OpenAI Swarm is best treated as an experimental, educational framework for learning how multiple agents can coordinate—not as the default choice for a new production system. Its most useful lesson is how to divide work between focused specialists: transfer control when a specialist should own the next response, or call a specialist as a tool when a manager should remain responsible for the final answer. For current OpenAI development, compare the Agents SDK, Agents API, and Responses API before choosing a runtime.
What OpenAI Swarm is—and how to think about it
Swarm is an open-source framework for exploring multi-agent orchestration: a design in which specialized agents handle different parts of a task. OpenAI characterized it as experimental and pointed developers toward its more developed Agents SDK. The announcement says, “Our new open-source Agents SDK simplifies orchestrating multi-agent workflows and offers significant improvements over Swarm, an experimental SDK we released last year.” OpenAI’s announcement uses “last year” relative to its own publication; check the page for its date rather than treating that phrase as a current calendar reference.
The practical value of Swarm is conceptual. It makes it easier to reason about which agent should act, what tools it can use, and when control should pass elsewhere. For a new application, verify the repository’s current status and documentation, then assess the current OpenAI options described below instead of assuming an older Swarm example remains supported or production-ready.
Choose how specialists participate
The central design choice is whether a specialist takes over the conversation or supplies bounded help to a manager. These patterns solve different problems; adding agents is useful only when branches genuinely need different instructions, tools, or policies.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11| Pattern | What happens | Use it when |
|---|---|---|
| Handoff | The current agent transfers control to a specialist, which continues the run and can own the next response. | The next branch needs a different agent to take responsibility, such as a focused specialist handling a distinct task. |
| Agents as tools | A manager calls a specialist for bounded assistance, then remains responsible for the user-facing reply. | You want a single manager to integrate specialist contributions and deliver the final answer. |
OpenAI’s handoff guidance and agent documentation describe these orchestration concepts. The distinction is about ownership, not a ranking: use a handoff when responsibility should move, and an agent-as-tool when it should stay with the manager.
Keep the team small and responsibilities clear
- Give each specialist a narrow, distinct responsibility.
- Provide the tools and policy that responsibility actually requires; avoid duplicating broad capabilities across every agent.
- Make handoff descriptions short and concrete so the routing decision is understandable.
- Do not split a task merely to increase the agent count. If one agent with tools can complete it, orchestration may add needless complexity.
How an Agents SDK run proceeds
In the OpenAI Agents SDK, the runner coordinates the loop rather than stopping after a single model response. It calls the model, executes requested tool calls, follows any handoff, and continues until no more work is required and an agent returns a final answer. The SDK’s run documentation explains this execution pattern.
- Start with an agent. The application supplies the agent and the input for the run.
- Call the model. The runner obtains the agent’s next response.
- Handle the next action. If the response requests a tool, the runner executes it; if it hands off, the runner switches to the receiving agent.
- Continue until completion. The runner returns a result when the work ends without another tool action or handoff.
This loop matters operationally: the model proposes actions, but the application’s runtime is responsible for carrying them out. How much visibility and control you need over that loop is one factor in choosing between OpenAI’s runtime options.
Where Swarm fits among OpenAI’s current runtime options
OpenAI’s runtime overview distinguishes the managed Agents API, the application-run Agents SDK, and integrations built more directly around the Responses API. They differ in where orchestration runs, who controls it, and how state is handled. The right choice depends on the application’s operational needs, not simply on whether it uses multiple agents.
| Option | Where orchestration runs | Who controls the loop and deployment | State and operational fit |
|---|---|---|---|
| Agents API | In OpenAI’s managed harness. | OpenAI provides the managed runtime; the application integrates with the service. | Consider it when a managed agent harness fits the application’s needs. Review the current API documentation for the specific state and operational behavior required. |
| Agents SDK | In the application’s runtime. | The developer integrates and runs the SDK, retaining control over deployment, tools, storage, approvals, and runtime behavior. | Consider it when code-first orchestration and application control over integrations and the execution environment matter. |
| Responses API integration | In application code around direct model calls. | The application has more direct control over model calls and agent behavior, and implements more of the orchestration itself. | Consider it when the application needs that degree of control and is prepared to own the surrounding loop and state handling. |
These are different responsibility boundaries, not interchangeable labels for one runtime. Before choosing, map the application’s requirements for tools, approvals, persistent state, deployment, and execution visibility to the control the option provides. Consult the live OpenAI agents guide and runtime documentation for current capabilities.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to design a multi-agent workflow
- Describe the task as a single workflow first. Identify the outcome the user needs and the steps that can be handled by one agent with tools.
- Find real boundaries. Split out a specialist only when a branch needs distinct instructions, tools, or policy.
- Decide who owns the next response. Choose a handoff if the specialist should take over; choose an agent-as-tool if a manager should synthesize the work and answer.
- Constrain each specialist. State its responsibility clearly and expose only relevant capabilities.
- Select the runtime deliberately. Decide whether a managed harness, application-run SDK, or more direct API integration best matches your control and operational requirements.
- Validate the implementation against current documentation. Check package versions, installation instructions, API signatures, repository maintenance, and supported runtime behavior before adapting an example.
The framework choice and the workflow design are separate decisions. A sound handoff or manager-and-tools structure can be evaluated independently of which runtime executes it.
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