🎯 Tutorial 1: Your First OpenAI Agent

Welcome to your first step in the OpenAI Agents SDK journey! This tutorial introduces you to the fundamental concept of creating a simple AI agent using OpenAI's Agents SDK.

🎯 What You'll Learn

• Basic Agent Creation: How to create your first OpenAI agent
• OpenAI SDK Workflow: Understanding the agent lifecycle
• Simple Text Processing: Basic input/output handling
• Agent Configuration: Essential parameters and settings

🧠 Core Concept: What is an OpenAI Agent?

An OpenAI agent is a programmable AI assistant that can:

• Process user inputs (text, voice, etc.)
• Use AI models (like GPT-4o) to understand and respond
• Perform specific tasks based on your instructions
• Return structured or unstructured responses

Think of it as creating a smart function that uses AI to handle complex tasks.

🔧 Key Components

1. Agent Class

The main building block for creating AI agents in OpenAI SDK:

from agents import Agent

2. Essential Parameters

• name: Unique identifier for your agent
• instructions: How your agent should behave
• model: The AI model to use (defaults to "gpt-4o")

3. Basic Workflow

1. Input: User sends a message
2. Processing: Agent uses AI model to understand and respond
3. Output: Agent returns a response

🚀 Tutorial Overview

This tutorial includes two focused agent examples:

1. Personal Assistant Agent (personal_assistant_agent/)

• Basic agent creation and configuration
• Simple instructions and role definition
• Core Agent class usage

2. Execution Demo Agent (execution_demo_agent/)

• Demonstrates different execution methods
• Sync, async, and streaming patterns
• Runner class usage examples

📁 Project Structure

1_starter_agent/
├── README.md                    # This file - concept explanation
├── requirements.txt             # Dependencies
├── personal_assistant_agent/    # Basic agent creation
│   ├── __init__.py
│   └── agent.py                # Simple agent definition (20 lines)
├── execution_demo_agent/        # Execution methods demonstration
│   ├── __init__.py
│   └── agent.py                # Sync, async, streaming examples
├── app.py                      # Streamlit web interface (optional)
└── env.example                 # Environment variables template

🎯 Learning Objectives

By the end of this tutorial, you'll understand:

• ✅ How to create a basic OpenAI agent
• ✅ Essential agent parameters and their purpose
• ✅ How to run agents synchronously and asynchronously
• ✅ Basic OpenAI SDK workflow and lifecycle
• ✅ How to use streaming responses

🚀 Getting Started

1. Set up your environment:

   # Make sure you have your OpenAI API key
   # Get your API key from: https://platform.openai.com/api-keys
   

2. Install OpenAI Agents SDK:

   pip install openai-agents
   

3. Install dependencies:

   # Install required packages
   pip install -r requirements.txt
   

4. Set up environment variables:

   # Copy the example environment file
   cp .env.example .env
   
   # Edit .env and add your OpenAI API key
   # OPENAI_API_KEY=sk-your_openai_key_here
   

5. Test the agent:

   # Run the agent directly
   python agent.py
   
   # Or run the Streamlit web interface
   streamlit run app.py
   

6. Try different execution methods:

   • Test synchronous execution: "What's the weather like today?"
   • Test asynchronous execution: "Tell me a story about AI"
   • Test streaming responses: "Explain machine learning in detail"

🧪 Sample Prompts to Try

• General Questions: "What's the capital of France?"
• Creative Tasks: "Write a short poem about technology"
• Problem Solving: "How can I improve my productivity?"
• Explanations: "Explain quantum computing in simple terms"

🔗 Next Steps

After completing this tutorial, you'll be ready for:

• Tutorial 2: Structured Output Agent - Learn to create type-safe, structured responses
• Tutorial 3: Tool Using Agent - Add custom tools and functions to your agent
• Tutorial 4: Runner Execution Methods - Master different execution patterns

💡 Pro Tips

• Start Simple: Begin with basic functionality and add complexity gradually
• Test Often: Try different prompts to understand agent behavior
• Read Instructions: Clear instructions lead to better agent behavior
• Experiment: Try different execution methods to see the differences

🚨 Troubleshooting

• API Key Issues: Make sure your .env file contains a valid OPENAI_API_KEY
• Import Errors: Ensure all dependencies are installed with pip install -r requirements.txt
• Rate Limits: If you hit rate limits, wait a moment before trying again