OpenAI Agents SDK Build Multi-Agent AI Systems in Python

What you'll learn

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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

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Basic Python. Comfortable with variables, functions, loops, and classes. No async/await experience required; it's introduced gradually as the course progresses.
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.
No prior experience with AI agents, LLM frameworks, or the OpenAI API required. This course starts from your very first agent.
Description

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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.
You will start with your first agent and end by building a complete multi-agent research assistant, live, line by line, in the course capstone. Along the way you will learn how to equip agents with function tools and hosted tools, stream responses in real time, route conversations between specialist agents with handoffs and triage logic, protect your pipelines with input and output guardrails, and debug and monitor everything you build using the SDK's built-in tracing and the OpenAI dashboard.
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
If you are a Python developer ready to move past single API calls and start building agents that actually coordinate, this course will take you there.
Who this course is for

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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
Anyone who has tried LangChain, CrewAI, or similar frameworks and wants to learn OpenAI's own first-party Python SDK instead
Homepage

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