Markdown has become a universal representation of information between humans and agents. It preserves core primitives needed to express stru…

Markdown has become a universal representation of information between humans and agents. It preserves core primitives needed to express structure (headings, lists), allows agents to read the information in a semantically interpretable manner, and allows for complex elements like tables and charts. You can natively embed HTML as well. But since most unstructured data is not natively within markdown (yet), the issue becomes the translation layer. We've created best models to convert any unstructured document container into markdown. Check out our blog! https://www.llamaindex.ai/blog/markdown-is-all-you-need Markdown is all you need. (Mostly.) A parser can get every word on the page right and still lose which column a number belongs to. Then your model has to guess. Markdown keeps headings, lists, and tables intact, and it stays readable when you're debugging a bad answer. For tables…

The secret to LiteParse lies in the grid projection algorithm. We project a complex page layout with text and tables into well-structured te…
Most extraction tools treat spreadsheets like PDFs. They flatten the file into text or markdown, then ask a model to infer the original stru…
Most document OCR solutions have parse (doc->markdown) and extract (doc + schema -> structured output) endpoints. Sometimes you want to extr…
As agents are generating more and more documents, they need a better agent-native document format 🤖📄 So far the two main containers are ma…

Source: Jerry Liu (X) | 2026-10-08

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