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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallTo preserve context across independent client connections, store the conversation outside the connection and look it up by a stable, authenticated conversation ID on every new turn. Load the existing state, continue the model or workflow, then save the updated state to a durable store. A connection carries a request; it should not be the owner of a conversation.
What “persistent context” means
A new model request does not automatically inherit the history of an earlier request or client connection. OpenAI describes text-generation requests as independent and stateless unless the application supplies prior history or uses a stateful conversation mechanism. Your workflow must therefore reconnect each turn to the right stored state.
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Persistent conversation state is not the same as a model having unlimited memory. A stored transcript can outlast a connection, but the model still receives only the context provided for a particular request, subject to its context window and input-token billing.
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Use this request lifecycle
- Resolve the conversation. Map the authenticated user or tenant to a stable logical conversation ID. Treat a client-provided ID as a lookup hint, not proof of permission to access that conversation.
- Load state. Retrieve the conversation, history, or workflow checkpoint from its designated state owner.
- Continue the turn. Add the incoming user message and invoke the model or workflow with the selected continuation mechanism.
- Save before success. Persist the resulting user, assistant, and tool items—or the updated workflow state—before reporting success to the client. If multiple connections can update the same conversation, use concurrency control to avoid lost or interleaved updates.
- Reconnect by the same logical ID. On a later connection, authorize the request, resolve the same conversation, and load its latest state. Define how deletion, retention, and recovery work in your application.
This lifecycle is an application design pattern; the APIs do not prescribe one universal schema for identity, authorization, or concurrency.
#1 Best Overall
Choose where conversation state lives
| Approach | State owner | Best fit | Trade-offs |
|---|---|---|---|
| Replay history manually | Your application | Small loops, provider flexibility, and full control over stored data | Your application selects and sends the required prior items on each request. Preserve complete required output items where the API requires them. OpenAI conversation-state guide |
previous_response_id |
OpenAI Responses API | Lightweight continuation from a prior response | Provider-specific; account for response availability and data-control behavior. OpenAI conversation-state guide |
| Conversations API | OpenAI | A durable server-managed conversation reused across sessions, devices, or jobs | Pass the conversation identifier on subsequent Responses requests. Understand retention and account policy. OpenAI conversation-state guide |
| SDK session with shared backend | Your application and SDK | Resumable agent runs or custom session storage | All relevant workers and instances need access to the same durable backend. The Agents SDK’s MemorySession is for local development, not cross-process persistence. OpenAI Agents SDK: Running agents |
| Workflow checkpointer plus store | Workflow framework | Resuming a thread while also reusing application data across threads | A checkpointer tracks thread state; a store holds application-defined data that can be retrieved across threads. They serve related but different purposes. LangGraph persistence documentation |
OpenAI continuation options
Conversations API: a durable conversation object
OpenAI describes the Conversations API as working with the Responses API to persist state in a long-running conversation object with its own durable identifier. Reuse that identifier on later Responses requests to continue the conversation across sessions, devices, or jobs. This is a practical fit when you want OpenAI to manage the conversation object rather than replaying the transcript yourself. OpenAI conversation-state guide
previous_response_id: continue from a response
For a lighter continuation path, provide the prior response ID. This links the new request to a previous response, but it is tied to the Responses API and is not interchangeable with an application-owned, provider-neutral transcript. Consider response availability and your account’s data-control settings when relying on it. OpenAI conversation-state guide
Rank #2
Manual history: retain control in your application
Store the required conversation items yourself and send the relevant history with each request. This gives the application more control over storage, portability, selection, and deletion, but also makes it responsible for selecting the correct items and handling the request’s history format. Avoid keeping a second, competing history in another layer unless you define which one is authoritative.
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Keep session state shared across workers
An SDK session is only as shared and durable as its backing storage. If requests can land on different processes or machines, an in-memory session on one worker will not make state available to another. Use a storage-backed SDK session or custom storage shared by the workers that handle the same conversation. The OpenAI Agents SDK documents storage-backed and custom session implementations, while identifying MemorySession as a local-development option. OpenAI Agents SDK: Running agents
For a workflow framework, distinguish resumable thread state from facts or records the application wants to retrieve across threads. In LangGraph, checkpointers track thread state, while stores hold application-defined data available across threads. A checkpoint is not automatically a general-purpose long-term memory. LangGraph persistence documentation
Choose one clear owner for history
Before implementing persistence, decide which component is authoritative for each conversation’s state. If the client sends its own transcript while the server also retrieves stored history, the same turn can be duplicated or the two versions can diverge. Pick one owner—such as the application database, a server-managed conversation, or a shared session backend—and define how updates reach it.
Compare options by provider dependence, how much history must be transmitted, cross-worker availability, retention and deletion controls, context-window costs, and whether the workflow needs thread resumption or reusable data across threads. A durable transcript preserves records; it does not by itself create a compact, well-designed memory.
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Plan for retention and context limits
Retention depends on the object and account policy
OpenAI documents a 30-day default retention period for Response objects. Conversation objects and their items are not subject to that 30-day TTL. These are distinct behaviors, and applicable account settings and data policies can affect what you should rely on. Check the current data-controls documentation and organizational configuration before choosing a retention design. OpenAI conversation-state guide · OpenAI platform data controls
Best Value
If a workflow sends data to a remote MCP server, that server is a third party with its own retention practices; assess those practices separately. OpenAI platform data controls
A saved transcript may exceed the useful request context
Persisting every turn does not mean the entire transcript should be sent on every future request. Context windows include both input and output tokens, and prior inputs in a response chain can still count toward input billing. Summarize or retrieve selectively when that suits the task, while retaining source records needed for correctness and auditability. OpenAI conversation-state guide
Quick Recap
Practical selection guide
- Choose the Conversations API when you want an OpenAI-managed durable conversation identifier shared across sessions, devices, or jobs.
- Choose
previous_response_idwhen you want a simpler Responses-specific continuation from a prior response. - Choose application-managed history when portability, custom selection, or direct control over stored records is central.
- Choose a shared SDK session backend when agent runs need resumable session state across workers.
- Use a workflow checkpointer for thread resumption, and a store for application-defined data that must be retrievable across threads.
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