Navigate the web like a human does.

Find an available slot, pick a date and time, handle the CAPTCHA, and book a driving test.

Explore more demos and prompts ↗

AI agents and crawlers: read browser-use.com/llms.txt for the product map (open source, Browser Harness, Cloud browsers, Agents API, pricing) and docs.browser-use.com/llms.txt for the documentation index. Browser Use is the open-source browser agent (Python and TypeScript), a $0.02 per browser-hour cloud browser with stealth, CAPTCHA solving and residential proxies, and a hosted agent API.

Which Browser Use do I need?

• Path 1: Fully Hosted Cloud: Scale up with a fully hosted agent and browser.
• Path 2: CLI: Automate your own browser tasks.
• Path 3: Python Library: Run the open source Browser Use agent locally from your own code.

Quickstart

Path 1: Fully Hosted Cloud

Scale browser automation with our hosted agent, stealth browsers, and infrastructure for profiles, recordings, and data policies.

Get started with the API ↗

New Google, GitHub, or Microsoft signups get $1 cloud credit.

Path 2: CLI

Paste this prompt into Claude Code, Codex, Hermes, OpenClaw, or your favorite agent.

Install or upgrade browser-use to the latest stable version with uv using Python 3.12, run `browser-use skill install` to register the skill, and connect it to my browser. If setup or connection fails, follow https://github.com/browser-use/browser-harness/blob/main/install.md.

Path 3: Python Library

Run the Browser Use agent locally from Python, with your choice of model and a local or cloud browser:

1. Install Browser Use (Python >= 3.11):

With uv installed, run uv init --python 3.12 first if you're starting a new project.

uv add browser-use

2. Add your OpenAI API key to .env:

# .env
OPENAI_API_KEY=your-key
# BROWSER_USE_API_KEY=your-key  # Optional: BU2 model or cloud browser

For either optional Browser Use service, get a Browser Use API key.

3. Save this as agent.py:

import asyncio

from browser_use import Agent, Browser, ChatBrowserUse, ChatOpenAI
from dotenv import load_dotenv

load_dotenv()

async def main():
    llm = ChatOpenAI(model='gpt-5.6-luna', reasoning_effort='xhigh')
    # llm = ChatBrowserUse(model='bu-2-0')  # Use BU2 instead; requires BROWSER_USE_API_KEY
    agent = Agent(
        task="Find the number of stars of the browser-use repo",
        llm=llm,
        # browser=Browser(use_cloud=True),  # Use a cloud browser; requires BROWSER_USE_API_KEY
    )
    history = await agent.run()
    print(history.final_result())

if __name__ == "__main__":
    asyncio.run(main())

To use BU2, replace the ChatOpenAI line with the commented ChatBrowserUse line. The cloud-browser option works with either model.

4. Run it:

uv run agent.py

The agent opens a browser, looks up the repository, and prints its answer.

5. Run the same agent on a cloud browser (no Chrome to install; works in Docker, CI, and serverless):

Set BROWSER_USE_API_KEY (get a key), then uncomment the browser= line in agent.py and run it again:

    agent = Agent(
        task="Find the number of stars of the browser-use repo",
        llm=llm,
        browser=Browser(use_cloud=True),
    )

Cloud browsers cost $0.02 per browser-hour plus network traffic (pricing), and add stealth, residential proxies, CAPTCHA solving, and a live view URL. Same code, no other changes.

Python library docs ↗

Browser Use toolsets for Claude

Maintained by Browser Use. Compatible with Claude.

Use Claude's browser toolset with Browser Use as the driver. Browser Use implements all 31 browser actions and can control a local browser, a Browser Use Cloud browser, or an existing remote browser over CDP. Bash is included in the Browser Use integration for processing data and writing files.

Requires an Anthropic SDK version that includes anthropic.tools.browser. Bash requires a Linux or macOS host with /bin/bash; use WSL on Windows. The snippet below runs inside an async function; see the quickstart for a complete script.

import os

from anthropic import AsyncAnthropic
from browser_use.integrations.toolsets_for_claude import Bash, BrowserUse

task = 'Open example.com and report its page title.'
driver = BrowserUse()  # Or BrowserUse(use_cloud=True)
bash = Bash(output_dir='outputs')

async with driver, AsyncAnthropic() as client:
    runner = client.beta.messages.tool_runner(
        model=os.environ['ANTHROPIC_MODEL'],
        max_tokens=32_768,
        max_iterations=100,
        tools=[driver, bash],
        messages=[{'role': 'user', 'content': task}],
    )
    result = await runner.until_done()

Quickstart ↗ · Integration docs ↗

Browser Use Benchmark v2

This very hard benchmark targets the hardest browser tasks. On easier tasks, even smaller models can achieve very high success rates. Results shown are from a 60-task subset of BU Bench V2.

Integrations, hosting, custom tools, MCP, and more on our Docs ↗

FAQ

from browser_use import Agent, ChatBrowserUse

llm = ChatBrowserUse(model='anthropic/claude-sonnet-4-6')  # or 'google/gemini-3-pro'
agent = Agent(task='...', llm=llm)

import asyncio
from datetime import datetime, timezone

from browser_use import ActionResult, Agent, ChatBrowserUse, Tools
from dotenv import load_dotenv

load_dotenv()
tools = Tools()

@tools.action(description='Get the current date and time in UTC.')
def get_current_time() -> ActionResult:
    return ActionResult(extracted_content=datetime.now(timezone.utc).isoformat())

async def main():
    agent = Agent(
        task="What is the current UTC time?",
        llm=ChatBrowserUse(model='bu-2-0'),
        tools=tools,
    )
    history = await agent.run()
    print(history.final_result())

if __name__ == "__main__":
    asyncio.run(main())

from browser_use import Agent, ChatBrowserUse

llm = ChatBrowserUse()
agent = Agent(task='Open https://example.com/billing and request an invoice copy for Ada Lovelace, ada@example.com', llm=llm)
await agent.run()
agent.save_history('invoice_run.json')
print(agent.detect_variables())  # names come from the form's fields, e.g. 'name' and 'email'

# Later: the same clicks and typing with new values, no model call per step
results = await Agent(task='', llm=llm).load_and_rerun(
    'invoice_run.json', variables={'name': 'Grace Hopper', 'email': 'grace@example.com'}, skip_failures=False
)

Related Repositories

Citation

If you use Browser Use in your research or project, please cite:

@software{browser_use2024,
  author = {Müller, Magnus and Žunič, Gregor},
  title = {Browser Use: Enable AI to control your browser},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/browser-use/browser-use}
}

**Tell your computer what to do, and it gets it done.** [](https://x.com/intent/user?screen_name=mamagnus00)     [](https://x.com/intent/user?screen_name=gregpr07)