# 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
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
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
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
# 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
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
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](/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.