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LlamaIndex

LlamaIndex

Data framework for LLM applications.

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About LlamaIndex

If you've been extracting data from PDFs with AWS Textract or a legacy IDP tool, you know the routine: build a template per document type, watch it break when a vendor changes their invoice layout, then write cleanup code for every table that spans two pages. LlamaIndex pitches LlamaParse as the replacement for that workflow. It doesn't ask you to define templates. It uses what it calls agentic OCR: task-specific agents split a page into text, tables, charts and handwriting, send each piece to a specialized expert, and then run auto-correction loops that check and fix errors before you get the output. The company says it handles 50+ unstructured file types, including multi-page tables, embedded images and handwritten notes.

The other comparison worth making is with LlamaIndex's own reputation. Many developers know it as the open-source RAG framework (the site cites 25M+ package downloads a month), and you'd expect it to compete mainly with LangChain. The homepage now leads with document processing instead. The product line covers Parse, Extract (schema-based extraction with no training, now at v2.5), Split, Classify and Index, plus LiteParse, a fully open-source local parser you install with npm install @llamaindex/liteparse. LiteParse skips VLMs and LLM tokens entirely, so it's quick and private, but it's a text-and-bounding-box tool rather than the agentic pipeline.

The free plan is a good way to test it: 10,000 credits a month, which works out to roughly 1,000 pages, with agentic OCR, structured extraction and document agents included. The site claims 300k+ LlamaParse users and 1B+ documents processed. The benchmarks against VLMs, commercial IDP and open-source OCR are LlamaIndex's own, though, so run your ugliest scans through it before you trust the charts.

Key features

Agentic OCR parsing

LlamaParse sends text, tables, charts and handwriting to task-specific agents and returns clean, LLM-ready output instead of a flat text dump.

Auto-correction loops

Recursive checks catch and fix parsing errors automatically, which LlamaIndex says keeps pass-through rates high even on messy scans and multi-modal documents.

Schema-based Extract

You define a schema and LLM-powered extraction agents pull structured fields from unstructured documents with no model training required.

Split and Classify

You can segment a document into logical sections or sort documents into categories using plain natural-language rules instead of hand-built templates.

Index for RAG

The Index product handles chunking and embedding so your parsed documents feed straight into retrieval for RAG applications.

LiteParse open-source parser

LiteParse parses PDFs, Office docs and images locally with bounding box output, using no cloud calls and no LLM tokens.

Best for

  • engineers building RAG over messy PDFs
  • teams replacing template-based IDP tools
  • finance and insurance teams processing invoices or claims
  • developers who need local, offline parsing via LiteParse
  • builders of multi-step document agents

Skip if

  • Skip this if you need full agentic parsing without sending documents to a hosted service, since LiteParse is the only local option and it doesn't use LLMs
  • Skip this if your documents are clean, plain-text PDFs, where a basic open-source extractor will do the job
  • Skip this if you want a general-purpose agent framework and don't work with documents much, since the product focus has clearly shifted to parsing and extraction

Pros & cons

Pros

  • Handles 50+ file types, including multi-page tables, charts and handwritten notes
  • The free plan's 10,000 monthly credits (~1,000 pages) is enough to properly test on real documents
  • Extract works from a schema you define, with no training or per-layout templates
  • LiteParse gives you a fully open-source, local fallback with no token costs or usage limits
  • Global and EU regions are offered, which matters if you're handling European data

Cons

  • Accuracy benchmarks are self-published, with no independent results in the source material
  • The site leans heavily on enterprise messaging and 'Book a demo' calls to action, and technical limits like file size caps or latency aren't stated
  • LiteParse is VLM-free, so it won't give you the chart-to-data or handwriting understanding that the hosted LlamaParse provides
  • Credits don't map one-to-one to pages (about 10 credits per page on the free plan), so estimating usage on complex documents takes some testing

Pricing

TierPriceIncludes
Free$010,000 credits per month (~1,000 pages), agentic OCR for layout-aware parsing, structured extraction of defined schemas, and building and deploying document agents
LiteParse (open source)FreeLocal parsing of PDFs, Office docs and images with bounding box output, no cloud and no LLM tokens

Frequently asked questions

Is LlamaIndex free to use?

Yes. LiteParse is fully open source, and LlamaParse's free plan includes 10,000 credits per month, which is roughly 1,000 pages.

How is LlamaParse different from AWS Textract?

Textract and most legacy IDP tools rely on fixed extraction or custom templates, while LlamaParse routes each part of a page to specialized agents and runs auto-correction loops. It's aimed at producing LLM-ready output for RAG and agents, not just raw OCR text.

Can I parse documents without sending them to the cloud?

Yes, with LiteParse, which you install via npm install @llamaindex/liteparse. It runs locally on PDFs, Office docs and images with no cloud calls and no LLM tokens, but it skips the agentic features of the hosted LlamaParse.

What file types does LlamaParse support?

LlamaIndex says it supports 50+ unstructured file types. That includes documents with embedded images, complex layouts, multi-page tables and handwritten notes.

Is LlamaIndex still a RAG framework like LangChain?

The open-source framework is still widely used, with 25M+ package downloads a month according to the site. The company's main pitch now is LlamaParse for document parsing, extraction and indexing, which makes it more specialized than LangChain.

How LlamaIndex compares

LlamaIndex vs Unstructured

Unstructured is a close open-source rival for document preprocessing, but LlamaParse's agent routing and auto-correction loops target harder layouts like dense tables and charts.

LlamaIndex vs AWS Textract

Textract fits well if you're already deep in AWS and your documents are predictable, while LlamaParse is the better pick when layouts vary and you need LLM-ready output.

LlamaIndex vs LangChain

LangChain is broader for orchestrating agents and chains, but it relies on outside loaders for parsing, which is exactly the gap LlamaParse fills.

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