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Showing content from https://github.com/browser-use/browser-use below:

browser-use/browser-use: 🌐 Make websites accessible for AI agents. Automate tasks online with ease.

🌐 Browser-use is the easiest way to connect your AI agents with the browser.

💡 See what others are building and share your projects in our Discord! Want Swag? Check out our Merch store.

🌤️ Skip the setup - try our hosted version for instant browser automation! Try the cloud ☁︎.

With pip (Python>=3.11):

Install the browser:

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
from browser_use.llm import ChatOpenAI

async def main():
    agent = Agent(
        task="Compare the price of gpt-4o and DeepSeek-V3",
        llm=ChatOpenAI(model="o4-mini", temperature=1.0),
    )
    await agent.run()

asyncio.run(main())

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

OPENAI_API_KEY=
ANTHROPIC_API_KEY=
AZURE_OPENAI_ENDPOINT=
AZURE_OPENAI_KEY=
GOOGLE_API_KEY=
DEEPSEEK_API_KEY=
GROK_API_KEY=
NOVITA_API_KEY=

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

You can test browser-use using its Web UI or Desktop App.

You can also use our browser-use interactive CLI (similar to claude code):

pip install "browser-use[cli]"
browser-use

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

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.

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

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

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 report.pdf in my documents and create a GitHub issue about it",
        llm=ChatOpenAI(model="gpt-4o"),
        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.

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.'

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.

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.

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

• Improve agent memory to handle +100 steps
• Enhance planning capabilities (load website specific context)
• Reduce token consumption (system prompt, DOM state)

• Enable detection for all possible UI elements
• Improve state representation for UI elements so that all LLMs can understand what's on the page

• Let user record a workflow - which we can rerun with browser-use as a fallback
• Make rerunning of workflows work, even if pages change

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

• 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.

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

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.

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.

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

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}
}

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