> ## Documentation Index
> Fetch the complete documentation index at: https://upstash.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Reading Pages

There are three ways to get data out of a tab: read the DOM as text, capture a screenshot, or have an AI agent extract structured data against a schema.

## Page content

`content()` reads the tab's current title, URL, visible text, and links from the real DOM, including JavaScript-rendered content:

<CodeGroup>
  ```typescript box.ts theme={"system"}
  const { title, url, text, links } = await tab.content()

  console.log(title)
  console.log(text.slice(0, 200))
  for (const link of links ?? []) {
    console.log(link.text, link.href)
  }
  ```

  ```python box.py theme={"system"}
  content = tab.content()

  print(content.title)
  print(content.text[:200])
  for link in content.links or []:
      print(link.text, link.href)
  ```
</CodeGroup>

## Screenshots

`screenshot()` captures the tab as a PNG. It works headless, with no display needed. By default you get raw PNG bytes. Ask for base64 if you are passing the image on, for example to an LLM:

<CodeGroup>
  ```typescript box.ts theme={"system"}
  import { writeFile } from "node:fs/promises"

  // PNG bytes (Uint8Array)
  const png = await tab.screenshot({ fullPage: true })
  await writeFile("page.png", png)

  // Base64-encoded PNG string
  const b64 = await tab.screenshot({ type: "base64" })
  ```

  ```python box.py theme={"system"}
  # PNG bytes
  png = tab.screenshot(full_page=True)
  with open("page.png", "wb") as f:
      f.write(png)

  # Base64-encoded PNG string
  b64 = tab.screenshot(encoding="base64")
  ```
</CodeGroup>

Pass `fullPage: true` to capture the entire scrollable page instead of just the viewport.

## Structured extraction

`extract()` hands the page to a DOM-aware AI agent and returns data validated against your schema. The schema is a [Zod](https://zod.dev) object schema (v3 or v4) in TypeScript, and a [pydantic](https://docs.pydantic.dev) model class or a raw JSON schema dict in Python:

<CodeGroup>
  ```typescript box.ts theme={"system"}
  import { z } from "zod"

  const article = await tab.extract(
    "extract the article title and author",
    z.object({
      title: z.string(),
      author: z.string(),
    }),
  )

  console.log(article.title, article.author)
  ```

  ```python box.py theme={"system"}
  from pydantic import BaseModel

  class Article(BaseModel):
      title: str
      author: str

  article = tab.extract(
      "extract the article title and author",
      Article,
  )

  print(article.title, article.author)
  ```
</CodeGroup>

<Note>
  `extract` uses an LLM and is metered. It needs a provider API key on the box
  or your account, and accepts an optional `model` override just like the other
  [AI Actions](/docs/box/overall/browser/ai-actions).
</Note>

The result is parsed with your schema before it is returned, so a successful call always gives you data in the shape you asked for.