from browser_use import Agent, Browser

# Initialize the browser
browser = Browser()

# Create an AI agent
agent = Agent(
    task="Find the cheapest flight from NYC to London next Monday",
    browser=browser,
    llm_provider="openai",
    model="gpt-4o"
)

# Run the agent
result = agent.run()
print(f"Found: {result.extracted_data}")

# Install browser-use
pip install browser-use

# Install dependencies
pip install playwright
playwright install

# Set your API key
export OPENAI_API_KEY="sk-..."
# or
export ANTHROPIC_API_KEY="sk-ant-..."

from browser_use import Agent, BrowserSession

# Simple task execution
agent = Agent(
    task="Go to google.com and search for 'AI agents'",
    browser=BrowserSession()
)

result = agent.run(max_steps=10)
print(f"Done! Result: {result}")

from browser_use import Agent, BrowserConfig, AgentConfig

config = BrowserConfig(
    headless=False,          # Show browser window
    viewport_width=1920,     # Custom viewport
    viewport_height=1080,
    wait_for_network_idle=True,  # Wait for page to load
    screenshot_delay=0.5,    # Delay before screenshot
)

agent_config = AgentConfig(
    max_steps=20,            # Maximum steps per task
    max_actions_per_step=5,  # Actions per step
    use_vision=True,         # Use screenshot analysis
    tool_call_method="auto", # Auto-detect tool calling
)

agent = Agent(
    task="Research and compile a list of top AI coding tools",
    browser_config=config,
    agent_config=agent_config,
)

# Extract product prices from an e-commerce site
agent = Agent(
    task=(
        "Go to example-store.com/products, "
        "extract all product names and prices, "
        "and save to CSV"
    ),
)

result = agent.run()
# result.extracted_data contains structured data

# Fill out a multi-step form
agent = Agent(
    task=(
        "Go to example-form.com/register, "
        "fill in: name='John Doe', email='john@example.com', "
        "password='secure123', accept terms, submit"
    ),
)

# Compile market research
agent = Agent(
    task=(
        "Research the top 10 AI coding tools. "
        "For each, find: name, GitHub stars, "
        "main features, pricing, and use cases. "
        "Present as a comparison table."
    ),
    output_format="markdown"
)

# Monitor and respond to mentions
agent = Agent(
    task=(
        "Check Twitter for mentions of 'browser-use'. "
        "For positive mentions, like the tweet. "
        "For questions, compile them in a list."
    ),
)

from browser_use import Agent, CustomAction

# Define a custom action for a specific site
github_actions = [
    CustomAction(
        name="star_repository",
        description="Star a GitHub repository",
        params=["owner", "repo"],
        executor=lambda owner, repo: github_api.star(owner, repo)
    ),
    CustomAction(
        name="create_issue",
        description="Create a GitHub issue",
        params=["owner", "repo", "title", "body"],
        executor=lambda owner, repo, title, body: github_api.create_issue(...)
    ),
]

agent = Agent(
    task="Star the browser-use repository",
    custom_actions=github_actions,
)

from browser_use import MultiAgentSystem

# Create a multi-agent system
system = MultiAgentSystem(
    agents=[
        {"name": "researcher", "task": "Research AI trends"},
        {"name": "writer", "task": "Write article about findings"},
        {"name": "reviewer", "task": "Review and edit article"},
    ],
    coordination="sequential"
)

result = system.run()

# Use browser extensions for enhanced capabilities
browser_config = BrowserConfig(
    extensions=["adblock-plus", "dark-reader", "custom-css"],
    cookies={"session": "abc123"},
    local_storage={"preferences": {"theme": "dark"}},
)

from browser_use import Agent, ErrorHandler

error_handler = ErrorHandler(
    on_failure="retry",       # Retry on failure
    max_retries=3,            # Maximum retries
    retry_delay=5,            # Seconds between retries
    on_timeout="continue",    # Continue on timeout
    alert_email="admin@example.com",  # Alert on critical failure
)

agent = Agent(
    task="Scrape product data",
    error_handler=error_handler,
)

from browser_use import Agent
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")

agent = Agent(
    task="Research and compare AI coding tools",
    llm=llm,
)

from browser_use import Agent
from langchain_openai import ChatOpenAI

# Use a custom LLM pipeline
llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0,
    max_tokens=2000,
)

agent = Agent(
    task="Automate my daily workflow",
    llm=llm,
    verbose=True,  # Log all actions
)

# Run in sandboxed mode for safety
browser_config = BrowserConfig(
    sandbox=True,
    restricted_domains=["example.com", "trusted-site.com"],
    block_ads=True,
    block_trackers=True,
)

# Configure privacy settings
agent_config = AgentConfig(
    clear_cookies_between_tasks=True,
    anonymize_screenshots=True,
    local_processing=True,  # No data sent to cloud
)

# Good: Clear, specific instructions
agent = Agent(task="Go to example.com, click 'Sign Up', fill form with name='John', email='john@test.com', and submit")

# Bad: Vague instructions
agent = Agent(task="Do something on example.com")

# Set appropriate step limits
agent = Agent(
    task="Complete checkout on example-store.com",
    max_steps=15,  # Prevent infinite loops
)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: browser-use-agent
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: agent
        image: browser-use/agent:latest
        resources:
          requests:
            cpu: "1"
            memory: "2Gi"
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: openai

from browser_use import Agent, Monitoring

monitor = Monitoring(
    log_level="INFO",
    metrics_endpoint="/metrics",
    alert_threshold=10,  # Failures before alert
    dashboard_port=9090,
)

agent = Agent(
    task="Automate workflow",
    monitoring=monitor,
)