# LlamaIndex Documentation

Source: https://developers.llamaindex.ai/python/cloud/llamaparse/

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# Overview of LlamaParse

**Transform unstructured documents into LLM-ready data**

Built for teams that care about quality, LlamaParse turns complex, messy files
into structured, clean outputs.

LlamaParse is a highly accurate parser for complex documents like financial
reports, research papers, and scanned PDFs. It handles tables, images, and
charts with ease—so you can skip the cleanup and get straight to using your
data

![parse document
comparison](/python/_astro/parse_comparison.h1v__IZo_2beC10.webp)

  
  

## Core Features

Section titled “Core Features”

  * **Flexible Parsing** \- Choose between _Cost Effective_ , _Agentic_ , _Agentic Plus_ and _Use-case Oriented_ [presets](/python/cloud/llamaparse/presets_and_modes/presets) to handle everything from simple text to visually complex documents. For more advanced parsing, the [Advanced Settings](/python/cloud/llamaparse/presets_and_modes/advance_parsing_modes) allows you to pick-and-choose what works for you.

  * **Broad File Support** \- Parse PDFs, DOCX, PPTX, XLSX, HTML, JPEG, XML, EPUB, and [many more →](/python/cloud/llamaparse/features/supported_document_types/).

  * **Multimodal & Custom Output** \- Accurately extract tables, charts, images, and diagrams into structured formats. Use custom prompt instructions to tailor the output the way you want it.

  
  

## Workflow

Section titled “Workflow”

  1. **Connect your documents**  
Upload or stream documents via our API, Clients, or UI—with built-in
connectors to sync with enterprise data sources.

  2. **Configure your parsing**  
Select a preset for a quick start, or define a custom configuration with
specific models, output formats, and parsing instructions tailored to your use
case.

  3. **Get clean, structured results**  
Receive parsed output in text, markdown, or JSON—ready to plug into your
application, database, or LLM pipeline.

  
  

## Why LlamaParse?

Section titled “Why LlamaParse?”

High-quality document parsing is one of the most overlooked—yet crucial—steps
in the LLM stack. Models can only reason with the information you give them,
and most documents today are hard for LLMs to interpret out of the box.

LlamaParse was built to solve this problem from the ground up. Unlike generic
OCR or PDF-to-text tools, LlamaParse uses AI-native methods to understand
structure, layout, and intent—ensuring every output is optimized for
downstream LLM consumption.

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