Getting Started with Strands Agents

A comprehensive hands-on course for learning AI agent development using the Strands Agents framework. This repository contains 6 progressive labs that teach you how to build, deploy, and monitor AI agents with advanced capabilities.

🎯 Course Overview

This course covers the complete journey of AI agent development, from basic usage to advanced topics like agent-to-agent (A2A) communication and observability. You'll learn to work with:

• Strands Agents Framework - Build intelligent AI agents
• Model Context Protocol (MCP) - Enable tool integration
• Agent-to-Agent Communication - Create multi-agent systems
• Observability & Evaluation - Monitor and improve agent performance

📚 Lab Structure

Lab 1: Basic Agent Usage

Files: basic-use.py, http-tool-use.py, system-prompt-use.py

Learn the fundamentals of creating and using Strands agents:

• Basic agent initialization and usage
• System prompt customization
• HTTP tool integration

Lab 2: Model Providers & Configuration

Files: anthropic-model-provider.py, anthropic-pet-breed-agent.py, bedrock-default-config.py, bedrock-detailed-config.py

(Certain portions of code in this lab require a pre-existing AWS account to make use of the 'generate_image' tool.)

Explore different model providers and configuration options:

• Anthropic model integration
• Amazon Bedrock model configuration

Lab 3: 'use_aws' Tool Integration

Files: aws-tool-use.py

(The code in this lab requires a pre-existing AWS account to properly utilize the 'use_aws' tool. An example Amazon DynamoDB Table is used to generate results when querying a table.)

Learn to integrate AWS services with your Strands agents:

• AWS service tool usage
• Examples using Amazon S3 and Amazon DynamoDB

Lab 4: Model Context Protocol (MCP) as Tools

Files: mcp-and-tools.ipynb, mcp_calulator.py

Deep dive into the Model Context Protocol:

• MCP server creation
• Tool definition and usage
• Calculator and Weather agents examples
• Interactive Jupyter notebook tutorial

Lab 5: Agent-to-Agent Communication

Files: a2a-communication.ipynb, run_a2a_system.py, employee_data.py, employee-agent.py, hr-agent.py

Build multi-agent systems with inter-agent communication:

• A2A communication patterns
• Employee/HR agent system example
• MCP server for data sharing
• REST API integration

Lab 6: Observability and Evaluation

Files: observability-with-langfuse-and-evaluation-with-ragas.ipynb, restaurant-data/

Monitor and evaluate agent performance:

• Restaurant recommendation agent example
• LangFuse integration for observability
• RAGAS evaluation framework
• Performance metrics and tracing

📖 Learning Path

1. Start with Lab 1 - Get familiar with basic agent creation
2. Progress through Labs 2-3 - Learn model providers and AWS integration
3. Master Lab 4 - Understand MCP and tool integration
4. Build with Lab 5 - Create multi-agent systems
5. Monitor with Lab 6 - Implement observability and evaluation

🔧 Configuration

The course uses several configuration approaches:

• Environment variables - API keys and sensitive data
• Inline configuration - Within individual lab files

🚀 Quick Start

Prerequisites

• Python 3.8+
• Virtual environment (recommended)
• API keys for:
  • Anthropic Claude (recommended)
  • Amazon Bedrock (for Lab 1-3)
  • LangFuse (for Lab 6)

Installation

1. Clone the repository:

   git clone https://github.com/aws-samples/sample-getting-started-with-strands-agents-course.git

2. Create and activate virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Running the Labs

Each lab can be run independently. Start with Lab 1 for the basics:

Lab 1-3 - Basic Usage

cd Lab1
python basic-use.py

Lab 4 - MCP Calculator (interactive)

cd Lab4

Follow the steps in mcp-and-tools.ipynb

Lab 5 - A2A System

cd Lab5

Follow the steps in a2a-communication.ipynb

Lab 6 - Observability and Evaluation

cd Lab6

Follow the steps in observability-with-langfuse-and-evaluation-with-ragas.ipynb

📝 Additional Resources

• Strands Documentation
• Model Context Protocol
• LangFuse Documentation
• RAGAS Documentation
• Setting up AWS CLI

🐛 Troubleshooting

Common issues and solutions:

1. API Key Issues - Ensure all required API keys are set in your environment
2. Port Conflicts - Labs use ports 8000-8002, ensure they're available
3. Dependencies - Run pip install -r requirements.txt if you encounter import errors
4. MCP Server - Allow time for servers to start before connecting clients

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Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.