Audit-Ready Memory
A local-first memory layer for AI agents with transparent logs, deterministic replay, and explicit deletion workflows.
This project is being developed to make AI memory more inspectable, reproducible, and accountable in practice.
This work is funded by SIDN Fund.
What it is
Audit-Ready Memory treats agent memory as a structured, time-stamped record instead of opaque model state. The goal is to make it possible to inspect what an agent remembered, trace how memory was used, replay prior state deterministically, and handle deletion explicitly.
Why it matters
Many current AI systems rely on hidden memory mechanisms or external services that are difficult to audit. That makes accountability, correction, and deletion harder than they should be, especially in public-interest settings. This project focuses on a simpler alternative: local-first memory with clear provenance, reproducible behavior, and operator control.
What this project will deliver
The project focuses on a minimal but reusable core: transparent memory logs, deterministic replay, explicit deletion workflows, a small demo, and open documentation. The intent is to produce a concrete artifact that researchers, civic-tech teams, and other public-interest builders can inspect and build on.
Status
The repository has been created and the public project page is live. Code, documentation, benchmarks, and demo materials will be published here as the project progresses.
Links
Repository: github.com/humemai/audit-ready-memory
Organization: github.com/humemai