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To preserve context across independent client connections, store the conversation or workflow state outside the connection and retrieve it using a stable, authorized conversation ID when the next request arrives. A connection is a transport, not a memory store: each new model request needs supplied history or a stateful continuation mechanism.
How persistent context works
Give each logical conversation a stable identifier, associate it with the correct user or tenant, and make a durable store the source of truth. When a connection handles a new turn, load that conversation’s state, continue the model or workflow, then save the resulting state before returning success. A later connection can then resume the same logical conversation.
- Resolve the conversation. Map the authenticated user or tenant to a stable conversation ID. Do not treat an ID supplied by an untrusted client as proof of access.
- Load state. Retrieve the history, checkpoint, or server-side conversation reference from the chosen state owner.
- Run the turn. Add the incoming user input and invoke the model or workflow with the selected continuation method.
- Save the result. Persist the user, assistant, and relevant tool items—or updated workflow state—before reporting success. Use concurrency control if multiple connections could update the same conversation at once.
- Define its lifecycle. Decide how state is retained, deleted, recovered, and made available to other workers.
This sequence is an implementation pattern, not a universal schema prescribed by the providers. The application must define access checks, update ordering, and concurrent-write behavior.
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Pick one clear owner for history. Mixing application-managed history with server-managed state without an explicit synchronization design can duplicate or omit messages.
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| Approach | State owner | Best fit | Trade-offs |
|---|---|---|---|
| Replay history manually | Your application | Small request loops, provider flexibility, or precise control | Your code stores, selects, and resends the required prior items on every request. Preserve complete required output items where the API calls for them. OpenAI conversation-state documentation |
previous_response_id |
OpenAI Responses API | Lightweight continuation from an earlier response | Specific to Responses; account for response availability and data-control behavior. OpenAI conversation-state documentation Agents SDK: Running agents |
| Conversations API | OpenAI | A durable server-managed conversation reused across sessions, devices, or jobs | Pass the conversation identifier on later Responses requests. Understand the distinct retention treatment for conversation items and applicable account policies. OpenAI conversation-state documentation OpenAI data controls |
| SDK session with shared backend | Your application plus SDK | Resumable agent runs or custom session storage | All workers that need the session must use the same durable backend. The Agents SDK’s in-memory MemorySession is for local development, not cross-process persistence. Agents SDK: Running agents |
| Workflow checkpointer plus store | Workflow framework | Resuming a thread while also reusing data across threads | In LangGraph, a checkpointer tracks thread state; a store holds application-defined data that can be retrieved across threads. They serve related but distinct purposes. LangGraph persistence documentation |
OpenAI Responses: continue a conversation across connections
OpenAI documents text-generation requests as independent and stateless unless the application supplies history or uses a stateful conversation mechanism. The Conversations API creates a durable conversation object with its own identifier; reuse that identifier in subsequent Responses requests to continue the stored conversation across sessions, devices, or jobs. OpenAI describes it as a long-running object for persisting conversation state. OpenAI conversation-state documentation
Use Conversations when the conversation should outlive a response
Store or securely associate the conversation ID with the application’s authenticated user or tenant. On a later connection, retrieve the appropriate ID and pass it to the Responses request. The API manages conversation items, while your application remains responsible for determining which user can access which conversation.
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Use previous_response_id for a lighter continuation chain
When continuing from a prior response is sufficient, supply its ID as previous_response_id. This is a Responses-specific continuation mechanism, not a provider-neutral session store. For manual history management, your application resends the prior conversation items instead. OpenAI conversation-state documentation
Share agent or workflow state between workers
A session identifier alone does not make state shared. If requests can land on different processes or machines, those workers need access to the same durable backing store. An in-memory session can work within a local development process but cannot, by itself, resume state in another process.
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For workflows, distinguish the current thread’s resumable execution state from reusable information that belongs to the application. LangGraph’s persistence guidance describes checkpointers for thread state and stores for application-defined data that can be accessed across threads. Use the mechanism that matches the data’s scope rather than treating every stored fact as transcript history. LangGraph persistence documentation
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OpenAI documents a 30-day default retention period for Response objects; Conversation objects and their items are not subject to that 30-day TTL. The applicable behavior can depend on account settings and data policies, so check the current OpenAI data-controls documentation before relying on a default. Data sent to a remote MCP server is also subject to that third party’s retention practices. OpenAI conversation-state documentation
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A durable transcript does not guarantee that the entire transcript fits in a future model input. Context windows include input and output tokens, and prior inputs in a response chain can still count toward input billing. Keep the source records needed for correctness and auditability, but summarize or retrieve relevant portions when the full history is unnecessary.
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Make reconnections reliable
- Authorize every lookup. Verify that the authenticated user or tenant may access the resolved conversation, even when the request includes a conversation ID.
- Save before acknowledging. If the client disconnects after a model run, durable state should still reflect whether the turn completed. Define retry behavior so a repeated request does not accidentally create duplicate turns.
- Handle concurrent turns deliberately. If two connections can update the same conversation simultaneously, use a locking, versioning, or other concurrency strategy rather than assuming writes arrive in order.
- Plan deletion and recovery. Set application rules for user-requested deletion, retention, and recovery; do not assume a provider’s retention behavior is identical to your application’s obligations.
- Keep history and memory distinct. A transcript records turns; a compact summary or retrieved application data can support future requests without resending every item.
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