---
title: Overview of Extract | Developer Documentation
description: Introduction to LlamaExtract, a tool for extracting structured data from unstructured documents, available as a web UI, Python SDK, and REST API.
---

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## Overview

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.

### Is LlamaExtract right for me?

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!

## Quick Start

### Using the web UI

The simplest way to try out LlamaExtract is to [use the web UI](./web_ui).

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

![Extraction Results](/_astro/results.CkWc7NqG_Z2mbVsx.png)

### Get an API key

Once you’re ready to start coding, [get an API key](../general/api_key) to use LlamaExtract with the Python SDK.

### Use our libraries

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](./sdk) to get started.

### REST API

If you are using a language other than Python, you can use the [REST API](./api).

### Tiers and versions

LlamaExtract offers three primary **tiers** in the UI:

- **Cost Effective** – best when you want lower cost and higher throughput for simpler extraction tasks.
- **Agentic** – recommended default tier that balances quality, speed, and cost for most real‑world documents.
- **Agentic Plus (coming soon)** – high‑fidelity tier for very complex or high‑stakes extractions.

LlamaExtract now runs on the **v2** APIs by default. If you need to use the **legacy Extract v1** experience, see [Using Extract v1](#using-extract-v1) below.

When using the SDK or REST API directly, V2 decouples parse and extract tiers. Here is how V2 configurations map to V1 equivalents:

| V2 extract `tier` | V2 parse `tier` | V1 equivalent (`extraction_mode`) |
| ----------------- | --------------- | --------------------------------- |
| `cost_effective`  | `fast`          | `FAST`                            |
| `agentic`         | `agentic`       | `MULTIMODAL`                      |
| `agentic`         | `agentic_plus`  | `PREMIUM`                         |

### Using Extract v1

LlamaExtract v2 is the default and recommended experience. If you need to use the legacy **Extract v1**:

- **Web UI**: Open the main LlamaCloud UI, go to **Settings → General**, and enable the **Extract v1** toggle for your workspace.
- **Python SDK**: Use the `llama-cloud-services` package (shown as the “Python (legacy)” tab in our SDK examples). See the [SDK page](./sdk) for details.
- **REST API**: The v1 endpoints are documented on the [REST API (v1 Legacy)](v1/getting_started/api) page.

Extract v1 is legacy and may be deprecated in the future. We recommend migrating to v2 for new projects.