LlamaParse

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

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 tool

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, tables, and images❌ That’s why we created LlamaParse and LiteParse Agent Skills, designed to give agents access to a deeper layer of document understanding, enabling more reliable knowledge extraction and automation across complex documents📝 📚Learn more about the problem, and how the skills solve it: https://t.co/dn33HE6Z0k 🦙 Get started with LlamaParse: https://t.co/ADG9CPTcAV

Common Failure Modes Break VLM-Powered OCR in Production. 🔁 Repetition Loops — model spirals into infinite whitespace, exhausts resources, cascades latency across your system 🛑 Recitation Errors

Common Failure Modes Break VLM-Powered OCR in Production. 🔁 Repetition Loops — model spirals into infinite whitespace, exhausts resources, cascades latency across your system 🛑 Recitation Errors — safety filters hard-stop legitimate extractions as "copyright violations" Same pipeline. Completely different root causes. Completely different fixes. Our enginerring leadership broke down what went wrong and how we solved both 👇 https://t.co/fFkLmnG11h

Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lanced

Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lancedb to build a structure-aware PDF QA pipeline🚀 Here’s how it works: 1. LiteParse extracts structured text and captures page screenshots📸 2. We embed the text with Gemini 2 Embedding⚙️ 3. Text, vectors, and images are stored in LanceDB🗄️ 4. A Claude agent retrieves the relevant context and, if text isn’t enough, it falls back to image-based reasoning on the screenshots🧠 In our evaluations, the agent achieved near-perfect scores across most tasks, showing how strong parsing (LiteParse) plus multimodal storage (LanceDB) can significantly improve agentic search pipelines📈 📚 Full breakdown: https://t.co/k3swCwPmme 🦙 Learn more about LiteParse: https://t.co/lHZWj9hhl1

Open call to fintech leaders in NYC 🏦 May 13, in-person workshop with @jerryjliu0 on turning complex financial docs into LLM-ready data using agentic OCR. Build real pipelines. Hear from a Top 5 PE f

Open call to fintech leaders in NYC 🏦 May 13, in-person workshop with @jerryjliu0 on turning complex financial docs into LLM-ready data using agentic OCR. Build real pipelines. Hear from a Top 5 PE firm's production agent. Make sure to bring your laptops→ https://t.co/j6nBDaaDqo

We took a brief break from parsing PDFs this First Thursday and welcomed the AI community to "Series B Lane" in San Francisco 🦙 New office. A-parse-rol Spritzes. LlamaIsland Iced Teas. 100+ builders

We took a brief break from parsing PDFs this First Thursday and welcomed the AI community to "Series B Lane" in San Francisco 🦙 New office. A-parse-rol Spritzes. LlamaIsland Iced Teas. 100+ builders. Then everyone walked one block to catch Reggie Watts at SF's street fest. More of this coming soon 🎥⬇️

After the release of Parse v2, Extract is also getting an upgrade — 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝘃𝟮! 🎉 We've been reworking the experience from the ground up to make document extraction m

After the release of Parse v2, Extract is also getting an upgrade — 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝘃𝟮! 🎉 We've been reworking the experience from the ground up to make document extraction more powerful and easier to use than ever. Here's what's new: ✦ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝘁𝗶𝗲𝗿𝘀: we've replaced modes with cleaner, more intuitive tiers. (And stay tuned: agentic plus is coming to Extract too, very soon.) ✦ 𝗣𝗿𝗲-𝘀𝗮𝘃𝗲𝗱 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀: load your saved extraction configs directly, so you can skip the setup and get straight to extracting. ✦ 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝗯𝗹𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗮𝗿𝘀𝗶𝗻𝗴: now you can control how your documents get parsed before extraction, giving you more flexibility and better results end to end. And for those who need a transition period: Extract v1 will remain accessible via the UI under 'Settings → General' for a limited time. Try Extract v2 today → https://t.co/yPVJzqoKal

Lawyers <3 documents We're proud to sponsor @StanfordLaw and @CodeXStanford's FutureLaw Week 2026! 🏛️⚖️ AI x Law bootcamps, hackathons, the UN AI For Good Law Track & the FutureLaw Conference — a

Lawyers <3 documents We're proud to sponsor @StanfordLaw and @CodeXStanford's FutureLaw Week 2026! 🏛️⚖️ AI x Law bootcamps, hackathons, the UN AI For Good Law Track & the FutureLaw Conference — all exploring the future of legal AI. Join us alongside friends from @DLA_Piper, @normativeai, @filevine, @harvey, @LexisNexis & the global legal tech community. April 11–16 👉 https://t.co/9MFWAn46ti

LlamaIndex is proud to be named to the 2026 Enterprise Tech 30, #3 in the Early Stage category. The ET30 is an annual list by @Wing_VC and Eric Newcomer, voted on by 90+ leading investors and corpora

LlamaIndex is proud to be named to the 2026 Enterprise Tech 30, #3 in the Early Stage category. The ET30 is an annual list by @Wing_VC and Eric Newcomer, voted on by 90+ leading investors and corporate development leaders. It recognizes the private companies wi th the most potential to shape the future of enterprise technology. Thank you to Wing Venture Capital and Eric Newcomer, and congratulations to all the companies honored this year.

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl

Transform your document processing with intelligent table extraction that goes beyond basic OCR. Tables in PDFs aren't just text - they're structured data trapped in visual formats. Our new deep dive

Transform your document processing with intelligent table extraction that goes beyond basic OCR. Tables in PDFs aren't just text - they're structured data trapped in visual formats. Our new deep dive explains how modern OCR for tables reconstructs spatial relationships, preserves header hierarchies, and ensures data integrity across complex documents. 📊 Why table extraction is fundamentally harder than standard text OCR - spatial relationships matter more than character recognition 🔧 The three core phases: detection, structure recognition, and data extraction with validation 💼 Real-world applications across financial services, healthcare, and logistics - from invoice processing to lab results ⚡ How LlamaParse handles multi-line rows, merged cells, and borderless tables while maintaining logical consistency We show a complete invoice processing example where complex line-item tables get converted to clean JSON with preserved relationships and validated totals - ready for immediate ERP integration. Read the complete guide: https://t.co/NjUf30AeC7

🚀 The @GoogleDeepMind team just added Gemini 3.1 to the Live API, so we built a small demo showing how Gemini voice agents can plug directly into the document processing ecosystem powered by LlamaInd

🚀 The @GoogleDeepMind team just added Gemini 3.1 to the Live API, so we built a small demo showing how Gemini voice agents can plug directly into the document processing ecosystem powered by LlamaIndex. 🔥 In this example, we integrate LiteParse to enable fast, fully-local document parsing. With our TUI-based voice assistant, you can literally talk to your terminal: - Speak commands - Trigger live document parsing via tool calls - Hear the agent read back results in real time 🔊 The assistant can extract content from single files or entire folders, leveraging the lightning-fast local parsing that LiteParse provides ⚡ Take a look at the demo👇 👩‍💻 GitHub repo https://t.co/ySmenP2HoY 📚 LiteParse docs https://t.co/NlpoI4CqEq

Bounding boxes are key for citations, and we just shipped a new guide showing how to use LiteParse for visual citations! LiteParse is our fast and open-source document parser. Using both bounding box

Bounding boxes are key for citations, and we just shipped a new guide showing how to use LiteParse for visual citations! LiteParse is our fast and open-source document parser. Using both bounding box extraction and page screenshots, anyone (including agents) can learn how to associate text with an element on the page. https://t.co/Vauhx5Yh9n

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl

👉 Signup to LlamaParse to try it out: https://t.co/QQzVCOwiVl

Word docs are one of the most common file formats people process in LlamaParse, and they've always been surprisingly frustrating to parse well. Here's the counterintuitive part: .docx actually has bet

Word docs are one of the most common file formats people process in LlamaParse, and they've always been surprisingly frustrating to parse well. Here's the counterintuitive part: .docx actually has better structural information than most document formats. We just haven't been able to fully use it. Until now. A .docx file is a ZIP archive of XML files. That XML knows everything: cell boundaries, merged cells, column and row spans, nested tables, formatting tags, hyperlinks. A PDF of the same table has none of that. It's just text positioned at coordinates and line intersections that a parser has to reverse-engineer into structure. The hard part with Word XML isn't extracting the table content. It's knowing which page it's on. Word is a flow format — there are no page boundaries in the XML. Pagination depends on the renderer, fonts, margins, line-height. The same .docx renders differently in Word, LibreOffice, and Google Docs. We built a technique to resolve this, mapping Word XML table elements to their correct page positions in the rendered output. We now get the original document structure AND know exactly where each table appears. The quality improvement is most significant for: · Tables with rich cell formatting (bold, italic, strikethrough, superscript, lists inside cells) · Merged cells and column/row spans · Nested tables (tables inside table cells) If you're processing Word docs with table-heavy content, try it out. 📖 Full writeup: https://t.co/aAEFkvfycG

Congratulations to @zubeensyed, one of our LlamAgent contest winners, for building an agentic AI workflow that automates GDPR breach report structuring! The agent takes incident reports and maps them

Congratulations to @zubeensyed, one of our LlamAgent contest winners, for building an agentic AI workflow that automates GDPR breach report structuring! The agent takes incident reports and maps them to a standardized GDPR breach schema, aligning with Article 33 notification requirements and Article 34 risk thresholds. It classifies documents, extracts the relevant fields, and surfaces them in a review UI where a human can approve or reject the output. Read about the solution here: https://t.co/ovHYzzFnhD Watch the full walkthrough of how this is built; https://t.co/PwbJqSDrDB