Enable AI to control your browser 🤖

🌤️ Want to skip the setup? Use our cloud for faster, scalable, stealth-enabled browser automation!

Quick start

With pip (Python>=3.11):

pip install browser-use

Install the browser:

uvx playwright install chromium --with-deps --no-shell

Spin up your agent:

import asyncio
from dotenv import load_dotenv
load_dotenv()
from browser_use import Agent, ChatOpenAI

async def main():
    agent = Agent(
        task="Find the number of stars of the browser-use repo",
        llm=ChatOpenAI(model="gpt-4.1-mini"),
    )
    await agent.run()

asyncio.run(main())

Add your API keys for the provider you want to use to your .env file.

OPENAI_API_KEY=

For other settings, models, and more, check out the documentation 📕.

Demos

Task: Add grocery items to cart, and checkout.

Prompt: Add my latest LinkedIn follower to my leads in Salesforce.

Prompt: Read my CV & find ML jobs, save them to a file, and then start applying for them in new tabs, if you need help, ask me.'

apply.to.jobs.8x.mp4

Prompt: Write a letter in Google Docs to my Papa, thanking him for everything, and save the document as a PDF.

Prompt: Look up models with a license of cc-by-sa-4.0 and sort by most likes on Hugging face, save top 5 to file.

hugging_face_high_quality.mp4

More examples

For more examples see the examples folder or join the Discord and show off your project. You can also see our awesome-prompts repo for prompting inspiration.

MCP Integration

Browser-use supports the Model Context Protocol (MCP), enabling integration with Claude Desktop and other MCP-compatible clients.

Use as MCP Server with Claude Desktop

Add browser-use to your Claude Desktop configuration:

{
  "mcpServers": {
    "browser-use": {
      "command": "uvx",
      "args": ["browser-use[cli]", "--mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

This gives Claude Desktop access to browser automation tools for web scraping, form filling, and more.

Connect External MCP Servers to Browser-Use Agent

Browser-use agents can connect to multiple external MCP servers to extend their capabilities:

import asyncio
from browser_use import Agent, Controller, ChatOpenAI
from browser_use.mcp.client import MCPClient

async def main():
    # Initialize controller
    controller = Controller()

    # Connect to multiple MCP servers
    filesystem_client = MCPClient(
        server_name="filesystem",
        command="npx",
        args=["-y", "@modelcontextprotocol/server-filesystem", "/Users/me/documents"]
    )

    github_client = MCPClient(
        server_name="github",
        command="npx",
        args=["-y", "@modelcontextprotocol/server-github"],
        env={"GITHUB_TOKEN": "your-github-token"}
    )

    # Connect and register tools from both servers
    await filesystem_client.connect()
    await filesystem_client.register_to_controller(controller)

    await github_client.connect()
    await github_client.register_to_controller(controller)

    # Create agent with MCP-enabled controller
    agent = Agent(
        task="Find the latest pdf report in my documents and create a GitHub issue about it",
        llm=ChatOpenAI(model="gpt-4.1-mini"),
        controller=controller  # Controller has tools from both MCP servers
    )

    # Run the agent
    await agent.run()

    # Cleanup
    await filesystem_client.disconnect()
    await github_client.disconnect()

asyncio.run(main())

See the MCP documentation for more details.

Vision

Tell your computer what to do, and it gets it done.

Roadmap

Agent

• Make agent 3x faster
• Reduce token consumption (system prompt, DOM state)

DOM Extraction

• Enable interaction with all UI elements
• Improve state representation for UI elements so that any LLM can understand what's on the page

Workflows

• Let user record a workflow - which we can rerun with browser-use as a fallback

User Experience

• Create various templates for tutorial execution, job application, QA testing, social media, etc. which users can just copy & paste.

Parallelization

• Human work is sequential. The real power of a browser agent comes into reality if we can parallelize similar tasks. For example, if you want to find contact information for 100 companies, this can all be done in parallel and reported back to a main agent, which processes the results and kicks off parallel subtasks again.

Contributing

We love contributions! Feel free to open issues for bugs or feature requests. To contribute to the docs, check out the /docs folder.

🧪 How to make your agents robust?

We offer to run your tasks in our CI—automatically, on every update!

• Add your task: Add a YAML file in tests/agent_tasks/ (see the README there for details).
• Automatic validation: Every time we push updates, your task will be run by the agent and evaluated using your criteria.

Local Setup

To learn more about the library, check out the local setup 📕.

main is the primary development branch with frequent changes. For production use, install a stable versioned release instead.

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Swag

Want to show off your Browser-use swag? Check out our Merch store. Good contributors will receive swag for free 👀.

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}
}

Made with ❤️ in Zurich and San Francisco