chore: import upstream snapshot with attribution

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())