Fast Agent

import asyncio
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-2.5-flash')

	# 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())

# Groq - Ultra-fast inference
from browser_use import ChatGroq
llm = ChatGroq(model='meta-llama/llama-4-maverick-17b-128e-instruct')

# Google Gemini Flash - Optimized for speed
from browser_use import ChatGoogle
llm = ChatGoogle(model='gemini-2.5-flash')

browser_profile = BrowserProfile(
    minimum_wait_page_load_time=0.1,    # Reduce wait time
    wait_between_actions=0.1,           # Faster action execution
    headless=True,                      # No GUI overhead
)

agent = Agent(
    task=task,
    llm=llm,
    flash_mode=True,                    # Skip LLM thinking process
    extend_system_message=SPEED_PROMPT, # Optimize LLM behavior
)