Knowledge Graph

Purpose

Maintains structured knowledge about project components and their relationships

When to Use This Skill

1. Dependency Analysis - Understand relationships
2. Impact Assessment - Predict change effects
3. Traceability - Track requirement to implementation
4. Knowledge Discovery - Find implicit connections

Responsibilities

1. Store entities - and relationships (GraphQL)
2. Complex relationship - traversals
3. Traceability (requirements - → code → tests)
4. Impact analysis - for changes
5. Hybrid retrieval - with RAG

Integration with Pipeline

Communication

Receives:

• Input data specific to agent's purpose

Sends:

• Processed output and analysis results

Usage Examples

Standalone Usage

python3 knowledge_graph.py --help

Programmatic Usage

from knowledge_graph import KnowledgeGraph

agent = KnowledgeGraph()
result = agent.execute()

Configuration

Environment Variables

# Agent-specific configuration
ARTEMIS_KNOWLEDGE_GRAPH_ENABLED=true
ARTEMIS_LLM_PROVIDER=openai
ARTEMIS_LLM_MODEL=gpt-4o

Hydra Configuration (if applicable)

knowledge_graph:
  enabled: true
  llm:
    provider: openai
    model: gpt-4o

Best Practices

1. Follow agent-specific guidelines
2. Monitor performance metrics
3. Handle errors gracefully
4. Log important events
5. Integrate with observability

Cost Considerations

Typical cost: $0.05-0.20 per operation depending on complexity

Limitations

• Depends on LLM quality
• Context window limits
• May require multiple iterations

References

• Artemis Documentation
• Agent Pattern

Version: 1.0.0

Maintained By: Artemis Pipeline Team

Last Updated: October 24, 2025