OpenAI Agents SDK - What You Need to Know Before Building

OpenAI released their Agents SDK a while back, and it's quietly become one of the more practical tools for building agentic applications. Not the flashiest announcement they've ever made, but the SDK itself is solid and worth understanding if you're building AI agents - even if you end up choosing a different framework.

I've spent time with both the Python and TypeScript versions across a few projects, and I want to share an honest assessment. What it does well, where it falls short, and how to think about it alongside the other options available right now.

What the Agents SDK Actually Is

At its simplest, the OpenAI Agents SDK is a library for building applications where an AI model can use tools, hand off to other specialised agents, stream partial results, and maintain a trace of everything that happened. That last point - tracing - is one of its underrated features.

The SDK is available in two flavours:

• Python (github.com/openai/openai-agents-python)
• TypeScript/JavaScript (openai.github.io/openai-agents-js)

Both provide the same core abstractions, so you can pick whichever matches your team's stack. If you're a .NET shop (as many of our clients are), you'll likely use the TypeScript version in a Node.js service or look at other frameworks - but more on that later.

The Core Concepts

The SDK is built around a few ideas that, once you understand them, make the whole thing click.

Agents are the main unit. An agent has a system prompt, a set of tools it can use, and rules about what it's allowed to do. Think of it as a configured persona with specific capabilities.

Tools are functions your agent can call. These could be API calls, database queries, file operations, or anything else you can wrap in a function. The SDK handles the plumbing of presenting tools to the model, parsing its tool calls, executing them, and feeding results back.

Handoffs let one agent pass control to another. This is where things get interesting for complex workflows. You might have a triage agent that understands the user's request and then hands off to a specialist agent - one for billing questions, another for technical support, a third for account management. Each specialist has its own tools and instructions.

Streaming gives you partial results as the model generates them. For user-facing applications, this is the difference between a blank screen for 10 seconds and a responsive experience.

Tracing records everything that happened during an agent run - which tools were called, what the model decided, where handoffs occurred. This is gold for debugging and for building confidence that your agent is behaving correctly.

What Works Well

The handoff pattern is genuinely useful. Most real-world agentic applications need some form of routing - not every query should go to the same set of tools and instructions. The SDK's handoff mechanism makes this straightforward without you having to build the orchestration from scratch. I've seen this work well for customer service applications where different types of enquiries need different handling.

Tracing saves hours of debugging. When an agent does something unexpected (and they will), being able to look at the full trace of decisions and tool calls is enormously helpful. Without tracing, debugging agent behaviour often turns into guesswork. The SDK bakes this in rather than making it an afterthought.

The abstraction level is right. Some agent frameworks try to do too much - they want to manage your prompts, your memory, your retrieval, your everything. The OpenAI SDK is more focused. It handles the agent loop, tool execution, and handoffs, and stays out of your way for everything else. You bring your own data layer, your own business logic, your own deployment strategy.

Streaming is well-implemented. The SDK's streaming support isn't just "dump tokens as they arrive." It gives you structured events for tool calls, handoffs, and completions alongside the streamed text. This makes it much easier to build responsive UIs that show users what the agent is doing.

Where It Falls Short

OpenAI lock-in. This is the obvious one. The Agents SDK works with OpenAI models. If you want to use Claude, Gemini, or open-source models, you'll need a different framework. For organisations that want model flexibility (and we recommend most of our clients maintain that flexibility), this is a real consideration.

No built-in memory management. The SDK handles individual conversations well, but long-term memory across sessions - remembering user preferences, past interactions, accumulated knowledge - is left entirely to you. That's fair enough for a focused SDK, but it means more work for production applications.

Limited workflow orchestration. For simple request-response patterns and basic handoffs, the SDK is great. But if you need complex workflows with conditional branching, parallel execution, approval gates, or human-in-the-loop steps, you'll outgrow it quickly. It's an agent SDK, not a workflow engine.

The ecosystem is still young. Compared to something like LangChain (which has its own problems, to be fair), the OpenAI Agents SDK has fewer integrations, fewer community examples, and less battle-testing in production. It's improving fast, but you should expect to build more things yourself.

How It Compares

The AI agent framework space is crowded right now, and picking the right one depends on your constraints.

If you're all-in on OpenAI models and want something well-integrated with their platform, the Agents SDK is probably the cleanest choice. The integration is tight, the API patterns are consistent, and you get features like tracing that work smoothly.

If you need model flexibility, look at frameworks that support multiple providers. Semantic Kernel (Microsoft's framework) is worth considering, especially if you're in the Azure ecosystem. It supports OpenAI, Azure OpenAI, and other providers.

If you need complex orchestration, the Agents SDK alone won't cut it. You'll either need to build orchestration on top of it or look at purpose-built workflow tools.

If you're building on Azure, the Azure AI Foundry and Semantic Kernel combination often makes more sense for enterprise deployments. You get model flexibility, enterprise security features, and better integration with the Microsoft stack.

Our Approach

At Team 400, we're model-agnostic in our recommendations. We've built production agents using OpenAI, Azure OpenAI, and Anthropic models depending on the use case and client requirements. The Agents SDK is one tool in the toolkit - a good one for certain scenarios, but not the only option.

For most Australian enterprise clients, we tend to recommend Azure AI Foundry as the foundation, with the specific agent framework chosen based on the use case. The OpenAI Agents SDK can work within that setup when you're using Azure OpenAI as the model provider, giving you the SDK's developer experience with Azure's enterprise features.

If you're exploring AI agent development for your organisation, whether that's customer-facing bots, internal automation, or something else entirely, our AI agent development team can help you evaluate the options and build something that actually works in production.

For the complete SDK documentation and getting started guides, check out the OpenAI Agents SDK docs for Python or the TypeScript version.