chore: import upstream snapshot with attribution

examples/getting_started/01_basic_search.py

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+"""
+Setup:
+1. Get your API key from https://cloud.browser-use.com/new-api-key
+2. Set environment variable: export BROWSER_USE_API_KEY="your-key"
+"""
+
+import asyncio
+import os
+import sys
+
+# Add the parent directory to the path so we can import browser_use
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from browser_use import Agent, ChatBrowserUse
+
+
+async def main():
+	llm = ChatBrowserUse(model='bu-2-0')
+	task = "Search Google for 'what is browser automation' and tell me the top 3 results"
+	agent = Agent(task=task, llm=llm)
+	await agent.run()
+
+
+if __name__ == '__main__':
+	asyncio.run(main())

examples/getting_started/02_form_filling.py

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+"""
+Getting Started Example 2: Form Filling
+
+This example demonstrates how to:
+- Navigate to a website with forms
+- Fill out input fields
+- Submit forms
+- Handle basic form interactions
+
+This builds on the basic search example by showing more complex interactions.
+
+Setup:
+1. Get your API key from https://cloud.browser-use.com/new-api-key
+2. Set environment variable: export BROWSER_USE_API_KEY="your-key"
+"""
+
+import asyncio
+import os
+import sys
+
+# Add the parent directory to the path so we can import browser_use
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from browser_use import Agent, ChatBrowserUse
+
+
+async def main():
+	# Initialize the model
+	llm = ChatBrowserUse(model='bu-2-0')
+
+	# Define a form filling task
+	task = """
+    Go to https://httpbin.org/forms/post and fill out the contact form with:
+    - Customer name: John Doe
+    - Telephone: 555-123-4567
+    - Email: john.doe@example.com
+    - Size: Medium
+    - Topping: cheese
+    - Delivery time: now
+    - Comments: This is a test form submission
+    
+    Then submit the form and tell me what response you get.
+    """
+
+	# Create and run the agent
+	agent = Agent(task=task, llm=llm)
+	await agent.run()
+
+
+if __name__ == '__main__':
+	asyncio.run(main())

examples/getting_started/03_data_extraction.py

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+"""
+Getting Started Example 3: Data Extraction
+
+This example demonstrates how to:
+- Navigate to a website with structured data
+- Extract specific information from the page
+- Process and organize the extracted data
+- Return structured results
+
+This builds on previous examples by showing how to get valuable data from websites.
+
+Setup:
+1. Get your API key from https://cloud.browser-use.com/new-api-key
+2. Set environment variable: export BROWSER_USE_API_KEY="your-key"
+"""
+
+import asyncio
+import os
+import sys
+
+# Add the parent directory to the path so we can import browser_use
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from browser_use import Agent, ChatBrowserUse
+
+
+async def main():
+	# Initialize the model
+	llm = ChatBrowserUse(model='bu-2-0')
+
+	# Define a data extraction task
+	task = """
+    Go to https://quotes.toscrape.com/ and extract the following information:
+    - The first 5 quotes on the page
+    - The author of each quote
+    - The tags associated with each quote
+    
+    Present the information in a clear, structured format like:
+    Quote 1: "[quote text]" - Author: [author name] - Tags: [tag1, tag2, ...]
+    Quote 2: "[quote text]" - Author: [author name] - Tags: [tag1, tag2, ...]
+    etc.
+    """
+
+	# Create and run the agent
+	agent = Agent(task=task, llm=llm)
+	await agent.run()
+
+
+if __name__ == '__main__':
+	asyncio.run(main())

examples/getting_started/04_multi_step_task.py

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+"""
+Getting Started Example 4: Multi-Step Task
+
+This example demonstrates how to:
+- Perform a complex workflow with multiple steps
+- Navigate between different pages
+- Combine search, form filling, and data extraction
+- Handle a realistic end-to-end scenario
+
+This is the most advanced getting started example, combining all previous concepts.
+
+Setup:
+1. Get your API key from https://cloud.browser-use.com/new-api-key
+2. Set environment variable: export BROWSER_USE_API_KEY="your-key"
+"""
+
+import asyncio
+import os
+import sys
+
+# Add the parent directory to the path so we can import browser_use
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from browser_use import Agent, ChatBrowserUse
+
+
+async def main():
+	# Initialize the model
+	llm = ChatBrowserUse(model='bu-2-0')
+
+	# Define a multi-step task
+	task = """
+    I want you to research Python web scraping libraries. Here's what I need:
+    
+    1. First, search Google for "best Python web scraping libraries 2024"
+    2. Find a reputable article or blog post about this topic
+    3. From that article, extract the top 3 recommended libraries
+    4. For each library, visit its official website or GitHub page
+    5. Extract key information about each library:
+       - Name
+       - Brief description
+       - Main features or advantages
+       - GitHub stars (if available)
+    
+    Present your findings in a summary format comparing the three libraries.
+    """
+
+	# Create and run the agent
+	agent = Agent(task=task, llm=llm)
+	await agent.run()
+
+
+if __name__ == '__main__':
+	asyncio.run(main())

examples/getting_started/05_fast_agent.py

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+import asyncio
+import os
+import sys
+
+# Add the parent directory to the path so we can import browser_use
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+from browser_use import Agent, BrowserProfile
+
+# Speed optimization instructions for the model
+SPEED_OPTIMIZATION_PROMPT = """
+Speed optimization instructions:
+- Be extremely concise and direct in your responses
+- Get to the goal as quickly as possible
+- Use multi-action sequences whenever possible to reduce steps
+"""
+
+
+async def main():
+	# 1. Use fast LLM - Llama 4 on Groq for ultra-fast inference
+	from browser_use import ChatGroq
+
+	llm = ChatGroq(
+		model='meta-llama/llama-4-maverick-17b-128e-instruct',
+		temperature=0.0,
+	)
+	# from browser_use import ChatGoogle
+
+	# llm = ChatGoogle(model='gemini-3.1-flash-lite')
+
+	# 2. Create speed-optimized browser profile
+	browser_profile = BrowserProfile(
+		minimum_wait_page_load_time=0.1,
+		wait_between_actions=0.1,
+		headless=False,
+	)
+
+	# 3. Define a speed-focused task
+	task = """
+	1. Go to reddit https://www.reddit.com/search/?q=browser+agent&type=communities 
+	2. Click directly on the first 5 communities to open each in new tabs
+    3. Find out what the latest post is about, and switch directly to the next tab
+	4. Return the latest post summary for each page
+	"""
+
+	# 4. Create agent with all speed optimizations
+	agent = Agent(
+		task=task,
+		llm=llm,
+		flash_mode=True,  # Disables thinking in the LLM output for maximum speed
+		browser_profile=browser_profile,
+		extend_system_message=SPEED_OPTIMIZATION_PROMPT,
+	)
+
+	await agent.run()
+
+
+if __name__ == '__main__':
+	asyncio.run(main())