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Looking for LiteParse V1? Follow this link to the old code
LiteParse is a standalone OSS PDF parsing tool focused exclusively on fast and light parsing. It provides high-quality spatial text parsing with bounding boxes, without proprietary LLM features or cloud dependencies. Everything runs locally on your machine.
Hitting the limits of local parsing? For complex documents (dense tables, multi-column layouts, charts, handwritten text, or scanned PDFs), you'll get significantly better results with LlamaParse, our cloud-based document parser built for production document pipelines. LlamaParse handles the hard stuff so your models see clean, structured data and markdown.
- Fast Text Parsing: Spatial text parsing using PDFium
- Flexible OCR System:
- Built-in: Tesseract (zero setup, bundled with the library)
- HTTP Servers: Plug in any OCR server (EasyOCR, PaddleOCR, custom)
- Standard API: Simple, well-defined OCR API specification
- Screenshot Generation: Generate high-quality page screenshots for LLM agents
- Multiple Output Formats: JSON and Text
- Bounding Boxes: Precise text positioning information
- Multi-language: Use from Rust, Node.js/TypeScript, Python, or the browser (WASM)
- Multi-platform: Linux, macOS (Intel/ARM), Windows
flowchart LR
subgraph Input["Input Formats"]
direction TB
PDF["PDF"]
DOCX["DOCX"]
XLSX["XLSX"]
PPTX["PPTX"]
IMG["Images"]
end
subgraph Core["Rust Core"]
direction TB
CONV["Format Conversion\nLibreOffice / ImageMagick"]
EXTRACT["Text Extraction\nPDFium C library"]
OCR["Selective OCR\nTesseract / HTTP / Custom"]
MERGE["OCR Merge\nNative text + OCR results"]
PROJ["Grid Projection\nSpatial layout reconstruction"]
CONV --> EXTRACT
EXTRACT --> OCR --> MERGE --> PROJ
EXTRACT --> MERGE
end
subgraph Output[" Output "]
direction TB
JSON["Structured JSON\ntext + bounding boxes"]
TEXT["Plain Text\nlayout-preserved"]
SCREEN["Screenshots\nPNG rendering"]
end
subgraph Bindings["Language Bindings"]
direction TB
NAPI["Node.js / TypeScript\nnapi-rs"]
PYO3["Python\nPyO3"]
WASM["Browser / WASM\nwasm-bindgen"]
CLI["CLI\ncargo / npm / pip"]
NAPI ~~~ PYO3 ~~~ WASM ~~~ CLI
end
PDF --> EXTRACT
DOCX & XLSX & PPTX & IMG --> CONV
PROJ --> JSON & TEXT & SCREEN
JSON & TEXT & SCREEN --> Bindings
style Input fill:#F5F5F5,color:#000000,stroke:#37D7FA,stroke-width:2px
style Core fill:#F5F5F5,color:#000000,stroke:#3E18F9,stroke-width:2px
style Output fill:#F5F5F5,color:#000000,stroke:#FF8705,stroke-width:2px
style Bindings fill:#F5F5F5,color:#000000,stroke:#FF8DF2,stroke-width:2px
style PDF fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
style DOCX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
style XLSX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
style PPTX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
style IMG fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
style CONV fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
style EXTRACT fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
style OCR fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
style MERGE fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
style PROJ fill:#4B72FE,color:#FFFFFF,stroke:#3E18F9,stroke-width:2px
style JSON fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px
style TEXT fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px
style SCREEN fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px
style NAPI fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
style PYO3 fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
style WASM fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
style CLI fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
Install via your preferred package manager. All versions (except WASM) ship with the same lit CLI.
| Language | Install | Library Docs |
|---|---|---|
| Node.js / TypeScript | npm i @llamaindex/liteparse |
Node.js README |
| Python | pip install liteparse |
Python README |
| Rust | cargo install liteparse (CLI) / cargo add liteparse (lib) |
Rust README (crates.io) |
| Browser (WASM) | npm i @llamaindex/liteparse-wasm |
WASM README |
You can use liteparse as an agent skill, downloading it with the skills CLI tool:
npx skills add run-llama/llamaparse-agent-skills --skill liteparse
Or copy-pasting the SKILL.md file to your own skills setup.
The CLI is the same across all installations (npm, pip, cargo install).
# Basic parsing lit parse document.pdf # Parse with specific format lit parse document.pdf --format json -o output.json # Parse specific pages lit parse document.pdf --target-pages "1-5,10,15-20" # Parse without OCR lit parse document.pdf --no-ocr # Parse a remote PDF curl -sL https://example.com/report.pdf | lit parse -
Parse an entire directory of documents:
lit batch-parse ./input-directory ./output-directory
Screenshots are essential for LLM agents to extract visual information that text alone cannot capture.
# Screenshot all pages lit screenshot document.pdf -o ./screenshots # Screenshot specific pages lit screenshot document.pdf --target-pages "1,3,5" -o ./screenshots # Custom DPI lit screenshot document.pdf --dpi 300 -o ./screenshots
lit parse [OPTIONS] <file>
Options:
-o, --output <file> Output file path
--format <format> Output format: json|text [default: text]
--no-ocr Disable OCR
--ocr-language <lang> OCR language, Tesseract format [default: eng]
--ocr-server-url <url> HTTP OCR server URL (uses Tesseract if not provided)
--tessdata-path <path> Path to tessdata directory
--max-pages <n> Max pages to parse [default: 1000]
--target-pages <pages> Pages to parse (e.g., "1-5,10,15-20")
--dpi <dpi> Rendering DPI [default: 150]
--preserve-small-text Keep very small text
--password <password> Password for encrypted documents
--num-workers <n> Concurrent OCR workers [default: CPU cores - 1]
-q, --quiet Suppress progress output
-h, --help Print help
lit batch-parse [OPTIONS] <input-dir> <output-dir>
Options:
--format <format> Output format: json|text [default: text]
--no-ocr Disable OCR
--ocr-language <lang> OCR language [default: eng]
--ocr-server-url <url> HTTP OCR server URL
--tessdata-path <path> Path to tessdata directory
--max-pages <n> Max pages per file [default: 1000]
--dpi <dpi> Rendering DPI [default: 150]
--recursive Recursively search input directory
--extension <ext> Only process files with this extension (e.g., ".pdf")
--password <password> Password for encrypted documents
--num-workers <n> Concurrent OCR workers
-q, --quiet Suppress progress output
-h, --help Print help
lit screenshot [OPTIONS] <file>
Options:
-o, --output-dir <dir> Output directory [default: ./screenshots]
--target-pages <pages> Pages to screenshot (e.g., "1,3,5" or "1-5")
--dpi <dpi> Rendering DPI [default: 150]
--password <password> Password for encrypted documents
-q, --quiet Suppress progress output
-h, --help Print help
Tesseract is bundled and works out of the box:
lit parse document.pdf # OCR enabled by default lit parse document.pdf --ocr-language fra # Specify language lit parse document.pdf --no-ocr # Disable OCR
For offline or air-gapped environments, set TESSDATA_PREFIX to a directory containing pre-downloaded .traineddata files:
export TESSDATA_PREFIX=/path/to/tessdata
lit parse document.pdf --ocr-language engOr pass the path directly:
lit parse document.pdf --tessdata-path /path/to/tessdata
For higher accuracy or better performance, you can use an HTTP OCR server. We provide ready-to-use example wrappers for popular OCR engines:
You can integrate any OCR service by implementing the simple LiteParse OCR API specification (see OCR_API_SPEC.md).
The API requires:
- POST
/ocrendpoint - Accepts
fileandlanguageparameters - Returns JSON:
{ results: [{ text, bbox: [x1,y1,x2,y2], confidence }] }
LiteParse supports automatic conversion of various document formats to PDF before parsing.
- Word:
.doc,.docx,.docm,.odt,.rtf,.pages - PowerPoint:
.ppt,.pptx,.pptm,.odp,.key - Spreadsheets:
.xls,.xlsx,.xlsm,.ods,.csv,.tsv,.numbers
Install LibreOffice for automatic conversion:
# macOS brew install --cask libreoffice # Ubuntu/Debian apt-get install libreoffice # Windows choco install libreoffice-fresh
On Windows, you may need to add LibreOffice's program directory (usually
C:\Program Files\LibreOffice\program) to your PATH.
- Formats:
.jpg,.jpeg,.png,.gif,.bmp,.tiff,.webp,.svg
Install ImageMagick for image-to-PDF conversion:
# macOS brew install imagemagick # Ubuntu/Debian apt-get install imagemagick # Windows choco install imagemagick.app
| Variable | Description |
|---|---|
TESSDATA_PREFIX |
Path to a directory containing Tesseract .traineddata files. Used for offline/air-gapped environments. |
The project is a Rust workspace with the core library and language-specific binding crates.
crates/
├── liteparse/ # Core library + CLI binary
├── liteparse-napi/ # Node.js bindings (napi-rs)
├── liteparse-python/ # Python bindings (PyO3)
├── liteparse-wasm/ # WASM bindings (wasm-bindgen)
├── pdfium/ # PDFium Rust wrapper
└── pdfium-sys/ # PDFium FFI bindings
packages/
├── node/ # npm package (TS wrapper + native binary)
├── python/ # PyPI package (Python wrapper + native binary)
└── wasm/ # WASM npm package
# Build the CLI cargo build --release -p liteparse # Build Node.js bindings cd packages/node && npm run build # Build Python bindings cd packages/python && maturin develop --release # Build WASM cd packages/wasm && npm run build
We provide a fairly rich AGENTS.md/CLAUDE.md that we recommend using to help with development + coding agents.
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