Python library for controlling a real browser with vision and DOM extraction, built for agent workflows.
Where it wins
Vision + DOM hybrid approach for robust page understanding
Large public traction and active development
Works with multiple LLM providers
Handles complex multi-step browser tasks
Where to be skeptical
Python-only — no native TypeScript/Node support
Still evolving reliability for complex flows
Heavier setup than MCP-based browser tools
Editorial verdict
Unchallenged category leader — 81K stars, 1M+ weekly PyPI downloads, 89.1% WebVoyager. The gap to #2 is enormous: 4x stars, 6x downloads vs the next autonomous agent.
Source
Videos
Reviews, tutorials, and comparisons from the community.
Browser Use: This New AI Agent Can Do Anything (Full AI Scraping Tutorial)
Browser Use: FREE AI Agent CAN CONTROL BROWSERS & DO ANYTHING! (Beats Anthropic!)
Browser Use - AI Agent with the Browser
Related
Playwright MCP
93Microsoft's official MCP server for Playwright. Uses accessibility snapshots instead of screenshots for structured browser control. Auto-configured in GitHub Copilot's Coding Agent.
Chrome DevTools MCP
92Google Chrome team's official MCP server for Chrome DevTools. Gives coding agents deep debugging, performance profiling, and Core Web Vitals analysis through 26 tools across 6 categories.
Stagehand
90AI-native browser automation SDK by Browserbase with natural language selectors and act/extract/observe primitives.
Skyvern
90Vision-LLM browser automation for enterprise workflows. Combines computer vision with LLM reasoning to handle websites never seen before. YC S23 backed with CAPTCHA solving, 2FA, and proxy networks.
Public evidence
$17M seed led by Felicis with Paul Graham participating. 20+ YC W25 batch companies used Browser Use. Manus (viral agent) built on top of it.
Browser Use achieved 89.1% success rate on the WebVoyager benchmark across 586 web tasks. State-of-the-art open-source performance, though below commercial competitors (Surfer 2 at 97.1%).
Firecrawl's independent ranking places Browser Use as the top open-source AI browser agent. Cites vision+DOM hybrid approach and multi-step task capability.
1M+ weekly PyPI downloads confirms massive real-world adoption. No other open-source browser agent comes close — Skyvern is at 167K/wk (6x gap).
YC W25 backing, SOC 2 Type II certified, cloud product at $30/month. Proprietary cost-optimized model BU-30B (200 tasks/$1). Enterprise runway no other open-source agent has.
Dominant AI browser agent framework by stars. Zero to 81K+ in ~18 months. Weekly release cadence. 314 contributors.
Hands-on comparison of 6 browser automation tools confirms Browser Use wins for 'complex multi-step tasks (form filling, autonomous workflows).'
Multiple independent sources confirm Browser Use in production contexts. AWS blog coverage, InfoWorld feature, and official Apify integration validate real-world deployment beyond star counts.
Confirms Browser Use's unique full-autonomy positioning: 'the LLM decides what to click, what to type, when to scroll, and when the task is complete.' Independent editorial, not sponsored.
Multiple commenters compared Browser Use and Stagehand architectures in depth. Organic community discussion validates Browser Use as the autonomous agent default.
Browser Use Cloud showed 43.9% success rate vs Skyvern's 64.4% in cloud mode. However, Browser Use was 2x faster and 2x cheaper per task. Reveals reliability gap in cloud deployment vs strong cost/speed profile.
Raw GitHub source
Constrained peek so you can sanity-check the source material without leaving the site.
🌤️ Want to skip the setup? Use our <b>cloud</b> for faster, scalable, stealth-enabled browser automation!
Browser Use 0.13 introduces a new beta agent powered by a Rust core and a browser harness built for current frontier models. It gives the model a real browser/computer action space, persistent tools, and recovery loops inspired by coding agents.
Python API -> Rust core -> Browser harness -> Web task done
1. Install Browser Use with the native core runtime (Python>=3.11):
uv add "browser-use[core]"
# or: pip install "browser-use[core]"
The [core] extra installs the native Browser Use runtime for your platform.
2. [Optional] Get your API key from Browser Use Cloud:
# .env
BROWSER_USE_API_KEY=your-key
# GOOGLE_API_KEY=your-key
# ANTHROPIC_API_KEY=your-key
3. Run your first agent:
from browser_use.beta import Agent, BrowserProfile, ChatBrowserUse
# from browser_use.beta import ChatOpenAI # ChatOpenAI(model='gpt-5.5')
# from browser_use.beta import ChatGoogle # ChatGoogle(model='gemini-3.1-pro-preview')
# from browser_use.beta import ChatAnthropic # ChatAnthropic(model='claude-opus-4-8')
import asyncio
async def main():
agent = Agent(
task="Find the number of stars of the browser-use repo",
llm=ChatBrowserUse(),
# llm=ChatOpenAI(model='gpt-5.5'),
# llm=ChatGoogle(model='gemini-3.1-pro-preview'),
# llm=ChatAnthropic(model='claude-opus-4-8'), # Sonnet also works well.
browser_profile=BrowserProfile(
headless=False,
allowed_domains=["*.github.com"],
),
)
history = await agent.run()
print(history.final_result())
if __name__ == "__main__":
asyncio.run(main())
Existing Python agent users can keep using from browser_use import Agent. The new Rust-powered beta agent is from browser_use.beta import Agent.
Check out the library docs and the cloud docs for more!
<br/>We benchmark Browser Use across 100 real-world browser tasks. Full benchmark is open source: browser-use/benchmark.
Use the Open-Source Agent
Use the Fully-Hosted Cloud Agent (recommended)
Example code ↗