<div align="center">
<a href="https://openkb.ai">
<img src="https://docs.pageindex.ai/images/openkb.png" alt="OpenKB (by PageIndex)" />
</a>
<br />
<br />
<p align="center">
<a href="https://trendshift.io/repositories/26145" target="_blank"><img src="https://trendshift.io/api/badge/repositories/26145" alt="VectifyAI%2FOpenKB | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>
# OpenKB: Open LLM Knowledge Base
<p align="center"><i>Scale to long documents • Reasoning-based retrieval • Native multi-modality • No Vector DB</i></p>
</div>
<details open>
<summary><h2>📢 Recent Updates</h2></summary>
- *Google Open Knowledge Format (OKF)*: Wiki pages follow the [Google OKF](https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing) specification for knowledge sharing.
- *Entity Pages*: People, orgs, places, and products as dedicated wiki pages, auto-extracted and kept in sync.
</details>
---
# 📑 What is OpenKB
**OpenKB (Open Knowledge Base)** is an open-source system (in CLI) that compiles raw documents into a structured, interlinked wiki-style knowledge base using LLMs, powered by [**PageIndex**](https://github.com/VectifyAI/PageIndex)'s vectorless, reasoning-based retrieval for long documents.
The idea is based on a [concept](https://x.com/karpathy/status/2039805659525644595) described by Andrej Karpathy: LLMs generate summaries, concept pages, and cross-references, all maintained automatically. Knowledge compounds over time instead of being re-derived on every query.
### Why not traditional RAG?
Traditional RAG rediscovers knowledge from scratch on every query. Nothing accumulates. OpenKB compiles knowledge once into a persistent wiki, then keeps it current. Cross-references already exist, contradictions are flagged, and synthesis reflects everything consumed.
OpenKB has two layers: a **wiki foundation** that compiles and maintains your knowledge, and **generators** (query / chat / Skill Factory) that turn it into useful output. See [Usage](#️-usage) for the full command list.
### Features
- **Broad format support:** PDF, Word, Markdown, PowerPoint, HTML, Excel, CSV, text, URLs, and more.
- **Scales to long documents:** Long and complex documents are handled via [PageIndex](https://github.com/VectifyAI/PageIndex) tree indexing, enabling accurate, vectorless, context-aware retrieval.
- **Native multi-modality:** Retrieves and understands figures, tables, and images, not just text.
- **Compiled wiki:** The LLM compiles your documents into summaries, concept pages, entity pages, and cross-links, all kept in sync.
- **Query & chat:** One-off questions or multi-turn conversations over your wiki, with persisted sessions to resume.
- **Skill Factory:** Distills redistributable agent skills from your wiki.
- **OKF-ready:** Wiki pages follow the [Google OKF](https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing) specification for knowledge sharing.
- **Obsidian-compatible:** The wiki is plain `.md` files with cross-links. Opens in Obsidian for graph view.
- **Knowledge Workbench (Web UI):** A bundled web UI served at `/` to browse the KB, upload and compile documents, and stream queries and chats — all in the browser.
# 🚀 Getting Started
### Install
```bash
pip install openkb
```
<details>
<summary><b><i>Other install options:</i></b></summary>
- **Latest from GitHub:**
```bash
pip install git+https://github.com/VectifyAI/OpenKB.git
```
- **Install from source** (editable, for development):
```bash
git clone https://github.com/VectifyAI/OpenKB.git
cd OpenKB
pip install -e .
```
</details>
### Quick Start
```bash
# 1. Create a directory for your knowledge base
mkdir my-kb && cd my-kb
# 2. Initialize the knowledge base
openkb init
# 3. Add documents
openkb add paper.pdf
openkb add ~/papers/ # Add a whole directory
openkb add https://arxiv.org/pdf/2509.11420 # Or fetch from a URL
# 4. Ask a question
openkb query "What are the main findings?"
# 5. Or chat interactively
openkb chat
# (Optional) Turn the wiki into other outputs
openkb skill new my-expert "Reason like an expert on <your-topic>" # a portable agent skill
openkb visualize # an interactive knowledge graph
openkb deck new my-deck "An intro deck on <your-topic>" # slides — a single-file HTML deck
```
### Set up your LLM
OpenKB supports [multiple LLM providers](https://docs.litellm.ai/docs/providers) (OpenAI, Claude, Gemini, etc.) via [LiteLLM](https://github.com/BerriAI/litellm) (pinned to a [safe version](https://docs.litellm.ai/blog/security-update-march-2026)).
Set your model during `openkb init` or in [`.openkb/config.yaml`](#configuration) using the `provider/model` LiteLLM format (e.g. `anthropic/claude-sonnet-4-6`). OpenAI models can omit the prefix (e.g. `gpt-5.4`).
Create a `.env` file with your LLM API key:
```bash
LLM_API_KEY=your_llm_api_key
```
Subscription-based providers that authenticate via OAuth device flow (e.g. `chatgpt/*`, `github_copilot/*`) need no API key; OpenKB skips the missing-key warning for them.
### Knowledge Workbench (Web UI)
OpenKB ships a bundled web UI, served by the REST API at `/`. Install the API extra and start the server — no configuration needed:
```bash
pip install "openkb[web]"
openkb-web # serves the API + Workbench at http://127.0.0.1:7566/
```
Open `http://127.0.0.1:7566/` for the Workbench. Auth is off by default (local-first); set `OPENKB_API_TOKEN` to require a bearer token before exposing the server. See the [full Web UI guide](examples/rest-api/README.md#knowledge-workbench-web-ui).
> Working on the UI itself? Run the Vite dev server with `cd frontend && npm install && npm run dev` (it proxies `/api` to a running `openkb-web`), or `npm run build` to regenerate the bundled `openkb/web/`.
# 🧩 How OpenKB Works
### Architecture
<div align="center">
<img src="assets/openkb-architecture.webp" alt="OpenKB Architecture: from raw documents (markitdown / PageIndex) through LLM wiki compilation to the wiki/ foundation, powering query/chat, the Skill Factory, and future generators" width="900" />
</div>
### Short vs Long Document Handling
| | Short documents | Long documents (PDF ≥ 20 pages) |
| ------------- | -------------------------- | ---------------------------------- |
| **Convert** | markitdown → Markdown | PageIndex → tree index + summaries |
| **Images** | Extracted inline (pymupdf) | Extracted by PageIndex |
| **LLM reads** | Full text | Document trees |
| **Result** | summary + concepts | summary + concepts |
Short documents are read in full by the LLM. Long PDFs are processed by [PageIndex](https://github.com/VectifyAI/PageIndex) into a hierarchical tree index. The LLM reads the tree instead of the full text, enabling accurate and scalable retrieval for long documents.
### Knowledge Compilation
When you add a document, the LLM:
1. Generates a **summary** page
2. Reads existing **concept** and **entity** pages
3. Creates or updates concepts with cross-document synthesis
4. Creates or updates **entity** pages (people, orgs, places, products)
5. Updates the **index** and **log**
A single source might touch 10--15 wiki pages. Knowledge accumulates: each document enriches the existing wiki rather than sitting in isolation.
# ⚙️ Usage
OpenKB commands fall into two layers: the **wiki foundation** (compile + manage your knowledge) and **generators** (turn that wiki into useful output). Each links to a concrete walkthrough — a real artifact OpenKB generated from one sample paper (browse them all in [`examples/`](examples/)).
## Layer 1: 🧱 Wiki Foundation — compile and maintain
| Command | Description |
| ------------------------------------------------------------ | --------------------------------------------------------------------------------------- |
| `openkb init` | Initialize a new knowledge base (interactive) |
| <code>openkb add <file_or_dir_or_URL></code> | Add files, directories, or URLs and compile to wiki (URL content type is auto-detected) |
| `openkb list` | List indexed documents and concepts |
| `openkb status` | Show knowledge base stats |
| `openkb watch` | Watch `raw/` and auto-compile new files |
| `openkb lint` | Run structural and knowledge health checks |
<details>
<summary><i>More wiki commands:</i></summary>
<br>
| Command | Description |
| ------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| <code>openkb remove <doc></code> | Remove a document and clean up its wiki pages, images, registry, and PageIndex state (`--dry-run` to preview, `--keep-raw` / `--keep-empty` to retain artifacts) |
| <code>openkb recompile [<doc>] [--all]</code> | Re-run the compile pipeline on already-indexed docs without re-indexing. Regenerates summaries and rewrites concept pages; manual edits are overwritten (`--dry-run` to preview, `--refresh-schema` to also update `wiki/AGENTS.md`) |
| <code>openkb feedback ["msg"]</code> | File feedback by opening a prefilled GitHub issue (`--type bug/feature/question` to tag it) |
</details>
→ **Example:** the everyday loop walked through end to end — [`examples/commands/`](examples/commands/).
## Layer 2: 💡 Generators — turn the wiki into output
A "generator" reads from the compiled wiki and produces something usable: an answer, a conversation, a skill folder. The wiki is the substrate; generators are the surfaces.
| Command | Output | Example |
| ------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | ---------------------------------- |
| <code>openkb query "question"</code> | A grounded answer with citations (`--save` to persist to `wiki/explorations/`) | [query & save](examples/commands/) |
| <code>openkb chat</code> | Interactive multi-turn session over the wiki (`--resume`, `--list`, `--delete` to manage sessions) | [chat](examples/chat/) |
| <code>openkb visualize</code> | A self-contained interactive knowledge graph at `output/visualize/graph.html` — 3D, mind-map, and radial views | [visualize](examples/visualize/) |
| <code>openkb skill new <skill-name> "<intent>"</code> | Distill a redistributable agent skill from your wiki (see [Skill Factory](#skill-factory) below) | [skills](examples/skills/) |
| <code>openkb deck new <name> "<intent>"</code> | Generate a single-file HTML slide deck (`--skill` picks a theme, `--critique` runs a quality pass) | [slides](examples/slides/) |
### (i) 💬 Query & Chat — *ask the wiki*
`openkb query "..."` answers a single question with a grounded, cited answer from your wiki. `openkb chat` is interactive, an ongoing multi-turn session over the same wiki (`--resume`, `--list`, `--delete` to manage sessions). → Walked through with real saved output in **[`examples/commands/`](examples/commands/)** (query) and **[`examples/chat/`](examples/chat/)** (chat).
Inside a chat, type `/` to access slash commands (Tab to complete).
<details>
<summary><i>More slash commands:</i></summary>
<br>
- `/help`: list available commands
- `/status`: show knowledge base status
- `/list`: list all documents
- `/add <path>`: add a document or directory without leaving the chat
- `/skill new <skill-name> "<intent>"`: compile a skill from this chat (see below)
- `/deck new <name> "<intent>"`: generate an HTML slide deck from the wiki
- `/critique <path>`: run the HTML critic over an existing deck
- `/save [name]`: export the transcript to `wiki/explorations/`
- `/clear`: start a fresh session (the current one stays on disk)
- `/lint`: run knowledge base lint
- `/exit`: exit (Ctrl-D also works)
</details>
<a id="skill-factory"></a>
### (ii) 🛠 Skill Factory — *drop in a book; out comes a digital expert.*
`openkb skill new` distills a portable [agent skill](https://docs.claude.com/en/docs/build-with-claude/skills) from your wiki that Claude Code, Codex, and Gemini can install and load natively. Drop in a book's worth of papers; out comes a specialist other agents can call on. → A real generated skill, plus install / share / `eval` / rollback, is walked through in **[`examples/skills/`](examples/skills/)**.
<details>
<summary><i>More skill commands:</i></summary>
<br>
| Command | Output |
| ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------ |
| <code>openkb skill validate [name]</code> | Validate compiled skills (auto-runs after `skill new`) |
| <code>openkb skill eval <name></code> | Check the skill triggers on the right prompts |
| <code>openkb skill history <name></code> / <code>openkb skill rollback <name></code> | Version history + rollback for skills |
</details>
# 🔧 Configuration
### Settings
OpenKB settings are initialized by `openkb init` and stored in `.openkb/config.yaml`:
```yaml
model: gpt-5.4 # LLM model (any LiteLLM-supported provider)
language: en # Wiki output language
pageindex_threshold: 20 # PDF pages threshold for PageIndex
```
The full settings reference — `entity_types`, OAuth providers (`chatgpt/*`, `github_copilot/*`), and LiteLLM tuning (timeouts for slow local runtimes like Ollama / LM Studio, `drop_params`, GitHub Copilot headers, install notes) — is in **[`examples/configuration/`](examples/configuration/)**.
### PageIndex Setup
Long-document retrieval is a [known challenge](https://x.com/karpathy/status/2039823314982744522) for LLMs. [PageIndex](https://github.com/VectifyAI/PageIndex) solves this with vectorless, reasoning-based retrieval, by building a hierarchical tree index that lets LLMs reason over the index for context-aware retrieval.
PageIndex runs locally by default using the [open-source version](https://github.com/VectifyAI/PageIndex), with no external dependencies required.
***Cloud Support*** *(Optional)*:
For large or complex PDFs, [PageIndex Cloud](https://docs.pageindex.ai/) can be used to access additional capabilities, including:
- OCR support for scanned PDFs (via hosted VLM models)
- Faster structure generation
- Scalable indexing for large documents
Set `PAGEINDEX_API_KEY` in your `.env` to enable cloud features:
```
PAGEINDEX_API_KEY=your_pageindex_api_key
```
→ **Example:** local vs. cloud indexing, and importing a cloud-indexed doc — [`examples/pageindex-cloud/`](examples/pageindex-cloud/).
### AGENTS.md
The `wiki/AGENTS.md` file defines wiki structure and conventions. It's the LLM's instruction manual for maintaining the wiki. Customize it to change how your wiki is organized.
The LLM reads `AGENTS.md` from disk at runtime, so your edits take effect immediately.
# 🔌 Integrations
### Using with Obsidian
The wiki is a directory of Markdown files with `[[wikilinks]]`. Obsidian renders it natively.
1. Open `wiki/` as an Obsidian vault
2. Browse summaries, concepts, and explorations
3. Use graph view to see knowledge connections
4. Use Obsidian Web Clipper to add web articles to `raw/`
### Using with Claude Code / Codex / Gemini CLI
OpenKB ships a `SKILL.md` so any agent can read your compiled wiki. No extra runtime, no MCP setup, just install the skill once.
<details>
<summary><i>Claude Code:</i></summary>
<br>
```
/plugin marketplace add VectifyAI/OpenKB
/plugin install openkb@vectify
```
</details>
<details>
<summary><i>OpenAI Codex CLI:</i></summary>
<br>
*(no marketplace command yet; manual symlink)*
```bash
git clone https://github.com/VectifyAI/OpenKB.git ~/openkb-src
mkdir -p ~/.agents/skills
ln -s ~/openkb-src/skills/openkb ~/.agents/skills/openkb
```
</details>
<details>
<summary><i>Gemini CLI:</i></summary>
<br>
```bash
gemini skills install https://github.com/VectifyAI/OpenKB.git --path skills/openkb --consent
```
</details>
The skill is read-only. It won't run `openkb add`, `remove`, or `lint --fix` without you asking. See [`skills/openkb/SKILL.md`](skills/openkb/SKILL.md) for the full instruction set.
# REST API
OpenKB ships a FastAPI service for HTTP clients. Install with `pip install -e ".[web]"`, then start with `python -m openkb.api`. The interactive API reference is at [`/docs`](http://127.0.0.1:7566/docs) (importable into Postman).
See the [full REST API reference](examples/rest-api/README.md#rest-api) for endpoints, auth, and SSE streaming.
# 🧭 Learn More
### Compared to Karpathy's Approach
| | Karpathy's workflow | OpenKB |
| ----------------- | --------------------------- | ------------------------------------------------- |
| Short documents | LLM reads directly | markitdown → LLM reads |
| Long documents | Context limits, context rot | PageIndex tree index |
| Input sources | Web clipper → .md | PDF, Word, PPT, Excel, HTML, text, CSV, .md, URLs |
| Wiki compilation | LLM agent | LLM agent (same) |
| Entity extraction | Manual | Automatic (people, orgs, places, products) |
| Q&A | Query over wiki | Wiki + PageIndex retrieval |
| Output | Wiki only | Wiki + Skill Factory + agent CLI integration |
### The Stack
- [PageIndex](https://github.com/VectifyAI/PageIndex) — Vectorless, reasoning-based document indexing and retrieval
- [markitdown](https://github.com/microsoft/markitdown) — Universal file-to-markdown conversion
- [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) — Agent framework (supports non-OpenAI models via LiteLLM)
- [LiteLLM](https://github.com/BerriAI/litellm) — Multi-provider LLM gateway
- [Click](https://click.palletsprojects.com/) — CLI framework
- [watchdog](https://github.com/gorakhargosh/watchdog) — Filesystem monitoring
### Roadmap
- [ ] Extend long document handling to non-PDF formats
- [ ] Scale to large document collections with nested folder support
- [ ] Hierarchical concept (topic) indexing for massive knowledge bases
- [ ] Database-backed storage engine
- [x] Web UI for browsing and managing wikis (Knowledge Workbench, served at `/`)
### Contributing
Contributions are welcome! Submit a pull request or open an [issue](https://github.com/VectifyAI/OpenKB/issues) for bugs and feature requests. For larger changes, consider opening an issue first to discuss the approach.
### License
Apache 2.0. See [LICENSE](LICENSE).
### 🌐 Open-Source Ecosystem
Other [open-source projects](https://docs.pageindex.ai/open-source) from the PageIndex ecosystem:
- [PageIndex](https://github.com/VectifyAI/PageIndex): Vectorless, reasoning-based RAG framework for long documents
- [ChatIndex](https://github.com/VectifyAI/ChatIndex): Tree indexing and retrieval for long conversational histories and memory
- [ConDB](https://github.com/VectifyAI/ConDB): A KV-cache native context database for tree-based retrieval at scale
- [PageIndex MCP](https://github.com/VectifyAI/pageindex-mcp): MCP server for PageIndex
### Support Us
If you find OpenKB useful, please give us a star 🌟 — and check out [**PageIndex**](https://github.com/VectifyAI/PageIndex) too!
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