run-llama/liteparse Alternatives

calls liteparse “if you want a fast Rust-based parser with a small footprint and Apache-2.0.”

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

calls liteparse “Choose when you need maximum parsing speed with a low-memory Rust core; the ecosystem is less mature and may require Rust integration.”

PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.

calls liteparse “Rust document parser from LlamaIndex; choose if you prefer that ecosystem over anydoc.”

Convert Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF to clean Markdown. Built in Rust, with Node.js and Python bindings.

calls liteparse “when you want a Rust-native document parser with a similar LLM-based approach to structured output.”

OCR & Document Extraction using vision models

calls liteparse “actively maintained Apache-2.0 Rust parser, a direct drop-in replacement for Parsr.”

Transforms PDF, Documents and Images into Enriched Structured Data

calls liteparse “choose when you need a fast, embeddable Rust document parser for low-latency services; seed is Python and includes a UI/API.”

Extract and convert data from any document, images, pdfs, word doc, ppt or URL into multiple formats (Markdown, JSON, CSV, HTML) with intelligent structured data extraction and advanced OCR.

calls liteparse “Rust-based parser, use when you need a fast native binary rather than Python.”

Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.

calls liteparse “if you need a Rust-native parser for performance or embedding in Rust codebases”

Get your documents ready for gen AI

calls liteparse “When you want a Rust-core parser with Python bindings for high throughput.”

Convert PDF to markdown + JSON quickly with high accuracy

calls liteparse “Choose when you want automated document parsing with LLM-based structuring, not a low-level OCR engine.”

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

calls liteparse “Rust-native document parser; choose for low-latency parsing in a Rust environment.”

Unlimited OCR Works: Welcome the Era of One-shot Long-horizon Parsing.

calls liteparse “fast LLM-based document parser for structured extraction instead of low-level OCR.”

OCR, layout analysis, reading order, table recognition in 90+ languages

calls liteparse “choose it as a Rust parser for more general document types, but it lacks pdf-inspector's classification focus.”

Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.

calls liteparse “when you need a fast Rust-based document parser with a minimal API from the LlamaIndex ecosystem.”

Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.

calls liteparse “Fast Rust document parser producing structured data; alternative when performance and multi-format support are priorities.”

Plumb a PDF for detailed information about each char, rectangle, line, et cetera — and easily extract text and tables.

calls liteparse “Choose for a fast Rust-based document parser that handles many formats, if you need high-throughput extraction and don't require PDF manipulation or rendering.”

PyMuPDF is a high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.

calls liteparse “when you need a Rust-native, fast document parser with low overhead instead of Python”

A Comprehensive Toolkit for High-Quality PDF Content Extraction

calls liteparse “Use for fast, open-source document parsing to LLM-ready structured data rather than visual retrieval.”

The end of web parsing. The beginning of scalable pixel-native search. link: https://pixelrag.ai/

calls liteparse “when you prefer a simpler Rust-core parser with Apache-2.0 and don't need xberg's 106-format/code-intelligence features.”

A polyglot document intelligence framework with a Rust core. Extract text, metadata, images, and structured data from 101 formats (115 file extensions) plus code intelligence for 371 code languages. 15 language bindings — Rust, Python, Ruby, Java, Go, PHP, Elixir, C#, TypeScript — plus CLI, REST API, and MCP server.

calls liteparse “choose when you prefer a Rust-native parser for various document formats over a Python VLM.”

Multilingual Document Layout Parsing in a Single Vision-Language Model