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

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

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

LlamaParse Agentic Plus mode now delivers precise visual grounding with bounding boxes for the most challenging document elements. Our latest update brings major improvements to how we handle complex visual content:

One of the hardest problems with document parsing is trust. How do you know the output actually corresponds to what's in the source? LlamaParse has visual grounding with bounding box citations for outputs, and it addresses exactly this. Two ways to use it:

Agentic AI transforms document extraction from simple text transcription into intelligent reasoning, dramatically reducing manual review queues and maintenance overhead.

Choosing between Skills and MCP tools for your AI agents? Here's an overview from @cle-does-things.bsky.social and @tuana.dev 🔧 MCP tools offer deterministic API calls with fixed schemas - perfect for precise, predictable operations but require dev knowledge and introduce network latency

Ever wondered what we mean by 'agentic' OCR? It's parsing that reasons about documents instead of just reading them. Agentic OCR adapts to layout changes by treating document processing as a goal-oriented task rather than simple text extraction.

🔎 semtools v3.0.0 is out, and it's a great step forward for anyone using semantic search and document parsing from the command line.

🚀 The team at @GoogleDeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality.

If you’re working with lots of slide decks and need a better way to search through them, Surreal Slides makes it simple 🌀

PDFs are the bane of every AI agent's existence: here's why parsing them is so much harder than you think 📄 Every developer building document agents eventually hits the same wall: PDFs weren't designed to be machine-readable. They're drawing instructions from 1982, not structured data.

"Just send the PDF to GPT-4o" Ok. We did. Here's what happened: • Reading order? Wrong. • Tables? Half missing. • Hallucinated data? Everywhere. • Bounding boxes? Nonexistent. • Cost at 100K pages? Brutal. So we're doing it live.

Creating agent workflows and architecting the logic is one thing, making them durable and fail-safe is another👇 New integration for durable agent workflows with @dbos.dev execution - Make sure your agents survive crashes, restarts, and errors without writing any checkpoint code.

Huge thank you to everyone who joined the Google DeepMind hackathon in NYC with us over the weekend 💛

If you need to split complex or composite documents into structured categories or sections, LlamaSplit is built for the job ✂️

LlamaIndex has evolved far beyond a RAG framework - we're now focused on agentic document processing that automates knowledge work. 🚀 Agent orchestration has fundamentally changed with sophisticated reasoning loops, tool discovery through Skills/MCP, and coding agents that write Python for you

When you parse a document with LlamaParse, you also get access to layout data for figures, charts, etc. Parse the document, specify to save layout images, and access those images on the response! Each image will be a cropped screenshot of that specific layout element.

Turn your PDF charts into pandas DataFrames with specialized chart parsing in LlamaParse! This tutorial walks you through extracting structured data from charts and graphs in PDFs, then running data analysis with pandas - no manual data entry required.

Build a private equity deal sourcing agent that automatically classifies investment opportunities and extracts key financial metrics using our LlamaAgents Builder. This step-by-step guide shows you how to create an agent that processes deal files like teasers and financial summaries:

Document OCR benchmarks are hitting a ceiling - and that's a problem for real-world AI applications.

🚀 LlamaAgents Builder just leveled up: File uploads are here! Our natural language interface for building agentic document workflows now supports file uploads.

🚀 Big drop from Google DeepMind: Gemini 3.1 Pro is here, and we built a hands-on demo powered by LlamaCloud to put it to work and turn your receipt photos into real financial insights!

More reasoning doesn't always mean better results - especially for document parsing. We tested GPT-5.2 at four reasoning levels on complex documents and found that higher reasoning actually hurt performance while dramatically increasing costs and latency.

🏆 We're running a LlamaAgents contest right now. Throw your hardest documents at our agent builder, and tell us how it goes. Want help getting started? We have a new walkthrough for the LlamaAgent Builder by @tuana.dev

Your extraction tool found the revenue figure. Great. But which page? Which paragraph? Can you prove it to an auditor? Most document extraction treats a 200-page PDF as one giant blob. You get the data, but lose the context. That's a problem when:

What if an AI agent could review every invoice against your contracts — and flag what doesn't match? That's exactly what our Invoice Reconciler demo does. Here's how it works: 📄 Upload your contracts and invoices → LlamaParse converts them into clean, LLM-readable Markdown

🚀 The @posthog.com team has just rolled out LlamaIndex support for their LLM Analytics, and we built a demo to showcase what’s possible. Using LlamaIndex, LlamaParse, and OpenAI, our Agent Workflow compares product specifications and matches users with the most suitable option for their use case 🛠️

Fuel up for hackathon success with free breakfast at the same bakery that fed the winning Superbowl team! 🥐 We're teaming up with @kilocode, @MiniMax_AI, and @withmartian to host breakfast for all @DeveloperWeek attendees next week in San Jose. Sign up for free breakfast: luma.com/devchampions

2026 is the year of long-horizon agents. @sequoia predicts that this year, agents will be able to tackle long-horizon tasks and work autonomously for hours to solve ambiguous tasks.

The rise of coding agents is fundamentally changing open source - Our head of OSS @LoganMarkewich breaks down how LLM-powered coding agents are impacting core pillars of open source: 👥 Community interaction, which is getting complicated by low-quality, massive AI-generated PRs

Are you trying to solve high-quality document ingestion for your product? Catch lessons from the field on how @stackai uses LlamaCloud to power high-accuracy document ingestion & retrieval across PDFs, images, spreadsheets & more — at enterprise scale.