Engram is a trust layer for AI agent memory. Agents that write down what they learn have no way to tell a hard-won fact from a lie. What's worse is that a wrong memory stays put: the agent does work based on it and writes new memories that inherit the error, so deleting the original leaves the damage behind. Engram gives every memory a confidence score it has to earn from real outcomes, and records which memories were relied on when each new one was written, generating a family tree. Memories that help get promoted; memories cited when the agent gets something wrong get slashed and benched. When a memory is caught being wrong, Engram follows the family tree and knocks down everything that memory taught, automatically. The demo does this live against a real movie database: I plant a single false claim that low-vote ratings should be filtered out: something nothing in the data can disprove. It passes the first task, because that filter doesn't change a ranking, so the false memory is rewarded and spawns a new memory beneath it. Then it fails a counting task (9 instead of 16), gets caught, and one database query traces and cleans up its offspring with no human involvement between the poisoning and the recovery. I built the whole engine from scratch: the scoring rules, the search that ranks by relevance times confidence, the family-tree trace, the agent loop, and a live terminal view, backed by 131 tests including 20 that run against the real cluster.
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