Agentmemory

Your agent can surface any conversation from six months ago verbatim, yet it is still making the same mistakes it made then. Recall and learning are architecturally distinct, and most agent memory systems only build the former. Removing memory from an agent hurts performance more than swapping the underlying LLM. You're probably investing in the wrong layer.

Your vector store has the right answer stored. Your agent still gets it wrong. The failure is in retrieval logic that fetches topically similar content instead of logically relevant facts. Backward chaining, a technique from 1970s Prolog theorem provers, closes a 21-point accuracy gap on multi-hop memory benchmarks without touching your storage layer.