To manage state in an AI agent, decide what must persist, who owns it, and who needs access. Keep the active run’s execution state separate from conversation history and durable application data. Then choose one conversation-continuation strategy—application-managed history, an SDK session, or provider-managed continuation—and add durable workflow orchestration only when work must survive long waits, approvals, retries, or restarts.
How do I manage state in an AI agent?
“State” can mean several different things. Treating it as one all-purpose memory feature makes it easy to persist too much, lose information needed for recovery, or send an ever-growing conversation back to the model.
- Execution state: information needed while the current run is in progress, such as its steps, tool results, or pending action. It may be temporary unless the run needs to pause and resume.
- Conversation history: prior messages and run items used to continue an interaction in later turns. It needs a persistence strategy and a policy for deciding what history to include.
- Durable application data or learned memory: business records, user preferences, or other information intended to remain available beyond one conversation. The application should define its access, retention, and deletion rules.
These categories may interact, but they do not have to share one store. OpenAI’s documented options illustrate the distinction: the managed Agents API provides managed session state; an application using the Agents SDK can manage storage or use SDK sessions; and an application using the Responses API can manage history itself or use documented server-managed continuation. These are different arrangements, not interchangeable labels for the same memory system.
Which persistence approach should I choose?
Choose based on control, sharing, recovery, context growth, data governance, and the operational work your application can take on. The official documentation describes implementation options and cautions, not comparative performance or total-cost rankings.
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| Approach | Who owns conversation persistence? | When it may fit | What to plan for |
|---|---|---|---|
| Pass history forward manually | Your application passes the previous result’s input list to the next call. | A small loop where you want direct control over what carries forward. | Your application must decide what to retain, filter, or summarize and manage the history it sends. |
| Agents SDK session | The SDK session reads prior items from a backing store before a run and stores new run items afterward. | An SDK-backed conversation that should persist across turns using a session attached to the same store. | Select and operate storage, decide how much history to include, and set suitable limits or filtering. |
| Server-managed continuation | The provider maintains continuation state using a conversation ID or prior-response mechanism documented for the Responses API. | You prefer provider-managed continuation over carrying the full history yourself. | Understand the provider’s data handling and retention terms, and do not also persist the same conversation through another layer without reconciliation. |
| Managed Agents API session state | The managed API retains state across turns. | You want to use the managed Agents API’s session arrangement. | Check the API’s current residency, retention, deletion, and data-control terms before use. |
The Agents SDK session guide gives SQLite, Redis, and hosted storage as implementation choices. Those examples do not establish that one is universally best. A local or single-process setup and a multi-worker service may have different sharing and operations needs; evaluate the actual access pattern and recovery requirements rather than choosing from the storage name alone.
How can an AI agent remember context between runs?
First decide whether “remember” means continue the same conversation or retrieve durable information. Conversation continuation uses history or provider-managed state. Durable preferences and business facts should be stored as application data with explicit rules about who can read, update, and delete them.
For a small, manually controlled loop
Pass forward the previous result’s input list when making the next call. This gives the application control over the history carried into the next turn. It also makes the application responsible for keeping that history useful as it grows.
For an SDK-backed conversation
Attach a session to the same backing store across turns. The SDK session mechanism retrieves prior items before a run and stores new run items afterward. Its documentation describes SQLite, Redis, and hosted storage as options.
For provider-managed continuation
Use the corresponding conversation ID or prior-response mechanism documented for the Responses API. Keep that approach distinct from client-managed history unless the application deliberately reconciles the two.
OpenAI’s Agents SDK guidance warns that combining client-managed history with server-managed continuation can duplicate context. It advises choosing one persistence strategy per conversation in most applications. If there is a reason to combine layers, define which one is authoritative and how duplicated or conflicting items will be detected and resolved.
How should an agent handle growing conversation history?
Persistence determines what can be retrieved; it does not mean every old item must be sent to the model on every turn. The SDK session documentation describes filtering or limiting history before model input, including keeping recent history and setting session limits. That is a practical mechanism, not evidence that a particular cutoff or truncation policy is optimal.
- Decide what the next turn needs: retain relevant instructions and facts, not automatically every intermediate item.
- Choose a policy deliberately: for example, limit the history or filter older items before they reach the model.
- Keep durable records separate: do not rely on a long transcript as the only record of a business fact that must be reliably retrieved or governed.
- Check the result: ensure the policy does not remove context the agent needs to answer or act correctly.
The documentation offers examples, not measured evidence comparing history policies. The right filter depends on the information your application needs and the consequences of omitting it.
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Keep application context and permission decisions under application control. The Agents SDK context guide states, “The context object is not sent to the LLM.” That is useful for separating application-side context from model-visible conversation, but it does not make every other kind of stored or transmitted state private.
- Keep secrets out of any context or state that may be serialized or transmitted.
- Store durable business records in application-owned systems with explicit access, retention, and deletion rules.
- Enforce authorization in the tool implementation or another application-side authorization layer.
- Validate model-selected arguments and the requested resource against the user’s permissions before performing an action.
Making a tool or capability available to an agent does not itself authorize every model-selected argument or resource. A model-generated request is an input to validate, not a permission decision.
When does an agent need durable workflow orchestration?
A conversation session is not a guarantee that a task will safely resume after a process stops. If a task must wait for human approval, survive a long delay, retry after failure, or recover progress after a restart, assess a durable workflow integration in addition to conversation persistence.
The Agents SDK guide names integrations including Dapr, Temporal, Restate, and DBOS for long-running workflows, approvals, and progress recovery. The cited documentation does not provide a comparative benchmark among them. Before adopting one, verify its current persistence semantics, limitations, and operational requirements for your own deployment.
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Confirm where state resides, who can access it, how long it is retained, how it can be deleted, and whether the service meets your data-residency requirements. Also consider whether serialized state could contain credentials, personal information, or other data your application should not persist there.
As stated in the OpenAI Agents API documentation accessed on October 5, 2026, the Agents API retains state across turns, has US-only data residency, and does not support Zero Data Retention (ZDR). These terms apply to the Agents API as described there; they should not be generalized to the Agents SDK, other OpenAI products, or other vendors. Product and data terms can change, so verify the current documentation for the specific service and deployment before choosing it.
A practical decision checklist
- Classify the information. Decide which state is temporary execution state, conversation history, or durable application data.
- Choose one conversation strategy. Use manual history, an SDK session, or the applicable provider-managed continuation mechanism. Reconcile layers explicitly if you have a reason to combine them.
- Choose storage for the access pattern. Consider whether the same conversation must be available across workers or services, and who operates the backing store.
- Set a history policy. Decide what is filtered or limited before the next model input, and test whether important context remains available.
- Put permissions in the application. Keep sensitive data out of state that may be serialized or transmitted, and validate tool actions against application authorization rules.
- Add workflow durability when needed. For pauses, approvals, retries, or restart recovery, assess a durable workflow integration separately from session storage.
- Review data terms. Check current residency, retention, deletion, and access controls for the exact service you plan to use.
OpenAI’s Agents overview, Agents API documentation, and Python Agents SDK guides for running agents, sessions, and context were the basis for the product-specific details above. Their documentation describes options and behavior, not a market-wide ranking of agent state architectures.
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