OpenAI Agents SDK Tutorial

The OpenAI Agents SDK is a software development kit that lets you control how AI agents run inside your own Python application. An "agent" here is an AI program that can plan actions, use tools, search the web, keep a conversation going across multiple turns, and correct itself. The SDK matters because it hands you the control panel for the agent loop — the cycle where the model thinks, calls a tool, gets a result, and thinks again. Without the SDK, you are just sending a message to a model and printing the reply. With the SDK, your application owns tool execution (running those external functions), approvals (allowing or blocking actions), state (remembering what happened), and traces (logs you can inspect). For example, a later lesson in the tutorial will look up orders and route hard cases using code you write, not just a chat response.

The SDK works best when you install the correct package. The right Python package is `openai-agents`, not `agents`. Setup also requires your API key to be loaded from a `.env` file using `load_dotenv`. When you see the machine is ready before the agent enters the story, that boring status is good — it means the foundation is solid. OpenAI also offers built-in tools like web search and file search, where you drop in a file and OpenAI handles embedding and indexing automatically. The SDK gives you a lightweight framework without a whole bunch of ceremony, making it practical for building real agents that do more than chat.

Start by installing the correct package: run `pip install openai-agents` (not the outdated "agents" package). The SDK is designed for orchestration (coordinating multi-step tasks) in your Python app, not just sending a single message. It lets your code own tool execution, approvals, state, and traces.

Create a minimal setup check in a file like `setup.py`. Import `load_dotenv` to make your local `.env` file visible, then use `os.getenv("OPENAI_API_KEY")` to verify your key is loaded. Import the `agents` package to confirm the SDK is on your path. Use `sys.exit(1)` to stop immediately if any check fails. The expected output should be "boring" — just confirmation messages.

To build an agent, create a file like `lib/agent.ts`. Import `ToolLoopAgent` and specify your model. If you want to use OpenAI, import the OpenAI provider. The SDK lets you augment context (extra information you pass into each request) and provide the agent with its first tool (a function the agent can call). For a practical starter, the agent can search the web, maintain conversation across turns, and call tools you define.

Common setup errors: using the wrong package name (must be `openai-agents`), or having the key file present but not loaded because `load_dotenv` is missing. The SDK supports streaming, where the API returns many events until a final output. For a beginner, focus on getting the setup script running before adding complexity. The SDK gives you control over the agent loop from your Python code, enabling you to route hard cases, look up data, and approve actions programmatically.

When starting with the OpenAI Agents SDK, beginners often hit two specific setup mistakes. First, running `pip install agents` installs the wrong package. The correct command is `pip install openai-agents`. Second, even if your API key file exists, it won’t load if you forget the `load_dotenv()` call. Use a small setup verifier script that checks each condition in order: confirm `load_dotenv()` ran, then `os.getenv("OPENAI_API_KEY")` returns a value, then `import agents` succeeds. If any check fails, call `sys.exit(1)` to stop immediately. This prevents guessing about downstream errors.

A common pitfall when building agents is forgetting to pass extra context or parameters into the model request. When you define a tool, the additional parameters you set on the agent side may not automatically forward to the OpenAI API call unless you explicitly include them in the tool definition. Double-check that your context augmentation is wired into the actual request payload.

Best practices include using the SDK when your application needs to own tool execution, approvals, state management, and tracing (recording agent steps). The SDK is designed for orchestration inside your Python app, not just sending a message and printing a reply. Also, consider separating your agent logic by responsibility. For example, one agent can look up orders, and another route hard cases in code. This mirrors the pattern of agent teams (multiple instances coordinating on tasks) seen in larger implementations. Finally, test your setup with a boring, silent pass — it means the machine is ready before the agent enters the story.