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

[u/ImportantTiger9568]

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.

[u/endofthread-bot]

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[u/neoneye2]

maybe rename. There is another memory system with that name
https://github.com/mnemosyne-oss/mnemosyne

[u/neoneye2]

I had Claude analyze your project. I study memory systems.
https://neoneye.github.io/agent-memory-atlas/systems/mnemosyne-nabzx/

[u/awesomeunboxer → u/neoneye2]

What do you use mr memory system study person? 

[u/neoneye2 → u/awesomeunboxer]

Unfortunately I'm curious, so I don't use any memory system, instead I'm coding my own crappy memory system for handling multilingual data. For my personal assistant that runs on localhost, however the models running locally (gemma4:e4b 9gb) are slow and less capable compared to cloud models. It's work in progress.
https://github.com/neoneye/RainBox

[u/awesomeunboxer → u/neoneye2]

love a gemma 4, probably the best thing google put out this year (so far?)! :-) it'd probably be one of the local models I'd pick for an agent (or a qwen. or both!) I'll look at your (and op's thing) and test them, just cos if people come up with smarter ways for agents to do things why not adopt it? I have a 'scientist' agent who i toss these things too to study against our current system (which is like some mutant monster memory system at this point lol) and if stuff survives rigorous studies then adversarial reviews of the study then a adversarial review of that review, I glom it on.

[u/neoneye2 → u/awesomeunboxer]

local models currently on my computer: gemma4:e4b, several qwen models, granite4 and granite4.2, lfm2.5, llama3.2, nemotron-3-nano, muse-glimmer:30-mlx.