Browser Use
Let an AI agent drive a real browser — open pages, click, type, fill in forms
Browser Use is an open-source Python library and CLI that lets an LLM drive a real browser — opening pages, clicking, typing and filling in forms — so an agent can do web work that has no API behind it. The library is MIT-licensed and runs on your own machine with whichever model you point it at; the same team sells a hosted cloud, and their own README sends you there for CAPTCHAs and for production. That split is the honest summary of the whole category: driving the browser is the solved part, and not being blocked by the sites you want to automate is the part that decides whether any of it works.
What Browser Use is
Browser Use (github.com/browser-use/browser-use) started in late 2024 and is now one of the most-starred agent projects on GitHub. It is Python, MIT-licensed, and installs in a line on Python 3.11 or newer. You hand an agent a task in plain language and a model — the team's own browser-tuned models, or OpenAI, Anthropic and Google reached through a single key, or something local through Ollama — and it works the page the way a person would: reads what is on screen, clicks, types, and extracts whatever you asked for.
There are two ways in, and choosing the wrong one is the most common early mistake. The Python library is for automation you are building into your own code: many runs on a schedule or in parallel, custom tools, structured output. The CLI is for an agent you already use — installing its skill lets a coding assistant take the browser over for a one-off job. The project's own rule of thumb is worth borrowing: one-off tasks through an agent, repeatable automation in code. It also publishes its benchmark of a hundred real-world browser tasks as an open repository, which is more accountability than most agent projects offer, and claims the top place on a public long-horizon web-task leaderboard — their measurement, not ours.
Where it fits a founder's growth stack
What this is genuinely good at is the web work that has no API and never will: sites you need something out of, or something into, that were only ever built for humans. For a small team the sensible order to attempt it in is the order of what a wrong run costs you.
- Read-only research Competitor pricing pages, changelogs, marketplace listings, review pages — the raw inputs behind competitor analysis. A wrong run here costs you a re-run, which is the cheapest kind of wrong.
- The same form for the fortieth time Directory and marketplace submissions are the clearest win: the work is identical every time and the payoff is real, which is exactly why getting listed in AI tool directories is such a grind by hand.
- Your own funnel, from a clean browser Signing up as a stranger every morning catches the broken step your logged-in session never sees. It is closer to QA than to growth, and it is the highest-value boring item on this list — see why nobody is signing up for what usually turns out to be wrong.
- Anything that publishes or spends Posting, messaging strangers, bidding. This is where the automation should stop and a person should look. An approval gate exists for exactly this class of action, and a capable browser agent is not a reason to remove it.
The wall you actually hit
Every honest account of browser agents arrives at the same place, and the project's own FAQ gets there in two answers: for CAPTCHAs it tells you to use its cloud, for fingerprinting and proxies, and for production it warns that Chrome eats memory and parallel runs are awkward to manage. Neither is a knock on the library. They are the real bill, and it is better to read it before you plan a growth workflow around free local runs.
| What you expect to be hard | What actually decides the run |
|---|---|
| Teaching the agent to click the right thing | Whether the site serves you a page at all |
| Model cost per task | Proxy, fingerprinting and browser infrastructure |
| The run failing | The run succeeding on a page that was never the real content |
That last row is ours, and it is the most expensive thing we have learned about web automation. AgentCeres — the AI Growth Officer at agentceres.com — runs automated browsers for research, and against sites with a serious anti-bot layer we simply never got through: the refusal arrives on the first byte, before any page renders, so nothing about how human the clicking looks changes the outcome, and a stealth plugin we had assumed would help turned out never to have been tested against that layer at all. The quieter failure was the worse one. Our own check for did we just hit a bot wall only recognised English challenge pages, so a challenge served in another language scored as ordinary content and was summarised as though it were the site. A blocked run is obvious; a run that confidently returns the wrong page is not.
So: point it at the repetitive, low-stakes web work, budget for browser infrastructure rather than assuming free, and put a second signal on anything a decision rests on — a check that the content is what you think it is, not just that a page came back. Keep a person on whatever goes out. That last part is the shape AgentCeres works in: a roster of specialists drafts the content and the outreach, and every outbound action waits for your approval before it ships.
FAQ
- Is Browser Use free?
- The library is, under the MIT licence — run it on your own machine and the code is yours to modify. What is not free is the model: every step the agent takes is an LLM call, so cost scales with how many pages it has to work through rather than with how many tasks you start. The hosted cloud, with its stealth browsers, proxy rotation and integrations, is a separate paid product from the same team.
- Do I need to write code to use it?
- Not for one-off jobs. Installing its CLI skill lets a coding assistant you already use drive the browser from a plain-language instruction. The Python library is the right choice once the same task runs repeatedly, in parallel, or inside your own product — the project's own rule of thumb is one-off through an agent, repeatable in code.
- Will it get past bot detection and CAPTCHAs?
- Not reliably on your own machine, and the project says so itself: its FAQ answers both CAPTCHAs and production by pointing at its cloud, which supplies proxies and browser fingerprinting. Plan for that cost. A site with a serious anti-bot layer refuses the connection before your automation gets a chance to be clever, and no amount of realistic clicking changes it.
- What should I not automate with a browser agent?
- Anything that publishes, messages a stranger, or spends money without a person seeing it first — and anything whose terms of service forbid automated access, which many platforms do. The useful test is what a wrong run costs: a bad scrape costs a re-run, a bad post costs your reputation, and only one of those is recoverable in an afternoon.
You built it. Now grow it.
AgentCeres is a managed AI marketing team — specialists draft the SEO, social, and outreach that fill your links, you approve what ships. 14-day free trial, from $39/month.