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README.md

layout default
title OpenAI Agents Tutorial
nav_order 201
has_children true
format_version v2

OpenAI Agents Tutorial: Building Production Multi-Agent Systems

OpenAI Agents SDKView Repo is the official OpenAI framework for building multi-agent systems in Python. As the production successor to Swarm, it provides first-class primitives for agent handoffs, tool use, guardrails, streaming, and tracing — all backed by the OpenAI API and designed for real-world deployment.

The SDK embraces a minimalist, opinionated design: agents are defined declaratively with instructions, tools, and handoff targets, while the Runner orchestrates the agentic loop, model calls, and tool execution behind a clean async interface.

Mental Model

flowchart TD
    A[User Input] --> B[Runner]
    B --> C[Agent]
    C --> D{Decision}
    D -->|Tool Call| E[Tool Execution]
    D -->|Handoff| F[Target Agent]
    D -->|Final Answer| G[Output]
    E --> C
    F --> H[New Agent Loop]
    H --> D

    C --> I[Guardrails]
    I -->|Pass| D
    I -->|Trip| J[Early Exit]

    B --> K[Tracing]
    K --> L[OpenAI Dashboard]

    classDef input fill:#e1f5fe,stroke:#01579b
    classDef core fill:#f3e5f5,stroke:#4a148c
    classDef execution fill:#fff3e0,stroke:#ef6c00
    classDef output fill:#e8f5e8,stroke:#1b5e20
    classDef safety fill:#fce4ec,stroke:#c2185b

    class A input
    class B,C,H core
    class D,E,F,K,L execution
    class G output
    class I,J safety
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Why This Track Matters

The OpenAI Agents SDK is increasingly the default choice for Python developers building multi-agent applications on the OpenAI platform. Latest Release: The SDK has matured rapidly since its March 2025 launch, with built-in support for GPT-4.1, streaming events, guardrail validation, and first-party tracing integrated with the OpenAI dashboard.

This track focuses on:

  • understanding the agent primitive and its declarative configuration
  • mastering tool integration and function calling patterns
  • building multi-agent systems with handoffs and routing
  • implementing guardrails for input/output safety
  • using streaming, tracing, and observability for production systems

Chapter Guide

Welcome to your journey through the OpenAI Agents SDK! This tutorial takes you from first install to production-grade multi-agent systems.

  1. Chapter 1: Getting Started - Installation, configuration, and your first agent
  2. Chapter 2: Agent Architecture - The Agent primitive, instructions, models, and lifecycle
  3. Chapter 3: Tool Integration - Function tools, hosted tools, and custom integrations
  4. Chapter 4: Agent Handoffs - Routing between agents, escalation, and specialization
  5. Chapter 5: Guardrails & Safety - Input/output validation and tripwire patterns
  6. Chapter 6: Streaming & Tracing - Real-time events, spans, and the tracing dashboard
  7. Chapter 7: Multi-Agent Patterns - Orchestrator, pipeline, and parallel agent topologies
  8. Chapter 8: Production Deployment - Scaling, error handling, cost control, and monitoring

Current Snapshot (auto-updated)

What You Will Learn

By the end of this tutorial, you'll be able to:

  • Build intelligent agents with declarative instructions, tools, and handoffs
  • Orchestrate multi-agent systems using handoffs, routing, and specialization
  • Implement safety guardrails for input validation and output filtering
  • Integrate tools including function tools, code interpreter, and web search
  • Stream agent responses with fine-grained event handling
  • Trace and debug agent runs using built-in tracing and the OpenAI dashboard
  • Design production architectures with error recovery, cost controls, and monitoring
  • Apply proven patterns for orchestrator, pipeline, and parallel agent topologies

Prerequisites

  • Python 3.9+ (3.11+ recommended)
  • An OpenAI API key with access to GPT-4o or later models
  • Basic understanding of async/await in Python
  • Familiarity with LLM concepts (prompts, tool calling, function calling)

What's New

Production Successor to Swarm: The OpenAI Agents SDK brings Swarm's lightweight agent-handoff philosophy into a production-grade framework with built-in tracing, guardrails, and streaming.

Stars License: MIT Python

Key features:

  • Agent Handoffs: First-class primitive for routing between specialized agents
  • Guardrails: Input and output validation with tripwire abort patterns
  • Streaming: Fine-grained event stream for real-time UIs
  • Tracing: Built-in OpenTelemetry-compatible tracing with OpenAI dashboard integration
  • Tool Use: Function tools, hosted tools (code interpreter, web search), and agents-as-tools

Learning Path

Beginner Track

Perfect for developers new to multi-agent systems:

  1. Chapters 1-2: Setup and agent fundamentals
  2. Focus on understanding the agent lifecycle and Runner

Intermediate Track

For developers building agent applications:

  1. Chapters 3-5: Tools, handoffs, and guardrails
  2. Learn to build interconnected multi-agent workflows

Advanced Track

For production multi-agent system development:

  1. Chapters 6-8: Streaming, tracing, patterns, and deployment
  2. Master enterprise-grade agent orchestration

Ready to build multi-agent systems with OpenAI? Let's begin with Chapter 1: Getting Started!

Related Tutorials

Navigation & Backlinks

Generated by AI Codebase Knowledge Builder

Full Chapter Map

  1. Chapter 1: Getting Started
  2. Chapter 2: Agent Architecture
  3. Chapter 3: Tool Integration
  4. Chapter 4: Agent Handoffs
  5. Chapter 5: Guardrails & Safety
  6. Chapter 6: Streaming & Tracing
  7. Chapter 7: Multi-Agent Patterns
  8. Chapter 8: Production Deployment

Source References