OpenAI Agents SDK: Build Multi-Agent AI Systems in Python

OpenAI Agents SDK: Build Multi-Agent AI Systems in Python
Instructor: Gopi Donthireddy

What you’ll learn

• Build and configure production-ready agents using the OpenAI Agents SDK in Python
• Equip agents with function tools, hosted tools, and structured outputs to complete real tasks
• Stream agent responses in real time and manage runs with RunConfig and context objects
• Orchestrate multi-agent systems using handoffs, triage routing, and parallel execution
• Protect agent pipelines with input and output guardrails that block invalid requests
• Debug and monitor agents using built-in tracing, the OpenAI dashboard, and lifecycle hooks
• Track token usage and cost across multi-agent runs to control spend in production
• Build a complete multi-agent research assistant combining tools, handoffs, guardrails, and tracing

Requirements

• A free OpenAI platform account with API access. Usage is pay-as-you-go and billed separately from Udemy; the full course typically costs only a few dollars in API calls.
• A free Google account for Google Colab. Every notebook runs in the browser; no local Python install, IDE, or virtual environment needed.
• Any computer with a modern browser and internet connection. No powerful hardware or GPU required.

Description

Build Production-Ready AI Agents Using the OpenAI Agents SDK in Python

Most tutorials stop at a single agent calling a single tool. This course goes the other ninety percent of the way: multi-agent orchestration, guardrails, structured outputs, real-time streaming, and full observability, all in Python, all built on OpenAI’s own official SDK.

Every lecture ships with a Google Colab notebook you run yourself. There is no local setup, no virtual environment, and no TypeScript; this course is Python only, from the SDK’s own official Python interface.

Here is what you will be able to do by the end of this course:

• Build and configure agents using the OpenAI Agents SDK, from your first agent through advanced configuration and structured outputs
• Equip agents with function tools, hosted tools, and multi-agent handoffs to complete real, multi-step tasks
• Stream agent responses in real time and manage runs with full control over configuration and context
• Orchestrate multi-agent systems using handoffs, triage routing, and parallel execution with asyncio
• Protect agent pipelines with input and output guardrails that block invalid requests before they cost you time or money
• Debug and monitor agents using the SDK’s built-in tracing, the OpenAI dashboard, and lifecycle hooks
• Track token usage and cost across multi-agent runs, a skill most courses skip entirely
• Build a complete multi-agent research assistant from scratch, combining every concept from the course into one capstone project

Who this course is for:

• Python developers who want to move beyond single LLM API calls and build autonomous, tool-using agents
• Backend or software engineers exploring agentic AI who want a structured, hands-on path instead of scattered tutorials
• Developers already using the OpenAI API directly who want a framework that manages the agent loop, tool calls, and handoffs for them
• AI or ML engineers moving from model training into agent orchestration, guardrails, and production observability

Homepage