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Show HN: Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)
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What is Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)?
Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph) is a digital product or tool described as:
Where did Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph) originate?
Data for Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph) was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph) publicly launched?
The initial public indexing or launch date for Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph) within our tracked developer communities was recorded on June 4, 2026.
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Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph) has achieved measurable traction, logging over 34 traction score and facilitating 16 recorded discussions or engagements.
Is Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph) recognized by media or academic researchers?
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Community Voice & Feedback
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.cloudfeel free to try and use it. i think it's useful
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Is there any relevance with another tool call mnemon?
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.
You forgot BM25 embeddings.https://github.com/MikeS071/ai-engramhttps://github.com/lamost423/openclaw-hybrid-memoryhttps://medium.com/@qdrddr/agentic-memory-framework-hindsigh...https://clawhub.ai/vnesin-sarai/hybrid-retrievalhttps://www.josecasanova.com/blog/openclaw-qmd-memoryhttps://medium.com/@richardhightower/stop-the-hallucinations...https://github.com/oomkapwn/enquire-mcp#-why-its-the-besthttps://github.com/rohitg00/agentmemory#key-capabilitieshttps://github.com/Melody-0321/NE-Memory-Corehttps://github.com/ClaudioDrews/memory-oshttps://en.wikipedia.org/wiki/Okapi_BM25> It is based on the probabilistic retrieval framework developed in the 1970s and 1980sAnyway, good for ya, hope you had fun building it.
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.
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