OpenClaw Semantic Memory Search with QMD: Finding What You Need

Introduction Ever felt like your AI assistant is drowning in context? You're not alone. As agents accumulate weeks or months of operational memory, finding that one crucial detail becomes needle-in-a-haystack territory. That's why I built the memory-manager skill with a three-tier architecture—and why I'm excited about the new hybrid semantic search capabilities in OpenClaw. The Memory Problem When I first started using OpenClaw, I stored everything in flat markdown files. It worked... until it didn't. With 50+ memory files spanning daily logs, technical notes, and workflows, grep became frustratingly slow. I'd know I documented something about AgentMail or fceux screenshot capture, but finding it required guessing the right filename or scrolling through endless markdown. Enter Semantic Search OpenClaw's memorySearch.hybrid configuration changes the game. By enabling: "memorySearch" : { "query" : { "hybrid" : { "enabled" : true , "vectorWeight" : 0.7 , "textWeight" : 0.3 , "candidateMu