# run-llama/liteparse

A fast, helpful, and open-source document parser

Repository: https://github.com/run-llama/liteparse
Canonical: https://ross.abutalabs.com/products/liteparse
Homepage: https://developers.llamaindex.ai/liteparse/
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: document-ocr, document-processing, ocr, ocr-recognition, pdf, pdf-parser, text-extraction
Last push: 2026-08-26T15:43:02+00:00

## Health v2 (maintenance only)
Score: 78/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 14
- inputs: {"age_days": 205, "days_push": 7, "days_rel": 8, "gap_med": 0.0, "n_releases_24m": 167}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12181, forks 843 (observed 2026-08-28T04:10:51.949324+00:00)

## What it is
LiteParse is a fast, open-source document parser written in Rust that extracts spatial text with bounding boxes from PDFs, Office files, and images, entirely locally with no cloud dependencies. It offers a CLI and library APIs for Rust, Python, Node.js, and the browser (WASM), with built-in Tesseract OCR, pluggable OCR servers, Markdown/JSON/text output, and page screenshots.

## Use cases
- parse pdfs to text locally
- extract text with bounding boxes from pdf
- convert pdf to markdown for rag pipelines
- ocr scanned documents offline
- parse docx xlsx pptx files
- generate pdf page screenshots for llm agents
- score pdf complexity before parsing
- run document parsing in the browser with wasm

## When to choose
- you need fast, local PDF text extraction without cloud APIs or LLMs
- you need spatial layout info and bounding boxes for downstream processing
- you want Markdown output to feed LLMs or RAG pipelines
- you need a parser usable from Rust, Python, Node.js, or the browser

## When to avoid
- you need to parse dense tables, multi-column layouts, charts, or handwriting accurately - a cloud parser like LlamaParse will do better
- you need cloud-scale or LLM-powered document extraction features
- you need browser parsing of complex scanned PDFs (WASM has limitations)

## Facets
- artifact type: library
- maturity: active
- function: parser, ocr, pdf, image-processing, cli, sdk
- domain: pdf, files, developer-tools, artificial-intelligence
- platform: rust, python, cli, wasm, browser, cross-platform
- tags: pdf-parsing, text-extraction, bounding-boxes, tesseract, rag-pipelines, document-conversion, wasm, llamaindex, natural-language-processing, nodejs

## Member repositories
- run-llama/liteparse (main) score 78

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:51.949324+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:14:51.820699+00:00, confidence not recorded.
  - readme: https://github.com/run-llama/liteparse (fetched 2026-08-28T04:10:51.949324+00:00, sha 2834e5fa40dd)
  - homepage: https://developers.llamaindex.ai/liteparse/ (fetched 2026-08-29T08:11:57.777200+00:00, sha 00340cae42ef)
  - registry_crates: https://crates.io/api/v1/crates/liteparse (fetched 2026-08-29T08:11:57.798857+00:00, sha 502eb6bb037c)
  - site_page: https://developers.llamaindex.ai/ (fetched 2026-08-29T08:11:57.789875+00:00, sha 7af42d618d4f)
  - site_page: https://developers.llamaindex.ai/liteparse/getting_started (fetched 2026-08-29T08:11:57.795902+00:00, sha 2a05ec90dbc3)
- Data as of 2026-08-30T08:39:29.467469+00:00.