Automate Data Extraction from Dynamic Websites in 3 Lines of Code

Scraping data from the web used to be straightforward. You'd send a request, parse the static HTML, and you were done. But the modern web is different. It's dominated by dynamic, JavaScript-heavy Single-Page Applications (SPAs) that load content on the fly. Trying to extract data from these sites can feel like a constant battle against complex loading states, obscure API calls, and fragile CSS selectors that break with the smallest UI update.

What if you could skip all that? What if you could just tell a browser what you want in plain English and get structured JSON data back?

Enter browse.do, an AI-powered web navigation API that turns complex browser actions into simple function calls. Let's explore how you can extract data from any dynamic website, not with a complex script, but with a simple, high-level objective.

The Challenge: Why Dynamic Sites Are Hard to Scrape

Traditional scraping tools work by downloading the initial HTML source of a page. With SPAs built on frameworks like React, Vue, or Angular, that initial source is often just a barebones shell with a <script> tag. The actual content—the product prices, the user comments, the data tables—is rendered client-side by JavaScript.

To scrape these sites, developers have historically relied on headless browser automation tools like Selenium or Puppeteer. While powerful, they come with their own set of challenges:

• Brittle Selectors: You have to write precise CSS selectors or XPath queries to find elements. A minor class name change by the website's developer can break your entire script.
• Managing State: You need to manually code waits for elements to appear, handle Ajax loading spinners, and manage complex user interaction flows.
• Complex Logic: Scraping a multi-step process like "log in, navigate to dashboard, click on 'reports', and download the latest PDF" requires a significant amount of conditional, error-prone code.

The Solution: AI-Powered Objectives

browse.do abstracts away this complexity. Instead of controlling a browser step-by-step, you provide a high-level goal, and an AI agent performs the necessary actions to achieve it. It interacts with the site just like a human would, understanding context and adapting to the UI.

Let's see it in action. Here’s how you can get the title and URL of the top story from Hacker News—a dynamic, interactive site—in just a few lines of code.

import { browse } from "@do-inc/agents";

async function getTopHackerNewsStory() {
  const result = await browse.do({
    url: "https://news.ycombinator.com",
    objective: "Find the title of the top story and its URL."
  });

  console.log(result.data);
  // {
  //   "title": "The specific title of the top story",
  //   "url": "https://the-url-of-the-top-story.com"
  // }
  return result.data;
}

getTopHackerNewsStory();

Breaking it Down

Let's look at what's happening in those three key lines:

1. import { browse } from "@do-inc/agents";: You import the agent. Simple.
2. const result = await browse.do({...});: This is the magic. We're calling the agent and giving it two simple parameters: the url to visit and a natural language objective. We didn't need to inspect the page, find the <span> with class titleline, or figure out the <a> tag's href attribute. We just described what we wanted.
3. console.log(result.data);: The agent doesn't just return raw HTML. It understands the "what" from your objective ("title" and "URL") and intelligently structures the extracted information into a clean, ready-to-use JSON object.

Why This is a Game-Changer for Data Extraction

This objective-based approach fundamentally changes the nature of web automation and anoints a new era of robotic process automation.

• Resilience: Because the AI understands the intent behind "the top story," it's not reliant on a specific id or class. If the website's developers change the HTML structure, the agent can still identify the correct elements, making your scripts dramatically more robust.
• Simplicity: You no longer need to be a CSS selector wizard. You can describe complex goals like, "log in with these credentials, navigate to the settings page, and tell me the current email address on file." The agent will handle finding the login form, inputting the data, and navigating through the site.
• Power: browse.do operates within a full, headless browser environment. It can render JavaScript, manage cookies, and handle sessions, allowing it to interact with the most complex SPAs as effectively as a human user.

Go Beyond Simple Scraping

Data extraction is just the beginning. This same simple API can be used for a vast range of web automation tasks:

• Automated Testing: Write end-to-end tests using plain English objectives.
• Form Submission: Automate filling out contact forms, applications, or surveys.
• Competitor Monitoring: Keep an eye on pricing changes or new product launches on competitor websites.

Stop fighting with brittle selectors and complex browser automation scripts. Describe your goal and let an AI agent handle the rest.

Ready to simplify your web automation workflow? Visit browse.do to learn more and get started today.