# OpenKB **Repository Path**: yonja/OpenKB ## Basic Information - **Project Name**: OpenKB - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-07-21 - **Last Updated**: 2026-07-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
OpenKB (by PageIndex)

VectifyAI%2FOpenKB | Trendshift

# OpenKB: Open LLM Knowledge Base

Scale to long documents • Reasoning-based retrieval • Native multi-modality • No Vector DB

📢 Recent Updates

 - *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.
--- # 📑 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 ```
Other install options: - **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 . ```
### 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 " # a portable agent skill openkb visualize # an interactive knowledge graph openkb deck new my-deck "An intro deck on " # 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
OpenKB Architecture: from raw documents (markitdown / PageIndex) through LLM wiki compilation to the wiki/ foundation, powering query/chat, the Skill Factory, and future generators
### 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) | | openkb add <file_or_dir_or_URL> | 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 |
More wiki commands:
| Command | Description | | ------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | openkb remove <doc> | 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) | | openkb recompile [<doc>] [--all] | 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`) | | openkb feedback ["msg"] | File feedback by opening a prefilled GitHub issue (`--type bug/feature/question` to tag it) |
→ **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 | | ------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | ---------------------------------- | | openkb query "question" | A grounded answer with citations (`--save` to persist to `wiki/explorations/`) | [query & save](examples/commands/) | | openkb chat | Interactive multi-turn session over the wiki (`--resume`, `--list`, `--delete` to manage sessions) | [chat](examples/chat/) | | openkb visualize | A self-contained interactive knowledge graph at `output/visualize/graph.html` — 3D, mind-map, and radial views | [visualize](examples/visualize/) | | openkb skill new <skill-name> "<intent>" | Distill a redistributable agent skill from your wiki (see [Skill Factory](#skill-factory) below) | [skills](examples/skills/) | | openkb deck new <name> "<intent>" | 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).
More slash commands:
- `/help`: list available commands - `/status`: show knowledge base status - `/list`: list all documents - `/add `: add a document or directory without leaving the chat - `/skill new ""`: compile a skill from this chat (see below) - `/deck new ""`: generate an HTML slide deck from the wiki - `/critique `: 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)
### (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/)**.
More skill commands:
| Command | Output | | ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------ | | openkb skill validate [name] | Validate compiled skills (auto-runs after `skill new`) | | openkb skill eval <name> | Check the skill triggers on the right prompts | | openkb skill history <name> / openkb skill rollback <name> | Version history + rollback for skills |
# 🔧 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.
Claude Code:
``` /plugin marketplace add VectifyAI/OpenKB /plugin install openkb@vectify ```
OpenAI Codex CLI:
*(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 ```
Gemini CLI:
```bash gemini skills install https://github.com/VectifyAI/OpenKB.git --path skills/openkb --consent ```
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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