LangChain explains LangSmith AI Search: trajectory views plus a small parallel model under half a second per check
LangChain · x · 2026-10-17
LangChain breaks down how AI Search in LangSmith works: SmithDB converts each ingested trace into a single standard message format and builds a readable trajectory view across agent sessions. Search first applies structured filters (e.g. time range) to narrow candidates, then a small, fast model checks each remaining trajectory against the user's description, with many checks running in parallel — each usually under half a second. Matches stream back as they're found rather than waiting for a full scan. A useful hybrid retrieval architecture (structured pre-filter + semantic model re-check) for teams building agent observability.
Related event: LangChain Launches LangSmith AI Search for Semantic Agent Trace Retrieval(4 posts)→
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