Most AI agent memory is just vector DB + semantic search. Store everything, retrieve by similarity. It works, but it doesn't scale well over time. The noise floor keeps rising and recall quality degrades.
I took a different approach and built memory using actual cognitive science models. ACT-R activation decay, Hebbian learning, Ebbinghaus forgetting curves. The system actively forgets stale information and reinforces frequently-used memories, like how human memory works.
After 30 days in production: 3,846 memories, 230K+ recalls, $0 inference cost (pure Python, no embeddings required). The biggest surprise was how much forgetting improved recall quality. Agents with active decay consistently retrieved more relevant memories than flat-store baselines.
And I am working on multi-agent shared memory (namespace isolation + ACL) and an emotional feedback bus.
Curious what approaches others are using for long-running agent memory.
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