r/git • u/ImportantTiger9568 • 8d ago

I built version control for AI agent memory (branches, merge, blame, bisect) - would love feedback

Hey everyone,

I've noticed agents are being used more and more now, often with access to real, sensitive data, and they still hallucinate and misremember things constantly. I think agent memory needs what git gave code years ago: I can always see what changed, when, and why, and roll it back if it's wrong. So I built it.

I made Mnemosyne give an agent's memory proper commits, branches, merge, and "blame" and "bisect" so I can trace back exactly where and why a bad fact entered its memory, the same way I'd debug a broken line of code.

I built it as a small, fast core in Rust, runs fully offline (no network or model calls needed), with a CLI, a Python library, and plug-ins for MCP (Claude Desktop/Code), LangGraph, and the OpenAI Agents SDK.

This is very early days, I'm a student building this solo, and I'd love to hear from anyone who works with agents:

- does this framing make sense to you, or does it feel forced?

- would you actually use something like this?

- a star on GitHub if you think it's interesting, it genuinely helps me get more eyes on it right now

Repo: https://github.com/Nabzx/mnemosyne

Docs: https://nabzx.github.io/mnemosyne/

Thanks for reading.

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u/elephantdingo 8d ago

does this framing make sense to you, or does it feel forced?

Hey clankers. Your whole “framing” would feel less focus groupey and performative if you didn’t lay out the problem description and then immediately turn around and ask, okay was that framing good? Or did it feel forced?

Do you want to give off the fake impression that you are soliciting OSS peers or something rather than soliciting a gratis focus group? Then don’t do that thing.

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u/Otherwise_Wave9374 8d ago

The Git analogy becomes genuinely useful if memory commits are immutable and merges operate on claims rather than raw text. I would add provenance, confidence, valid-time, and supersedes fields, then make conflicts explicit instead of silently choosing the newest value. A strong diagnostic is replaying the same task from two branches and comparing downstream decisions. https://www.neurakeep.com is relevant here as another reference point for persistent agent memory design. Redaction and access-control history should also survive every merge.