vipune is a local CLI memory store for AI agents. You tell your LLM agents to run vipune add "..." and vipune search "..." — it stores embeddings locally in SQLite and retrieves by semantic meaning, reranking and recency baked in, not keyword matching.
The problem it solves: agents needing historical knowledge of your project or other tasks that other agents are working on. Or just getting context transferred from session to session in clean and semantically searchable way. Most solutions require an API, a cloud service, or a running server. vipune is a single binary, no daemon, no keys, works offline.
I had a predecessor to vipune in my personal agentic flow for a long time, but it got bloated and decided on a rewrite and thought to share, maybe someone else will find it useful also.
And yes, this is written 99% by LLM agents, 100% monitored by yours truly.
It also does conflict detection — if you try to add something semantically similar to an existing memory, it flags it instead of silently duplicating.
Built in Rust, Apache-2.0. Binaries for macOS ARM64 and Linux. Early release — would love feedback on the use cases people try it for.
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Show HN: Vipune – Simple Memory for Agents · HackerTrans
The problem it solves: agents needing historical knowledge of your project or other tasks that other agents are working on. Or just getting context transferred from session to session in clean and semantically searchable way. Most solutions require an API, a cloud service, or a running server. vipune is a single binary, no daemon, no keys, works offline.
I had a predecessor to vipune in my personal agentic flow for a long time, but it got bloated and decided on a rewrite and thought to share, maybe someone else will find it useful also.
And yes, this is written 99% by LLM agents, 100% monitored by yours truly.
It also does conflict detection — if you try to add something semantically similar to an existing memory, it flags it instead of silently duplicating.
Built in Rust, Apache-2.0. Binaries for macOS ARM64 and Linux. Early release — would love feedback on the use cases people try it for.