Content History
The secret to LiteParse lies in the grid projection algorithm. We project a complex page layout with text and tables into well-structured text, that humans can read and agents can understanding. This contains of a few core steps (no LLMs!): 1. Grouping text fragments to lines 2. Identify left,center,right anchors 3. Snap each text item to an anchor 4. Handle flowing paragraphs separately 5. Render each text item in a carefully tuned order so that each piece of text aligns to a grid column 6. Post-processing For more details check out this great blog post we wrote a month ago! https://www.llamaindex.ai/blog/how-liteparse-turns-pdfs-into-text-a-deep-dive-into-the-grid-projection-algorithm
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
Parse PDFs at lightspeed (this video is at 1x) Absolute cinema
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
Parse PDFs in the browser, or the edge, in milliseconds Our LiteParse WASM package can be literally run anywhere, from cloudflare workers, mobile runtimes, to the browser. Starter template for Cloudflare: https://github.com/run-llama/liteparse-cloudflare-worker-quickstart LiteParse repo: https://github.com/run-llama/liteparse LiteParse docs: https://developers.llamaindex.ai/liteparse/
When we say “LiteParse runs everywhere,” we mean it. Our WASM package is lightweight, minimal, and built for browser and edge runtimes, which makes it a perfect fit for @cloudflare Workers. Using WebAssembly, you can spin up a parser that runs directly on the Worker, takes PDF
RT LlamaIndex 🦙 When we say “LiteParse runs everywhere,” we mean it. Our WASM package is lightweight, minimal, and built for browser and edge runtimes, which makes it a perfect fit for @cloudflare Workers. Using WebAssembly, you can spin up a parser that runs directly on the Worker, takes PDF bytes as input, and returns extracted text plus page count (all in under 25 lines of code!)🚀 👩💻 Try it out now: https://github.com/run-llama/liteparse-cloudflare-worker-quickstart 📚️ Get started with LiteParse: https://developers.llamaindex.ai/liteparse/
We comprehensively benchmarked Opus 4.8 on document understanding tasks, and compared it to Opus 4.7. It's fairly apparent that Opus 4.8 wasn't explicitly post-trained on visual document understanding: it does slightly better on tables/semantic formatting/layout, but worse on content faithfulness and more. Full results ready on ParseBench: https://www.parsebench.ai/
Opus 4.8 dropped today. ParseBench results are out. ✅ Slight gains: tables, semantic formatting, layout ⚠️ Slight regressions: charts, content faithfulness 💰 Slight price/page increase Lots of alpha left in teaching LLMs to read docs like humans do. LlamaParse remains the
RT LlamaIndex 🦙 Opus 4.8 dropped today. ParseBench results are out. ✅ Slight gains: tables, semantic formatting, layout ⚠️ Slight regressions: charts, content faithfulness 💰 Slight price/page increase Lots of alpha left in teaching LLMs to read docs like humans do. LlamaParse remains the best doc-ingestion API for AI agents.
Who's going first
Introducing Claude Opus 4.8: it builds on Opus 4.7 with sharper judgment, more honesty about its own progress, and the ability to work independently for longer than its predecessors. Available today at the same price.
RT ADITYA KABRA I tried the liteparse's web browser version today to convert a couple of PDF to text and was shocked at the speed. I had to recheck twice to see whether it even did the complete processing or not 😅 https://run-llama.github.io/liteparse/
Beyond being fast, LiteParse is designed to provide highly accurate, semantically coherent text for LLM use. We benchmarked every open-source, model-free PDF parser on LLM QA tasks - from PyPDF to PyMuPDF to Markitdown. ✅ We ~roughly tied for #1 in accuracy (along with
Beyond being fast, LiteParse is designed to provide highly accurate, semantically coherent text for LLM use. We benchmarked every open-source, model-free PDF parser on LLM QA tasks - from PyPDF to PyMuPDF to Markitdown. ✅ We ~roughly tied for #1 in accuracy (along with pdftotext, which is decently accurate but a bit slower) ✅ PyMuPDF is the closest to us in term of latency, but we found it struggles in projecting complex text layouts (multi-columns, tables) in formats that LLMs can understand Besides being accurate and #1 in speed, LiteParse is also a general-purpose parser taht supports dozens of other file formats (incl .docx, .pptx, .xlsx), and also supports convenience tools for both OCR and screenshotting. Come check it out! LiteParse: https://github.com/run-llama/liteparse
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
RT LlamaIndex 🦙 Is grep 𝘳𝘦𝘢𝘭𝘭𝘺 all your AI agent needs for search? For a small codebase or a docs folder, the answer might be yes, but in most enterprise environments, agents face millions of PDFs, spreadsheets, and scanned documents. Lexical search alone can't read those formats, doesn't scale, and misses synonyms entirely. In our latest post, we break down: → Where grep shines (and why it's not going away) → Why RAG and semantic search are necessary at enterprise scale → How to layer lexical + semantic search for the best of both worlds The answer isn't grep vs. RAG, it is knowing when to reach for each and how to combine them. 📚️ Read the full breakdown: https://llamaindex.ai/blog/is-grep-all-you-need-lexical-vs-sematic-search-for-agents
RT kogu https://github.com/run-llama/liteparse これか。日本語PDFでどんなか試しとこう。
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
RT simon we are a car
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
RT Nick Craske ⚡️
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
RT Dmitry Lyalin This is pretty cool!
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
RT K-Dense New skill from K-Dense: LiteParse in Scientific Agent Skills — built for fast, local research paper ingestion. Your AI co-scientist can now: * Parse PDFs and supplementary files on your machine (no document cloud) * Pull layout-preserved text with bounding boxes for grounded citations and RAG * OCR scanned papers, protocols, and figure-heavy supplements * Batch-ingest literature folders for reviews and evidence synthesis * Grab page screenshots when the model needs figures, tables, or charts * Efficient extraction for the papers you already have https://github.com/K-Dense-AI/scientific-agent-skills
We've created the world's fastest PDF parser ⚡️ And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm) Introducing LiteParse v2 - we rewrote the entire library into Rust and
RT Clelia Bertelli (🦙/acc) LiteParse 🤝 Rust🦀 We refactored the LiteParse library and CLI porting it to Rust, and here's what that means for you: ⚡ Parsing up to 100X faster, with sub-second processing for documents as large as 450+ pages and 100MB in size 🈳 Has native multi-language support, with bindings for python and typescript (and the rust library and CLI) 🌐 Can be truly deployed on edge devices and in the browser, as we cross-compiled it to WASM. We also have a fun demo for it 👉 http://run-llama.github.io/liteparse Kudos to @LoganMarkewich for driving this!🎉 📚 Check out the blog: https://www.llamaindex.ai/blog/liteparse-v2-0-runs-everywhere ⭐ Star the repo (we crossed 5k!): https://github.com/run-llama/liteparse
Scan documents with your iPhone 📱, digitalize it with LlamaParse ✍️ We now support parsing HEIC formats natively in LlamaParse (along with 50+ other formats, incl. PDF, Word, Powerpoint, HTML) Try it out now: https://cloud.llamaindex.ai/?utm_source=xjl&utm_medium=social
LlamaParse now parses HEIC files natively 🎉 . HEIC is Apple's default image format, so it shows up all over enterprise file systems. Photos of whiteboards, scanned docs, receipts snapped on an iPhone. You no longer need to convert to JPEG first. Point LlamaParse at the .heic
RT LlamaIndex 🦙 Automate a loan underwriting pipeline in just a few lines of code✨️ A typical loan file is a stack of pay stubs and brokerage statements, every one formatted differently, every number re-typed by hand. Here's a pipeline that does it automatically with LlamaParse: PDFs to clean markdown, fields into Pydantic models, then cross-document analysis that produces an underwriting summary with discrepancy flags. Full post and repo: https://www.llamaindex.ai/blog/building-a-financial-document-pipeline-with-llamaparse
RT Clelia Bertelli (🦙/acc) I'm going to Applied AI Conference by @techeurope_ in Berlin on Thursday! I'll be having my presentation on the side stage along with other great speakers from @OpenAI, @GoogleDeepMind, @arizeai, @stripe, @SlackHQ, @modal, @Linkup_platform and more! My talk is at 10.50, but I'll be around the conference all day, so if you want to catch up just leave a comment/send a DM and I'll happily stop by you :)) See you in Berlin🚀
We're excited to be an official shoutout at the Google I/O Developer Keynote 🔥 @llama_index is building the document infrastructure for AI agents, and we plan to integrate even more heavily with both the model layer (Gemini API) and agent harness layer (Antigravity agents) to support all developers within the Google ecosystem.
We're live at @googleio! Thanks, @OfficialLoganK for the shoutout in the developer keynote. Lots of exciting features comining to the @GeminiApp API🔥 and we're exciting to provide the document infrastructure for Google ecosystem builders.
RT LlamaIndex 🦙 We're live at @googleio! Thanks, @OfficialLoganK for the shoutout in the developer keynote. Lots of exciting features comining to the @GeminiApp API🔥 and we're exciting to provide the document infrastructure for Google ecosystem builders.
There are a lot of coding and reasoning benchmarks for AI agents, but not a lot for document understanding - which is a prerequisite for all downstream knowledge work. We released ParseBench ~a month ago, and it is one of the most comprehensive benchmarks that test whether frontier models can understand real-world enterprise documents. This includes complex pages with dense tables, charts, layouts, and more. Most real-world documents around finance, insurance, and legal have one or more of these dimensions. We're hosting a live webinar next Wednesday to talk about document understanding benchmarking, come check it out: https://landing.llamaindex.ai/-webinar-parsebench You can access the full benchmark, paper, and leaderboards through our main site here: https://www.parsebench.ai/
How do you know your document parser is ready for production? 🤔 Existing benchmarks miss what AI agents actually need. That's the gap ParseBench, the first doc OCR benchmark for AI agents, fills. We'll unveil all the magic behind it in a live webinar👇 https://streamyard.com/watch/dkbf3GWDWKbt
View quoted postThere’s an open question on whether grep is all you need for agentic search. This recent paper by @PwCUS (Sen et al.) seems to suggest that. It’s titled “Is Grep All You Need? How Agent Harnesses Reshape Agentic Search”. They test various agentic harnesses (in-house, Claude Code, Codex), and equip the agent with both vector search and grep. They find that grep generally yields higher accuracy than semantic search. IMO the main gap of the paper is that it tests retrieval over conversational memory, not over a real-world corpus of enterprise documents. Standard enterprise RAG setups involve asking complex questions over a static document corpus (e.g. 10-Ks, legal contracts, SOPs). The corpus here is per-user chat history, which is quite a different document distribution. I do think that evolving agentic harnesses simplify the problem of retrieval - hence the popularity with file sandboxes and a vector db is “just a database” - but IMO there’s still more work to be done here. Paper: https://arxiv.org/pdf/2605.15184
We gave a full 90 minute workshop on how to build agentic workflows over your enterprise documents at @aiDotEngineer Singapore 🇸🇬🦙 The majority of unstructured information is locked up within PDFs. @hexapode walks through how to extract information out of these documents and combine it into a deterministic agentic workflow. We'll share the slides soon. Thanks Singapore, and see you in a few weeks at the World Fair in SF, there's going to be even more good content!
That's a wrap at @aiDotEngineer Singapore 🇸🇬 Thanks for all the devs that tuned into our workshop, keynote, and executive dinner. See you in a few weeks at the world fair in SF 🌉
Many AI agents in finance rely on extremely high quality context engineering from documents 📑 They can be roughly divided into two categories: 1️⃣ Repetitive, operational work common in back-office use cases - invoice processing, loan origination, KYC 2️⃣ Assistive agents for open-ended research and generation of reports/presentations - e.g. diligence, equity research We gave a workshop last week in NYC on how to build a high-quality document context layer to enable these AI agent use cases. At this stage, you need a rigorous OCR layer, evaluation checks, and good UI/UX for HITL review/audit - even a slight mistake in number can have catastrophic consequences downstream. Check out the resources below: ✅ My slides: talk a lot about document processing and the general landscape of knowledge work: https://www.figma.com/slides/QUUMQqhCsmV6tz8s5Iq9Iu ✅ Logan’s repo on building an agentic document parsing pipeline over financial documents, with full HITL review: https://github.com/logan-markewich/finparse-pipeline Our core mission is extracting the highest-quality document context for AI agents in finance and more. Come talk to us if you’re facing relevant challenges: https://www.llamaindex.ai/contact
A new set of open-weight models is topping the leaderboard for document understanding 🔥 INF just released two models: Infinity-Parser2-Pro (35B) and Infinity-Parser2-Flash (2B) that top our @huggingface leaderboard for ParseBench. Two key insights: ✅ An expanded synthetic data engine over 5 million diverse parsing samples ✅ A novel Joint RL algorithm that co-optimizes multiple complex tasks: document parsing, element parsing, chart parsing, and more. ParseBench is an open benchmark designed to test semantic document understanding over real-world enterprise documents; it has comprehensive metrics over tables, charts, semantic formatting, and more. Come check out the results on ParseBench! HuggingFace 🤗: https://huggingface.co/datasets/llamaindex/ParseBench Site: https://www.parsebench.ai/ Infinity-Parser Flash model: https://huggingface.co/infly/Infinity-Parser2-Flash
Yesterday we gave an in-person NYC workshop on automating your financial document ingestion pipelines with AI. If you're an investment bank, accounting team, finance AI startup, fintech company, and you're processing a massive amount of consumer/regulatory/public financial paperwork at scale - you might find these resources useful! @LoganMarkewich built a full set of tutorials showing how you can stitch together VLM-enabled document parsing with schemas and business logic into an e2e workflow. This is both much more accurate than existing OCR solutions in extracting out data with much less human review, and can be easily stitched together with your downstream agent applications. Check it out: https://github.com/logan-markewich/finparse-pipeline If you're interested in VLM-powered doc processing, come check out LlamaParse: https://cloud.llamaindex.ai/
LiteParse is the best open-source, model-free document parser for AI agents. Run it over over 50+ document types, and it will parse dense pages with complex text layouts and tables, and it will extract out clean text in seconds ⚡️ (and contains lightweight OCR integrations too!) Today we released `liteparse-server`, which serves LiteParse through an HTTP API. This lets you can use it from any language or service, without sending any data to the cloud. Parse your sensitive, complex docs without calls to 3rd-party VLM APIs. Check out our blog post and release! Blog: https://www.llamaindex.ai/blog/liteparse-server-self-hostable-document-parsing?utm_medium=socials&utm_source=twitter&utm_campaign=2026-- liteparse-server: https://github.com/run-llama/liteparse-server liteparse: https://github.com/run-llama/liteparse
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
RT Clelia Bertelli (🦙/acc) Do you actually own your document parsing infrastructure? 👀 At @llama_index, we wanted to make that easier, so we built 𝗹𝗶𝘁𝗲𝗽𝗮𝗿𝘀𝗲-𝘀𝗲𝗿𝘃𝗲𝗿, a lightweight HTTP backend built on top of LiteParse that can parse and generate page screenshots from PDFs, images, and Office documents🦙 It’s 100% open source, fully self-hostable, and your data stays yours✅ Built in TypeScript with @UseExpressJS, liteparse-server can run as a @Docker container or in serverless environments. We also included ready-to-use examples for rate limiting, caching, and OpenTelemetry-compatible traces and metrics collection with tools like @Redisinc, @JaegerTracing, @PrometheusIO, and @grafana🔭 📚 Read the blog post I wrote about it: https://www.llamaindex.ai/blog/liteparse-server-self-hostable-document-parsing ⭐ Star the GitHub repo: https://github.com/run-llama/liteparse-server
Agents + file sandboxes are all in the range in 2026 🤖🗃️ This is a nifty reference implementation by @itsclelia showing you how to run your agent over a collection of docs (PDFs, images, Office) with full access to a secure, local-first sandbox. ✅ Uses LiteParse for extremely fast parsing of all these docs ⚡️ ✅ Uses agent harness + native bash commands available to the sandbox (@microsandbox ) to do retrieval Check it out! Reference repo: https://github.com/run-llama/sandboxed-lit LiteParse: https://github.com/run-llama/liteparse
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: - LiteParse,
RT LlamaIndex 🦙 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: - LiteParse, our lightning-fast local parser for PDFs, images, Office files, and more - A secure sandbox powered by @microsandbox - Full filesystem mounting, so your agent can safely interact with local files inside the sandbox Mount your local workspace, give the agent shell access, and let it do its magic 🪄 👩💻 GitHub: http://github.com/run-llama/sandboxed-lit 📚 Learn more about LiteParse: https://developers.llamaindex.ai/liteparse?utm_medium=socials&utm_source=twitter&utm_campaign=2026–
RT Conor Bronsdon New episode of the @chain_ofthought Podcast with @jerryjliu0 of @llama_index coming soon! https://chainofthought.show/
We just wrapped recording with @jerryjliu0 of @llama_index 👀 His thesis: the AI framework era is over. Scaffolding collapses into the model. Context quality = moat that survives. Frontier models still hit ~20% hallucination on enterprise docs. That's the gap. Dropping soon👇
RT Chain of Thought Podcast We just wrapped recording with @jerryjliu0 of @llama_index 👀 His thesis: the AI framework era is over. Scaffolding collapses into the model. Context quality = moat that survives. Frontier models still hit ~20% hallucination on enterprise docs. That's the gap. Dropping soon👇
Congrats on the launch! Filesystems are all you need (?) There wasn't a huge demand for "managed RAG" services in 2023, but it's possible the infra and market was just not mature enough. Maybe filesystems are the right abstraction that allows users to run their production document indexing workloads
Introducing Mirage, a unified virtual filesystem for AI agents! 6 weeks. 1.1M+ lines of code. We rewrote bash from the ground up so cat, grep, head, and pipes work across heterogeneous services. S3, Google Drive, Slack, Gmail, GitHub, Linear, Notion, Postgres, MongoDB, SSH, and
Last week I gave a talk at AI Dev ’26 by @DeepLearningAI on “AI can’t read PDFs, how do we fix it” . I’m sharing the slides publicly if others are interested in doing a deep dive into document understanding. AI agents are going to automate huge amounts of knowledge work, but knowledge work depends on data, a lot of that data is in documents/PDFs, and existing OCR tools suck. PDFs are a format that is inherently hard to read, and I dive into specific reasons why (tables, layouts), and why frontier VLMs and benchmarks are still insufficient. Even as agents get better and more general, they need the right tools to read and act over PDFs. They both need this at the data ingest layer, as well as tools they can call on the fly. Check out the slides: https://www.figma.com/deck/v4xhu6Q797nLNvuhVGyqfY We’re building high-quality AI document processing, both with LlamaParse, along with OSS efforts like LiteParse and ParseBench. If you have a ton of PDFs that you’re hoping to unlock with AI, come talk to us! https://www.llamaindex.ai/contact?utm_source=xjl&utm_medium=social
I ❤️ NYC We're hosting two in-person events next Wednesday: 1️⃣ FinParse workshop: Build AI agents to extract and act over the most complex financial documents 2️⃣ AI Happy Hour with Tabs: drinks, conversations, and L'Industrie Pizza 🍕 Workshop: https://luma.com/updli8i6 Happy hour: https://luma.com/tklfgwh8 Come check it out!
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 → https://luma.com/updli8i6 🍕 AI Engineers on Tap — happy hour w/ @tabs → https://luma.com/tklfgwh8
View quoted postI’m excited to announce that @llama_index is on the @CBInsights AI 100 list for 2026 🔥 We’re on a mission to parse all of the world’s PDFs, and make them accessible to both humans and AI agents. List: https://www.cbinsights.com/research/report/artificial-intelligence-top-startups-2026/ If you haven’t done so already, our website design is awesome, check it out: https://www.llamaindex.ai/
🎉 @CBinsights AI 100 2026 is out and LlamaIndex made the list. We're proud to provide the leading document understanding API for AI agents. Congrats to all honorees in the AI Infrastructure category. Full list here: https://www.cbinsights.com/research/report/artificial-intelligence-top-startups-2026/
RT LlamaIndex 🦙 🎉 @CBinsights AI 100 2026 is out and LlamaIndex made the list. We're proud to provide the leading document understanding API for AI agents. Congrats to all honorees in the AI Infrastructure category. Full list here: https://www.cbinsights.com/research/report/artificial-intelligence-top-startups-2026/
We're hosting a pregame for SF First Thursdays 🎉 In honor of May 4th today, we're making it Star Wars themed 🛸 There's no agenda, just fun, vibes, and both alcoholic and non-alcoholic libations 🍻🚰 We are on the exact street that First Thursdays are hosted, so feel free to check out the party after! You may also be subjected to my Spotify playlist. This Thursday: https://luma.com/i98ittfi Spots are limited, come sign up!
May the 4th be with you!✨ Celebrate with us, you must. Join our Start Up Party Up this Thursday before SF's free 2nd St Fest 🎵 Calling all #AI Jedis - leave your agents at home and sign up for our next monthly meet up to find: 🍕 Outta Sight's specialty pizza 🧋 Jawa juice &
View quoted postParsing PDFs is hard This past week I gave a few talks (at both AI Dev '26 by @DeepLearningAI and @Capgemini ) on why this is still such an open problem, and it’s even more important as agents become the consumers of documents, and need the OCR tools to read them properly. The fundamental issue is that PDFs are designed for print and display purposes, not to give back a linearized, semantically meaningful string of text. Text and tables are represented as a bunch of chars and lines, without any guaranteed order. This is what the community is solving with VLM-based approaches, including our own efforts around LlamaParse and ParseBench. If you’re interested in learning more about the problem, check out the blog post I wrote on this a while ago! https://www.llamaindex.ai/blog/why-reading-pdfs-is-hard
Building a document processing pipeline at scale is hard, and is one of the reasons that it's hard to DIY your own document OCR solution by relying on LLM APIs. Your orchestration pipeline needs to handle rate-limit issues, handle parsing failure exceptions, handle retries due to timeouts without restarting the whole workflow. We're excited to collab with @render on this blog post. Get extremely high-quality, scalable document parsing APIs with LlamaParse, and make it even more scalable/resilient in a multi-step workflow through @render's infrastructure! Blog: https://render.com/blog/building-document-pipelines-that-actually-scale Sample repo: https://github.com/render-examples/render-workflows-llamaindex LlamaParse: https://cloud.llamaindex.ai/?utm_source=xjl&utm_medium=social
Building scalable, distributed document processing pipelines isn’t easy. That’s why we teamed up with @render to build a system that: 📝 Leverages the LlamaParse platform to parse, classify, extract, and retrieve information from documents ⚙️ Uses Render Workflows to distribute
View quoted postI need a new laptop sticker 🙂 Big thanks to @DeepLearningAI for the awesome event!
Thank you AI Dev Day '26 @DeepLearningAI @jerryjliu0 shares why SOTA LLMs can build an app but can't read a PDF 🤯
This is really well thought out. Filesystems are the new default abstraction for agents to interact with documents (the new RAG stack in 2026). The issue is actually figuring out how to productize this; you can't "productize" Claude Code over a local file system. Seems like this tool has all the semantics of filesystems with the versioning of git
Introducing Mesa: the most powerful filesystem ever built, designed specifically for enterprise AI agents. Every team building agents eventually hits the same wall: where do the files live? Not the chat history, the actual artifacts the agent works on. > The contracts your
Want to see which frontier models do the best on document understanding? Check out our ParseBench leaderboard on @kaggle! https://www.kaggle.com/benchmarks/llamaindex-org/parsebench For more details on everything else, check out our http://parsebench.ai site: https://www.parsebench.ai/
ParseBench: A benchmark for document parsing agents @llama_index just shipped a benchmark with 2k verified pages for real enterprise documents. Benchmarks are the major underrated component in the ML ecosystem, so I'm excited to see more entities doing open work in the space
Processing loan applications traditionally takes a staggering amount of time going through paperwork - ~dozens of hours every month looking at the loan application and cross-checking it with user-submitted data: tax returns, bank statements, pay stubs, and more. A big reason it takes so much time is that you need to check numbers (e.g. income) are consistent between different documents. You can automate this with AI agents, but this requires that you have extremely high accuracy document OCR that can properly extract the right information out of each document. I wrote this blog post to show you how you can build an agentic workflow to automate the e2e process. It uses LlamaParse for high-accuracy document OCR, and integrates with Claude to give back structured outputs. Blog: https://www.llamaindex.ai/blog/build-automated-loan-income-verification-with-llamaparse-claude-agent-sdk?utm_medium=socials&utm_source=xjl&utm_campaign=2026-apr- Full repo is here: https://github.com/jerryjliu/llamaparse_use_cases
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
View quoted postRT Sasha Sheng (Hiring) 🫶🏼 We need more evals for document understanding. ParseBench is a really great start. I respect @llama_index ‘s work on this. 📈
We benchmarked GPT-5.5 on document understanding 📄📊 We ran it through ParseBench, our comprehensive OCR benchmark over enterprise documents. We evaluated metrics across various dimensions: visual grounding, tables, charts, and more. We evaluated GPT-5.5 on mid thinking and
Grateful to @Wing_VC to be a part of ET30 and @Nasdaq for the event 🔥 Hope to be back to ring the bell for real 🙂 (I am in the photo! In the bottom right)
Congrats to the ET30 Class of 2026 who just rang the @Nasdaq Closing Bell. 🔔
Our own team was trying to figure out how to get LiteParse working in the browser 😂 Shouldn't have doubted Claude. Claude knows best.
LiteParse is really neat! It does a great job of extracting text from annoying layouts in PDFs (multiple columns for example) It's only available as a Node.js CLI app, so I vibe-coded up this version that runs in a browser
RT Simon Willison LiteParse is really neat! It does a great job of extracting text from annoying layouts in PDFs (multiple columns for example) It's only available as a Node.js CLI app, so I vibe-coded up this version that runs in a browser
LiteParse, our OSS document parser, is really good at parsing complex PDF layouts, text, and tables into a clean spatial grid. The best part is it doesn't use VLMs or any ML models at all. It's entirely heuristics based and super fast ⚡️ The secret lies in our sophisticated
LiteParse, our OSS document parser, is really good at parsing complex PDF layouts, text, and tables into a clean spatial grid. The best part is it doesn't use VLMs or any ML models at all. It's entirely heuristics based and super fast ⚡️ The secret lies in our sophisticated grid projection algorithm. This blog post by @LoganMarkewich gives a comprehensive walkthrough on how it works: 1️⃣ Sort lines based on similar Y coordinates 2️⃣ Extract left, right, and center anchors 3️⃣ Classify every text item into one of these anchors 4️⃣ Project every text item into a grid column (the exception is any paragraph of flowing text, which is rendered separately) 5️⃣ For any item projected into a grid column, that item is the forward anchor for all subsequent text items with the same anchor 6️⃣ Postprocess the final outputs to remove extraneous spaces and margins As an example, take a look at the results below. You can see text in the left column, with a nicely overlaid table on the right. LiteParse is fully free and open-source, you can use it today! Either directly through the CLI or integrated into your coding agent. Blog: https://www.llamaindex.ai/blog/how-liteparse-turns-pdfs-into-text-a-deep-dive-into-the-grid-projection-algorithm?utm_medium=socials&utm_source=xjl&utm_campaign=2026-apr- LiteParse repo: https://github.com/run-llama/liteparse
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
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