Model Releases
A downside with VLM-based parsing is that they’re generally slower than text-based heuristic approaches. As a result they add latency to any…
A downside with VLM-based parsing is that they’re generally slower than text-based heuristic approaches. As a result they add latency to any ad-hoc file processing *in-the agent loop* (e.g. if you upl
A downside with VLM-based parsing is that they’re generally slower than text-based heuristic approaches. As a result they add latency to any ad-hoc file processing in-the agent loop (e.g. if you upload a file to Claude) We built LiteParse to be a really good text-based, heuristic extractor so that you can use it as the default parser in the agent loop. It takes 4ms to parse 200 pages ⚡️, is more accurate than other OSS parsers, and you can install it within Claude Cowork/Code or Codex in one-line with our agent skill. It also supports 50+ different doc formats and has a native complexity router so you can route it to OCR/VLMs for any complex pages Come check it out! Repo: https://github.com/run-llama/liteparse
Related
- LiteParse 🤝 Rust🦀 We refactored the LiteParse library and CLI porting it to Rust, and here's what that means for you: ⚡ Parsing up to 100X…
- LiteParse runs anywhere, including inside agent runtimes. To prove it, we built an email processing assistant using @flueai (the agentic fra…
- A downside with using VLMs to parse PDFs is guaranteeing that the output text is correct and output in the correct reading order. 1️⃣ Text…
Source: Jerry Liu (X) | 2026-08-10