openkb

---

📑 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 for vectorless long document retrieval.

The idea is based on a concept 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. Synthesis reflects everything consumed.

Features

• Broad format support — PDF, Word, Markdown, PowerPoint, HTML, Excel, text, and more via markitdown
• Scale to long documents — Long and complex documents are handled via PageIndex tree indexing, enabling accurate, vectorless long-context retrieval
• Native multi-modality — Retrieves and understands figures, tables, and images, not just text
• Compiled Wiki — LLM manages and compiles your documents into summaries, concept pages, and cross-links, all kept in sync
• Query — Ask questions (one-off) against your wiki. The LLM navigates your compiled knowledge to answer
• Interactive Chat — Multi-turn conversations with persisted sessions you can resume across runs
• Lint — Health checks find contradictions, gaps, orphans, and stale content
• Watch mode — Drop files into raw/, wiki updates automatically
• Obsidian compatible — Wiki is plain .md files with [[wikilinks]]. Open in Obsidian for graph view and browsing

🚀 Getting Started

Install

pip install openkb

Other install options

• Latest from GitHub:

  pip install git+https://github.com/VectifyAI/OpenKB.git
  

• Install from source (editable, for development):

  git clone https://github.com/VectifyAI/OpenKB.git
  cd OpenKB
  pip install -e .
  

Quick Start

# 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

Set up your LLM

OpenKB comes with multi-LLM support (e.g., OpenAI, Claude, Gemini) via LiteLLM (pinned to a safe version).

Set your model during openkb init, or in .openkb/config.yaml, using provider/model LiteLLM format (like anthropic/claude-sonnet-4-6). OpenAI models can omit the prefix (like gpt-5.4).

Create a .env file with your LLM API key:

LLM_API_KEY=your_llm_api_key

🧩 How OpenKB Works

Architecture

raw/                              You drop files here
 │
 ├─ Short docs ──→ markitdown ──→ LLM reads full text
 │                                     │
 ├─ Long PDFs ──→ PageIndex ────→ LLM reads document trees
 │                                     │
 │                                     ▼
 │                         Wiki Compilation (using LLM)
 │                                     │
 ▼                                     ▼
wiki/
 ├── index.md            Knowledge base overview
 ├── log.md              Operations timeline
 ├── AGENTS.md           Wiki schema (LLM instructions)
 ├── sources/            Full-text conversions
 ├── summaries/          Per-document summaries
 ├── concepts/           Cross-document synthesis ← the good stuff
 ├── explorations/       Saved query results
 └── reports/            Lint reports

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