Browser Use

Browser Use is an open-source Python library that lets AI agents control a real web browser, clicking, typing, and navigating the way a person would instead of scraping HTML or calling a site-specific API. You give it a plain-language goal, such as “find the cheapest flight to Tokyo,” and it works out the steps itself, drawing on any LLM including Claude, GPT, and Gemini. The project has crossed 99,000 GitHub stars since launch, and for developers building agents that need to act on websites with no API, it is often the fastest path from idea to a working automation in production.

How Browser Use works

At its core, Browser Use puts an LLM in a loop with a real browser. You describe the task in natural language, and the agent reads the page, decides on an action, executes it, and checks the result before deciding what to do next.

Task input: you start the agent with a plain-language goal and, optionally, constraints such as which domains it is allowed to visit.
Page perception: the agent reads the current page, combining a condensed DOM (the Document Object Model, a structured map of a webpage’s elements) with screenshots, so the LLM can reason about what is visible and clickable.
Action selection: the LLM picks the next action, such as clicking an element, typing text, or scrolling, from a defined set of browser actions.
Execution and recovery: Browser Use’s Rust-based harness runs this loop with built-in recovery, so if a click misses or a page loads slowly, the agent retries instead of failing outright.
Model choice: you can point the agent at Browser Use’s own optimized models, or bring your own key for Claude, GPT, or Gemini through the ChatBrowserUse interface.

This loop repeats until the agent decides the task is complete or hits a limit you set, and the whole run can be logged and replayed for debugging.

What you can build with Browser Use

Job application agents: an agent that reads a resume and a job posting, then fills out the application form field by field across dozens of different applicant tracking systems, each with a different layout.
Grocery and shopping automation: an agent that takes a shopping list and places the order on a site like Instacart, selecting substitutions and a delivery window without a dedicated integration for that store.
Research and data collection agents: an agent that searches a set of sites against specific criteria, such as cars under a price threshold, and writes the matching results to a CSV file instead of a person doing it by hand.
QA and regression testing: a test suite that describes user flows in plain language, so tests keep passing through a redesign instead of breaking on a changed CSS selector.
Personal assistant workflows: an agent that finds compatible parts for a custom PC build, compares prices across retailers, and returns a shortlist, work that used to mean opening a dozen tabs by hand.
Production-scale scraping: teams that need thousands of parallel browser sessions with proxy rotation and CAPTCHA handling run the same agent logic against Browser Use Cloud instead of managing that infrastructure themselves.