It solves a critical problem of flat shared memory causing hallucinations, demonstrated by doubling task success (40% to 80%) and cutting hallucination to a third, while requiring no cloud infrastructure or LLM calls.
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
Similar projects matched by category, topics, and programming language.
Harness Engineering is a methodology for improving coding agent outputs by carefully crafting the environment around them—providing curated context, tools, and executable constraints that encode an organization’s nonfunctional requirements and cumulative lessons.
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
mimic captures traffic from any iOS or web app and automatically generates a Python client library that lets you call the app's API like a regular library.