Over the past few years, AI has ceased to be just an experimental technology and can now be called a digital worker. It can search for information, fill out forms, work with CRM, analyze documents, and perform complex multi-step processes.
However, along with this growing capability, a new problem has emerged: most existing solutions use the browser exclusively as an automation tool. These are most often Playwright or Selenium, where the agent's actions are limited to specific actions in a script.
This approach is great for website testing, but it's insufficient for autonomous artificial intelligence. Therefore, a full-fledged browser layer is needed to transform a web page into a structured working environment for the model. BrowserAct provides such a layer.
Well, let's get started.
Playwright is an excellent tool for testing. It gives you complete low-level control: click selectors, type into inputs, run JavaScript, take screenshots. That's perfect when you know the page structure in advance and you're testing a specific application.
This is more than sufficient for testing, but an AI agent typically has a much broader range of tasks. For example, it needs to know which page to open, which elements to view, and much more. Each new website becomes a new context that the AI must understand independently. Playwright only provides low-level browser control APIs. All the logic for understanding the page resides in the language model itself, so this approach is unsuitable.
const browserA = await chromium.launch();
const pageA = await browserA.newPage();
const contextA = await browserA.createBrowserContext({
storageState: './profiles/account-a.json', // Manual session file
});
When working with sessions, you may notice that we'll need to store files and update them manually for the website. If we want to test our application, we'll have to configure everything this way each time.
// Playwright forces you to choose selectors before running
// If the page updates or selectors change, your script breaks
await page.click('#button-id-123'); // What if this you move button in component?
// You'd need to re-scan the page and update your script
// There's no built-in "read current page state and tell me what changed"
We need to know exactly which button is on the page. Otherwise, the script simply won't work.
What AI really needs to work effectively is:
To do this, let's now try the browser layer, which will help us.
BrowserAct is a browser automation CLI built for AI agents.
Its partners include Google Cloud, Oracle and others.
Let's now try to make a browser layer. First, let's register, and then go to the dashboard.
There will be a block like this, click on the install button and simply copy the text into AI on your computer. I will insert this prompt into Cursor.
Since I'm in ask mode in the cursor, the agent simply output the file, installing dependencies from which I installed the cli.
After this, we'll be prompted to install several dependencies.
uv tool install browser-act-cli --python 3.12
You can check the version as follows:
browser-act --version
If you're in a mode where AI can edit files, everything will happen automatically. If you're doing it manually, after the cli appears in the terminal, you'll need to get the actual workflow content:
browser-act get-skills core --skill-version 2.0.2
The CLI serves skill content that always matches the installed version, so instructions never go stale. Do NOT truncate the output — none of which are available through --help
.
Browser-act can run basic Chrome and chrome-direct browser automation without an API key. But if you want to access other features, you can register, as we did earlier, and enter the following:
browser-act auth login
browser-act auth poll
Or set a key directly:
browser-act auth set <your_api_key>
Now, let's imagine a case where we need to automatically test our application for, say, several people visiting the site and using the same functionality.
// You'd have to manually manage profiles for each account
const browserA = await chromium.launch();
const pageA = await browserA.newPage();
await pageA.click('#login-button-id');
const contextA = await browserA.createBrowserContext({
storageState: './profiles/account-a.json', // Manual session file
});
// testSomeFunction()
const browserB = await chromium.launch();
const pageB = await browserB.newPage();
await pageB.click('#login-button-id');
const contextB = await browserB.createBrowserContext({
storageState: './profiles/account-b.json', // Another session file
});
// testSomeFunction()
// Now you're managing session files, proxy rotation, and fingerprints by hand.
If one account's context crashes, you need to reload session manually. This means that if you want to do something like what happened with GitHub Actions when your functionality was tested, you won't be able to do it here.
browser-act --session account-a browser open <browser_a> https://example.com
browser-act --session login-redirect button 1
browser-act --session account-a login_workflow
browser-act --session account-b browser open <browser_b> https://example.com
browser-act --session login-redirect button 1
browser-act --session account-b login_workflow
In this case, if you have BrowserAct configured, your tests won't need to be constantly updated. You can continue working on the functionality without any worries, and if any issues occur, such as users' IDs being generated identically within a second, the tests will detect them, while you'll never learn about the bug manually.
Another advantage of BrowserAct is the ability to import profiles from a running browser:
browser-act browser list-profiles
browser-act browser create \
--type chrome \
--name "work-automation" \
--desc "Logged-in work account for testing" \
--source-profile profile_a
browser-act --session test_some_function_workflow browser open work-automation https://example.com/dashboard
In addition to multi-account, this makes testing much easier, whereas PlayWright uses files for everything, which is generally inconvenient.
At first glance, Playwright and BrowserAct may seem to solve the same problem—they both interact with web browsers. In reality, they operate at different levels of abstraction and are designed for different use cases.
With Playwright, every interaction must be explicitly programmed. You need to know which page to open, which selector to use, when to wait for an element, and how to handle every possible variation of the website. This deterministic approach is ideal for automated testing, where the expected behavior of the application is already known. Instead of exposing only low-level browser APIs, BrowserAct provides an AI-friendly browser layer that enables language models to interact with websites in a more structured and reliable way. It handles many of the browser-related complexities, allowing the AI agent to focus on reasoning and decision-making rather than DOM manipulation.
| Feature | Playwright | BrowserAct |
|---|---|---|
| Primary purpose | Browser automation and testing | Browser layer for AI agents |
| Interaction model | Imperative scripts | AI-driven workflows |
| Login flow handling | Manual implementation | Built-in support |
| Workflow designer | No | Visual workflow builder |
| AI integration | External implementation | Native AI-oriented architecture |
| Structured browser actions | Limited | Yes |
For traditional QA automation or end-to-end testing, Playwright remains one of the best choices available. However, if your goal is to build autonomous AI agents that can navigate websites, complete multi-step workflows, handle authentication, extract structured data, or interact with modern web applications, BrowserAct provides a significantly higher-level abstraction.
Rather than replacing Playwright, BrowserAct builds on the idea of browser automation and extends it with the capabilities that modern AI agents require.
You can write your thoughts about the new features in the comments, it will be interesting to read! If you'd like to learn more about the product, BrowserAct has a YouTube channel and a Discord account where you can discuss how the tool works.
Playwright remains an excellent automation tool, but its capabilities are limited. Autonomous AI agents require much more: context awareness, memory, planning, and the ability to adapt to constantly changing interfaces.
A fully-fledged Browser Layer becomes the missing link between the language model and the browser. It transforms the complex DOM into a semantic representation, reducing the load on the LLM. With BrowserAct, you can implement it quickly and easily.
Official website: https://www.browseract.ai/Anthony
BrowserAct GitHub: https://www.browseract.com/?co-from=Anthony&redirect=https://github.com/browser-act/skills/tree/main
BrowserAct skill: https://github.com/browser-act/skills/tree/main/browser-act
Affiliate program: https://www.browseract.com/affiliate
Thanks for reading this article! ❤️