💡 Summary
Browser-Use enables automated browser tasks using AI agents for efficient web interactions.
🎯 Target Audience
🤖 AI Roast: “Powerful, but the setup might scare off the impatient.”
Risk: Medium. Review: shell/CLI command execution; outbound network access (SSRF, data egress); API keys/tokens handling and storage; filesystem read/write scope and path traversal. Run with least privilege and audit before enabling in production.
🌤️ Want to skip the setup? Use our cloud for faster, scalable, stealth-enabled browser automation!
🤖 LLM Quickstart
- Direct your favorite coding agent (Cursor, Claude Code, etc) to Agents.md
- Prompt away!
👋 Human Quickstart
1. Create environment with uv (Python>=3.11):
uv init
2. Install Browser-Use package:
# We ship every day - use the latest version! uv add browser-use uv sync
3. Get your API key from Browser Use Cloud and add it to your .env file (new signups get $10 free credits):
# .env
BROWSER_USE_API_KEY=your-key
4. Install Chromium browser:
uvx browser-use install
5. Run your first agent:
from browser_use import Agent, Browser, ChatBrowserUse import asyncio async def example(): browser = Browser( # use_cloud=True, # Uncomment to use a stealth browser on Browser Use Cloud ) llm = ChatBrowserUse() agent = Agent( task="Find the number of stars of the browser-use repo", llm=llm, browser=browser, ) history = await agent.run() return history if __name__ == "__main__": history = asyncio.run(example())
Check out the library docs and the cloud docs for more!
🔥 Deploy on Sandboxes
We handle agents, browsers, persistence, auth, cookies, and LLMs. The agent runs right next to the browser for minimal latency.
from browser_use import Browser, sandbox, ChatBrowserUse from browser_use.agent.service import Agent import asyncio @sandbox() async def my_task(browser: Browser): agent = Agent(task="Find the top HN post", browser=browser, llm=ChatBrowserUse()) await agent.run() # Just call it like any async function asyncio.run(my_task())
See Going to Production for more details.
🚀 Template Quickstart
Want to get started even faster? Generate a ready-to-run template:
uvx browser-use init --template default
This creates a browser_use_default.py file with a working example. Available templates:
default- Minimal setup to get started quicklyadvanced- All configuration options with detailed commentstools- Examples of custom tools and extending the agent
You can also specify a custom output path:
uvx browser-use init --template default --output my_agent.py
💻 CLI
Fast, persistent browser automation from the command line:
browser-use open https://example.com # Navigate to URL browser-use state # See clickable elements browser-use click 5 # Click element by index browser-use type "Hello" # Type text browser-use screenshot page.png # Take screenshot browser-use close # Close browser
The CLI keeps the browser running between commands for fast iteration. See CLI docs for all commands.
Claude Code Skill
For Claude Code, install the skill to enable AI-assisted browser automation:
mkdir -p ~/.claude/skills/browser-use curl -o ~/.claude/skills/browser-use/SKILL.md \ https://raw.githubusercontent.com/browser-use/browser-use/main/skills/browser-use/SKILL.md
Demos
📋 Form-Filling
Task = "Fill in this job application with my resume and information."
🍎 Grocery-Shopping
Task = "Put this list of items into my instacart."
https://github.com/user-attachments/assets/a6813fa7-4a7c-40a6-b4aa-382bf88b1850
💻 Personal-Assistant.
Task = "Help me find parts for a custom PC."
https://github.com/user-attachments/assets/ac34f75c-057a-43ef-ad06-5b2c9d42bf06
💡See more examples here ↗ and give us a star!
Integrations, hosting, custom tools, MCP, and more on our Docs ↗
FAQ
We optimized ChatBrowserUse() specifically for browser automation tasks. On avg it completes tasks 3-5x faster than other models with SOTA accuracy.
Pricing (per 1M tokens):
- Input tokens: $0.20
- Cached input tokens: $0.02
- Output tokens: $2.00
For other LLM providers, see our supported models documentation.
Yes! You can add custom tools to extend the agent's capabilities:
from browser_use import Tools tools = Tools() @tools.action(description='Description of what this tool does.') def custom_tool(param: str) -> str: return f"Result: {param}" agent = Agent( task="Your task", llm=llm, browser=browser, tools=tools, )
Yes! Browser-Use is open source and free to use. You only need to choose an LLM provider (like OpenAI, Google, ChatBrowserUse, or run local models with Ollama).
Check out our authentication examples:
- Using real browser profiles - Reuse your existing Chrome profile with saved logins
- If you want to use temporary accounts with inbox, choose AgentMail
- To sync your auth profile with the remote browser, run
curl -fsSL https://browser-use.com/profile.sh | BROWSER_USE_API_KEY=XXXX sh(replace XXXX with your API key)
These examples show how to maintain sessions and handle authentication seamlessly.
For CAPTCHA handling, you need better browser fingerprinting and proxies. Use Browser Use Cloud which provides stealth browsers designed to avoid detection and CAPTCHA challenges.
Chrome can consume a lot of memory, and running many agents in parallel can be tricky to manage.
For production use cases, use our Browser Use Cloud API which handles:
- Scalable browser infrastructure
- Memory management
- Proxy rotation
- Stealth browser fingerprinting
- High-performance parallel execution
Tell your computer what to do, and it gets it done.
Pros
- Supports various AI models for flexibility
- Easy setup with clear instructions
- Offers cloud-based options for scalability
- Includes CLI for quick browser automation
Cons
- Requires API key for full functionality
- Dependency on external services for some features
- Learning curve for new users unfamiliar with Python
- Limited examples for advanced use cases
Related Skills
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S“It's the Swiss Army knife of deep learning, but good luck figuring out which of the 47 installation methods is the one that won't break your system.”
agno
S“It promises to be the Kubernetes for agents, but let's see if developers have the patience to learn yet another orchestration layer.”
nuxt-skills
S“It's essentially a well-organized cheat sheet that turns your AI assistant into a Nuxt framework parrot.”
Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.
Copyright belongs to the original author browser-use.
