Most people who try to build a knowledge base from a pile of documents end up doing it by hand — reading each file, writing a summary, linking it to related notes — which is slow and rarely kept up to date. OpenKB automates that: point it at a folder of documents and it reads them with an AI model, writes structured summary pages, and cross-links related topics automatically, so you end up with a browsable wiki instead of a flat pile of files. OpenKB is an open-source (Apache 2.0) command-line tool built by VectifyAI. It ingests PDFs, Word docs, Markdown, PowerPoint, HTML, Excel and CSV files — including figures, tables and images — and compiles them into a persistent, interlinked Markdown wiki compatible with Obsidian's graph view. Instead of the usual RAG approach (chunk documents into a vector database and re-search on every query), it uses "vectorless" tree indexing via VectifyAI's PageIndex technology, meaning it reasons over full document structure rather than fragmented chunks — a better fit for long, structured documents. On top of the wiki foundation it adds generators for querying, chatting with your knowledge base, and a "Skill Factory" for turning compiled knowledge into reusable outputs. It supports multiple LLM providers (OpenAI, Claude, Gemini) via LiteLLM and the OpenAI Agents SDK, and has around 3.8k GitHub stars.
| # | Tool | Best for | Price |
|---|---|---|---|
| 1 | Équipes sécurité et CISOs d'entreprise qui gèrent le déploiement d'agents IA, la sécurité des apps tierces et la gouvernance des risques IA — clients notables : T-Mobile, Databricks, S&P Global, Snowflake | — | |
| 2 | Professionnels | Entreprises | — | |
| 3 | Étudiants | Professionnels | — | |
| 4 | Développeurs et équipes voulant un assistant de code IA auto-hébergé avec mémoire persistante | — | |
| 5 | Équipes IT et sécurité d'entreprises déployant des agents IA internes construits par leurs employés | — | |
| 6 | Développeurs construisant des agents IA en production nécessitant un contrôle fin de l'état et de la fiabilité | — | |
| 7 | Équipes produit et développeurs construisant des chatbots, agents IA ou pipelines RAG sans vouloir tout coder à la main | — | |
| 8 | Développeurs et équipes construisant des systèmes IA ancrés dans leurs propres documents (RAG), de la startup à la grande entreprise | — | |
| 9 | Développeurs et équipes techniques construisant des agents IA autonomes longue durée (recherche, code, tâches multi-étapes) | — | |
| 10 | Développeurs et équipes qui font tourner des agents de code IA (Claude Code, Cursor, Codex...) de façon semi-autonome sur une vraie codebase | — | |
| 11 | Organisations en secteurs réglementés (santé, droit, finance, gouvernement) qui ne peuvent pas envoyer leurs données à un cloud tiers | — | |
| 12 | Développeurs et ingénieurs DevOps qui veulent des agents IA légers et composables intégrés à des pipelines existants (cron, git hooks, scripts) | — |
A security tool for IT teams that answers a question most companies can't: which of the hundreds of apps and AI agents connected to our Google Workspace, Salesforce or Slack actually have access to what, and are any of them behaving suspiciously right now
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