LlamaIndex 🦙(@llama_index)

Your agentic workflows are only as good as the context you feed them. In finance, that context is s...

LlamaIndex 🦙 (@llama_index) — Wed, 22 Jul 2026 18:09:57 GMT

Your agentic workflows are only as good as the context you feed them.

In finance, that context is stuck inside your messiest documents: dense tables, footnoted adjustments, and the details buried in the notes that actually carry the risk. Most tooling flattens exactly the structure that matters, so what reaches your systems is already degraded.

On July 30, join Harsha, a Solution Architect on our team, as he shows how to turn a real, dense financial document into clean, structured, decision-ready context.

On the agenda:
→ Tables and footnotes that stay intact, not flattened into text
→ Extracted figures traceable to the exact source page
→ Values checked against your rules, with exceptions surfaced

Thursday, July 30 · 9:00 AM PT / 12:00 PM ET 
Save your seat: watch.getcontrast.io/register/llama…

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The team flew into SF for a week onsite. 🌉 2x'd in size since we last did this — first time this m...

LlamaIndex 🦙 (@llama_index) — Tue, 21 Jul 2026 17:55:20 GMT

The team flew into SF for a week onsite. 🌉

2x'd in size since we last did this — first time this many of us have been in the same room.

The recap:
🧠 Locked in our roadmap for AI agent document infra
🎨 Spraypainting, Topgolf, soccer, & escape rooms
⚽️ Caught a few World Cup matches between sessions
🍗 Raising Cane's for lunch
🎯 Darts + way too many dinners across the city

Ready for Q3.

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We're kicking off a founder dinner series in SF. First one's July 28 - one table, 16 seats, invite-o...

LlamaIndex 🦙 (@llama_index) — Mon, 20 Jul 2026 17:00:06 GMT

We're kicking off a founder dinner series in SF. First one's July 28 - one table, 16 seats, invite-only, co-hosted by @jerryjliu0 and @dexhorthy. Topic is loop engineering.

A room of founders and CTOs building agents in production, comparing notes on what holds up.

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Teaming up with https://t.co/IBgKPZBnJY for their Agentic Hackathon at the AWS Builder Loft in SF, J...

LlamaIndex 🦙 (@llama_index) — Fri, 17 Jul 2026 17:00:02 GMT

Teaming up with You.com for their Agentic Hackathon at the AWS Builder Loft in SF, July 24th. One day to build agents on live web data - real-time intelligence, deep research, or multi-agent systems - then demo to the judges.

RSVP here: luma.com/youdotcom-agen…

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Document processing pipelines are often made up of multiple services: parsing → chunking → embedding...

LlamaIndex 🦙 (@llama_index) — Thu, 16 Jul 2026 16:25:07 GMT

Document processing pipelines are often made up of multiple services: parsing → chunking → embeddings → storage.

Until now, LiteParse was only available over REST. That works well for browsers and simple integrations, but it’s not always the best fit for service-to-service communication.

Today we’re introducing liteparse-grpc.

With  @𝘭𝘭𝘢𝘮𝘢𝘪𝘯𝘥𝘦𝘹/𝘭𝘪𝘵𝘦𝘱𝘢𝘳𝘴𝘦-𝘨𝘳𝘱𝘤 (npm) or the  𝘨𝘩𝘤𝘳.𝘪𝘰/𝘳𝘶𝘯-𝘭𝘭𝘢𝘮𝘢/𝘭𝘪𝘵𝘦𝘱𝘢𝘳𝘴𝘦-𝘨𝘳𝘱𝘤 Docker image, you can run LiteParse as a gRPC service and:

📄 Parse PDFs, Office documents, and images into JSON, text, or Markdown
📸 Render PDF pages as screenshots
📊 Estimate document complexity and whether OCR is needed

The package also includes a CLI client for testing, generated TypeScript client stubs and the protobuf definitions, so you can generate clients for Python, Go, Java, or any other language supported by 𝘱𝘳𝘰𝘵𝘰𝘤

The REST server isn’t going anywhere, this is simply another interface for teams building polyglot, microserllamaindex.ai/blog/introduci…elines.

📖github.com/run-llama/lite…n: https://t.co/iAIBoXzhEH
💻 GitHub: https://t.co/K0d8XVE7Rc

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We're part of the Amazon Web Services (AWS) AI Builder Lab in New York on Friday, July 24 - a Clash ...

LlamaIndex 🦙 (@llama_index) — Wed, 15 Jul 2026 17:00:20 GMT

We're part of the Amazon Web Services (AWS) AI Builder Lab in New York on Friday, July 24 - a Clash of Agents competition with OpenAI, LangChain, HiddenLayer, Protopia AI, Fiddler AI, and Coder.

One day of building multi-agent systems on Bedrock, hands-on the whole way - partner challenges, a live leaderboard, and the top builders walk away with AirPods and Meta glasses. No panels, no name-tag mingling.

Capped at 200 developers and free to RSVP. If you're building agents in New York, grab a spot: aicamp.ai/event/eventdet…

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Your extraction schema is now a conversation away. ⁣ 📄 Upload a doc — the agent drafts the schema f...

LlamaIndex 🦙 (@llama_index) — Tue, 14 Jul 2026 21:51:07 GMT

Your extraction schema is now a conversation away.
⁣
📄 Upload a doc — the agent drafts the schema from it⁣
💬 Want changes? Just ask⁣
📚 Or grab a template and go⁣
⁣
Writing JSON Schema by hand? That's over.⁣
⁣
Conversational Extract is live in LlamaParse. Try it now cloud.llamaindex.ai/?utm_medium=so…ip6

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Co-hosting 'Swap and Pitch Night' this Thursday in SF with @tembo, @clerk, and @autonomaai. Pitch ...

LlamaIndex 🦙 (@llama_index) — Tue, 14 Jul 2026 17:00:21 GMT

Co-hosting 'Swap and Pitch Night' this Thursday in SF with @tembo, @clerk, and @autonomaai. 

Pitch night where founders pitch each other's startups. Good test of whether you actually listen, and sometimes someone pitches your startup better than you do.

855 Brannan, 6 to 8:30pm. 

RSVP here: luma.com/swapandpitch

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We eat docs🫢

LlamaIndex 🦙 (@llama_index) — Tue, 14 Jul 2026 14:40:13 GMT

We eat docs🫢

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Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent, without havi...

LlamaIndex 🦙 (@llama_index) — Mon, 13 Jul 2026 16:08:09 GMT

Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent, without having use CLIs or open the browser?

We built exactly that.

Using @TauriAp ps, with a Rust backend powered by LiteParse and a React frontend, we created a cross-platform desktop app that lets you:

• Drag and drop PDFs
• Convert them into clean Markdown
• Preview page screenshots with extracted bounding boxes overlaid for easy inspection

It's a simple way to understand your document's structure and verify the parsing results before handing them off to an LLM.

Check out the demo below! 👇

GitHub:github.com/run-llama/lite…W
Get started with LiteParse:developers.llamaindex.ai/liteparse/S

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@OpenAI released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in d...

LlamaIndex 🦙 (@llama_index) — Thu, 09 Jul 2026 22:48:21 GMT

@OpenAI  released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in document understanding. The new family of models continues to excel at reading text and tables, but continues to struggle with charts and layout. 

What's most interesting is that Luna is about 6 x cheaper than Sol and only results in minor degradations across all ParseBench metrics, showing that an increase in reasoning tokens doesn't always result in a commensurate improvement in visual understanding.

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@OpenAI released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in d...

LlamaIndex 🦙 (@llama_index) — Thu, 09 Jul 2026 22:47:13 GMT

@OpenAI  released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in document understanding. The new family of models continues to excel at reading text and tables, but continues to struggle with charts and layout. 

What's most interesting is that Luna is about 6 x cheaper than Sol and only results in minor degradations across all ParseBench metrics, showing that an increase in reasoning tokens doesn't always result in a commensurate improvement in visual understanding.

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Low user caps in LlamaParse? Gone.🎉 Every plan — Free through Pro — now unlocks up to 100 team mem...

LlamaIndex 🦙 (@llama_index) — Thu, 09 Jul 2026 17:12:52 GMT

Low user caps in LlamaParse? Gone.🎉

Every plan — Free through Pro — now unlocks up to 100 team members.
No more picking who gets a seat. No more shared logins. No more upgrading just to invite a teammate.

Whether you're a two-person startup on the free tier or a team scaling on Pro, your whole team can now build together, same workspace, same projects, every tier.

True team collaboration is now the default, not a premium feature.

Signup for free and invite your team today -->cloud.llamaindex.ai/?utm_medium=so…6

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Introducing: Granular job tracking and cost attribution in LlamaParse 🦙 Parsing thousands of page...

LlamaIndex 🦙 (@llama_index) — Wed, 08 Jul 2026 21:13:09 GMT

Introducing: Granular job tracking and cost attribution in LlamaParse 🦙 

Parsing thousands of pages a day is a lot to manage. Engineering teams need to have a pulse on what's running, who ran it, and what it costs. Our latest tagging system make it easier to track and attribute usage and costs at scale in LlamaParse:

🏷️ 𝗨𝘀𝗲𝗿 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮: Always know where every job belongs. Attach your own labels to any parse job and they come back on every response.

🏷️ 𝗨𝘀𝗮𝗴𝗲 𝗧𝗮𝗴𝘀: Know what your document processing actually costs, by project and by team. Tag your requests, and your spend becomes filterable by those tags: by customer, by team, by product line, whatever makes sense for your org.

🏷️ 𝗦𝗶𝗴𝗻𝗲𝗱 𝗪𝗲𝗯𝗵𝗼𝗼𝗸𝘀: Trust every callback you receive. Every webhook delivery now includes an HMAC signature, so your systems can verify a callback really came from us before acting on it.

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We've rolled out improvements to LlamaParse Cost Optimizer. Our intelligent tier routing now more ...

LlamaIndex 🦙 (@llama_index) — Tue, 07 Jul 2026 20:27:42 GMT

We've rolled out improvements to LlamaParse Cost Optimizer. 

Our intelligent tier routing now more reliably ensures you always strike the right balance between cost and accuracy when processing large documents. Simple pages default to our cost-effective tier, complex pages are still processed by our higher accuracy and higher cost agentic and agentic plus tiers.

Only pay for the quality each page actually needs.

Live now for all customers.

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Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often di...

LlamaIndex 🦙 (@llama_index) — Mon, 06 Jul 2026 16:35:50 GMT

Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often different, consisting of messy PDFs where critical information is buried across tables, figures, and complex layouts.

That’s why we teamed up with @lancedb to explore how LiteParse (our lightning-fast parser) combined with LanceDB’s native multimodal storage can improve retrieval quality and agent response accuracy.

By separating PDFs into multiple information layers - pages (text + screenshots + embeddings), chunks, and extracted assets - and storing them in LanceDB for fast multimodal retrieval, we built a hybrid pipeline that unlocks information traditional RAG systems often miss.

Instead of relying on chunk-level retrieval alone, agents can retrieve and reason across pages, chunks, and visual assets, making complex enterprise PDFs far more accessible.

The result is a significantly stronger retrieval foundation for agentic workflows.

📖 Read the full breakdown in the blog post:lancedb.com/blog/from-mess…h
💻 Explore the full implementation on GitHubgithub.com/lancedb/litepa…Rx

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The team at @vercel recently released the Eve agent framework, so we built a template that integrate...

LlamaIndex 🦙 (@llama_index) — Fri, 03 Jul 2026 17:30:11 GMT

The team at @vercel recently released the Eve agent framework, so we built a template that integrates LiteParse with it🦙 

The template provides a set of read-only filesystem tools that let Eve resolve paths, list directories, and read text-based files. We then pair those with LiteParse, which parses files from their source and returns clean, structured Markdown⚙️ 

Finally, we equipped the agent with detailed instructions on when and how to combine these tools effectively, giving it a reliable workflow for navigating and understanding document collections out of the box📁 

The result is a solid starting point that you can extend with your own channels, tools, and skills🔧 

Check it ougithub.com/run-llama/eve-…3E0

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LiteParse runs anywhere, including inside agent runtimes. To prove it, we built an email processing ...

LlamaIndex 🦙 (@llama_index) — Thu, 02 Jul 2026 16:08:07 GMT

LiteParse runs anywhere, including inside agent runtimes.
To prove it, we built an email processing assistant using @flueai (the agentic framework by @astrodotbuild), @resend webhooks, and @tursodatabase for persistence📩 

Here's how it works:
→ Send an email
→ The agent reads its corpus, fetches attachments, and parses any PDFs with LiteParse
→ It summarizes the message and drafts a reply for you

This integration is an example of how parsing tools shouldn't just handle files, they should deepen what agents understand about them, augmenting their context📂 

👩‍💻 Code hegithub.com/run-llama/flue…Luy4

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Game. Match. That's a wrap on the inaugural Agent Open 🦙🌤️🏓 Hundreds of AI builders, 64 matches,...

LlamaIndex 🦙 (@llama_index) — Wed, 01 Jul 2026 21:01:19 GMT

Game. Match. That's a wrap on the inaugural Agent Open 🦙🌤️🏓

Hundreds of AI builders, 64 matches, 7 partners, 2 courts. 
No dink shots or demos, just backhands and hot takes.

Thanks to our partners @BrainTrust, @Modal, @turbopuffer, @p0, @Browserbase, and @Cursor_ai for everyone's time and sweat spent to ace yesterday's event.

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Agentic retrieval is changing the way retrieval-augmented applications are built, especially in doma...

LlamaIndex 🦙 (@llama_index) — Wed, 01 Jul 2026 17:30:14 GMT

Agentic retrieval is changing the way retrieval-augmented applications are built, especially in domains like legal and fintech, where agents need to autonomously navigate large, evolving knowledge bases.

That’s exactly the use case we designed Index v2 for.

To demonstrate what’s possible, we built legal-kb, a reference application that integrates Index v2 into an agentic knowledge automation workflow. It uses Index v2 as the underlying knowledge base and exposes its retrieve, read, grep, and find APIs as tools that an AI agent can use to autonomously explore and reason over your documents.

With legal-kb, you get:

📚 Project-scoped knowledge bases for agent-powered chat
📖 Visual citations directly in agent responses
🌿 Version control for your knowledge base
📦 Data export capabilities

Try it olegal-kb.devMEGx
Explore the cogithub.com/run-llama/lega…KZdn
Get started with LlamaParcloud.llamaindex.ai/signup1Brf

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Doors are open at Agent Open, right next door to AI Engineer Two courts, blue sky, no fog (a San Fr...

LlamaIndex 🦙 (@llama_index) — Tue, 30 Jun 2026 22:35:35 GMT

Doors are open at Agent Open, right next door to AI Engineer 
Two courts, blue sky, no fog (a San Francisco miracle) 🌤️

This is the perfect walk-over window, we are just 3 minutes from Moscone.

Don't miss the State of AI panel at 5PM, tournament + party til 10PM.

RSVP:luma.com/the-agent-open…c

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We just made it a lot easier for AI agents to work with your documents. LlamaParse MCP now does mor...

LlamaIndex 🦙 (@llama_index) — Tue, 30 Jun 2026 16:08:02 GMT

We just made it a lot easier for AI agents to work with your documents.

LlamaParse MCP now does more than parse or classify files: it can pull structured data out of contracts, invoices, and reports automatically, and give agents direct access to your knowledge base (PDFs, Office docs, images, and more) so they can search, read, and retrieve information just like a human would.

We also reorganized everything into focused, purpose-built tools: whether you're classifying documents, extracting data, or building search over your company's files, agents can do it faster, more reliably, and in parallel.

This allows for smarter, more capable AI workflows for anyone working with documents at scale.

Curious to try it out? 
Get started now with the LlamaParse Platform → cloud.llamaindex.ai/?utm_medium=so…
Blog post: llamaindex.ai/blog/extending…
Take a look at the GitHub repository → github.com/run-llama/mcp-…

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Semantic search alone doesn't cut it. Neither does brute-force grep. Agents need both. Today we're ...

LlamaIndex 🦙 (@llama_index) — Mon, 29 Jun 2026 18:05:29 GMT

Semantic search alone doesn't cut it. Neither does brute-force grep.
Agents need both.

Today we're shipping the Retrieval Harness in LlamaParse Index: semantic search, server-side grep, and file-level navigation working together in a single agent reasoning loop. 🦙🌤️

Grep a file, list what's in an index, read past a chunk boundary,
run hybrid search with reranking — all as native agent tools.

Now in beta across all paid tiers. Full breakdown in the blog 👇

Learn Morllamaindex.ai/blog/announcin…dOI

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We're all over AI Engineer World's Fair on June 29 to July 2. 🦙 📍Visit us at booth L-G47. LlamaPa...

LlamaIndex 🦙 (@llama_index) — Mon, 29 Jun 2026 17:30:02 GMT

We're all over AI Engineer World's Fair on June 29 to July 2. 🦙

📍Visit us at booth L-G47. LlamaParse demos + Fear of Docs swag

🎤 @jerryjliu0, our Founder & CEO, on agentic document parsing and shipping agents that survive production. Tue 6/30, 11:10 AM, Vision & OCR (Room 2006)

🎤 George He, our Head of Engineering, in a live parse-off, LlamaParse vs the leading LLMs. Thu 7/2, 1:55 PM, Expo Stage 4

🥒 The Agent Open (6/30)
RSluma.com/the-agent-open…Y1vX8l

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The @n8n_io node for the LlamaParse Platform is now an officially verified community node, as part o...

LlamaIndex 🦙 (@llama_index) — Fri, 26 Jun 2026 16:05:04 GMT

The @n8n_io node for the LlamaParse Platform is now an officially verified community node, as part of a broader partnership with n8n to bring cutting-edge document intelligence to the low-code and no-code world🚀 

The new version of the node brings together document parsing, classification, extraction, splitting, and retrieval in one place, all wired to a single LlamaParse API credential🦙
 
Each resource can now also act as a callable tool inside an n8n AI Agent: so instead of building static pipelines, you can let the agent decide when to retrieve context, parse a file, or extract structured data based on what the user actually needs🤖 

A few workflows worth highlighting: routing documents by type before extracting structured fields, plugging retrieval directly into an agent backed by your own knowledge base, and running parse outputs through different tiers side by side to find the right balance between accuracy and cost🔃 

If you're already using n8n, install it directly from your workflow canvas by searching 𝘓𝘭𝘢𝘮𝘢𝘗𝘢𝘳𝘴𝘦 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮 and give it a try!🔧 

📚️ Full breallamaindex.ai/blog/bring-you…https://t.co/8LJB80HCJ8

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We built LiteParse, the fastest document parsing solution on the planet and made it open source. An...

LlamaIndex 🦙 (@llama_index) — Thu, 25 Jun 2026 16:26:37 GMT

We built LiteParse, the fastest document parsing solution on the planet and made it open source.

And it just hit 10k github stars. 🦙 
Fast to run. Fast to love.

Thanks for building with us. If you haven't tried it already, repo at:github.com/run-llama/lite…q

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Bring your hot takes and your drop shots.🔥 Next week, we're co-hosting The Agent Open: an afternoo...

LlamaIndex 🦙 (@llama_index) — Mon, 22 Jun 2026 20:29:01 GMT

Bring your hot takes and your drop shots.🔥

Next week, we're co-hosting The Agent Open: an afternoon of pickleball, food, drinks, and industry-leading speakers who ace their code and commit to their serves. 🏓

Catch our co-founder and CEO, Jerry Liu after Day 2 of AIE World's Fair on a panel moderated by Gergely Orosz (𝘛𝘩𝘦 𝘗𝘳𝘢𝘨𝘮𝘢𝘵𝘪𝘤 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳). Get ready for a real conversation about the Future of AI and learn how to avoid the hype, stay out of the kitchen, and strategically position your team for what's next.

🗓️ Tuesday, June 30th @ 5:00pm PT
📍 San Francisco (register for the address)
🦙 Co-hosted by LlamaIndex with Braintrust, turbopuffer, Cursor, Parallel Web Systems, Modal, and Browserbase

Come for the panel, stay for the chaluma.com/the-agent-open…: #AIEngineeringG#AIAgents#DocumentAIing #AIAgents#DocumentAI

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LiteParse v2.1 is here, and its bringing the fastest markdown output possible. In this release, we ...

LlamaIndex 🦙 (@llama_index) — Thu, 18 Jun 2026 17:17:04 GMT

LiteParse v2.1 is here, and its bringing the fastest markdown output possible.

In this release, we are fulfilling our top request: markdown output. But in the spirit of "lite"-ness, we are doing this completely LLM-free and fast.

Not only is it fast, it also beats all other model-free competitors in 3 separate benchmark datasets.

Read more about it in our release blog: llamaindex.ai/blog/markdown-…

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~90% of enterprise data is unstructured, locked in the documents that power the majority of knowledg...

LlamaIndex 🦙 (@llama_index) — Thu, 18 Jun 2026 15:10:54 GMT

~90% of enterprise data is unstructured, locked in the documents that power the majority of knowledge work. The next massive frontier? AI agents that can deeply understand, reason over, and edit these files at scale to automate entire workflows.

Our CEO, Jerry Liu is speaking at @Databricks#DataAISummit today on the core advances in OCR and agent orchestration making this a reality.

📍 Yerba Buena Salon 7 · 10:20 AM
🔗 Link to Jerry's sessiondataaisummit.databricks.com/flow/db/dais20…MB

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Vector databases or pure grep? Teams are split on the right retrieval architecture for agents. ⁣ ⁣ T...

LlamaIndex 🦙 (@llama_index) — Wed, 17 Jun 2026 18:20:40 GMT

Vector databases or pure grep? Teams are split on the right retrieval architecture for agents. ⁣
⁣
The reality? You need both. Semantic search for a fast first pass; grep and file reads for surgical precision when top-k chunks cut off mid-answer. ⁣
⁣
On June 29, our Head of Engineering George He goes under the hood on the architecture decisions and dead ends behind building this harness into LlamaParse Index.⁣
⁣
 Register here : landing.llamaindex.ai/retrieval-harn…

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Our CEO Jerry Liu is joining founders from LangChain, CrewAI, and others at @Databricks #DataAISummi...

LlamaIndex 🦙 (@llama_index) — Wed, 17 Jun 2026 16:32:57 GMT

Our CEO Jerry Liu is joining founders from LangChain, CrewAI, and others at @Databricks#DataAISummit today for a panel on the The Agentic Stack — what the stack looks like, where it's headed, and what happens when agents become the primary consumers of infrastructure, not humans.

Panel begins at 11.30 am, see you there. 👋

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How much can good documentation save an AI agent in cost and time? Turns out, a lot. We built a cus...

LlamaIndex 🦙 (@llama_index) — Tue, 16 Jun 2026 16:08:02 GMT

How much can good documentation save an AI agent in cost and time? Turns out, a lot.

We built a custom skill that teaches Claude how to parse PDFs more efficiently, then used real usage traces to find where it was wasting time and money (re-reading the same file over and over, taking unnecessary "screenshots" of pages, etc.)

After a few rounds of fixes based on what we observed, the results vs. just having Claude read PDFs the default way:
→ 37% lower cost per question
→ Better answer quality across the board
→ Fewer wasted steps

The big takeaway: look at what an agent actually leaves in its traces, and fix bottlenecks from there.

Full case study 👉️llamaindex.ai/blog/building-…m
Benchmark code 👉github.com/run-llama/benc…m5

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Contracts are where business commitments live, but most organizations still manage them manually, se...

LlamaIndex 🦙 (@llama_index) — Mon, 15 Jun 2026 16:08:02 GMT

Contracts are where business commitments live, but most organizations still manage them manually, searching PDFs for renewal dates, chasing down payment terms, and hoping nothing slips through the cracks.

The problem isn't just volume. Legacy OCR treats contracts like flat text, unable to interpret what it reads: the same payment term appears under three different headings, renewal conditions are buried mid-paragraph, and termination clauses span multiple amendments.

We wrote up how LlamaParse solves this by:
✅️ Preserving document hierarchy
✅️  Using semantic reasoning to identify key fields regardless of how they're drafted
✅️ Mapping everything into validated, schema-aligned output

Ultimately, it transforms contracts from flat PDF text into structured data your downstream systems can actually use.

Read the full breakdown here: 
llamaindex.ai/blog/extract-c…

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We're headed back to @Databricks #DataAISummit to parse your PDFs next week 🦙 Catch our co-founder...

LlamaIndex 🦙 (@llama_index) — Fri, 12 Jun 2026 19:44:41 GMT

We're headed back to @Databricks#DataAISummit to parse your PDFs next week 🦙

Catch our co-founder & CEO @jerryjliu0  twice:

📄 Automating Document Work with Long-Horizon AI Agents databricks.com/dataaisummit/s…gT
🧱 The Agentic Stack: founder panel with LangChain, CrewAI, Agno + Databricksdatabricks.com/dataaisummit/s…Xzc

Then swing by booth #137, talk document agents, see LlamaParse, leave with 🔥 swag.

San Francisco, June 15–18. See you there. 👋

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Day 0 Anthropic Fable 5 in ParseBench: We tested the model's advancements when it comes to document ...

LlamaIndex 🦙 (@llama_index) — Wed, 10 Jun 2026 00:18:06 GMT

Day 0 Anthropic Fable 5 in ParseBench: We tested the model's advancements when it comes to document understanding. The model clearly peaks when it comes to adherence to the original text:

📃 Content faithfulness: 90.02% vs 86.19% (Gemini 3 Flash) and 86.81% (GPT-5.5)
🔢 Semantic formatting: 72.62% vs 58.35% and 60.12%, a 12+ point lead

These are two of the most important metrics for SOTA document understanding: does the output preserve what the document actually says, and does it preserve formatting that carries meaning? 

But ... it's not a sweep there continues to be a lot of alpha in unlocking document understanding for frontier models.

Full results below 👇

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Parsing a document accurately is one thing. Proving where every value came from is another. When ...

LlamaIndex 🦙 (@llama_index) — Tue, 09 Jun 2026 14:36:34 GMT

Parsing a document accurately is one thing. Proving where every value came from is another.  

When a compliance team reviews an AI extraction, or an auditor needs to sign off on a figure pulled from a financial filing, "it came from this document" isn't enough. They need to see exactly where.  The specific cell in the table, the exact line on the page, the precise word the agent used.  

Most parsers can get you to a paragraph or a table block. That's where the trail ends.  

Today we're shipping Granular Bounding Boxes in LlamaParse — word, line, and cell level coordinates for every value in your document.  

The result is a complete, verifiable trail from every extracted value back to its exact source in the document. Built for audit workflows, compliance review, and any pipeline where verification isn't optional.  

Read the full announcement → llamaindex.ai/blog/announcin…

Your browser does not support the video tag.

🔗 View on Twitter

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The Agent Open: AI's Pickleball Tournament 🏓 Come put your code and backhand to the test and embr...

LlamaIndex 🦙 (@llama_index) — Mon, 08 Jun 2026 14:56:26 GMT

The Agent Open: AI's Pickleball Tournament 🏓 

Come put your code and backhand to the test and embrace the full Open experience. 

Custom built out courts. Stadium seating. Exhibition matches by AI leaders. Fresh agent merch. Every infra startup you love, all in one place.

Brought to you by in collaboration with: 
@braintrust , @browserbase , @cursor_ai , @modal , @p0 , @turbopuffer

Where were you during the first Agent Open? Come make history. SF Edition. 👇lnkd.in/gMmpg8QHMJ

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Most AI pipelines are only as good as the data we provide them with, and that usually means PDFs or ...

LlamaIndex 🦙 (@llama_index) — Thu, 04 Jun 2026 16:08:02 GMT

Most AI pipelines are only as good as the data we provide them with, and that usually means PDFs or other unstructured documents.

Contracts, invoices, reports... All have special layout, language, and context mixed together, and getting reliable structured data out of them is still one of the hardest unsolved problems in enterprise AI.

Parse-Flow is an open-source project we built to tackle this head-on. It puts four document processing primitives at the center of a visual workflow designer:

📄 Parse — clean markdown and text from raw documents
🔍️ Classify — assign documents to user-defined categories
✂️ Split — segment documents into typed chunks
🪏 Extract — pull structured JSON against a schema

You drag steps onto a canvas, drop in a document, and watch events stream back as the pipeline runs. Under the hood it's powered by a LlamaAgents workflow that walks your flow one step at a time, making every transition observable and every failure a first-class value.

📚️ Full write-up on the architecture hellamaindex.ai/blog/designing…wGnX
👩‍💻 Source github.com/run-llama/pars…7vXt9i

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We're presenting ParseBench at CVPR 2026 today. 🦙 Come learn why document understanding is an AGI-...

LlamaIndex 🦙 (@llama_index) — Thu, 04 Jun 2026 13:21:43 GMT

We're presenting ParseBench at CVPR 2026 today. 🦙

Come learn why document understanding is an AGI-complete problem (an agent can't act on a doc it can't correctly read, and reading a real enterprise table is harder than it looks).

The first doc-parsing benchmark built for AI agents:

2,000+ human-verified pages
167K+ test rules
5 dimensions: tables, charts, faithfulness, formatting, grounding

Fully open source.
📍 Talk TODAY, June 4, 9–10 AM at CVPR. Come say hi 👇huggingface.co/datasets/llama…GVTgithub.com/run-llama/Pars…TWYarxiv.org/abs/2604.08538b48oJl

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❄️ Come meet the LlamaIndex Team at Snowflake Summit 2026. It might be chilly in Snow Park ☃️ but ...

LlamaIndex 🦙 (@llama_index) — Mon, 01 Jun 2026 14:09:44 GMT

❄️ Come meet the LlamaIndex Team at Snowflake Summit 2026. 

It might be chilly in Snow Park ☃️ but the AI infrastructure market is red hot 🔥. Come visit our team at the expo floor and explore how you can parse your most complex documents and teach your agents to read unstructured context with human-level accuracy.

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When we say “LiteParse runs everywhere,” we mean it. Our WASM package is lightweight, minimal, and ...

LlamaIndex 🦙 (@llama_index) — Fri, 29 May 2026 16:08:01 GMT

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 nogithub.com/run-llama/lite…YQS
📚️ Get started with LitePardevelopers.llamaindex.ai/liteparse/kbpS

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Opus 4.8 dropped today. ParseBench results are out. ✅ Slight gains: tables, semantic formatting, la...

LlamaIndex 🦙 (@llama_index) — Fri, 29 May 2026 01:04:32 GMT

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.

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Is grep 𝘳𝘦𝘢𝘭𝘭𝘺 all your AI agent needs for search? For a small codebase or a docs folder, the ...

LlamaIndex 🦙 (@llama_index) — Thu, 28 May 2026 13:00:21 GMT

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 breallamaindex.ai/blog/is-grep-a…8X1l3E5

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LiteParse v2.0 is out now, and it is blazing fast + runs everywhere! We rewrote everything from sc...

LlamaIndex 🦙 (@llama_index) — Wed, 27 May 2026 16:39:29 GMT

LiteParse v2.0 is out now, and it is blazing fast + runs everywhere! 

We rewrote everything from scratch in Rust, and now:
- up to 100x faster parsing
- install natively in Rust, JS/TS, and Python
- a custom WASM package enables browser and edge runtime usage

pip install liteparse
npm i ＠llamaindex/liteparse
npm i ＠llamaindex/liteparse-wasm
cargo install liteparse

Blog: llamaindex.ai/blog/liteparse…
Repo: github.com/run-llama/lite…

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Automate a loan underwriting pipeline in just a few lines of code✨️ A typical loan file is a stack...

LlamaIndex 🦙 (@llama_index) — Tue, 26 May 2026 14:11:58 GMT

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: llamaindex.ai/blog/building-…

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LlamaParse now parses HEIC files natively 🎉 . HEIC is Apple's default image format, so it shows up...

LlamaIndex 🦙 (@llama_index) — Mon, 25 May 2026 13:59:01 GMT

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 file and it parses.

Go ahead, parse that messy whiteboard.

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How do you know your document parser is ready for production? 🤔Existing benchmarks miss what AI a...

LlamaIndex 🦙 (@llama_index) — Fri, 22 May 2026 20:02:44 GMT

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 👇landing.llamaindex.ai/-webinar-parse…kz

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New in LlamaParse: Latency Metrics is now live. For every Parse, Extract, and Classify job, you can...

LlamaIndex 🦙 (@llama_index) — Thu, 21 May 2026 19:25:49 GMT

New in LlamaParse: Latency Metrics is now live.

For every Parse, Extract, and Classify job, you can now get a full latency breakdown.

All broken down by tier.
⏱ Queue time 
⚡Processing time 
📊 Total latency

There's also a new Metrics tab with a latency scatter plot and job volume histogram if you want to dig into patterns across your usage over time.

Head to your Parse History tab to check it out. 
�cloud.llamaindex.aigD

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New in LlamaParse: Latency Metrics is now live. For every Parse, Extract, and Classify job, you can...

LlamaIndex 🦙 (@llama_index) — Thu, 21 May 2026 16:08:01 GMT

New in LlamaParse: Latency Metrics is now live.

For every Parse, Extract, and Classify job, you can now get a full latency breakdown.

All broken down by tier.
⏱ Queue time 
⚡Processing time 
📊 Total latency

There's also a new Metrics tab with a latency scatter plot and job volume histogram if you want to dig into patterns across your usage over time.

Head to your Parse History tab to check it out. 
�cloud.llamaindex.ai/signuprf

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Financial analysts spend ~70% of their time pulling numbers out of PDFs. We built a demo agent that ...

LlamaIndex 🦙 (@llama_index) — Wed, 20 May 2026 15:28:35 GMT

Financial analysts spend ~70% of their time pulling numbers out of PDFs.
We built a demo agent that ingests SEC filings and answers questions with exact citations highlighted on the original PDF page.

About 600 lines of Next.js. No vector DB. Just LiteParse.

llamaindex.ai/blog/building-…

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LlamaIndex 🦙(@llama_index)

Your agentic workflows are only as good as the context you feed them. In finance, that context is s...

The team flew into SF for a week onsite. 🌉 2x'd in size since we last did this — first time this m...

We're kicking off a founder dinner series in SF. First one's July 28 - one table, 16 seats, invite-o...

Teaming up with https://t.co/IBgKPZBnJY for their Agentic Hackathon at the AWS Builder Loft in SF, J...

Document processing pipelines are often made up of multiple services: parsing → chunking → embedding...

We're part of the Amazon Web Services (AWS) AI Builder Lab in New York on Friday, July 24 - a Clash ...

Your extraction schema is now a conversation away. ⁣ 📄 Upload a doc — the agent drafts the schema f...

Co-hosting 'Swap and Pitch Night' this Thursday in SF with @tembo, @clerk, and @autonomaai. Pitch ...

We eat docs🫢

Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent, without havi...

@OpenAI released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in d...

@OpenAI released GPT 5.6 today and we ran a day 0 benchmark in ParseBench to test improvements in d...

Low user caps in LlamaParse? Gone.🎉 Every plan — Free through Pro — now unlocks up to 100 team mem...

Introducing: Granular job tracking and cost attribution in LlamaParse 🦙 Parsing thousands of page...

We've rolled out improvements to LlamaParse Cost Optimizer. Our intelligent tier routing now more ...

Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often di...

The team at @vercel recently released the Eve agent framework, so we built a template that integrate...

LiteParse runs anywhere, including inside agent runtimes. To prove it, we built an email processing ...

Game. Match. That's a wrap on the inaugural Agent Open 🦙🌤️🏓 Hundreds of AI builders, 64 matches,...

Agentic retrieval is changing the way retrieval-augmented applications are built, especially in doma...

Doors are open at Agent Open, right next door to AI Engineer Two courts, blue sky, no fog (a San Fr...

We just made it a lot easier for AI agents to work with your documents. LlamaParse MCP now does mor...

Semantic search alone doesn't cut it. Neither does brute-force grep. Agents need both. Today we're ...

We're all over AI Engineer World's Fair on June 29 to July 2. 🦙 📍Visit us at booth L-G47. LlamaPa...

The @n8n_io node for the LlamaParse Platform is now an officially verified community node, as part o...

We built LiteParse, the fastest document parsing solution on the planet and made it open source. An...

Bring your hot takes and your drop shots.🔥 Next week, we're co-hosting The Agent Open: an afternoo...

LiteParse v2.1 is here, and its bringing the fastest markdown output possible. In this release, we ...

~90% of enterprise data is unstructured, locked in the documents that power the majority of knowledg...

Vector databases or pure grep? Teams are split on the right retrieval architecture for agents. ⁣ ⁣ T...

Our CEO Jerry Liu is joining founders from LangChain, CrewAI, and others at @Databricks #DataAISummi...

How much can good documentation save an AI agent in cost and time? Turns out, a lot. We built a cus...

Contracts are where business commitments live, but most organizations still manage them manually, se...

We're headed back to @Databricks #DataAISummit to parse your PDFs next week 🦙 Catch our co-founder...

Day 0 Anthropic Fable 5 in ParseBench: We tested the model's advancements when it comes to document ...

Parsing a document accurately is one thing. Proving where every value came from is another. When ...

The Agent Open: AI's Pickleball Tournament 🏓 Come put your code and backhand to the test and embr...

Most AI pipelines are only as good as the data we provide them with, and that usually means PDFs or ...

We're presenting ParseBench at CVPR 2026 today. 🦙 Come learn why document understanding is an AGI-...

❄️ Come meet the LlamaIndex Team at Snowflake Summit 2026. It might be chilly in Snow Park ☃️ but ...

When we say “LiteParse runs everywhere,” we mean it. Our WASM package is lightweight, minimal, and ...

Opus 4.8 dropped today. ParseBench results are out. ✅ Slight gains: tables, semantic formatting, la...

Is grep 𝘳𝘦𝘢𝘭𝘭𝘺 all your AI agent needs for search? For a small codebase or a docs folder, the ...

LiteParse v2.0 is out now, and it is blazing fast + runs everywhere! We rewrote everything from sc...

Automate a loan underwriting pipeline in just a few lines of code✨️ A typical loan file is a stack...

LlamaParse now parses HEIC files natively 🎉 . HEIC is Apple's default image format, so it shows up...

How do you know your document parser is ready for production? 🤔Existing benchmarks miss what AI a...

New in LlamaParse: Latency Metrics is now live. For every Parse, Extract, and Classify job, you can...

New in LlamaParse: Latency Metrics is now live. For every Parse, Extract, and Classify job, you can...

Financial analysts spend ~70% of their time pulling numbers out of PDFs. We built a demo agent that ...

Vector databases or pure grep? Teams are split on the right retrieval architecture for agents. ⁣ ⁣ T...