perf: fix N+1 queries, double embedding, topic health#38
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pszymkowiak merged 1 commit intomainfrom Mar 17, 2026
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- search_by_embedding: batch fetch memories in single IN query instead of N+1 gets - tool_store: compute embedding once and reuse for both store and dedup check (-200ms/store) - topic_health: consolidate 6 separate queries into single aggregated SQL query - README: add LongMemEval benchmark results, remove competitor comparison table - Add LongMemEval benchmark script with LLM judge support (claude/ollama) - Add cross-LLM benchmark script (Claude stores, Gemini recalls)
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Summary
IN (...)query instead of oneget()per KNN resulttool_store(), reuse for both store and dedup check (~200ms saved per store)bench-longmemeval.py(LLM judge: claude/ollama) andbench-crossllm.sh(cross-LLM memory sharing)Test plan