Tessera

Need document parsing that stays fully local and private? 👀 Meet liteparse-server, a self-hostable, open-source HTTP server for parsing documents and generating screenshots from PDFs, Office files, and images. ✅ 100% self-hosted ✅ Private by default ✅ Open source ✅ Built for production deployments

Ever wished your agent could read PDFs, images, and Office documents as easily as plain text? Or combine the safety of a secure sandbox with the full power of Bash access? We built exactly that. Meet 𝘀𝗮𝗻𝗱𝗯𝗼𝘅𝗲𝗱-𝗹𝗶𝘁, a Rust 🦀 CLI agent that combines:

A few weeks ago @simonw got Claude to port LiteParse to the browser. Today, we are launching that work as a complete guide in our docs! developers.llamaindex.ai/liteparse/g...

Browser Usage

Run LiteParse in the browser with Vite.

developers.llamaindex.ai

What if you could extract text from any photo on your phone? We built LlamaParse Mobile, an @expo.dev + @reactnative.dev app for iOS & Android, powered by the LlamaParse TypeScript SDK 📱

LlamaIndex NYC takeover, 5/13 🗽 Our CEO Jerry Liu is in town. Two events, open to every NYC builder: 🛠️ FinParse Workshop — laptops out, hands-on with @jerryjliu0 → luma.com/updli8i6 🍕 AI Engineers on Tap — happy hour w/ @tabs → luma.com/tklfgwh8

NYC AI Engineer On Tap · Luma

Calling all AI Engineers in NYC. What happens when an AI infrastructure powerhouse (LlamaIndex) and a fintech darling (Tabs) walk into a bar? You get the…

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Building scalable, distributed document processing pipelines isn’t easy. That’s why we teamed up with @render.com to build a system that:

Parsing documents with AI agents just got a lot more seamless🚀 We've rebuilt the LlamaParse MCP server to handle your document processing workflows, and you can connect it today to any MCP-compatible client at mcp.llamaindex.ai/mcp 🌐

Loan processors spend 40–60% of their time reconciling income across tax returns, pay stubs, W-2s, and bank statements. We built an end-to-end pipeline that automates it with LlamaParse + the Claude Agent SDK: 📄 Schema-driven extraction across 4 doc types with confidence scores + citations

ParseBench is now live on @Kaggle. The first document OCR benchmark built for AI agents — 2,000 enterprise pages, 167K+ test rules, 5 dimensions that actually break downstream agents. Benchmark your parser against 14 methods including GPT-5 Mini, Gemini 3, Textract, and LlamaParse.

LiteParse: our open-source, layout-aware PDF parser for AI agents. The secret? Grid projection. Instead of heavy ML layout models or flat text extraction, it projects text onto a monospace grid so alignment preserves structure. Full deep dive into the grid projection algorithm behind the magic ↓

LiteParse hit 4K+ GitHub stars in 3 weeks. ~500 pages in 2 seconds. No GPU. No API keys. 50+ file formats. Now @LoganMarkewich, our Head of Open Source, will show you how to build with it. Live workshop — April 28, 9 AM PST: Build a Financial Due Diligence Agent with LiteParse.

Agents like OpenClaw are incredibly powerful, as long as the information they receive is clean and structured🦞

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:

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→ luma.com/updli8i6

Turn Complex Financial Docs into LLM-ready Data with Jerry Liu and LlamaIndex · Luma

A hands-on workshop for engineers building VLM-powered OCR that works on real-world financial documents. Most document pipelines fail quietly. They work on a…

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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:

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

We’ve moved to a new office and it’s time to celebrate. Swing by this Thursday to meet our team, grab a bite, and make new friends. Note: Space is limited, so please RSVP early. luma.com/mkh44c7w

Startup Party Up for First Thursday · Luma

We’ve moved to the 'AI Waterfront' and it’s time to celebrate. Swing by on April 2nd to see our new office on 2nd street, meet our team, and make new…

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Our OSS engineer @cle-does-things.bsky.social recently built 𝗹𝗶𝘁𝗲𝘀𝗲𝗮𝗿𝗰𝗵, a fully local document ingestion and retrieval CLI/TUI application powered by LiteParse ⚡ litesearch demonstrates how developers can assemble a high-performance, local-first pipeline using tools from across the ecosystem:

Transform your document processing with intelligent table extraction that goes beyond basic OCR.

🚀 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.

Bounding boxes are key for citations, and we just shipped a new guide showing how to use LiteParse for visual citations! developers.llamaindex.ai/liteparse/g...

Word docs are one of the most common file formats people process in LlamaParse, and they've always been surprisingly frustrating to parse well.

Congratulations to Zubeen, one of our LlamAgent contest winners, for building an agentic AI workflow that automates GDPR breach report structuring!

We’ve published a new blog with ‪@developers.google.com‬ on how to build a smart financial assistant using LlamaParse, our state-of-the-art agentic document parser, together with Gemini 3.

If you've ever worked in or around legal, you know that discovery is where document parsing really gets stress-tested.

LlamaParse now has an official Agent Skill you can use across 40+ agents. With built-in instructions for parsing complex documents, including different formats, tables, charts, and images, your agents gain access to deeper document understanding, not just raw text extraction. 👇 Watch the demo

Our new open-source LiteParse now comes with ready-to-use agent skills that work seamlessly with coding agents. `npx skills add run-llama/llamaparse-agent-skills --skill liteparse`

We've spent years building LlamaParse into the most accurate document parser for production AI. Along the way, we learned a lot about what fast, lightweight parsing actually looks like under the hood. Today, we're open-sourcing a light-weight core of that tech as LiteParse 🦙

Context engineering is the new prompt engineering — and if you're building AI agents, you need to understand the difference and why parsing your data correctly sits at the heart of it