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KnowNote A local-first research workspace. Research your documents. Trust every answer. Desktop · Local RAG · Bring your own model · Open source English | 简体中文 KnowNote turns your own documents into a knowledge base you can question, and answers from those documents with a reference back to the passage the answer came from. It is a desktop application rather than a self-hosted stack: no Docker, no server, no account. Parsing, splitting, embedding and vector search all run inside the app, and the only thing that leaves your machine is the request you send to the model endpoint you configured. If that endpoint is a local server, nothing leaves at all. The website is the full story: knownote.pages.dev — the mechanism, the limits, and an honest comparison with NotebookLM and AnythingLLM. Preview <img src="./.github/images/screenshot-main.png" alt="KnowNote main interfac
| Stars | 1,154 |
| Forks | 154 |
| Language | TypeScript |
| Category | AI Tool |
| License | GPL-3.0 |
| Quality Score | 72.8267702839207/100 |
| Open Issues | 6 |
| Last Updated | 2026-10-02 |
| Created | 2025-12-15 |
| Platforms | node |
| Est. Tokens | ~15k |
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Looking for a KnowNote alternative? If you're comparing KnowNote with other ai tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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KnowNote is A local-first AI knowledge base & NotebookLM alternative built with Electron. More convenient, more lightweight, and understands you better!. It is categorized as a AI Tool with 1.2k GitHub stars.
KnowNote is primarily written in TypeScript. It covers topics such as anthropic, electron-app, google.
You can find installation instructions and usage details in the KnowNote GitHub repository at github.com/mrsibe/KnowNote. The project has 1.2k stars and 154 forks, indicating an active community.
KnowNote is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to KnowNote on Agent Skills Hub include gptme, ax, witsy. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: