How it works under the hood
Browser Use launches a real Chromium browser via Playwright. It takes screenshots, extracts the DOM as an accessibility tree, and sends both to an LLM (GPT-4 or Claude). The LLM decides what action to take: click, type, scroll, or navigate. Browser Use executes that action, takes a new screenshot, and loops. Each iteration costs one LLM API call. A simple task like 'search for AI tools and extract the top 10 names' takes 5—10 iterations and costs $0.30—0.80 in API credits.
What it handled well
Tasks with clear visual targets worked reliably. Search Google for a query, click the first non-ad result, extract the article text: 5/5 success. Fill a contact form with name, email, and message: 4/5 success. Navigate to a GitHub repo and star it: 3/5. The agent is good at recognizing buttons, input fields, and links in standard web layouts. Sites with clean, accessible HTML and good contrast ratios work best.
What broke it
CAPTCHAs killed it immediately: the agent cannot solve visual challenges. Dynamic content that loads after 3—5 seconds of scrolling caused the agent to act on stale DOM state and click invisible elements. Complex multi-step workflows where step 3 depends on the result of step 2 caused compounding errors: if step 2 fails silently, steps 3—5 all operate on wrong data. And any website with a modal popup or cookie banner derailed the agent because it tried to interact with the page content behind the modal.
The cost makes it impractical for repeated tasks
At $0.50 per task, running 100 form submissions costs $50. A human doing the same work earns $15/hour and handles 60 tasks per hour. Browser Use is 4x more expensive than a human at minimum wage. The value proposition flips for tasks that require scale: scraping 1,000 pages overnight while you sleep is worth the API cost. But for daily automation of 5—10 tasks, the API bill will exceed the value.
Where this technology is headed
The core idea is right: give an AI a browser and let it figure out the UI. The weak link is the vision-to-action translation. Current LLMs understand what is on the screen but make planning errors: clicking before the page loads, typing into the wrong field, misreading button labels. Each of these costs one extra API call. The next generation of vision models (GPT-5, Claude 5) with better spatial reasoning should close this gap. I would bet on this technology being production-ready by early 2027.
