A hands-on comparison where I tested BrowserAct and Agent Browser using the SannySoft browser fingerprint test and the Cloudflare Challenge benchmark to evaluate their stealth execution and anti-bot capabilities under identical conditions.
You switched to a browser automation tool built specifically for AI agents.
That should have solved the hardest part... right?
After all, these tools are designed for production workflows, autonomous agents, and modern browser automation. So you launch your agent against a website protected by Cloudflare, expecting everything to work.
Instead, you find yourself in front of another verification page. Or the browser keeps asking you to verify you're human. Or the automation simply never reaches the content you need.
So I was asking myself:
If both BrowserAct and Agent Browser are built for AI agents, how different are they when it comes to stealth execution and anti-detection?
Instead of comparing documentation or feature lists, I decided to run both tools through the same hands-on tests under the same conditions.
For this comparison, I focused on one capability: stealth execution.
I tested both tools against the two most widely used benchmarks in the automation community.
These two tests measure different aspects of browser automation, and together they provide a clearer overview than a simple feature comparison.
I'll walk through exactly what I observed during both tests, where each tool performed well, where I noticed differences, and which one I would choose depending on the type of automation I'm building.
Before getting into the results, it's important to understand why stealth execution has become one of the biggest challenges in modern browser automation.
A few years ago, getting browser automation working mostly meant writing reliable scripts.
If your selectors were correct and your timing was good, there was a good chance your automation would work consistently.
Today, that's only half of the challenge.
Modern websites don't just respond to browser actions. They also evaluate the browser itself almost immediately after the page starts .
That creates two different layers of anti-bot protection.
The first layer is browser fingerprint detection.
This focuses on identifying whether the browser behaves like a normal user or an automated environment. Websites inspect signals such as:
navigator.webdriver
Many of these checks happen before your automation even clicks its first button.
The second layer is real-world anti-bot protection.
Services such as Cloudflare combine browser fingerprinting with additional signals like browser behavior, challenge-response verification, network reputation, and other detection techniques before deciding to trust a session.
That's an important distinction because passing a fingerprint test doesn't automatically mean a browser will get through Cloudflare or similar protection systems.
This is exactly why I used two different benchmarks for this article.
On paper, both BrowserAct and Agent Browser offer stealth capabilities.
The important thing is how they approach it. And that difference isn't obvious from their documentation.
It becomes easier to understand the difference when you run the same tests against both tools.
Before running the benchmarks, I wanted to understand how each tool approaches browser execution in environments that actively inspect automation.
Although both tools target AI agent workflows, they expose their browser environments differently.
BrowserAct provides a dedicated stealth browser that is ready to use once you've created a stealth browser profile.
According to BrowserAct's documentation, its stealth browser is designed to reduce common automation signals by providing characteristics such as:
To launch the browser, I simply opened my existing stealth browser profile:
browser-act --session stealth-test browser open <browser-id> https://example.com --headed
No additional stealth configuration was required before running my tests.
For Agent Browser, I used the standard CLI installation and launched a browser session directly without adding any extra plugins or manually modifying the browser fingerprint.
The browser was started with:
agent-browser --session stealth-test open https://example.com --headed
Since my goal was to compare the tools using their default workflows after installation, I intentionally avoided adding third-party extensions or making manual browser modifications.
To keep the comparison as fair as possible, I tested both tools under the same environment using fresh test runs for this article.
I selected two benchmarks because they evaluate different aspects of browser automation.
Together, these tests provide a practical view of both browser detectability and real-world behavior.
With both environments ready, I moved on to the fingerprint and Cloudflare challenge tests.
The first benchmark I ran was SannySoft.
Instead of checking whether a browser can bypass a specific anti-bot provider, SannySoft focuses on the browser fingerprint itself.
I ran the test separately with BrowserAct and Agent Browser using fresh browser sessions.
For BrowserAct, I opened a new stealth browser session and navigated directly to SannySoft.
browser-act --session fingerprint browser open <browser-id> https://bot.sannysoft.com --headed
BrowserAct also provides a useful CLI command (state
) that lets you inspect the current page directly from the terminal.
browser-act --session fingerprint state
The report looked very clean.
The key observations I noted were:
BrowserAct passed all of the major fingerprint checks reported by SannySoft during my testing.
The browser exposed very few characteristics that would immediately identify it as automation.
For Agent Browser, I also launched a browser session directly.
agent-browser --session fingerprint open https://bot.sannysoft.com --headed
The overall report was fairly strong, but one result immediately stood out.
The key observations were:
From my test, WebDriver was the only major fingerprint check that failed. The remaining fingerprint characteristics appeared much closer to a regular browser session.
| Fingerprint Check | BrowserAct | Agent Browser |
|---|---|---|
| WebDriver | ✅ Passed | ❌ Failed |
| Chrome Object | ✅ Present | ✅ Present |
| Plugin Detection | ✅ Passed | ✅ Passed |
| Overall Fingerprint | Passed all major checks | Failed WebDriver |
Before running the test, I expected both tools to perform similarly because both advertise stealth capabilities.
BrowserAct passed all of the major checks reported by SannySoft during my test, while Agent Browser exposed one remaining automation indicator through the WebDriver check.
Of course, browser fingerprinting is only one layer of modern anti-bot detection, and websites evaluate many more signals before deciding whether to trust a browser session.
But, still, this benchmark gave me a good first impression. BrowserAct presented a browser fingerprint that looked slightly closer to a regular user session based on the checks reported by SannySoft.
Now, let's see how both tools would behave when facing an actual anti-bot challenge instead of a diagnostic website.
Fingerprint tests are useful because they reveal how detectable a browser is.
That's why I also tested both tools against the Cloudflare Challenge page provided by ScrapingCourse.
Unlike SannySoft, this isn't a diagnostic report. The browser either reaches the protected page or it doesn't.
I opened a fresh BrowserAct stealth browser session and navigated directly to the Cloudflare challenge page.
browser-act --session cloudflare browser open <browser-id> https://www.scrapingcourse.com/cloudflare-challenge --headed
Within a few moments, the verification completed successfully.
The page displayed:
BrowserAct successfully reaching the protected page
"You bypassed the Cloudflare challenge! :D"
I inspected the current page again directly from the terminal using:
browser-act --session cloudflare state
Unlike several Cloudflare tests I've previously run with other browser automation tools, this session didn't get stuck repeatedly asking for additional verification.
It simply completed the challenge and proceeded to the protected content.
That was the outcome I was hoping to evaluate with this benchmark.
Next, I repeated the same test using Agent Browser under the same conditions.
For Agent Browser, I followed the same process and opened the Cloudflare challenge page using a fresh browser session.
agent-browser --session cloudflare open https://www.scrapingcourse.com/cloudflare-challenge --headed
The result was noticeably different from BrowserAct.
Instead of reaching the protected content, the browser remained on Cloudflare's verification screen.
On the terminal, it displayed:
"Just a moment"
And on the Cloudflare page, it repeatedly displayed:
"Verify you are human"
As shown in the GIF below, the challenge kept refreshing, and even after 4 attempts, it never progressed to the protected page.
Agent Browser keeps showing "Verify you are human"Unlike BrowserAct, which completed the verification during my first attempt, Agent Browser remained stuck in the verification loop throughout the test.
I also kept the terminal running during the test to make sure nothing else was happening in the background.
| Cloudflare Challenge | BrowserAct | Agent Browser |
|---|---|---|
| Challenge completed | ✅ Yes | ❌ No |
| Protected page reached | ✅ Yes | ❌ No |
| Verification loop | ❌ No | ✅ Yes |
| Result during my test | Passed on first attempt | Continued asking for verification |
BrowserAct completed the verification and reached the protected page on my first attempt.
Agent Browser, on the other hand, never moved beyond Cloudflare's verification screen during my testing. The browser continued asking me to verify that I was human and never reached the protected content.
Of course, Cloudflare constantly updates its detection systems, so no single test guarantees that a browser will always succeed or always fail in every environment.
Still, these were the results I consistently observed while running both tools under the same conditions.
These observations come directly from what I saw during the tests.
| Capability | BrowserAct | Agent Browser |
|---|---|---|
| SannySoft fingerprint test | Passed all major checks | Failed the WebDriver check |
| Cloudflare Challenge | Reached the protected page | Remained on the verification screen |
| Setup experience | Built-in stealth browser | Default CLI browser session |
| Overall experience during testing | Worked smoothly in both benchmarks | Good fingerprint results, but struggled with Cloudflare |
The important difference for me was what happened when the browser reached a protected website.
The fingerprint benchmark showed only one failed check for Agent Browser, which initially made me think the Cloudflare test might produce similar results.
Instead, the two tools behaved very differently.
Both BrowserAct and Agent Browser are designed for AI-powered browser automation, but based on my testing, they currently perform differently when stealth execution is the primary concern.
BrowserAct is a better fit if you:
During my testing, BrowserAct passed every major SannySoft fingerprint check and successfully completed the Cloudflare challenge test on the first attempt. If those are the kinds of environments you work with every day, that was a meaningful advantage.
Agent Browser is worth considering if you:
In my tests, Agent Browser produced a good fingerprint overall, with the exception of the WebDriver check. The larger limitation appeared during the Cloudflare benchmark.
That doesn't necessarily mean it will struggle with every protected website, but it was the outcome I consistently observed.
So the right choice depends on the problems you're trying to solve.
Before starting this comparison, I expected the two tools to produce fairly similar results.
Since both are built for AI agents and both offer stealth capabilities, I assumed the differences would mostly come down to developer experience or workflow preferences.
After running the tests, I found different results.
The browser fingerprint benchmark already showed a small difference between the two tools, but the Cloudflare challenge test made the distinction more obvious.
So, it's easy to compare features on a website or read through documentation, but running the same benchmark under the same conditions often makes the differences much clearer.
Of course, anti-bot systems evolve constantly, and no browser automation tool will succeed against every protected website forever. Results can also vary depending on the target site, browser version, network environment, and future updates to both tools.
These findings simply reflect what I observed while testing BrowserAct and Agent Browser side by side.
If you'd like to reproduce the same tests I ran in this article, you can try BrowserAct yourself. BrowserAct is also running a promotion at the moment: if you star the GitHub repository, you'll receive 500 free credits to help you get started with your own experiments.
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