mirror of
https://github.com/browser-use/browser-use
synced 2026-05-06 17:52:15 +02:00
fd5482d9cc
- Rearranged pages in the documentation for better organization. - Updated icons and descriptions in `browser-basic` and `chain-agents` sections for clarity. - Added new sections for "Secure Setup" and "More Examples" to provide comprehensive guidance on advanced use cases. - Improved the `fast-agent` example with clearer speed optimization instructions. - Refined the `browser-real-browser` documentation to simplify the connection description.
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
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-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())
|