---
id: liteparse
version: "2.12.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# liteparse — Python bindings for LiteParse - fast, lightweight PDF and document parsing

License: permissive · Maintenance: active · Downloads: 384.0K/mo

## What it is and what it does

LiteParse is a Python wrapper around a Rust-based PDF and document parser that extracts text while preserving spatial layout information. It supports multiple input formats (PDF, Office documents, images) and can output structured data as JSON, plain text, or reconstructed Markdown. The package includes built-in OCR via Tesseract, configurable image extraction, annotation and form-field parsing, and a CLI tool (`lit`) for command-line workflows.

Typical use cases include feeding documents into RAG pipelines and LLMs by converting PDFs to clean Markdown, routing documents to different processing pipelines based on complexity detection, and extracting structured data (images, links, annotations) from mixed document types. The package is designed to be lightweight and fast, with no runtime Python dependencies, though some features (like OCR and Office format support) require optional system libraries.

Use it for:

- Convert PDFs to Markdown for ingestion into LLM and RAG systems with preserved structure and links.
- Extract and route documents based on complexity signals (scanned vs. digital text) to optimize processing cost.
- Batch-parse large document collections via the CLI or Python API with configurable OCR and image extraction.
- Recover structured data from PDFs including images, annotations, form fields, and tagged logical structure.
- Generate PNG screenshots of specific document pages for preview or archival purposes.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

LiteParse provides Python bindings for fast, lightweight PDF and document parsing with spatial text extraction, OCR support, and output to JSON, plain text, or Markdown.

Yes. LiteParse is actively maintained, has no known vulnerabilities, supports modern Python versions, and solves a real problem (document parsing for LLM/RAG workflows) with a clean API. The permissive Apache-2.0 license and zero runtime Python dependencies keep friction low. Install it if you need to parse PDFs or mixed document formats into structured text or Markdown; skip it only if you have a simpler use case or a strong preference for pure-Python solutions.

## Install

pip install liteparse
uv add liteparse
poetry add liteparse

## Installing liteparse

Before you install: Medium install friction due to compiled wheels, but well-supported across Python 3.10–3.14 on Linux, macOS, and Windows. Active maintenance with a recent release (1 day old) and 12093 repository stars suggest solid upkeep.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most production and proprietary projects.

Quickstart:

pip install liteparse

from liteparse import LiteParse

parser = LiteParse()
result = parser.parse("document.pdf")
print(result.text)
print(f"Source document pages: {result.total_pages}")

Requires Python 3.10 or later. LibreOffice is required to parse Microsoft Office and OpenDocument formats (.docx, .xlsx, .pptx, .odt, .ods, .odp).

Verify before relying:

- Performance characteristics (parsing speed, memory usage) for large documents or batch operations.
- Accuracy of OCR and Markdown reconstruction quality on complex or scanned documents.
- Whether the `lit` CLI command is fully featured or a subset of the Python API.
- Compatibility or integration with specific RAG frameworks or LLM pipelines.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 384.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pdf parsing python, document text extraction, ocr pdf extraction, markdown from pdf, spatial text layout, lightweight pdf parser, rag document processing, pdf-parsing, ocr, rag-pipeline

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