Show HN: Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)
32 points
6 hours ago
| 5 comments
| github.com
| HN
georgespencer
5 hours ago
[-]
Given the abundance of vaguely similar local-first AI memory layers, it might be a good idea to add a "Why Mnemo" section right at the top of README.md to explain why folks should consider using it.
reply
zaydmulani
1 hour ago
[-]
Done "Why mnemo" section added to the README with a comparison table. Short version: single Rust binary, zero cloud, petgraph knowledge graph with multi-hop traversal, scored retrieval. Link in case you want to check it: github.com/zaydmulani09/mnemo
reply
cush
3 hours ago
[-]
Or just wait a week and whatever’s built into your harness de jour will be as good or better than whatever homebrew solutions are out there

> Most LLMs forget everything the moment a conversation ends. mnemo fixes that

Even the opening line of the README is obviously very out of date. Might be true if you’re raw-dogging a model or using a basic agent SDK

reply
SwellJoe
2 hours ago
[-]
After working with LLMs a bunch, I now want them to forget everything every time I end the conversation. Otherwise they get dumber and more confused over time.

LLMs do not have memory and these "memory" systems that everyone makes don't change that fact. They just clutter up context with probably irrelevant noise. I don't want the LLM to remember everything I've ever said and try to make every project align with often contradictory or unrelated facts, rules, guidelines, practices, whatever, because when it tries it gets messier and makes worse software.

I don't want the LLM to be my friend and remember my birthday. I have it write plans, developer docs, test suites, and static analysis into every project. That's the "memory". It's compatible with every agent, it's in their native tongue (Markdown and code), and it's focused on the specific project.

reply
ksajadi
2 hours ago
[-]
I tend to agree with the rest of the commenters that the most likely outcome is that harnesses will include features like this. I had a slightly different issue and that was 'project-level memory' that i can use across models or harnesses (chat, claude code, etc).

for a while i used Obsidian but it was not very good with hosted tools like claude.ai then i moved to a combination of Linear and Notion. Still using Linear but Notion ended up being a royal pain: it is built for humans not agents. It is block based and when multiple agents use it there is a lot of corruption in the process.

I wanted a markdown only, notion built for agents that can work with multiple agents so built one: markbase.cloud

feel free to try and use it. i think it's useful

reply
bilbo-b-baggins
5 hours ago
[-]
reply
zaydmulani
3 hours ago
[-]
BM25 is in my other project vecdb. mnemo's retrieval is graph-first — entity deduplication, multi-hop traversal, session-scoped scoring. Different tradeoff, not an oversight.
reply
asdev
4 hours ago
[-]
I haven't seen one unique product in AI, everyone is building the same thing
reply
zaydmulani
3 hours ago
[-]
Fair. The differentiator is the Rust single binary + petgraph knowledge graph. No Python runtime, no cloud, survives restarts. Built it because nothing local fit that profile.
reply
andai
4 hours ago
[-]
Do any of them work properly yet?
reply
SwellJoe
4 hours ago
[-]
Everybody builds one. And, then they usually figure out that making the model fill its context with a bunch of memories hurts performance more often than it helps.
reply
esafak
3 hours ago
[-]
That's why I always ask: got benchmarks?
reply
zaydmulani
2 hours ago
[-]
Yes — cargo run -p mnemo-bench. Ships with 12 benchmarks. Full retrieval pipeline is ~4ms on debug build. Numbers are in the README performance table.
reply
SwellJoe
1 hour ago
[-]
I don't care if it's fast, if it makes the model dumber by cluttering up context.
reply
phantomathkg
3 hours ago
[-]
Is there any relevance with another tool call mnemon?
reply
zaydmulani
2 hours ago
[-]
Different project — mnemon is a Python-based memory tool. mnemo is a Rust binary with a knowledge graph layer and REST API sidecar. Similar name, different approach.
reply