chore: import upstream snapshot with attribution

examples/features/fallback_model.py

@@ -0,0 +1,55 @@
+"""
+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())