openclaw/MEMORY.md

MEMORY.md — GraphRAG Agent Knowledge Base

Learned Facts

GraphRAG Performance Characteristics

• GraphRAG achieves +21% F1 improvement over baseline RAG on bridge-type questions (HotpotQA)
• GraphRAG uses ~2.5x more tokens per query than baseline RAG
• Adaptive routing eliminates token overhead for simple queries (complexity < 0.6)
• Schema-bounded extraction reduces entity extraction cost by ~90% vs unconstrained
• Multi-hop traversal (2 hops) is the sweet spot — 3+ hops adds noise without proportional accuracy gain

Provider Performance Notes

• Claude Sonnet 4: Best at structured entity extraction (JSON mode via tool_use)
• GPT-4o-mini: Best cost/quality ratio for answer generation
• Gemini 2.0 Flash: Fastest response times, good for keyword extraction
• Ollama llama3.2: Acceptable quality for entity extraction, zero cost
• Groq llama-3.3-70b: Near-cloud quality at very low latency (LPU hardware)
• DeepSeek R1: Excellent reasoning quality but slower

TigerGraph Best Practices

• Batch upsert limit: 10,000 vertices per call on free tier
• GSQL query compilation: 30-120 seconds (install once, run many times)
• Vector search is brute-force cosine similarity on free tier (no HNSW index)
• Entity deduplication via hash(name.lower() + type.lower()) is essential

HotpotQA Dataset Notes

• Bridge questions: require connecting information across 2 documents
• Comparison questions: require comparing attributes of 2 entities
• Supporting facts: gold standard for context evaluation
• Distractor setting: 8 distractor passages + 2 relevant passages per question

User Preferences

• Prefer concise answers with explicit evidence
• Show graph reasoning paths for complex queries
• Always display token counts and costs
• Default to adaptive routing enabled