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openclaw/MEMORY.md

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+# 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