run-llama/liteparse

# LiteParse [](https://github.com/run-llama/liteparse/actions/workflows/ci.yml) | [](https://crates.io/crates/liteparse) | [](https://www.npmjs.com/package/@llamaindex/liteparse) | [](https://www.npmjs.com/package/@llamaindex/liteparse-wasm) | [](https://pypi.org/project/liteparse/) | [](https://opensource.org/licenses/Apache-2.0) | [Docs](https://developers.llamaindex.ai/liteparse/) 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](https://developers.llamaindex.ai/python/cloud/llamaparse/?utm_source=github&utm_medium=liteparse), our cloud-based document parser built for production document pipelines. LlamaParse handles the hard stuff so your models see clean, structured data and markdown. ## Overview - **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 ## Installation 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](packages/node/README.md) | | **Python** | `pip install liteparse` | [Python README](packages/python/README.md) | | **Rust** | `cargo install liteparse` (CLI) / `cargo add liteparse` (lib) | [Rust README (crates.io)](crates/liteparse/README.md) | | **Browser (WASM)** | `npm i @llamaindex/liteparse-wasm` | [WASM README](packages/wasm/README.md) | ### Agent Skill 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`](https://github.com/run-llama/llamaparse-agent-skills/blob/main/skills/liteparse/SKILL.md) file to your own skills setup. ## CLI Usage The CLI is the same across all installations (`npm`, `pip`, `cargo install`). ### Parse Files # 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 - ### Batch Parsing Parse an entire directory of documents: lit batch-parse ./input-directory ./output-directory ### Generate Screenshots 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 ### CLI Reference #### Parse Command lit parse [OPTIONS] Options: -o, --output Output file path --format Output format: json|text [default: text] --no-ocr Disable OCR --ocr-language OCR language, Tesseract format [default: eng] --ocr-server-url HTTP OCR server URL (uses Tesseract if not provided) --tessdata-path Path to tessdata directory --max-pages Max pages to parse [default: 1000] --target-pages Pages to parse (e.g., "1-5,10,15-20") --dpi Rendering DPI [default: 150] --preserve-small-text Keep very small text --password Password for encrypted documents --num-workers Concurrent OCR workers [default: CPU cores - 1] -q, --quiet Suppress progress output -h, --help Print help #### Batch Parse Command lit batch-parse [OPTIONS] Options: --format Output format: json|text [default: text] --no-ocr Disable OCR --ocr-language OCR language [default: eng] --ocr-server-url HTTP OCR server URL --tessdata-path Path to tessdata directory --max-pages Max pages per file [default: 1000] --dpi Rendering DPI [default: 150] --recursive Recursively search input directory --extension Only process files with this extension (e.g., ".pdf") --password Password for encrypted documents --num-workers Concurrent OCR workers -q, --quiet Suppress progress output -h, --help Print help #### Screenshot Command lit screenshot [OPTIONS] Options: -o, --output-dir Output directory [default: ./screenshots] --target-pages Pages to screenshot (e.g., "1,3,5" or "1-5") --dpi Rendering DPI [default: 150] --password Password for encrypted documents -q, --quiet Suppress progress output -h, --help Print help ## OCR Setup ### Default: Tesseract 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 eng Or pass the path directly: lit parse document.pdf --tessdata-path /path/to/tessdata ### Optional: HTTP OCR Servers For higher accuracy or better performance, you can use an HTTP OCR server. We provide ready-to-use example wrappers for popular OCR engines: - [EasyOCR](ocr/easyocr/README.md) - [PaddleOCR](ocr/paddleocr/README.md) You can integrate any OCR service by implementing the simple LiteParse OCR API specification (see [`OCR_API_SPEC.md`](OCR_API_SPEC.md)). The API requires: - POST `/ocr` endpoint - Accepts `file` and `language` parameters - Returns JSON: `{ results: [{ text, bbox: [x1,y1,x2,y2], confidence }] }` ### Supported Input Formats #### Office Documents (via LibreOffice) - **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 #### Images (via ImageMagick) - **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 ## Environment Variables | Variable | Description | |----------|-------------| | `TESSDATA_PREFIX` | Path to a directory containing Tesseract `.traineddata` files. Used for offline/air-gapped environments. | ## Development 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 ### Building # 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. ## License Apache 2.0

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