"""
Example: Using a fallback LLM model.

When the primary LLM fails with rate limits (429), authentication errors (401),
payment/credit errors (402), or server errors (500, 502, 503, 504), the agent
automatically switches to the fallback model and continues execution.

Note: The primary LLM will first exhaust its own retry logic (typically 5 attempts
with exponential backoff) before the fallback is triggered. This means transient errors
are handled by the provider's built-in retries, and the fallback only kicks in when
the provider truly can't recover.

This is useful for:
- High availability: Keep your agent running even when one provider has issues
- Cost optimization: Use a cheaper model as fallback when the primary is rate limited
- Multi-provider resilience: Switch between OpenAI, Anthropic, Google, etc.

@dev You need to add OPENAI_API_KEY and ANTHROPIC_API_KEY to your environment variables.
"""

import asyncio
import os
import sys

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
from browser_use.llm import ChatAnthropic, ChatOpenAI

llm = ChatAnthropic(model='claude-sonnet-4-0')
fallback_llm = ChatOpenAI(model='gpt-4o')

agent = Agent(
	task='Go to github.com and find the browser-use repository',
	llm=llm,
	fallback_llm=fallback_llm,
)


async def main():
	result = await agent.run()
	print(result)

	# You can check if fallback was used:
	if agent.is_using_fallback_llm:
		print('Note: Agent switched to fallback LLM during execution')
		print(f'Current model: {agent.current_llm_model}')


if __name__ == '__main__':
	asyncio.run(main())