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| """ | |
| Example of using LangChain models with browser-use. | |
| This example demonstrates how to: | |
| 1. Wrap a LangChain model with ChatLangchain | |
| 2. Use it with a browser-use Agent | |
| 3. Run a simple web automation task | |
| @file purpose: Example usage of LangChain integration with browser-use | |
| """ | |
| import asyncio | |
| from langchain_openai import ChatOpenAI # pyright: ignore | |
| from browser_use import Agent | |
| from examples.models.langchain.chat import ChatLangchain | |
| async def main(): | |
| """Basic example using ChatLangchain with OpenAI through LangChain.""" | |
| # Create a LangChain model (OpenAI) | |
| langchain_model = ChatOpenAI( | |
| model='gpt-4.1-mini', | |
| temperature=0.1, | |
| ) | |
| # Wrap it with ChatLangchain to make it compatible with browser-use | |
| llm = ChatLangchain(chat=langchain_model) | |
| # Create a simple task | |
| task = "Go to google.com and search for 'browser automation with Python'" | |
| # Create and run the agent | |
| agent = Agent( | |
| task=task, | |
| llm=llm, | |
| ) | |
| print(f'π Starting task: {task}') | |
| print(f'π€ Using model: {llm.name} (provider: {llm.provider})') | |
| # Run the agent | |
| history = await agent.run() | |
| print(f'β Task completed! Steps taken: {len(history.history)}') | |
| # Print the final result if available | |
| if history.final_result(): | |
| print(f'π Final result: {history.final_result()}') | |
| return history | |
| if __name__ == '__main__': | |
| print('π Browser-use LangChain Integration Example') | |
| print('=' * 45) | |
| asyncio.run(main()) | |