queries based on usage patterns
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### Integration Patterns
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1. **Layered approach**: Use both Memory MCP and Cognee for different purposes
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2. **Context switching**: Different instruction files for different workflows
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3. **Selective automation**: Not every interaction needs knowledge graph queries
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## Examples
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### Code Review Assistant
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```bash
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# Setup
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> Goose, codify this repository and remember that I prefer: functional programming patterns, comprehensive tests, and clear documentation
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# Usage
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> Review this pull request and check it against my coding preferences
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```
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### Meeting Assistant
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```bash
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# Before meeting
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> Goose, cognify the agenda and participant backgrounds from these documents
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# During/after meeting
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> Based on the knowledge graph, what are the key action items and how do they relate to our previous discussions?
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```
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### Research Assistant
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```bash
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# Literature review
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> Goose, cognify these 10 research papers and create a knowledge graph of the relationships between their methodologies
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# Synthesis
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> What are the emerging patterns in the research and what gaps exist?
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```
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This advanced usage guide should help you maximize the potential of Cognee with Goose for sophisticated knowledge management and automation workflows.
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