OpenClaw QMD: Local Hybrid Search for 10x Smarter Memory

Why Default Memory Fails at Scale OpenClaw's built-in memory is simple: append to MEMORY.md, inject the whole file into every prompt. Works fine at 500 tokens. Falls apart at 5,000. The problems compound: Token explosion : Every message pays the full context tax. A 10-token query drags 4,000 tokens of memory. Your $0.01 API call becomes $0.15. Relevance collapse : The model sees everything, prioritizes nothing. Ask about "database connection pooling" and it weighs your lunch preferences equally. No semantic understanding : Keyword matching alone misses synonyms. "DB connection" won't find notes about "PostgreSQL pooling" unless you used those exact words. Cloud dependency : Vector search usually means Pinecone, Weaviate, or some hosted service. Your private notes now live on someone else's servers. QMD solves all four. It indexes your markdown files locally, runs hybrid retrieval combining three search strategies, and returns only the relevant snippets. 700 characters max per result, 6