OpenAI Agents SDK Crash Course

Complete tutorial series for mastering OpenAI Agents SDK from basics to production

OpenAI Agents SDK Crash Course

A comprehensive tutorial series for learning OpenAI’s Agents SDK from zero to hero. Build powerful AI agents with OpenAI’s cutting-edge framework, from simple text processing to advanced voice-enabled multi-agent systems.

What is OpenAI Agents SDK?

OpenAI Agents SDK is a powerful framework for developing and deploying AI agents that provides:

Agent Orchestration

Create and manage intelligent AI agents with sophisticated workflows and coordination patterns.

Tool Integration

Extend agents with custom functions and built-in tools like WebSearch, CodeInterpreter, and FileSearch.

Structured Outputs

Type-safe responses using Pydantic models for reliable data extraction and validation.

Multi-Agent Workflows

Coordinate multiple specialized agents with handoffs, delegation, and parallel execution.

Real-time Execution

Support for sync, async, and streaming execution methods for any use case.

Voice Integration

Static, streaming, and realtime voice capabilities for conversational AI applications.

Session Management

Automatic conversation memory and history with SQLiteSession.

Production Ready

Built-in tracing, guardrails, monitoring, and observability tools.

Complete Learning Path

This crash course covers 11 comprehensive tutorials organized into progressive layers:

🌱 Foundation Layer

Build your understanding of core concepts:

1. Tutorial 1: Your First Agent

   Starter Agent - Create your first OpenAI agent

   Learn basic agent creation, configuration, and execution methods. Understand the agent lifecycle and simple text processing.

   from agents import Agent
   
   agent = Agent(
       name="Personal Assistant",
       instructions="You are a helpful personal assistant."
   )
   

   What you’ll build: Personal assistant agents with sync, async, and streaming execution

2. Tutorial 2: Structured Outputs

   Type-Safe Responses - Work with Pydantic models

   Convert unstructured AI responses into validated, structured JSON data. Perfect for building reliable integrations.

   from pydantic import BaseModel
   from enum import Enum
   
   class Priority(str, Enum):
       LOW = "low"
       MEDIUM = "medium"
       HIGH = "high"
   
   class SupportTicket(BaseModel):
       customer_name: str
       priority: Priority
       issue_description: str
   

   What you’ll build:

   • Support ticket extractor
   • Product review analyzer
   • Email generator with structured metadata

🔧 Core Capabilities Layer

Extend agents with powerful features:

1. Tutorial 3: Tool-Using Agents

   Agent Tools & Functions - Extend agent capabilities

   Add custom functions and built-in tools to give your agents real-world superpowers:

   from agents import function_tool
   
   @function_tool
   def calculate_compound_interest(
       principal: float, 
       rate: float, 
       time: int
   ) -> float:
       """Calculate compound interest"""
       return principal * (1 + rate) ** time
   
   agent = Agent(
       name="Calculator",
       tools=[calculate_compound_interest]
   )
   

   Built-in tools available:

   • WebSearchTool: Real-time web search
   • CodeInterpreterTool: Execute Python code safely
   • FileSearchTool: Search through uploaded documents

   What you’ll build:

   • Custom function tools for calculations
   • Research agent with web search
   • Data analysis agent with code execution
   • Agents as tools orchestration
2. Tutorial 4: Running Agents

   Execution Mastery - Master the agent execution loop

   Deep dive into how agents run, make decisions, and handle different execution patterns:

   The Agent Loop:

   1. Receive user input
   2. LLM processes and decides actions
   3. Execute tools if needed
   4. Generate response
   5. Handle handoffs or continue

   Execution Methods:

   # Synchronous
   result = Runner.run_sync(agent, "Hello")
   
   # Asynchronous
   result = await Runner.run(agent, "Hello")
   
   # Streaming
   async for event in Runner.run_streamed(agent, "Hello"):
       print(event.content)
   

   What you’ll learn:

   • Advanced streaming events
   • Run configuration and customization
   • Conversation management
   • Exception handling
3. Tutorial 5: Context Management

   State & Context Handling - Manage conversation flow

   Learn to maintain state across interactions, pass context between runs, and control conversation flow:

   # Pass context to agent
   result = Runner.run_sync(
       agent,
       "Continue the story",
       context={"previous_chapter": chapter_1}
   )
   

   What you’ll build: Stateful agents that remember context across multiple interactions

🧠 Advanced Features Layer

Implement sophisticated functionality:

1. Tutorial 6: Guardrails & Validation

   Safety & Validation - Add protective boundaries

   Implement input validation and output filtering for safe, reliable agents:

   from agents import Agent
   
   agent = Agent(
       name="Safe Agent",
       input_guardrails=[validate_user_input],
       output_guardrails=[filter_sensitive_data]
   )
   

   Guardrail types:

   • Input guardrails: Validate user inputs before processing
   • Output guardrails: Filter and validate agent responses
   • Custom validators: Business rule enforcement

   What you’ll build: Agents with content filtering, validation rules, and safety boundaries

2. Tutorial 7: Sessions & Memory

   Session Management - Automatic conversation history

   Use SQLiteSession for persistent conversation memory:

   from agents import SQLiteSession
   
   session = SQLiteSession("conversations.db")
   result = Runner.run_sync(
       agent,
       "What did we discuss yesterday?",
       session=session
   )
   

   Features:

   • Automatic conversation history
   • Memory operations (update, delete, correct)
   • Multiple session management
   • Conversation organization

   What you’ll build:

   • Agents with persistent memory
   • Multi-session conversation managers
   • Interactive Streamlit session interface

🤝 Multi-Agent Layer

Orchestrate complex agent workflows:

1. Tutorial 8: Handoffs & Delegation

   Agent-to-Agent Task Delegation - Specialized agent teams

   Create triage systems where agents intelligently delegate to specialists:

   from agents import Agent, handoff
   
   billing_agent = Agent(
       name="Billing Specialist",
       instructions="Handle billing inquiries"
   )
   
   support_agent = Agent(
       name="Tech Support",
       instructions="Handle technical issues"
   )
   
   triage_agent = Agent(
       name="Triage",
       instructions="Route customers to specialists",
       handoffs=[billing_agent, support_agent]
   )
   

   Advanced handoff features:

   • Custom tool names and descriptions
   • Input filtering for context control
   • Handoff callbacks for logging
   • Structured input data passing

   What you’ll build:

   • Customer support triage system
   • Advanced handoff with callbacks
   • Multi-specialist coordination
2. Tutorial 9: Multi-Agent Orchestration

   Complex Workflows - Parallel and coordinated execution

   Parallel Execution:

   import asyncio
   
   # Run multiple agents in parallel
   results = await asyncio.gather(
       Runner.run(translator_agent, "Hello"),
       Runner.run(analyzer_agent, data),
       Runner.run(writer_agent, topic)
   )
   

   Agents as Tools Pattern:

   # Use specialized agents as tools
   orchestrator = Agent(
       name="Orchestrator",
       tools=[
           translator.as_tool("translate"),
           analyzer.as_tool("analyze")
       ]
   )
   

   What you’ll build:

   • Parallel multi-agent research systems
   • Agent orchestration with agents-as-tools
   • Multi-stage workflow coordination

🔍 Production Layer

Prepare for real-world deployment:

1. Tutorial 10: Tracing & Observability

   Monitoring & Debugging - Production visibility

   Built-in tracing for execution visualization:

   from agents import Agent, Runner, trace
   
   # Default tracing
   with trace():
       result = Runner.run_sync(agent, "Process this")
   
   # Custom traces and spans
   with trace(name="custom_workflow") as t:
       with t.span("research_phase"):
           research = await Runner.run(researcher, query)
       with t.span("analysis_phase"):
           analysis = await Runner.run(analyzer, research)
   

   What you’ll learn:

   • Built-in execution tracing
   • Custom traces for complex workflows
   • Performance monitoring
   • Debugging multi-agent systems

🎙️ Voice & Advanced Features

1. Tutorial 11: Voice Agents

   Real-time Conversation - Voice-enabled agents

   Three voice processing modes:

   1. Static Voice Processing:

   # Turn-based voice interaction
   audio_input = record_audio()
   result = Runner.run_sync(voice_agent, audio_input)
   play_audio(result.audio_output)
   

   2. Streaming Voice:

   # Real-time conversation with streaming
   async for event in Runner.run_streamed(voice_agent, audio):
       if event.audio_chunk:
           play_audio_chunk(event.audio_chunk)
   

   3. Realtime Voice (Ultra-Low Latency):

   # WebSocket-based realtime conversation
   async with RealtimeSession() as session:
       await session.connect(voice_agent)
       # Ultra-low latency voice conversation
   

   What you’ll build:

   • Turn-based voice assistants
   • Streaming voice conversation apps
   • Realtime voice agents with WebSocket
   • Speech-to-text and text-to-speech pipelines

Quick Start

1. Set Up Environment

   Install Python 3.8+ and get your OpenAI API key:

   # Get API key from:
   # https://platform.openai.com/api-keys
   
2. Install OpenAI Agents SDK

   pip install openai-agents
   
3. Create Your First Agent

   from agents import Agent, Runner
   
   # Create agent
   agent = Agent(
       name="Personal Assistant",
       instructions="You are a helpful assistant."
   )
   
   # Run agent
   result = Runner.run_sync(agent, "Hello!")
   print(result.final_output)
   
4. Follow the Tutorials

   Start with Tutorial 1 and progress through the learning path sequentially for the best experience.

Tutorial Structure

Each tutorial follows a consistent, learner-friendly structure:

README.md

Detailed concept explanations, learning objectives, and “why” behind each feature

Python Files

Working implementations with clear, commented code examples you can run immediately

Interactive Interfaces

Streamlit web apps for hands-on testing and experimentation

Submodules

Organized examples for different concepts and variations

Requirements & Setup

Clear dependencies and environment setup instructions

Key Features & Capabilities

Agent Execution Methods

Synchronous

Perfect for simple scripts and blocking operations:

result = Runner.run_sync(agent, "Process this")
print(result.final_output)

Asynchronous

For concurrent operations and better performance:

result = await Runner.run(agent, "Process this")
print(result.final_output)

Streaming

For real-time, progressive responses:

async for event in Runner.run_streamed(agent, "Tell me a story"):
    print(event.content, end="")

Tool Integration Patterns

Custom Function Tool

from agents import function_tool

@function_tool
def get_weather(city: str, units: str = "metric") -> str:
    """Get current weather for a city"""
    # API call logic
    return f"Weather in {city}: 72°F, Sunny"

agent = Agent(
    name="Weather Assistant",
    tools=[get_weather]
)

from agents import Agent
from agents.tools import WebSearchTool, CodeInterpreterTool

agent = Agent(
    name="Research Assistant",
    tools=[
        WebSearchTool(),
        CodeInterpreterTool()
    ]
)

from agents import Agent

translator = Agent(name="Translator")
analyzer = Agent(name="Analyzer")

orchestrator = Agent(
    name="Orchestrator",
    tools=[
        translator.as_tool("translate"),
        analyzer.as_tool("analyze")
    ]
)

Real-World Applications

By completing this course, you’ll be able to build:

Customer Support Systems

Multi-agent triage systems with specialized support agents for different departments

Research Assistants

Agents that search the web, analyze data, and generate comprehensive reports

Data Processing Pipelines

Extract structured data from unstructured inputs with type-safe validation

Voice Applications

Build voice-enabled assistants with real-time conversation capabilities

Content Generation

Automated content creation with quality validation and structured outputs

Multi-Agent Workflows

Complex systems where multiple specialized agents collaborate to solve problems

Prerequisites

Required: Python 3.8+ (Python 3.9+ for voice features), OpenAI API key, basic Python knowledge

Helpful but not required: Familiarity with async/await patterns, API concepts, JSON data structures

Environment Setup

Each tutorial requires your OpenAI API key. Create a .env file:

.env

OPENAI_API_KEY=sk-your_openai_key_here

Get your API key from OpenAI Platform

Learning Tips

1. Start Sequential

   Follow tutorials in order - each builds on previous concepts

2. Experiment Freely

   Modify code, try different prompts, and see what happens

3. Use Web Interfaces

   Interactive Streamlit apps make learning more engaging

4. Read Error Messages

   They often contain helpful guidance for troubleshooting

5. Join Community

   Engage with other learners and share experiences

Common Issues & Solutions

API Key Problems

• Ensure .env file is in the tutorial directory
• Verify API key is valid and has sufficient credits
• Check for typos in environment variable name

Import Errors

• Install requirements: pip install -r requirements.txt
• Verify Python 3.8+ (3.9+ for voice features)
• Try creating a virtual environment

Rate Limiting

• OpenAI has rate limits based on your plan
• Wait before retrying if you hit limits
• Consider upgrading your OpenAI plan

Progress Tracker

Track your learning journey:

• Tutorial 1: Basic agent creation ✨
• Tutorial 2: Structured outputs with Pydantic
• Tutorial 3: Tool integration and custom functions
• Tutorial 4: Execution methods mastery
• Tutorial 5: Context and state management
• Tutorial 6: Guardrails and validation
• Tutorial 7: Sessions and memory management
• Tutorial 8: Agent handoffs and delegation
• Tutorial 9: Multi-agent orchestration
• Tutorial 10: Tracing and observability
• Tutorial 11: Voice agents and real-time conversation 🎯

Additional Resources

Official Documentation

OpenAI Agents SDK comprehensive documentation

OpenAI Platform

API keys, usage dashboard, and model information

Pydantic Docs

Learn more about data validation and schemas

Streamlit Docs

Build interactive web interfaces for your agents

Next Steps

Start Learning

Set up your environment and begin with Tutorial 1: Your First Agent

Happy learning! 🚀