run-llama/liteparse

A fast, helpful, and open-source document parser

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Summary Information

Updated 6 minutes ago
Added to GitGenius on May 31st, 2026
Created on February 9th, 2026
Open Issues & Pull Requests: 27 (+0)
GitHub issues: Enabled
Number of forks: 849
Total Stargazers: 12,220 (+0)
Total Subscribers: 38 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 8.0 hours
Mean response time: 4.1 days
90th percentile: 9.7 days
Tracked items: 130

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 16% of issues opened in the past year have been closed. Three people close 96% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 20
New in 7 days: 2
Closed in 7 days: 4
Avg open age: 17 days
Stale 30+ days: 10
Stale 90+ days: 5

Recent activity

Opened in 7 days: 2
Closed in 7 days: 4
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (54)
  • enhancement (30)

Detailed Description

LiteParse is an open-source document parser written in Rust that specializes in fast, lightweight PDF parsing with spatial text extraction. The project is maintained by the LlamaIndex team and provides high-quality text parsing with bounding box information without relying on proprietary language models or cloud dependencies. All processing runs locally on the user's machine, making it suitable for offline and air-gapped environments.

The core functionality centers on spatial text parsing using PDFium for PDF rendering and text extraction. LiteParse includes a flexible OCR system with Tesseract bundled by default for zero-setup operation, while also supporting integration with HTTP-based OCR servers like EasyOCR and PaddleOCR for users who need higher accuracy. The library provides a standardized OCR API specification that allows integration of custom OCR services.

A distinctive feature is the complexity detection capability, which performs a cheap text-layer-only analysis to determine whether a document needs OCR or heavier processing before committing to a full parse. This allows users to route documents to different pipelines, reject unsuitable documents, or estimate processing costs. The detection identifies specific reasons a page might need OCR, including scanned content, missing text, sparse text, embedded images, garbled text, and vector text issues.

LiteParse supports multiple output formats including Markdown with reconstructed headings, tables, lists, images, and links, as well as JSON and plain text. The Markdown output is designed specifically for feeding documents into language models and RAG pipelines. The library can also generate high-quality page screenshots for LLM agents to extract visual information that text alone cannot capture.

The project is available across multiple programming languages and platforms. Users can install LiteParse via npm for Node.js and TypeScript, pip for Python, cargo for Rust, or as a WebAssembly package for browser use. The same command-line interface is available across all installations, supporting batch parsing of entire directories, individual file parsing, screenshot generation, and complexity checking.

LiteParse also handles automatic conversion of various document formats to PDF before parsing, including Office documents like Word, PowerPoint, and spreadsheets through LibreOffice integration, and image formats through ImageMagick. This multi-format input support makes it versatile for different document sources.