Agent Memory Is a Search Problem, Not a Storage Problem

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Learn how agentic search with PostgreSQL, MCP, and Claude achieved 0.665 F1 on LoCoMo, challenging the need for complex AI memory preprocessing pipelines.
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A conversational memory system built with PostgreSQL, MCP tools, and Claude achieved a raw F1 score of 0.665 on the LoCoMo benchmark, exceeding Omni-SimpleMem’s reported 0.613. Instead of extracting facts, generating summaries, or building memory graphs, the system stores raw conversation turns and lets the agent search using semantic, full-text, temporal, and hierarchical indexes. Experiments suggest that tool design, retrieval freedom, and careful adversarial evaluation can matter more than elaborate preprocessing, though model differences and limited benchmark scale remain important caveats.