Introducing DocJev - a lightning-fast OSS library for document classification and splitting with jev ⚡️ Give a document alongside some natural language category rules. Jev will predict the document category (classify) or the boundaries between sub-documents (split). It is 6x faster than gpt-5.6-luna with equivalent accuracy. You can choose between different OCR backends: liteparse: the fastest/most accurate free+OSS text parser out there. Perfect for digitalized documents. The speedups vs. luna above are inclusive of liteparse times. LlamaParse: the most advanced VLM-based solution for complex docs. Using this will of course add latency on document preprocessing. But it’s a good option if you want to cache the digitalized representations of documents for a variety of downstream tasks. Check it out: github.com/jerryjliu/docjev If you want to dive deeper into liteparse or LlamaParse check the links below: liteparse: github.com/run-llama/litepar… LlamaParse: cloud.llamaindex.ai/

Today, we're excited to launch Extract Turbo ⚡️ - the fastest VLM-powered document extraction solution. It is 3-5x faster than all other comparable OCR solutions, including our other LlamaParse tiers, at equivalent or higher accuracy.

We Parse PDFs We spent 7 figures to put this on billboards throughout SF. I thought long and hard about putting something more creative and whimsical. But then you wouldn’t know what we do. AI agents (and humans) are consuming exponentially more documents as they do real work. They need the best quality document parser to not output garbage on downstream tasks. This is what we do today as a company. If you have any PDFs (or other documents), we parse them :) If you’re around SF in June for one of the following events, come stop by our booths: ✅ Snowflake Summit (this week, Booth 1123) ✅ Databricks Data+AI Summit (June 15-18, Booth 137) ✅ AI Engineer World Fair(June 29-July 2, Booth L-G47) You can find us by the same sign we put on our billboards! We Parse PDFs @llama_index

If you're stopping by the SF Caltrain station over Memorial Day weekend, you might catch a glimpse of our digital ads 📺 We parse (PDFs) (50+ other document types)

This is why we released liteparse :) Free, open-source, designed for agents. Natively supports OCR / screenshotting for deeper visual understanding in a document when needed.

We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench. Document parsing is the foundation of every AI agent that works with real-world files. ParseBench is a benchmark that measures parsing quality specifically for agent knowledge work: ✅ It optimizes for semantic correctness (instead of exact similarity) ✅ It has the most comprehensive distribution of real-world enterprise documents It contains ~2,000 human-verified enterprise document pages with 167,000+ test rules across five dimensions that matter most: tables, charts, content faithfulness, semantic formatting, and visual grounding. We benchmarked 14 known document parsers on ParseBench, from frontier/OSS VLMs to specialized parsers to LlamaParse. Here are some of our findings: 💡 Increasing compute budget yields diminishing returns - Gemini/gpt-5-mini/haiku gain 3-5 points from minimal to high thinking, at 4x the cost. 💡 Charts are the most polarizing dimension for evaluation. Most specialized parsers score below 6%, while some VLM-based parsers do a bit better. 💡 VLMs are great at visual understanding but terrible at layout extraction. GPT-5-mini/haiku score below 10% on our visual grounding task, all specialized parsers do much better. 💡 No method crushes all 5 dimensions at once, but LlamaParse achieves the highest overall score at 84.9%, and is the leader in 4 out of the 5 dimensions. This is by far the deepest technical work that we’ve published as a company. I would encourage you to start with our blog and explore our links to Hugging Face to GitHub. All the details are in our full 35-page (!!) ArXiv whitepaper. 🌐: Blog: llamaindex.ai/blog/parsebenc… 📄 Paper: arxiv.org/abs/2604.08538?utm… 💻 Code: github.com/run-llama/ParseBe… 📊 Dataset: huggingface.co/datasets/llam… 🎥 YouTube: youtube.com/watch?v=g5p7G-Nw…

We're excited to collaborate with @googledevs on building an agentic workflow over complex financial documents - using LlamaParse and Gemini 3.1 Pro Brokerage statements have complex layouts, dense tables, and oftentimes visual elements like charts. Our multi-step agentic workflow does the following: 1. Ingest PDF into LlamaParse 2. Extract text and tables 3. Generate human-readable summary using Gemini Shoutout to @Vish_ow and @itsclelia 🙌 Check it out: developers.googleblog.com/bu…

The DOJ messed up some redactions on the latest Epstein files 🗄️🔏 - they didn’t flatten the PDF layers and you can highlight/copy the underlying text. If you want to extract this text at scale, you *can’t* just feed everything to a VLM (gpt-5.2, sonnet-4.5, gemini 3). VLMs only look at the top-level visual layer of the page, and will output the redacted blocks. You need to also reconstruct the text from the PDF binary itself, which is more in line with “traditional” techniques. LlamaParse uses a combination of both VLMs along with reading the underlying binary. * If you try out our agentic mode by default, it will output the redacted blocks in the markdown `md` field, but extract out the full text in the `text` field * With a simple prompt change you can also extract out the full text in `md`. Prompt: "Do not output redactions if the underlying extracted text already exists - output the full extracted text instead" Whether you want to comb through any set of released government documents or any other file, come check out LlamaParse! Source reddit thread: reddit.com/r/Epstein/s/ax3we… File: justice.gov/multimedia/Court… To use LlamaParse, sign up to LlamaCloud: cloud.llamaindex.ai/

Claude Code over Excel++ 🤖📊 Claude already 'works' over Excel, but in a naive manner - it writes raw python/openpyxl to analyze an Excel sheet cell-by-cell and generally lacks a semantic understanding of the content. Basically the coding abstractions used are too low-level to have the coding agent accurately do more sophisticated analysis. Our new LlamaSheets API lets you automatically segment structure complex Excel sheets into well-formatted 2D tables. This both gives Claude Code immediate semantic awareness of the sheet, and allows it to run Pandas/SQL over well-structured dataframes. We've written a guide showing you how specifically to use LlamaSheets with coding agents! Guide: developers.llamaindex.ai/pyt… Sign up to LlamaCloud: cloud.llamaindex.ai/

You might’ve known us as a “RAG framework” company - but we’ve been a best-in-class, agentic document OCR/workflow company for the past 1.5+ years! 📑🤖 We’re building the future of knowledge work over documents. Our website is awesome - check it out if you haven’t already 👇 llamaindex.ai/