Browser-use: Autonomous Web Automation with 91k+ GitHub Stars
Browser-use is the leading open-source framework for building AI agents that autonomously navigate websites, fill forms, and complete multi-step web tasks. With 91k+ GitHub stars and 89.1% success rate on the WebVoyager benchmark, it's the state-of-the-art for AI-powered web automation in 2026.
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Browser-use has become the go-to open-source framework for building AI agents that can autonomously navigate websites, fill forms, extract data, and complete multi-step web tasks. With 91.1k+ GitHub stars and active development (last commit 3 days ago), it's the most popular framework for web automation in the AI agent ecosystem. The project is actively maintained by a community of 314+ contributors and is trusted by teams at Anthropic, Amazon, and Airbnb.
In 2026, the AI browser automation market is projected to grow from $4.5 billion to $76.8 billion by 2034 (32.8% CAGR). Browser-use sits at the center of this explosion, achieving 89.1% success rate on the WebVoyager benchmark — the highest among open-source frameworks — making it the state-of-the-art for autonomous web interaction.
What is Browser-use?
Browser-use is a Python framework that gives AI agents the ability to control web browsers like humans do. Instead of writing brittle Selenium or Playwright scripts that break when websites change, you describe what you want the agent to accomplish in natural language, and Browser-use handles the navigation, clicking, form-filling, and data extraction.
The framework was created to solve a fundamental problem: traditional browser automation requires explicit instructions for every action (click button with class X, fill input Y, wait for element Z). When websites update their HTML structure, these scripts fail. Browser-use flips this model by using LLMs to reason about page structure and adapt to changes in real time.
Built on top of Playwright (for browser control) and LiteLLM (for model flexibility), Browser-use abstracts away the complexity of browser automation while maintaining full control over the underlying browser instance. It works with any LLM provider: OpenAI, Anthropic, Google, or local models via Ollama.
Core Features and Architecture
1. Model-Agnostic LLM Support
Browser-use works with any LLM provider through LiteLLM. You can use OpenAI's GPT-4o, Anthropic's Claude, Google's Gemini, or run local models with Ollama. The framework includes a specialized ChatBrowserUse() model optimized specifically for browser automation tasks, achieving 3-5x faster task completion than general-purpose models.
from browser_use import Agent, Browser, ChatBrowserUse
import asyncio
async def main():
browser = Browser()
agent = Agent(
task="Find the top 10 trending repositories on GitHub today",
llm=ChatBrowserUse(), # Optimized for browser tasks
browser=browser,
)
result = await agent.run()
print(result)
asyncio.run(main())2. DOM Distillation and Token Optimization
Browser-use strips web pages down to their essential interactive elements, reducing token consumption by up to 67% compared to raw HTML. This means faster execution and lower API costs. The framework intelligently identifies clickable elements, form fields, and navigation targets, then presents them to the LLM in a compact, semantic format.
3. Multi-Tab Support
Agents can work across multiple browser tabs simultaneously, enabling complex workflows that require context switching. This is critical for research tasks, competitive analysis, and data aggregation across multiple sources.
4. Screenshot and Accessibility Tree Analysis
Browser-use captures both visual screenshots and the accessibility tree (DOM structure) of each page. The LLM can reason about both representations, making it resilient to layout changes and visual obfuscation. If a button's color changes or CSS is updated, the agent still recognizes it as a button.
5. Memory and Context Management
The framework maintains conversation history and page context across navigation steps. This allows agents to remember previous interactions, learn from mistakes, and maintain state across multi-step workflows.
6. Custom Tools and Skills
You can extend Browser-use with custom tools that agents can invoke. This enables integration with external APIs, databases, or specialized services.
from browser_use import Tools
tools = Tools()
@tools.action(description='Extract structured data from the current page')
def extract_data(selector: str) -> dict:
# Custom extraction logic
return {"data": "extracted"}
agent = Agent(
task="Your task",
llm=ChatBrowserUse(),
browser=browser,
tools=tools,
)7. Built-in Benchmarking and Evaluation
Browser-use includes the WebVoyager benchmark (586 diverse web tasks) for evaluating agent performance. The framework achieved 89.1% success rate, significantly outperforming competitors like Skyvern (85.85%) and ChatGPT Atlas (87%).
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Getting Started
Prerequisites: Python 3.11+, an LLM API key (OpenAI, Anthropic, or Google), and Chromium installed.
Installation with uv (recommended):
uv init && uv add browser-use && uv sync
# Optional: Install Chromium if not already present
uvx browser-use installYour first agent:
from browser_use import Agent, Browser, ChatBrowserUse
import asyncio
async def main():
browser = Browser()
agent = Agent(
task="Search for 'AI agents 2026' on Google and list the top 5 results",
llm=ChatBrowserUse(),
browser=browser,
)
result = await agent.run()
print(result.output)
if __name__ == "__main__":
asyncio.run(main())Using with other LLM providers:
from browser_use import Agent, Browser
from browser_use import ChatAnthropic # or ChatGoogle, ChatOpenAI
async def main():
browser = Browser()
agent = Agent(
task="Your task here",
llm=ChatAnthropic(model='claude-sonnet-4-6'),
browser=browser,
)
await agent.run()
asyncio.run(main())Real-World Use Cases
1. Competitive Intelligence and Price Monitoring
E-commerce teams use Browser-use to monitor competitor pricing across 50+ websites daily. The agent navigates each site, extracts product prices, and feeds the data into dynamic pricing models. Unlike traditional scrapers that break when websites update, Browser-use adapts automatically.
2. Form Automation at Scale
Insurance companies automate quote requests across multiple carriers. Browser-use fills complex multi-page forms with customer data, handles CAPTCHAs (with additional services), and extracts quotes. Benchmarks show 30-field forms completed in 90 seconds versus 12+ minutes manually.
3. Research and Data Aggregation
Researchers use Browser-use to compile competitive analysis reports. The agent searches multiple sources, navigates to relevant pages, extracts structured data, and synthesizes findings into a single report — a task that would take hours manually.
4. Automated Testing and QA
QA teams generate end-to-end tests from natural language descriptions. Browser-use runs the tests, adapts when UI changes, and identifies regressions without brittle CSS selectors.
How It Compares
Browser-use vs. Skyvern: Skyvern uses computer vision and is stronger for form-filling (85.85% vs 89.1% on WebVoyager), but Browser-use is faster and more cost-effective for general web tasks. Skyvern excels when you need a no-code visual builder.
Browser-use vs. Stagehand: Stagehand is TypeScript-only and built on Playwright with an AI layer. Browser-use is Python-first and more flexible with LLM choice. Stagehand is better if you're already in the TypeScript ecosystem; Browser-use is better for Python teams.
Browser-use vs. Firecrawl: Firecrawl is a web data layer (search, scrape, extract) with managed browser infrastructure. Browser-use is a framework for building custom agents. They complement each other: use Firecrawl for web data extraction, Browser-use for complex multi-step workflows.
What's Next
The Browser-use roadmap includes improved CAPTCHA handling, better stealth mode for anti-bot detection, and native support for more LLM providers. The community is also working on a cloud-hosted version (Browser Use Cloud) that handles browser infrastructure, scaling, and proxy rotation automatically.
With 91k+ stars and growing adoption across enterprises, Browser-use is becoming the standard for AI-powered web automation. As LLMs improve and the ecosystem matures, expect browser agents to move from experimental to production-critical infrastructure in 2026.