ControlFlow vs OpenAI Agents SDK: Which Agent Framework to Use?

ControlFlow and OpenAI Agents SDK both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them.

OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

Pick ControlFlow if controlFlow's task-centric model is a genuinely different way to think about agent orchestration — define what you want, not how to get it. The Prefect integration adds real production value. But if your workflow is linear and your tasks are simple, plain function composition does the same job with less ceremony. The tradeoffs in its intro should match how your team already thinks about agents; OpenAI Agents SDK will feel like translation if they don't.

Pick OpenAI Agents SDK if the Agents SDK is the thinnest framework on this list — it barely abstracts beyond what you'd write yourself. Use it when you want OpenAI's conventions and auto-schema generation. Skip it when you want full control or use non-OpenAI models. The tradeoffs in its intro should match how your team already thinks about agents; ControlFlow will feel like translation if they don't.

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. ControlFlow has its own class hierarchy and tool registration conventions; OpenAI Agents SDK has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.