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OpenKB is an open-source Python CLI tool that compiles raw documents into a structured, wiki-style knowledge base using LLMs and vectorless retrieval. It organizes documents into summaries, concept pages, and cross-referenced entities, then lets you query, chat, and generate agent skills from the compiled knowledge.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | VectifyAI/OpenKB |
| Owner | VectifyAI |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 2.9k |
| Forks | 320 |
| Open issues | 24 |
| Latest release | v0.4.3 (2026-07-02) |
| Last updated | 2026-07-08 |
| Source | https://github.com/VectifyAI/OpenKB |
What OpenKB is
OpenKB integrates markitdown (document parsing), PageIndex (hierarchical tree indexing for long PDFs), and LiteLLM (multi-provider LLM abstraction) to build a persistent, compiled wiki in plain Markdown with cross-links. It supports multi-modal retrieval (text, tables, images) and follows Google OKF specification for knowledge representation.
Get the OpenKB source
Clone the repository and explore it locally.
git clone https://github.com/VectifyAI/OpenKB.gitcd OpenKB# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- LLM API dependency: All knowledge compilation and query reasoning rely on external LLM calls (OpenAI, Claude, etc.); budget API costs and set up `.env` LLM_API_KEY before init.
- Document format support breadth: PDF, Markdown, Word, PowerPoint, HTML, Excel, CSV, text, and URLs supported; test complex layouts (multi-column, embedded media) with your corpus first.
- Long document handling via PageIndex: PDFs ≥20 pages auto-route through tree indexing; verify PageIndex extraction quality on domain-specific PDFs (legal, medical, technical).
- Wiki Markdown convention: Generated wiki is plain `.md` with cross-links; integrates with Obsidian for graph visualization but requires manual wiki maintenance workflows if hand-edited.
- Concept and entity page drift: Auto-extraction and cross-document synthesis can produce over-generalized or redundant concept pages; lint and manual review of generated wiki recommended.
When to avoid it — and what to weigh
- Real-Time Document Streaming — OpenKB compiles knowledge once into a static wiki. If you need live sync with evolving documents or sub-second document ingest, traditional RAG or real-time indexing is better.
- Hybrid Full-Text + Vector Search — The tool deliberately avoids vector databases. If your use case requires dense similarity search or embedding-based filtering, you'll need to augment or integrate external vector systems.
- Low-Latency Query Performance — Multi-turn LLM reasoning and PageIndex tree traversal add latency. Typical queries require an LLM call, not microsecond responses. Real-time autocomplete or sub-100ms QA is not the design target.
- Fully Offline, Air-Gapped Deployment — OpenKB requires an external LLM API (OpenAI, Claude, Gemini, etc.) and LiteLLM communication. No built-in offline LLM support or local-only inference documented.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimer.
Apache 2.0 permits commercial use without royalty or disclosure requirements. You may use OpenKB in proprietary products. Ensure you retain/display Apache 2.0 license terms and provide a copy. Consult legal counsel if bundling with other licenses or if your org has restrictive IP policies.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
LLM API keys stored in `.env` file in project directory; ensure `.env` is in `.gitignore` and not committed. LiteLLM is pinned to a safe version (post-March 2026 security update). Input documents passed to external LLM APIs; do not add confidential/PII documents without understanding data residency. No built-in encryption for wiki at rest or in transit to LLM endpoints. Review LLM provider SLA/compliance (SOC 2, HIPAA, etc.) if handling regulated data.
Alternatives to consider
Langchain + Pinecone/Weaviate
Traditional RAG with vector database. Lower compilation overhead; real-time indexing. Trade-off: no persistent wiki, vectorless reasoning, or entity extraction. Better for high-velocity, low-latency retrieval.
Obsidian + Markdown + Manual Curation
Manual knowledge graph; no LLM compilation. Trade-off: time-intensive but full control, no API dependency, offline-capable. Best for small, curated knowledge bases.
Notion AI / Confluence AI
Closed-source cloud-hosted knowledge platforms with AI features. Trade-off: no local ownership, vendor lock-in, limited customization. Better for small teams seeking simplicity over control.
Build on OpenKB with DEV.co software developers
Start with `pip install openkb` and `openkb init`. Compile your first document in minutes. Free, open-source, Apache 2.0 licensed.
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OpenKB FAQ
Do I need a vector database?
What LLM providers are supported?
Can I use this offline?
How do I version-control the wiki?
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Build Your Knowledge Base Today
Start with `pip install openkb` and `openkb init`. Compile your first document in minutes. Free, open-source, Apache 2.0 licensed.