Long-term conversational memory is the ability to recall and reason over months of past conversations. It is one of the hardest unsolved problems in AI assistants, and the field has converged on a shared assumption about how to solve it: the model cannot be trusted to find what it needs, so the system has to pre-process the conversation for it. Extract atomic facts. Build entity graphs. Summarize each session. Page memory in and out of a custom memory-management layer.

We think that assumption is worth testing, and we have the numbers to argue against it.

We put raw conversation turns into a single Postgres table, exposed three search tools over it via MCP, and let the agent decide how to look things up. No fact extraction. No summarization. No graphs. No memory manager. That system scores F1 = 0.665 on the LoCoMo benchmark with Claude Sonnet (raw, full 10 samples), above Omni-SimpleMem's best reported configuration at 0.613.…