LlamaIndex 🦙(@llama_index)

confidence scores in LlamaParse just got an upgrade 🦸‍♀️ our new high-effort mode provides granul...

LlamaIndex 🦙 (@llama_index) — Fri, 11 Sep 2026 13:46:01 GMT

confidence scores in LlamaParse just got an upgrade 🦸‍♀️ 

our new high-effort mode provides granular page-level scores with text explanations to help you intimately understand parsing quality of your documents. we even refer back to the original document for an extra check when generating the score. 

use high-effort only when you need it, at 5 additional credits per page.

try it on your docs:developers.llamaindex.ai/llamaparse/par…h

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Last week we brought LlamaParse to Claude. Now it’s available in ChatGPT, too 🦙 ChatGPT can alrea...

LlamaIndex 🦙 (@llama_index) — Wed, 09 Sep 2026 16:58:16 GMT

Last week we brought LlamaParse to Claude. Now it’s available in ChatGPT, too 🦙 

ChatGPT can already read PDFs. But document work gets harder when the information you need is buried in messy scans, dense tables, spreadsheets, and charts. Strike the right balance between burning tokens and accuracy, with a specialized API for document understanding.

Turn complex documents into clean, structured context and let Astra run its magic. Use our connector to:

✅ Parse documents into clean Markdown, JSON, or HTML. 
✅ Extract specific fields into a schema you define.
✅ Search across document collections to find the information you need. 
✅ Classify files and split documents into logical sections.

Live in the ChatGPT plugin directory now! →chatgpt.com/plugins/plugin…5

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Does paying 5x more per page actually get you better document extraction? We ran the data to find ou...

LlamaIndex 🦙 (@llama_index) — Fri, 04 Sep 2026 16:43:38 GMT

Does paying 5x more per page actually get you better document extraction? We ran the data to find out.

We evaluated 14 frontier systems across 370 enterprise documents, then plotted their accuracy against cost per page. The biggest takeaway? Higher cost does not equal better extraction.

• Agentic Plus hit the highest accuracy overall at <1/3 the cost of the runner-up.
• Agentic & Cost Effective routinely beat systems costing several times more per page.

More expensive doesn’t necessarily mean more accurate. Now we have the data to show it.

Try Extract on your own documents with 10,000 free credits when you sign up for LlamaParse → cloud.llamaindex.ai/?utm_medium=so…

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Document extraction just got a new gear. ⚡️ Introducing Turbo mode for Extract, our fastest way yet...

LlamaIndex 🦙 (@llama_index) — Thu, 03 Sep 2026 16:49:39 GMT

Document extraction just got a new gear. ⚡️

Introducing Turbo mode for Extract, our fastest way yet to pull structured data from documents.

It runs roughly 4× faster than our Cost Effective Tier at comparable accuracy, with a median latency of 3.7 seconds a page. Turbo processes pages in parallel, so latency stays nearly flat as document size grows. 

Available in beta today.

Read the full breakdown → llamaindex.ai/blog/introduci…

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ExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents mo...

LlamaIndex 🦙 (@llama_index) — Wed, 02 Sep 2026 16:01:20 GMT

ExtractBench is now live on @Kaggle.

It tests schema-guided document extraction on the documents most likely to break downstream agents and workflows, including long record lists, noisy scans, handwriting, and complex tables.

The benchmark covers 370 enterprise documents across 8 business domains and 67 document types. See how LlamaParse, Codex, Claude Code, and more stack up.

Read the full story →llamaindex.ai/blog/llamainde…

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LlamaParse is officially a verified connector on Claude! 🦙 Claude is perfectly fine for the occasi...

LlamaIndex 🦙 (@llama_index) — Mon, 31 Aug 2026 16:11:46 GMT

LlamaParse is officially a verified connector on Claude! 🦙

Claude is perfectly fine for the occasional text-only PDF. But most of the context your agent needs lives in messy PDFs, spreadsheets, scanned forms, dense tables, and charts. 

Feeding those docs straight to the model burns through tokens and can lead to hallucinations when layouts get scrambled, chart values are missed, or numbers are pulled from the wrong column. 

LlamaParse converts those documents into clean, structured context Claude can work with. With the connector you can

✅️ Parse documents into clean Markdown, JSON, or HTML
✅️ Extract specific fields directly into your schema
✅️ Search across document collections with filesystem-style tools
✅️ Classify documents or split them into predefined sections

Live in the Claude Connectors Directory now !
claude.ai/directory/llam…4

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Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to tr...

LlamaIndex 🦙 (@llama_index) — Fri, 28 Aug 2026 15:02:11 GMT

Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to traditional embedding models. Can we make them better for retrieval?

We tried several things:
✅️  raw maxsim scoring on the per-token embeddings
✅️  training a small adapter model
✅️  changing the distillation training target and teacher

While none of these saw the results we wanted, it's an excellent dive into static embeddings and how they do (and don't) work!

Blog: llamaindex.ai/blog/exploring…

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Most extraction tools treat spreadsheets like PDFs. They flatten the file into text or markdown, the...

LlamaIndex 🦙 (@llama_index) — Thu, 27 Aug 2026 16:26:20 GMT

Most extraction tools treat spreadsheets like PDFs. They flatten the file into text or markdown, then ask a model to infer the original structure.

But spreadsheets depend on structure. Headers, formulas, merged cells, and hidden rows give every value its context. Strip that away, and you map the right number to the wrong metric or period.

That's why we built native spreadsheet extraction into the LlamaParse platform.

Instead of flattening your workbook to text, it reads the raw cells directly and maps the data to your schema.

Available today in beta on the agentic_plus tier. Give it a spin on your messiest .xlsx, .xls, or .csv files.

Docs: developers.llamaindex.ai/llamaparse/ext…

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Our 2nd founder dinner in SF co-hosted by @jerryjliu0 and @GuangyuRobert at @Fundamental - the team ...

LlamaIndex 🦙 (@llama_index) — Wed, 26 Aug 2026 15:00:08 GMT

Our 2nd founder dinner in SF co-hosted by @jerryjliu0 and @GuangyuRobert at @Fundamental - the team behind @tryshortcutai Talking about existing moats in the AI era.

Frontier labs are moving past model APIs into vertical agents - ChatGPT Health, Claude for Legal.

So where does the moat sit now?

✅ Agent engineering
✅ Infra optimization
✅ Domain evals and data
✅ Workflow expertise
✅ GTM and brand

If you're a founder or CTO shipping agents in production and want to know what other teams are doing to maintain their moat. Request a seat 👉️luma.com/llamai-8hryz

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Every extraction API demos well on a clean invoice. But what about the scanned form, the nested tabl...

LlamaIndex 🦙 (@llama_index) — Tue, 25 Aug 2026 17:30:23 GMT

Every extraction API demos well on a clean invoice. But what about the scanned form, the nested table, the 40-page financial report with merged headers?

We tested 14 frontier systems to find out. ExtractBench evaluates schema-guided extraction across 370 enterprise documents, 67 document types, and 4,800+ pages.

Join Simon Suo, CTO and co-founder of LlamaIndex, for a technical walkthrough of the results: what makes extraction hard, where existing benchmarks miss it, and how cost trades off against accuracy across VLMs, coding agents, and specialized APIs.

Wednesday, Aug 26 · 9:00 AM PT / 12:00 PM ET 

Save your seat 👉lnkd.in/gMbFNHPg8

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Our 2nd founder dinner in SF co-hosted by @jerryjliu0 and @GuangyuRobert at @Fundamental - the team ...

LlamaIndex 🦙 (@llama_index) — Mon, 24 Aug 2026 20:10:45 GMT

Our 2nd founder dinner in SF co-hosted by @jerryjliu0 and @GuangyuRobert at @Fundamental - the team behind @tryshortcutai Talking about existing moats in the AI era.

Frontier labs are moving past model APIs into vertical agents - ChatGPT Health, Claude for Legal.

So where does the moat sit now?

✅ Agent engineering
✅ Infra optimization
✅ Domain evals and data
✅ Workflow expertise
✅ GTM and brand

If you're a founder or CTO shipping agents in production and want to know what other teams are doing to maintain their moat. Request a seat 👉️luma.com/llamai-8hryz

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Our CEO @jerryjliu0 is speaking at @CoreWeave Fully Connected 2026 in SF. 🎤 "Automating Document W...

LlamaIndex 🦙 (@llama_index) — Fri, 21 Aug 2026 13:56:08 GMT

Our CEO @jerryjliu0 is speaking at @CoreWeave Fully Connected 2026 in SF. 🎤

"Automating Document Work with Long-Horizon AI Agents" Sept 30, Moscone Center.

Come see how long-horizon agents are taking on real document workflows in production. See you there!

Checkout the full lineup �coreweave.com/fully-connecte…TM

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Most parsers treat tracked changes as noise. Result: a deleted clause comes back as live text, and y...

LlamaIndex 🦙 (@llama_index) — Tue, 18 Aug 2026 15:31:37 GMT

Most parsers treat tracked changes as noise. Result: a deleted clause comes back as live text, and your pipeline reads a document that says the opposite of what it means.

LlamaParse now handles revision tracking. You get clean markdown of the document's final state, plus every revision (edits, deletions, comments) as structured data with author, content, and location. 

Contracts, regulatory submissions, policy drafts: if your documents go through redline rounds, this is for you.

Docs 👉️ :developers.llamaindex.ai/llamaparse/par…3

Sign Up:cloud.llamaindex.ai/?utm_medium=so…y

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Most document extraction APIs can't tell you where a value came from. For ExtractBench, we scored g...

LlamaIndex 🦙 (@llama_index) — Mon, 17 Aug 2026 15:59:35 GMT

Most document extraction APIs can't tell you where a value came from.

For ExtractBench, we scored grounding strictly: a field only counts if the value AND its citation are correct, word-level box at IoU 0.5. A perfect box around a wrong value earns nothing.

Results: VLMs and coding agents return no evidence at all — zero at both levels. Among systems that do return boxes, the best word-level F1 is still under 50%. And grounding collapses with length: one specialized API goes from 61.7% page-level on short docs to 0.0% on long ones.

LlamaExtract Agentic Plus leads at both levels — 84.9% page-level, 46.4% word-level — and holds at 87.1% on long documents where others hit zero.

Every extracted value should come with receipts. ExtractBench now gives the field a baseline to track it 👇

Blog:lnkd.in/gNm97fXp6
Paper:lnkd.in/euAfScWxF

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Get ready to break racks and agents. If you're in SF, next Thursday we're co-hosting a billiards n...

LlamaIndex 🦙 (@llama_index) — Fri, 14 Aug 2026 18:30:01 GMT

Get ready to break racks and agents. 

If you're in SF, next Thursday we're co-hosting a billiards night for AI engineers and founders with Judgment Labs , Modal, and ClickHouse.

Limited spots available. RSVPs are being approved now 👇️
luma.com/bj9o4jzdc

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🦙☀️ It’s a hot Llama summer, and it pays to be a PRO at document parsing. For a limited time, upgra...

LlamaIndex 🦙 (@llama_index) — Thu, 13 Aug 2026 18:18:24 GMT

🦙☀️ It’s a hot Llama summer, and it pays to be a PRO at document parsing. For a limited time, upgrade to the LlamaParse Pro plan and we'll triple your credits:

400K → 1.2M credits in your first month. That's $1,000 (~80k pages) of parsing, free.

No code, applied automatically. Upgrade today 👇️ llamaindex.ai/pricingZP

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Every document extraction system has a perception blind spot. We mapped them. For ExtractBench, we ...

LlamaIndex 🦙 (@llama_index) — Thu, 13 Aug 2026 14:05:11 GMT

Every document extraction system has a perception blind spot. We mapped them.

For ExtractBench, we tested 14 systems on documents that weren't born digital: 1950s regulatory filings, hand-filled tax forms, and pages degraded with fax thresholding, photocopier tone curves, sensor noise, and phone-camera capture.

The failures don't overlap. 

💡 Codex reads scans and handwriting above 93%, then drops to ~80% on rotated or image-only pages. 
💡 Specialized APIs are the exact inverse: fine on rotation and handwriting, 81% on scans
💡 Gemini 3.5 Flash falls from 88.6% to 71.1% the moment a page is scanned.

You benchmark on clean PDFs. Production sends you a shadowed photocopy from 1953.

Our new Extract Tier, Agentic Plus, was the only system with no blind spot: 95.9% / 93.9% / 93.8% across rotated, scanned, and handwritten, a 2-point spread where others swing 10+.

Learn more about ExtractBench 👇️ 

Bllnkd.in/gNm97fXpceB6
Paplnkd.in/euAfScWxkQpF

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The most dangerous document extraction failure isn't a wrong value. It's a missing row that looks li...

LlamaIndex 🦙 (@llama_index) — Wed, 12 Aug 2026 16:20:34 GMT

The most dangerous document extraction failure isn't a wrong value. It's a missing row that looks like nothing is wrong.

We released ExtractBench yesterday: 370 enterprise docs, 14 systems. The hardest test: long-list completeness. An unclaimed-property list with 26,725 rows. A creditor matrix with 8,624 records. A 13F with 3,063 holdings.

Frontier VLMs don't misread these docs, they abandon them. Precision stays high, recall collapses: 8.9–35.8% F1 on the longest documents. Every row they return looks correct, so spot checks pass while most of the document never came back.

Our new Extract tier, Agentic Plus, processes long docs iteratively instead of one pass: 96.1% F1 on long-list tasks, and the only system that holds flat as docs get longer.

Learn more about ExtractBench below👇

Blog:llamaindex.ai/blog/introduci…Y
Paper:arxiv.org/pdf/2607.29677u

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Introducing 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗕𝗲𝗻𝗰𝗵: the most comprehensive benchmark for information extraction fr...

LlamaIndex 🦙 (@llama_index) — Tue, 11 Aug 2026 13:00:51 GMT

Introducing 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗕𝗲𝗻𝗰𝗵: the most comprehensive benchmark for information extraction from complex enterprise documents. Our applied research team tested:

14 systems — frontier VLMs, coding agents, extraction APIs — on 370 enterprise docs, 4,869 pages, 67 doc types. Zero LLM judges, fully deterministic.

Biggest finding: past 50 pages, commercial VLMs collapse below 35% recall. Precision stays high, but they silently drop most of the table rows.

What is your extraction agent missing? Run ExtractBench to see todllamaindex.ai/blog/introduci…o/5UE8frCgithub.com/run-llama/Extr…o/27vJ36bL54
Hhuggingface.co/datasets/llama…o/oeGlwPjcQh

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SF builders, it's time to ditch your agents and head out on a 🏃 Join us for a run tomorrow with t...

LlamaIndex 🦙 (@llama_index) — Mon, 10 Aug 2026 18:40:26 GMT

SF builders, it's time to ditch your agents and head out on a 🏃 

Join us for a run tomorrow with the Lore community at 6pm at Sue Bierman Park (across from the Ferry Building) for a casual 5k down the Embarcadero. All paces welcome, founders and startup teams especially👇luma.com/4iiiprxj?utm_m…6g

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POV: It's the last day at @Ai4conferences and your: 🧢 colorful hats and stickers are all over the...

LlamaIndex 🦙 (@llama_index) — Thu, 06 Aug 2026 17:20:36 GMT

POV: It's the last day at @Ai4conferences and your:

🧢  colorful hats and stickers are all over the conference floor
👨‍🏫  CEO, @jerryjliu0 , delivered a speech to a full room 
🍽️  executive dinner brought in some of the best and brightest in AI 
👟  feet might hurt and new white shoes have a few scuffs, but still 🥴

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"OCR is just a feature now. Frontier models will eat it." We hear this constantly. The data says ot...

LlamaIndex 🦙 (@llama_index) — Wed, 05 Aug 2026 16:14:40 GMT

"OCR is just a feature now. Frontier models will eat it."

We hear this constantly. The data says otherwise.

Across three GPT generations, parsing accuracy gained ~24 points, while cost per page 4x'd. And the newest frontier models still trail specialized parsers.

Read more below on why the gap persists (and compounds) 👇️llamaindex.ai/blog/document-…b

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Parsing a W-2 into markdown was always the easy part. Getting the fields out was a second pipeline: ...

LlamaIndex 🦙 (@llama_index) — Tue, 04 Aug 2026 16:30:58 GMT

Parsing a W-2 into markdown was always the easy part. Getting the fields out was a second pipeline: define a schema, map the fields, handle the edge cases.

Set processing_options.forms='𝗲𝗻𝗿𝗶𝗰𝗵', and LlamaParse returns a dedicated JSON with field names, values, and checkbox states alongside your standard markdown. No schema. No second API call.

Now available in beta on all paid plans.

Signup for 10k free crelogin.llamaindex.ai/sign-upYFbtG2
developers.api.llamaindex.ai/api/resources/…rv3HTN(resource

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You shouldn't need a vision model to know your PDF has checkboxes. LiteParse can now pull structure...

LlamaIndex 🦙 (@llama_index) — Mon, 03 Aug 2026 13:08:39 GMT

You shouldn't need a vision model to know your PDF has checkboxes.

LiteParse can now pull structured data directly from your PDFs: form field values, checkbox states, annotations, embedded images, vector graphics, tagged document structure, and word-level bounding boxes, all running in ms per page.

For pages that do need a model, new complexity signals tell you why. Scanned pages, multi-column text, tables (ruled and borderless), and dense figures help you route your parsing to the best tools (like LlamaParse!)

Docs 👉️ :developers.llamaindex.ai/liteparse/guid…Udevelopers.llamaindex.ai/liteparse/guid…V

👩‍💻 Try igithub.com/run-llama/pars…8qn

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🦙 is coming coming to Las Vegas for @Ai4Conferences next week (Aug 4–6). Three ways to find us: ...

LlamaIndex 🦙 (@llama_index) — Sat, 01 Aug 2026 03:09:09 GMT

🦙 is coming coming to Las Vegas for @Ai4Conferences next week (Aug 4–6). Three ways to find us:

📍 Booth 1561 on the Expo floor
🎤 @jerryjliu0 will be speaking about unlocking document context for AI agents
🥂 Waitlist for our (very oversubscribed) exec dinner at Tao Bistro (find us on Luma)

Come say hi 👋

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Not every page in your PDF needs the same treatment. A scanned cover, a dense table, a clean text p...

LlamaIndex 🦙 (@llama_index) — Thu, 30 Jul 2026 19:35:05 GMT

Not every page in your PDF needs the same treatment.

A scanned cover, a dense table, a clean text page, a figure-heavy diagram.... most parsing pipelines throw all of them at the same parser, forcing a tradeoff between cost, speed, and accuracy across the whole document.

We built 𝗣𝗮𝗿𝘀𝗲 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 to fix that.

It uses LiteParse's 𝘪𝘴_𝘤𝘰𝘮𝘱𝘭𝘦𝘹 functionality to estimate complexity page-by-page and routes each page to the right tier based on why it's complex (scanned, sparse text, garbled encoding, vector text, embedded images) and how severely. Simple pages get parsed for free, in-process. Genuinely hard pages get routed to more capable LlamaParse tiers. No manual heuristics, no flat one-size-fits-all pipeline.

And it's also available as an MCP server, so your agents can estimate complexity and choose a parsing tier themselves!

The result: you stop paying premium prices for pages that never needed it, without sacrificing accuracy on the ones that dllamaindex.ai/blog/parse-gat…ttps://t.co/K5github.com/run-llama/pars…https://t.co/Gl0Dggv8qn

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Batch parsing used to mean writing API scripts. Now it's a button. 🦙 Parse or extract across up to...

LlamaIndex 🦙 (@llama_index) — Wed, 29 Jul 2026 16:34:08 GMT

Batch parsing used to mean writing API scripts. Now it's a button. 🦙

Parse or extract across up to 10,000 files in one run, straight from the LlamaParse UI. Point it at a folder and go — no code required.

Available now on all paid plans. Bring your files and start parsing �cloud.llamaindex.ai/?utm_medium=so…Dj

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Want to run your LlamaParse worker at the edge in minutes? We just released 𝗰𝗿𝗲𝗮𝘁𝗲-...

LlamaIndex 🦙 (@llama_index) — Mon, 27 Jul 2026 16:34:09 GMT

Want to run your LlamaParse worker at the edge in minutes?

We just released 𝗰𝗿𝗲𝗮𝘁𝗲-𝗹𝗹𝗮𝗺𝗮-𝘄𝗼𝗿𝗸𝗲𝗿 🚀

It scaffolds a ready-to-go @Cloudflare Worker that you can configure for Parse, Classify and Extract.

No boilerplate. No setup headaches. Just generate, configure, and deploy.

Get started:
𝘯𝘱𝘮 𝘤𝘳𝘦𝘢𝘵𝘦 @𝘭𝘭𝘢𝘮𝘢𝘪𝘯𝘥𝘦github.com/run-llama/crea…�𝘳

Check out the code:
https://t.co/jsCqbZ7KwX

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As of LiteParse v2.8.0, image → PDF conversion no longer depends on ImageMagick. Instead, it’s now h...

LlamaIndex 🦙 (@llama_index) — Fri, 24 Jul 2026 17:30:07 GMT

As of LiteParse v2.8.0, image → PDF conversion no longer depends on ImageMagick. Instead, it’s now handled natively in Rust 🦀

The result? Conversion is now 1.2× to 7.2× faster, depending on the image format⚡️ 

But the bigger win is what this enables: we’re one step closer to making LiteParse fully self-contained, with zero external system dependencies 🚀

If you’re using LiteParse, make sure to upgrade and to check out the project on GitHub �github.com/run-llama/lite…CQ0

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You asked, we delivered! 🚀 Expanding our developer toolkit with: 🐹 Go SDK 🏢 Java SDK >_ CLI ...

LlamaIndex 🦙 (@llama_index) — Thu, 23 Jul 2026 17:46:28 GMT

You asked, we delivered!  🚀

Expanding our developer toolkit with: 
🐹 Go SDK
🏢 Java SDK
>_  CLI

Parse a document from Go or Java in a few lines, or straight from your terminal.
🦙 Grab an API key and start parsing today!

Dodevelopers.llamaindex.ai/llamaparse4OyE
Start frcloud.llamaindex.ai/?utm_medium=so…1ip6

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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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