RAG & Knowledge
llm-wiki-agent
A personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki.
★ 3.6k starsMIT
View guide →OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
OpenKB is an open-source rag & knowledge project by VectifyAI (VectifyAI/OpenKB on GitHub). It has 4,759 GitHub stars and 495 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 22 July 2026.
Collect the papers, docs, or URLs you want compiled into one knowledge base.
Install OpenKB from PyPI, create a folder, and run the init command, choosing your LLM model.
Add files, a whole directory, or a URL; OpenKB compiles them into wiki pages with cross-links.
Ask questions with cited answers, chat interactively, or generate outputs like skills and slide decks.
Documents become summary, concept, and entity pages with cross-references that accumulate over time; contradictions get flagged.
PageIndex tree indexing handles long PDFs by reasoning over a hierarchical index — no vector database required.
PDF, Word, Markdown, PowerPoint, HTML, Excel, CSV, text, and URLs, with native handling of figures, tables, and images.
One-off cited answers or multi-turn chat over the wiki, with persisted sessions you can resume.
Distill redistributable agent skills from the wiki, visualize an interactive knowledge graph, or generate single-file HTML slide decks.
The wiki is plain Markdown with wikilinks that opens in Obsidian, and pages follow Google's Open Knowledge Format specification.
Clone for a source install, or simply install from PyPI in the next step.
git clone https://github.com/VectifyAI/OpenKB.git
cd OpenKBInstall the package (or `pip install -e .` from the clone for development).
pip install openkbCreate a knowledge base, add a document, and ask your first question (set your LLM API key in .env first).
openkb init
openkb add paper.pdf
openkb query "What are the main findings?"VectifyAI, the team behind PageIndex; OpenKB is powered by PageIndex's open-source vectorless retrieval.
Yes, it is Apache-2.0 licensed. You pay only for the LLM API usage of the provider you configure (OpenAI, Claude, Gemini, and others via LiteLLM); some subscription providers can authenticate via OAuth instead of a key.
It needs an LLM — usually your own API key set in .env. PageIndex runs locally by default; the optional PageIndex Cloud features need a separate key.
RAG rediscovers knowledge from chunks on every query and nothing accumulates. OpenKB compiles knowledge once into a persistent wiki with cross-references already built, then keeps it current as documents are added.
Run `pip install openkb`, then `openkb init` in a fresh folder, `openkb add <file>`, and `openkb query` or `openkb chat`.
A personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki.
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Free and open — grab the source, star the repo, and start creating.