Skip to content
Guide
Extract
Extract
Getting Started

Getting Started

Introduction to LlamaExtract, a tool for extracting structured data from unstructured documents, available as a web UI, Python SDK, and REST API.

LlamaExtract provides a simple API for extracting structured data from unstructured documents like PDFs, text files, and images.

LlamaExtract is available as a web UI, Python SDK and REST API.

LlamaExtract is a great fit for when you need:

  • Well-typed data for downstream tasks: You want to extract data from documents and use it for downstream tasks like training a model, building a dashboard, entering into a database, etc. LlamaExtract guarantees that your data complies with the provided schema or provides helpful error messages when it doesn’t.
  • Accurate data extraction: We use the best in class LLM models to extract data from your documents.
  • Iterative schema development: You want to quickly iterate on your schema and get feedback on how well it works on your sample documents. Do you need to provide more examples to extract a certain field? Do you need to make a certain field optional?
  • Support for multiple file types: LlamaExtract supports a wide range of file types, including PDFs, text files, and images. Let us know if you need support for another file type!

The simplest way to try out LlamaExtract is to use the web UI.

Just define your Extraction Agent (schema and settings), drag and drop any supported document into LlamaCloud and extract data from your documents.

Extraction Results

Once you’re ready to start coding, get an API key to use LlamaExtract with the Python SDK.

We have a library available for Python and Typescript. This is the recommended way to use LlamaExtract for running extraction jobs at scale. Check out the SDK quick start to get started.

If you are using a language other than Python, you can use the REST API.

Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/