AI/TLDR

LlamaIndex · 2026-05-27 · notable

LlamaIndex Rewrites LiteParse in Rust for v2.0 — 5–100× Faster Open-Source PDF and Office Parser, 457-Page Doc in 0.777 Seconds, Native + Python + Node + WASM

LiteParse v2.0 swaps a TypeScript parser for a Rust core wrapping a custom PDFium build. Same CLI, 5–100× speedup on small docs, 0.777s for a 457-page PDF, with native, Python, Node, and WASM bindings shipping side-by-side.

LiteParse v2.0 announcement banner from LlamaIndex showing the new Rust-based parser running across native, Python, Node, and WASM targets.
LlamaIndex

LlamaIndex rewrote its open-source PDF parser in Rust — same CLI, 5–100× faster, runs on Linux, macOS, Windows, Python, Node, and in the browser via WASM.

Key specs

LicenseApache-2.0
GitHub stars7.2K
Small doc speedup5–100×
457 page parse time0.777s
Total releases46

What is it?

LiteParse is a local, model-free document parser from LlamaIndex covering PDFs plus 50+ Office and image formats with built-in OCR and screenshot output for agents. Version 2.0 is a full rewrite from TypeScript to a Rust core that ships as a CLI, a Rust library, a Python package, a Node.js binding, and a WASM build — all sharing the same parser. It's a sibling to the hosted LlamaParse but stays entirely on your machine: no LLM calls, no API keys, no cloud.

How does it work?

The new core wraps a forked-and-rebuilt PDFium for text + spatial coordinates and uses tesseract-rs for OCR; the Rust binary auto-detects when OCR is needed via text-coverage metrics so it skips unnecessary work. Office documents are converted via LibreOffice on Linux/macOS/Windows. Python and Node bindings are generated through PyO3 and napi-rs; the WASM target exposes the same parser with a callback-based OCR hook so browsers and edge runtimes (Cloudflare Workers, Vercel Edge) can parse PDFs without spawning a process.

Why does it matter?

Every retrieval pipeline, agent, and ingest job has a PDF problem. LiteParse v2.0 turns a slow ETL step into a sub-second one — a 457-page, 100MB PDF parses in 0.777 seconds — without sending the document to a cloud parser or feeding pages to an LLM. The WASM build also unlocks parsing fully in the browser, which matters for privacy-sensitive RAG and on-device coding agents.

Who is it for?

RAG engineers, indie devs shipping document-aware agents, anyone running a self-hosted ingest pipeline.

Try it

cargo install liteparse  # or:  pip install liteparse

Sources · 4 outlets

Tags

  • liteparse
  • llamaindex
  • pdf-parsing
  • document-parsing
  • rust
  • wasm
  • pdfium
  • tesseract
  • ocr
  • open-source

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