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Overview of Strands Agents
Strands Agents is an open-source SDK from AWS that simplifies the development of AI agents through a model-driven approach. It allows you to define a task-specific prompt and tools the agent can use, and relies on foundation models like Claude, LLaMA, or models available in Amazon Bedrock.
With Strands, developers can create intelligent agents that plan, reason, and reflect while completing tasks using minimal code. It is already used internally by several AWS services such as Amazon Q Developer and AWS Glue.
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Prerequisites
Before setting up your Strands Agent, ensure the following:
- AWS CLI installed and configured
- Active AWS account with Amazon Bedrock access
- Python 3.10+
- A virtual environment manager (e.g. venv)
Create Project Environment
Set up a Python virtual environment and install required packages over the terminal:
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<em>python3 -m venv venv</em> <em>source venv/bin/activate</em> <em>pip install strands-agents strands-agents-tools </em> |
4) Configure AWS Access
Make sure AWS credentials are configured properly using:
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<em>aws configure </em> |
Build and Run a Basic Strands Agent
In agent.py, define your agent:
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<em>import os</em> <em>import logging</em> <em># CRITICAL: Set AWS environment variables BEFORE importing strands</em> <em>os.environ['AWS_PROFILE'] = 'profile_name'</em> <em>os.environ['AWS_REGION'] = 'us-east-1' </em> <em> </em> <em>from strands import Agent</em> <em>from strands_tools import calculator, current_time, use_aws</em> <em># Create the agent with tools and specify the model</em> <em>agent = Agent(</em> <em> tools=[calculator, current_time,use_aws],</em> <em> model="anthropic.claude-3-5-sonnet-20240620-v1:0" # Specify your model</em> <em>)</em> <em> </em> <em> </em> <em># Define a message to test the agent</em> <em>message = """</em> <em>I have 3 requests:</em> |
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1<em> What is 7 * 8?</em>
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1<em> What is the time right now in india?</em>
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1<em> list count of s3 buckets in my aws account?</em>
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<em>"""</em> <em># Run the agent and handle any errors</em> <em>if __name__ == "__main__":</em> <em> try:</em> <em> print("Running the agent...")</em> <em> response = agent(message)</em> <em> print("\nAgent Response:")</em> <em> print(response)</em> <em> except Exception as e:</em> <em> print(f"Error: {e}")</em> <em> import traceback</em> <em> traceback.print_exc()</em> |
Run your agent on the terminal:
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<em>Python agent.py</em> |
Output:
Handle Common Errors
AccessDeniedException for Model ID
- Make sure you’re using a region where your AWS account has access to the Bedrock model.
- Double-check IAM permissions for Bedrock.
Region Configuration
- Ensure AWS_REGION is exported correctly before importing the strands module.
Conditional Write Error
- If you encounter an error like “pre-condition failed”, verify if you’re trying to overwrite an existing object in S3 via a conditional call — this doesn’t apply directly to Strands, but could arise if tools use S3.
Clean Up Resources
Once testing is done:
- Deactivate Bedrock Model access enabled
- Deactivate and remove your virtual environmentml
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WRITTEN BY Kamlesh N
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