67d3f136eb
This PR improves the evaluation judge system by adding explicit criteria for scoring tasks that produce no output. **Changes:** - Added clarification that tasks producing no output must receive low scores - Improved evaluation consistency for incomplete task scenarios - Ensures judges properly penalize agents that fail to produce results **Problem Addressed:** Previously, the evaluation criteria didn't explicitly state how to handle cases where agents complete their steps but produce no meaningful output. This could lead to inconsistent scoring where some judges might give moderate scores for "effort" even when no results were delivered. **Benefits:** - **Clearer guidance**: Judges now have explicit instruction on scoring no-output scenarios - **Consistent evaluation**: Reduces variability in how different judges score incomplete tasks - **Better agent assessment**: Ensures that failure to produce output is properly reflected in scoring - **Improved training data**: More accurate scores help improve future agent development **Impact:** This change helps maintain evaluation accuracy by making it clear that lack of output is a significant failure condition that should result in low scores, regardless of the apparent "effort" shown in the agent's trajectory. **Testing:** - No breaking changes to existing functionality - Pre-commit hooks pass successfully - Change only affects evaluation scoring guidance
Enable AI to control your browser 🤖
🌐 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 ☁︎.
Quick start
With pip (Python>=3.11):
pip install browser-use
For memory functionality (requires Python<3.13 due to PyTorch compatibility):
pip install "browser-use[memory]"
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="gpt-4o"),
)
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 📕.
Test with UI
You can test browser-use using its Web UI or Desktop App.
Test with an interactive CLI
You can also use our browser-use interactive CLI (similar to claude code):
pip install "browser-use[cli]"
browser-use
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.'
https://github.com/user-attachments/assets/171fb4d6-0355-46f2-863e-edb04a828d04
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.
https://github.com/user-attachments/assets/de73ee39-432c-4b97-b4e8-939fd7f323b3
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.
Vision
Tell your computer what to do, and it gets it done.
Roadmap
Agent
- Improve agent memory to handle +100 steps
- Enhance planning capabilities (load website specific context)
- Reduce token consumption (system prompt, DOM state)
DOM Extraction
- Enable detection for all possible UI elements
- Improve state representation for UI elements so that all LLMs can understand what's on the page
Workflows
- Let user record a workflow - which we can rerun with browser-use as a fallback
- Make rerunning of workflows work, even if pages change
User Experience
- Create various templates for tutorial execution, job application, QA testing, social media, etc. which users can just copy & paste.
- Improve docs
- Make it faster
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 theREADME therefor 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.
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}
}