Show HN: Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)

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.

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

You forgot BM25 embeddings.

https://github.com/MikeS071/ai-engram

https://github.com/lamost423/openclaw-hybrid-memory

https://medium.com/@qdrddr/agentic-memory-framework-hindsigh...

https://clawhub.ai/vnesin-sarai/hybrid-retrieval

https://www.josecasanova.com/blog/openclaw-qmd-memory

https://medium.com/@richardhightower/stop-the-hallucinations...

https://github.com/oomkapwn/enquire-mcp#-why-its-the-best

https://github.com/rohitg00/agentmemory#key-capabilities

https://github.com/Melody-0321/NE-Memory-Core

https://github.com/ClaudioDrews/memory-os

https://en.wikipedia.org/wiki/Okapi_BM25

> It is based on the probabilistic retrieval framework developed in the 1970s and 1980s

Anyway, good for ya, hope you had fun building it.

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.

Is there any relevance with another tool call mnemon?

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