Agent Memory Is a Search Problem, Not a Storage Problem

This story was originally published on HackerNoon at: https://hackernoon.com/agent-memory-is-a-search-problem-not-a-storage-problem. Learn how agentic search with PostgreSQL, MCP, and Claude achieved 0.665 F1 on LoCoMo, challenging the need for complex AI memory preprocessing pipelines. Check more stories related to undefined at: https://hackernoon.com/c/undefined. You can also check exclusive content about #postgresql-ai-memory, #ai-agent-memory-architecture, #agentic-retrieval, #mcp-memory-tools, #ai-memory-retrieval, #claude-sonnet, #conversational-ai-memory, #good-company, and more. This story was written by: @tigerdata. Learn more about this writer by checking @tigerdata's about page, and for more stories, please visit hackernoon.com. 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.