它解决了共享平面记忆导致幻觉的关键问题，将任务成功率从 40% 提升至 80%，并将幻觉降低至三分之一，同时无需云基础设施或 LLM 调用。
Local memory for AI agents, served by persona, domain, and task. Survives restarts. One machine, many agents.
Most agent memory is a shared pile: every agent gets the same top-k for the same query. In our benchmark (methodology below), an agent fed flat shared memory hallucinated more than an agent given no memory at all. Serving each agent the memory that fits who it is (its persona, its domain, the task in front of it) doubled task success (40% to 80%) and cut hallucination to a third.
And the memory layer never calls an LLM. On LoCoMo — the long-conversation memory benchmark — engram-lite scores J 68.3 with zero LLM calls and $0 to build the memory. The protocol, footnotes, and the full comparison against the leading LLM-based memory systems live in benchmarks/locomo/.
engram-lite is that serving layer, small enough to run on your laptop:
pip install and you are running.Memory.decisions()). "Why don't you remember X?" has an answer — something an LLM-written memory can't give you, because its keep/drop decisions live inside model weights.Local Agentic Memory
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