Agents

Our company mission today is to give AI agents the highest-quality document context. The native open-source libs that agents have access to …

Our company mission today is to give AI agents the highest-quality document context. The native open-source libs that agents have access to (e.g. PyPDF) do naive text extraction. But this is incomplet

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Our company mission today is to give AI agents the highest-quality document context. The native open-source libs that agents have access to (e.g. PyPDF) do naive text extraction. But this is incomplete for most advanced knowledge work. AI agents need the following from documents: ✅ Clean, linearized markdown from multimodal complex documents (charts, tables, scans) ✅ Rich layout / bounding boxes - agents should be able to trace every generated answer/decision back to the source! ✅ Proper image segmentation. Don't just give the agent access to the full page, also give access to image segmentations to enable it to generate more targeted citations. ✅ The ability to define custom schemas so that they can extract from documents in a structured format. We're working on this with LlamaParse (our VLM-powered service) and LiteParse (our free OSS parser). Check out the blog here! https://www.llamaindex.ai/blog/beyond-raw-text-how-llamaparse-and-liteparse-give-agents-real-document-understanding?utm_medium=socials&utm_source=twitter&utm_campaign=2026-apr- Agents like @openclaw are incredibly powerful, as long as the information they receive is clean and structured🦞 When it comes to PDFs and other unstructured documents, most agents struggle. The tools they rely on often return only raw text, losing critical context like layout, t…

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Source: Jerry Liu (X) | 2026-04-09

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