zycaskevin

zycaskevin / Vault-for-LLM

Public

🧠 Local-first knowledge system for LLM agents — sqlite-vec + ONNX embeddings, no cloud/Docker/PyTorch dependency

embeddings knowledge-base llm local-first mcp-server
33
8
100% credibility
Found Apr 26, 2026 at 33 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A local knowledge management tool that structures personal notes, project details, and lessons into searchable layers for AI assistants without any cloud or heavy software needs.

How It Works

1
💡 Discover a smart memory for your AI

You learn about a simple local system that lets your AI assistant remember your personal projects, lessons, and tips forever.

2
🛠️ Set it up in minutes

Download and start your personal knowledge vault with a quick install—no complicated setup needed.

3
👤 Tell it about you

Share who you are, your current projects, and daily context so your AI always knows your world.

4
📝 Add your notes and wisdom

Drop in simple notes about what you've learned, fixes that worked, and techniques from your experience.

5
✨ Organize into smart layers

Hit go and it automatically sorts everything into always-ready, always-searchable memory layers.

6
🔍 Search and share with AI

Ask any question and get instant relevant memories to paste into your AI chats.

🎉 Your AI never forgets

Now your assistant recalls your exact projects, pitfalls, and successes every time, making it truly yours.

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AI-Generated Review

What is Vault-for-LLM?

Vault-for-LLM is a local-first knowledge system for LLM agents, giving them persistent, searchable memory without cloud, Docker, or PyTorch dependencies. Built in Python with sqlite-vec for storage and ONNX embeddings for hybrid keyword-vector search, it structures your notes into a four-layer hierarchy—from core user identity to deep techniques—for precise retrieval. Drop Markdown files into raw/, run vault compile, and query via CLI for atomic claims with citations and graph expansion.

Why is it gaining traction?

It stands out by ditching heavy ML stacks for a pip-install setup that runs on any machine, delivering agent-ready knowledge with freshness tracking, trust scores, and self-questioning convergence checks. Developers love the MCP server for mid-chat integration with tools like Claude Code, plus scripts for deduping and validation—pure local-first GitHub gold without vendor lock-in. Early adopters praise the 6x AAAK compression slashing token costs.

Who should use this?

AI engineers building offline LLM agents need this for embedding personal knowledge like separation and extraction methods into workflows. Solo devs prototyping RAG systems or maintaining project notes across WSL/Mac/Linux will appreciate the CLI lifecycle from import to linting. It's ideal for those tired of cloud embeddings and wanting a portable vault for daily syncs.

Verdict

Try it if local-first peoples knowledge for agents fits your stack—solid alpha with multilingual support and no GPU reqs, but 33 stars and 1.0% credibility signal early days; docs shine but expect rough edges in edge cases. Great for tinkerers, skip for production until more battle-tested.

(198 words)

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