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| """ | |
| AWS Bedrock Examples | |
| This file demonstrates how to use AWS Bedrock models with browser-use. | |
| We provide two classes: | |
| 1. ChatAnthropicBedrock - Convenience class for Anthropic Claude models | |
| 2. ChatAWSBedrock - General AWS Bedrock client supporting all providers | |
| Requirements: | |
| - AWS credentials configured via environment variables | |
| - boto3 installed: pip install boto3 | |
| - Access to AWS Bedrock models in your region | |
| """ | |
| import asyncio | |
| from browser_use import Agent | |
| from browser_use.llm import ChatAnthropicBedrock, ChatAWSBedrock | |
| async def example_anthropic_bedrock(): | |
| """Example using ChatAnthropicBedrock - convenience class for Claude models.""" | |
| print('🔹 ChatAnthropicBedrock Example') | |
| # Initialize with Anthropic Claude via AWS Bedrock | |
| llm = ChatAnthropicBedrock( | |
| model='us.anthropic.claude-sonnet-4-20250514-v1:0', | |
| aws_region='us-east-1', | |
| temperature=0.7, | |
| ) | |
| print(f'Model: {llm.name}') | |
| print(f'Provider: {llm.provider}') | |
| # Create agent | |
| agent = Agent( | |
| task="Navigate to google.com and search for 'AWS Bedrock pricing'", | |
| llm=llm, | |
| ) | |
| print("Task: Navigate to google.com and search for 'AWS Bedrock pricing'") | |
| # Run the agent | |
| result = await agent.run(max_steps=2) | |
| print(f'Result: {result}') | |
| async def example_aws_bedrock(): | |
| """Example using ChatAWSBedrock - general client for any Bedrock model.""" | |
| print('\n🔹 ChatAWSBedrock Example') | |
| # Initialize with any AWS Bedrock model (using Meta Llama as example) | |
| llm = ChatAWSBedrock( | |
| model='us.meta.llama4-maverick-17b-instruct-v1:0', | |
| aws_region='us-east-1', | |
| temperature=0.5, | |
| ) | |
| print(f'Model: {llm.name}') | |
| print(f'Provider: {llm.provider}') | |
| # Create agent | |
| agent = Agent( | |
| task='Go to github.com and find the most popular Python repository', | |
| llm=llm, | |
| ) | |
| print('Task: Go to github.com and find the most popular Python repository') | |
| # Run the agent | |
| result = await agent.run(max_steps=2) | |
| print(f'Result: {result}') | |
| async def main(): | |
| """Run AWS Bedrock examples.""" | |
| print('🚀 AWS Bedrock Examples') | |
| print('=' * 40) | |
| print('Make sure you have AWS credentials configured:') | |
| print('export AWS_ACCESS_KEY_ID=your_key') | |
| print('export AWS_SECRET_ACCESS_KEY=your_secret') | |
| print('export AWS_DEFAULT_REGION=us-east-1') | |
| print('=' * 40) | |
| try: | |
| # Run both examples | |
| await example_aws_bedrock() | |
| await example_anthropic_bedrock() | |
| except Exception as e: | |
| print(f'❌ Error: {e}') | |
| print('Make sure you have:') | |
| print('- Valid AWS credentials configured') | |
| print('- Access to AWS Bedrock in your region') | |
| print('- boto3 installed: pip install boto3') | |
| if __name__ == '__main__': | |
| asyncio.run(main()) | |