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Agentic Document Extraction now supports field extraction! Many doc extraction use cases extract specific fields from forms and other structured documents. You can now input a picture or PDF of an invoice, request the vendor name, item list, and prices, and get back the extracted fields. Or input a...

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Andrew Ng

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193,139 Aufrufe • vor 1 Jahr •via X (Twitter)

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We built an AI agent that lets you vibe-code document extraction - high accuracy and citations over the most complex documents. Our latest release lets you upload documents as context. All you then have to do is describe what you want extracted in natural language. 💡 Our agent will then read the document with file tools to infer the right schema, validation rules, and other pre/postprocessing logic. ✅ It will give you back a workflow that can extract over thousands/millions of documents at scale. You can still of course review and edit every output before approving. Stop handling paperwork manually; just upload files, describe your task, and let our agent handle the rest. Our vision for LlamaAgents is to provide the most advanced and easy-to-use way for you to orchestrate document work. Walkthrough: Check it out: If you’re interested in reducing the operational burden of document extraction (invoices, claims, onboarding forms), come talk to us!
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We built an AI agent that lets you vibe-code document extraction - high accuracy and citations over the most complex documents. Our latest release lets you upload documents as context. All you then have to do is describe what you want extracted in natural language. 💡 Our agent will then read the document with file tools to infer the right schema, validation rules, and other pre/postprocessing logic. ✅ It will give you back a workflow that can extract over thousands/millions of documents at scale. You can still of course review and edit every output before approving. Stop handling paperwork manually; just upload files, describe your task, and let our agent handle the rest. Our vision for LlamaAgents is to provide the most advanced and easy-to-use way for you to orchestrate document work. Walkthrough: Check it out: If you’re interested in reducing the operational burden of document extraction (invoices, claims, onboarding forms), come talk to us!

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New course: Document AI: From OCR to Agentic Doc Extraction, built with LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp. Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations. Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields. Skills you'll gain: - Build agents to convert unstructured files into structured Markdown/HTML and JSON - Use ADE to parse complex data like forms, handwriting, or equations - Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation - Deploy RAG applications with event-driven document processing Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently:
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New course: Document AI: From OCR to Agentic Doc Extraction, built with LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp. Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations. Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields. Skills you'll gain: - Build agents to convert unstructured files into structured Markdown/HTML and JSON - Use ADE to parse complex data like forms, handwriting, or equations - Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation - Deploy RAG applications with event-driven document processing Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently:

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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! →
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Astra is awesome, but if you're using it specifically to parse and extract a massive volume of documents, you will break the bank (10c+ per page on parsing and extraction!) We've added a native LlamaParse connector to ChatGPT that gives the model access to high-quality document OCR and extraction at the pareto frontier of accuracy and cost. It contains native access to our parse and extract endpoints. Upload your most complex documents, and you can do the following: * digitalize it into extremely accurate context for any QA task * perform large-scale extraction from a set of documents with accurate grounding and citations Offload the higher-level reasoning to Astra, leave specialized doc parsing and extraction to us. ChatGPT plugin: Come sign up here:
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Tutorial: Automating KYC with AI agents 🪪🕵 I’m creating a new tutorial series of automating practical document workflows with agents. Every financial institution needs to perform KYC (know your customer) to verify a customer’s identity, and this involves manually sifting through IDs, bank statements, etc. and doing the cross-checking by hand. This is a great first use case for agentic document workflows: 1. Extract identification information from the user supplied ID (license, passport) 2. Extract fields from utility bills/bank statements and then use LLMs to cross-validate extracted fields with the extracted ID fields It obviously doesn’t cover the full e2e process and uses publicly available online data, but should be a good reference guide to get started. To make this work well, you do need high-quality document extraction with confidence scores and citations! Check out the tutorial: If you’re interested come check out LlamaParse:
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After the release of Parse v2, Extract is also getting an upgrade — 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝘃𝟮! 🎉 We've been reworking the experience from the ground up to make document extraction more powerful and easier to use than ever. Here's what's new: ✦ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝘁𝗶𝗲𝗿𝘀: we've replaced modes with cleaner, more intuitive tiers. (And stay tuned: agentic plus is coming to Extract too, very soon.) ✦ 𝗣𝗿𝗲-𝘀𝗮𝘃𝗲𝗱 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀: load your saved extraction configs directly, so you can skip the setup and get straight to extracting. ✦ 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝗯𝗹𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗮𝗿𝘀𝗶𝗻𝗴: now you can control how your documents get parsed before extraction, giving you more flexibility and better results end to end. And for those who need a transition period: Extract v1 will remain accessible via the UI under 'Settings → General' for a limited time. Try Extract v2 today →
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LlamaIndex 🦙

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data extraction is now live on browse dot new Induced - prompt and describe what you want to extract - get structured data as JSON / CSV works on most websites out of the box. detailed prompts recommended. also available as an API.
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Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:
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We’re on a mission to parse the world’s hardest PDFs, and we’d love your help There are so many document types that introduce a million edge cases for current VLMs / OCR: handwritten forms, badly scanned/rotated pages, charts, diagrams, and more. We are running a contest right now for you to try to extract the hardest PDFs you can find. Come sign up on our agent builder, describe what you want to extract through natural language, upload your document, and show the results. If our platform doesn’t work, even better; this is great feedback for us to improve our service. Either way submit your project and we’d love to get your feedback! Check out LlamaCloud here:
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Your contract review agent can read an NDA and summarize the key terms. But ask it "what are the risky clauses?" and you get a different answer every time. Neither gets solved by a bigger model. Here's a deep dive of the Box AI Extract NDA risk workflow. The agent calls POST /2.0/ai/extract_structured with a fixed five-field schema: governing law, term, termination, confidentiality period, liability cap. Confidence scores and source references included. A transparent rule set scores each clause automatically. The Deal Risk Summary saves back to Box as a companion file alongside the source document. Same output shape every time, regardless of how the document is written. Extract, don't prompt.

Box

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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 !

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🔥 UEFN Python Verse Fields Tool is finally here! Direct Python Tool: Claude/Codex Skill: This tool lets you create, manage, and bind Verse fields, either individually or in batches, making UI workflows much faster and easier. I'd love for you to put it through its paces! If you have ideas for improvements, whether it's the UI, workflow, or anything else, feel free to leave feedback or open a PR on the repository. Current limitations: - Event bindings and event Verse fields are not supported. - Bindings that require conversion functions (such as ToVisibility, MakeBrush, etc.) are not supported. It really shines for Verse Field to Verse Field bindings. For example, if you have a slot widget with fields like Name and Image, and a parent widget containing 10 of those slots, you can quickly generate the corresponding fields in the parent widget and bind each slot's fields to them automatically. 🔥 If you have any questions, feel free to DM me or leave a comment below. I'd love to hear your feedback! 🙏 #UEFN #Python #Verse Fortnite Developers
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🔥 UEFN Python Verse Fields Tool is finally here! Direct Python Tool: Claude/Codex Skill: This tool lets you create, manage, and bind Verse fields, either individually or in batches, making UI workflows much faster and easier. I'd love for you to put it through its paces! If you have ideas for improvements, whether it's the UI, workflow, or anything else, feel free to leave feedback or open a PR on the repository. Current limitations: - Event bindings and event Verse fields are not supported. - Bindings that require conversion functions (such as ToVisibility, MakeBrush, etc.) are not supported. It really shines for Verse Field to Verse Field bindings. For example, if you have a slot widget with fields like Name and Image, and a parent widget containing 10 of those slots, you can quickly generate the corresponding fields in the parent widget and bind each slot's fields to them automatically. 🔥 If you have any questions, feel free to DM me or leave a comment below. I'd love to hear your feedback! 🙏 #UEFN #Python #Verse Fortnite Developers

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Most organizations run on unstructured content, but it’s the data locked within that content moves business forward. Box Extract is now generally available, delivering agentic data extraction at scale for smart process automation. Combining the latest AI models with advanced OCR capabilities, and agentic approaches that understand document structure and meaning, Box Extract automatically and accurately extracts high quality data from a variety of content to automate workflows, speed content discovery, and drive smarter business decisions.

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We tuned an AI agent that can do large-scale document extraction from long docs (50+ pages, some with 10k-100k fields) with 94%+ accuracy 📈 It uses a harness + model set that is tuned specifically for reasoning over extracting out complex information from complex docs. Each extracted field comes with a confidence score as well as a bounding box denoting where it came from. It does 10-20% better in accuracy than generalized coding agent harnesses (e.g. Claude Code Opus 4.8 and Codex GPT-5.6). Check out the video below for a demo. The mode is called LlamaExtract Agentic Plus. Learn more about our extraction benchmark and agentic plus mode here: If you have very complex document extraction needs, come check out LlamaParse!
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We tuned an AI agent that can do large-scale document extraction from long docs (50+ pages, some with 10k-100k fields) with 94%+ accuracy 📈 It uses a harness + model set that is tuned specifically for reasoning over extracting out complex information from complex docs. Each extracted field comes with a confidence score as well as a bounding box denoting where it came from. It does 10-20% better in accuracy than generalized coding agent harnesses (e.g. Claude Code Opus 4.8 and Codex GPT-5.6). Check out the video below for a demo. The mode is called LlamaExtract Agentic Plus. Learn more about our extraction benchmark and agentic plus mode here: If you have very complex document extraction needs, come check out LlamaParse!

Jerry Liu

18,359 Aufrufe • vor 27 Tagen

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If you’re building an “agent over your PDFs” in 2026, one of the most underrated things you can do is to provide exact grounding back to the source document for any agentic answers. Specifically, showing the exact word, line, or region that the answer came from. Frontier vision models do a terrible job at this. They have a hard time predicting exact bounding boxes over region, tying it back to the source answer, and surfacing it as context in a cost-effective and accurate manner. Dedicated VLM-based document OCR/extraction tools like LlamaParse are a lot better at visual grounding and specifically tying it to extracted outputs. One of our main evaluation areas in ExtractBench, our recently released real-world benchmark, is to evaluate the visual grounding capabilities of document extraction. ExtractBench: If you want to give LlamaParse a shot, come check it out:

Jerry Liu

14,515 Aufrufe • vor 25 Tagen

Astra might finally help me automate what I've spent ~hundreds of hours doing since the start of this company: Making live product demos through Loom or Screen Studio Check out the video walkthrough on our new Agentic Plus mode for document extraction - where we're able to populate 60 fields worth of financials from a sample earnings release. This entire video was fully generated by Astra. With its default set of tools it can already login to my LlamaParse account and piece together a set of slides with static screenshots. I then gave it full access to computer use and Chrome, and let it try out Loom and Screen Studio on my desktop and record the current chrome session. In the meantime, if you do have use cases for gnarly extraction from extremely complex documents, come check out our LlamaParse product demo below 🙂
1:55

Astra might finally help me automate what I've spent ~hundreds of hours doing since the start of this company: Making live product demos through Loom or Screen Studio Check out the video walkthrough on our new Agentic Plus mode for document extraction - where we're able to populate 60 fields worth of financials from a sample earnings release. This entire video was fully generated by Astra. With its default set of tools it can already login to my LlamaParse account and piece together a set of slides with static screenshots. I then gave it full access to computer use and Chrome, and let it try out Loom and Screen Studio on my desktop and record the current chrome session. In the meantime, if you do have use cases for gnarly extraction from extremely complex documents, come check out our LlamaParse product demo below 🙂

Jerry Liu

18,579 Aufrufe • vor 6 Tagen

Today, we’re excited to announce Deep Extract. We’re pushing the boundary of complex document extraction by utilizing an agent harness approach, iterating and verifying outputs until they are at human-level accuracy. The results speak for themselves: we’ve handled documents exceeding 2,500 pages, extracting a total of 30M+ fields in the last couple weeks with results in our production beta reaching 99-100% accuracy. It’s particularly effective on complex documents with long lists, such as invoice line items, brokerage statement transactions, equipment manifests, and more. See more on how we built it, how to use it, and more👇
0:59

Today, we’re excited to announce Deep Extract. We’re pushing the boundary of complex document extraction by utilizing an agent harness approach, iterating and verifying outputs until they are at human-level accuracy. The results speak for themselves: we’ve handled documents exceeding 2,500 pages, extracting a total of 30M+ fields in the last couple weeks with results in our production beta reaching 99-100% accuracy. It’s particularly effective on complex documents with long lists, such as invoice line items, brokerage statement transactions, equipment manifests, and more. See more on how we built it, how to use it, and more👇

Reducto

23,949,618 Aufrufe • vor 5 Monaten

Announcing a significant upgrade to Agentic Document Extraction! LandingAI's new DPT (Document Pre-trained Transformer) accurately extracts even from complex docs. For example, from large, complex tables, which is important for many finance and healthcare applications. And a new SDK makes using it require only 3 simple lines of code. Please see the video for technical details. I hope this unlocks a lot of value from the "dark data" currently stuck in PDF files, and that you'll build something cool with this!
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Announcing a significant upgrade to Agentic Document Extraction! LandingAI's new DPT (Document Pre-trained Transformer) accurately extracts even from complex docs. For example, from large, complex tables, which is important for many finance and healthcare applications. And a new SDK makes using it require only 3 simple lines of code. Please see the video for technical details. I hope this unlocks a lot of value from the "dark data" currently stuck in PDF files, and that you'll build something cool with this!

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