liteparse

LiteParse now supports extracting structured data from PDFs—form fields, checkbox states, annotations, images, vector graphics, and word-level bounding boxes—without a vision model, plus complexity signals to route harder pages to tools like LlamaParse.

LlamaIndex introduces Parse Gateway, a page-level document OCR router that estimates each page's complexity and routes it to the appropriate parsing tier (LiteParse or LlamaParse), balancing cost, latency, and quality.

LlamaIndex and LanceDB collaborated on a pipeline using LiteParse for PDF parsing and LanceDB for multimodal storage, enabling better retrieval from complex enterprise PDFs for agentic workflows.

Jerry Liu announces LiteParse, a fast and accurate file parser for agent loops, now integrated with Vercel's Eve framework.

Jerry Liu presents a framework for document parsing across accuracy, cost, and latency tradeoffs, introducing LiteParse as an open-source, low-latency parsing tool for AI agent loops, along with LlamaParse for high-accuracy modes.

A comparison of on-prem document processing tools—Docling, Liteparse, Mineru, and Unstructured—for university use, evaluating their suitability for local deployment.

LlamaIndex's Jerry Liu demonstrates building a financial due diligence AI agent with LiteParse, a free open-source PDF parser that provides exact citations and bounding box coordinates, enabling trust and transparency in agentic workflows.

sandboxed-lit is a Rust CLI agent that parses PDFs, images, and Office documents securely via LiteParse and microsandbox, combining local file access with a sandboxed Bash environment.