PageIndex

MIT self-hosted vectorless tree-RAG core — local mode with your own LLM key

AI Agents / RAG · OSS/MIT · 39.3k

Reviewed 2026-10-05 · last push 2026-10-19 · repo checked 2026-10-19

What's Good

MIT (VectifyAI/PageIndex). PyPI `pageindex` (v0.2.21) local mode: index, retrieve, and chat locally with your own LLM key (e.g. OPENAI_API_KEY), index stored in a local directory, no vector DB and no PageIndex account. Tools drop into the OpenAI Agents SDK, Claude Agent SDK, or your own framework. Published OSS benchmark covers exactly the local no-OCR setup. Useful when long, structured, text-heavy PDFs (filings, manuals, contracts) defeat chunk-and-embed RAG. ★~38,648.

The Catch

Cloud: OCR and image understanding, Metadata, Folders, the MCP server, and the PageIndex File System (corpus-scale indexing) are PageIndex Cloud only — need a PageIndex API key. Local mode is text-heavy PDFs, one document tree at a time. Not a duplicate of the pageindex-mcp gem: that one is the freemium hosted MCP surface; this is the self-hosted MIT tree-RAG core you run in your own code. Your LLM key pays for every index and query — tree reasoning is token-hungry on big docs. SDK is young (0.2.x); pin it.

Verdict

MIT self-hosted tree-RAG core for long text-heavy PDFs with your own LLM key — not the freemium pageindex-mcp; OCR, folders, metadata, MCP, and File System are cloud.

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