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To coordinate state across AI agents, decide separately who chooses the next step and what information survives between steps. Then define who owns that information, which workers may access it, and how a run resumes after a wait, retry, or process restart. Those choices—not a single framework or storage product—determine whether a multi-step agent workflow stays coherent.
What state coordination means in an agent workflow
State coordination is the set of design decisions that keeps a workflow’s control flow and information aligned. In a multi-agent application, that means specifying:
- Control: which step or agent runs next, and who makes that decision.
- State: what information is carried forward, and what is deliberately left out.
- Ownership: which component is authoritative for each piece of state.
- Sharing: which agents, workers, or services can read or update it.
- Recovery: what happens when execution pauses, fails, or outlives the process that started it.
These are related choices, but they are not interchangeable. Orchestration determines the path through a workflow; persistence determines what data remains available along that path or after an interruption.
Choose control flow independently from persistence
OpenAI’s Agents SDK documentation describes model-directed and code-directed orchestration, and notes that “You can mix and match these patterns.” That statement concerns how the workflow is routed; it does not mean that orchestration itself guarantees durable state.
#1 Best Overall
| Orchestration approach | Who selects the next step? | When it may fit | Design consideration |
|---|---|---|---|
| Model-directed | The model has more discretion to choose or route work. | Tasks whose next action depends on open-ended interpretation. | Keep safety and business constraints explicit; decide which routing decisions the model may make. |
| Code-directed | Application code defines the workflow path. | Fixed or inspectable sequences and rules. | Application code controls transitions, but state still needs an explicit owner and persistence plan. |
| Mixed | Code and model reasoning each control selected parts of the flow. | Workflows with both defined guardrails and flexible task routing. | Make the boundary clear: specify which decisions are delegated and which remain application-controlled. |
As an implementation rule of thumb, prefer explicit code control for fixed safety or business rules, and consider model-directed routing when the task is open-ended. This is a design inference from the documented control distinction, not a measured performance comparison.
Decide what each state object represents
Before choosing storage, name the lifecycle and identity of each state object. A conversation, a workflow run, an agent handoff, and durable business data are different scopes. Treating them as one undifferentiated history can make it unclear which worker may update what, or whether two simultaneous runs are sharing mutable data unintentionally.
- Conversation state represents information associated with a user conversation.
- Run state represents progress and data for one execution of a workflow.
- Handoff state contains the context a receiving agent needs to perform its task.
- Business data is durable application data whose lifecycle may extend beyond a conversation or run.
These categories are a useful design boundary, not a universal schema prescribed by the SDK documentation. Define stable identifiers and ownership in your application, especially when concurrent runs or multiple workers are involved.
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OpenAI’s Agents SDK documentation describes multiple persistence strategies and recommends choosing one strategy per conversation. That is a useful default: give each conversation a clear persistence authority rather than layering mechanisms without a specific reason. The documented choices differ in who manages the history and where continuation data lives.
Rank #3
| Approach | State owner | Documented options or scope | Key decision |
|---|---|---|---|
| Application-managed history | Your application and its storage. | The application controls how history is stored and passed along. | Use when your application needs to own the storage and history lifecycle; plan for access by every worker that needs the data. |
| SDK session | The SDK session mechanism, backed by a selected storage option. | Documented options include SQLite, Redis, a Dapr state store, and OpenAI-hosted storage. | Choose based on runtime, storage ownership, and whether the session must be shared across workers. Verify current provider and runtime requirements in the SDK documentation. |
| Responses API continuation | Responses API resources. | The Responses API offers conversation and response-continuation options. | Confirm which API resource matches the intended lifecycle and application design; these resources are not interchangeable with an SDK session. |
Do not assume that an OpenAI conversation object is the same thing as an SDK session, a response continuation, or a sandbox. They represent distinct resources and should be selected according to the state you need to preserve. The SDK and API documentation retrieved on October 7, 2026 describe these capabilities; check the current documentation for the version and environment you deploy.
Plan for sharing between workers
A state mechanism that works for one process may not suit a workflow whose next step runs on another worker. Evaluate sharing as a first-class requirement rather than assuming that persistence automatically makes state available everywhere.
- Identify which worker or service can read and write each state object.
- Decide whether a worker receives a complete history or only the context needed for its handoff.
- Specify which component may update authoritative state, and how other components learn that an update occurred.
- Check whether the chosen storage and runtime support the access pattern your deployment needs.
- Keep per-run or per-conversation state separate from shared business data unless their lifecycles genuinely match.
The documented SDK session options include local or storage-backed choices, as well as OpenAI-hosted storage. Their presence does not establish that every choice has the same sharing behavior or runtime requirements. Confirm those details for the particular option you plan to use.
Design recovery around waits, retries, and restarts
If a workflow can wait for a person, an external system, or a later event, ordinary continuation may not be enough. Work that must resume after retries, process restarts, or long pauses needs an explicit recovery model: determine what progress is recorded, how the workflow identifies the correct run, and how it avoids repeating or losing work when resumed.
Best Value
The OpenAI Agents SDK guide names Dapr, Temporal, and Restate integrations for durable-execution use cases. LangGraph documents persistence and durable execution for stateful, long-running workflows. These are documented capabilities, not an apples-to-apples comparison: the cited sources provide no common reliability or latency measurements. Check each integration’s current documentation for its status, capabilities, and compatibility before choosing it.
A useful distinction is whether the application only needs to continue a conversation or whether it must resume a workflow across operational interruptions. Conversation continuation preserves conversational context; a durable workflow design addresses the execution lifecycle. Do not assume one automatically supplies the guarantees of the other.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use this decision sequence
- Map the workflow. List the agents and steps, the conditions that move work between them, and any points where a person or external service may delay progress.
- Set control boundaries. Mark which transitions are fixed application rules, which may be model-directed, and where a mixed approach is appropriate.
- Define state scopes. Separate conversation, run, handoff, and durable business data. Assign an identity and authoritative owner to each.
- Choose persistence ownership. Select application-managed history, an SDK session option, or a Responses API continuation based on lifecycle and storage needs; avoid combining strategies for one conversation without a specific architectural reason.
- Check worker access. Verify that the components responsible for later steps can reach the required state using the chosen runtime and storage.
- Choose the recovery model. If runs must survive waits, retries, or restarts, evaluate a durable-workflow integration or framework capability and confirm what it supports.
- Instrument transitions. Record state changes, handoffs, retries, and persistence failures so you can diagnose where a run diverged or stopped.
What the available documentation does—and does not—establish
The OpenAI Agents SDK and API documentation and the LangGraph reference retrieved on October 7, 2026 describe orchestration, persistence options, and durable-execution capabilities. They do not provide a shared benchmark establishing that one framework or storage option is faster, more reliable, or less expensive than another. Nor do those sources establish pricing, quotas, or a universal state schema. Treat performance and operational guarantees as implementation-specific questions to verify against current documentation and your own requirements.
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