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You can pause an agent mid-task and resume it four minutes later with its context intact—but four minutes is not a documented retention threshold or guarantee. The reliable approach is to preserve the specific state your implementation needs: conversation history, a paused run, a thread-level objective, workspace files, and any pending application-side actions. These are separate kinds of state, not one universal agent memory.
First decide what “memory” must include
A resumed agent may need more than its previous messages. Identify which of these must survive the interruption:
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- Conversation history: messages and other stored conversation items the agent can use in a later turn.
- Interrupted run state: the execution state needed to continue a run paused for a decision, such as human approval.
- Thread objective: a durable task goal attached to a particular thread.
- Workspace state: files, snapshots, and reusable notes or memory stored separately from the conversation.
- Application task state: the status of actions or jobs handled by your own backend, including whether an operation has already completed.
Continuity depends on storing each type in the right place and using the appropriate resume mechanism. An intact chat transcript alone does not prove that a paused execution can continue, that workspace changes remain available, or that an external action is safe to repeat.
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The Python Agents SDK session layer loads a session’s conversation history before a run and stores new items after it. For a later run to continue using that stored history, reuse the same session identity and storage. The Sessions documentation says that after a run pauses for approval, resumption should use the same session instance or another instance configured with the same session ID and underlying storage backend.
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This preserves the stored conversation history; it is distinct from restoring the execution state of an interrupted run.
Resume a run paused for human approval
When a tool requires approval, the runner surfaces a pending decision as an interruption. The human-in-the-loop flow uses RunState to serialize the paused run so the application can resume it after the approval or rejection is handled. That mechanism applies to tools used by the top-level agent, a handoff, or nested Agent.as_tool() execution. See the Human-in-the-loop documentation for the run-state flow.
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If sessions are enabled, preserve the session identity and backing store as well as the paused run state. Restoring only the run state does not replace the need to keep the intended conversation history available.
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For a Codex Goal, the documented controls are /goal pause and /goal resume. The Codex Goals cookbook describes a Goal as a durable objective attached to the current thread, alongside relevant thread context such as inspected files, commands, diffs, and logs. It is persisted thread state—not global memory or project-level instructions—so do not assume a fresh, unrelated thread has the same context.
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Keep sandbox history, memory, and workspace distinct
OpenAI’s Sandbox Agents documentation distinguishes SDK-managed sessions, which preserve message history, from sandbox memory, which distills reusable lessons into files. Workspace continuity is another concern: a snapshot or retained files preserve workspace state, but do not make files, reusable memory, and conversation history interchangeable.
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The documented approaches include keeping the same live sandbox session, resuming session state, starting from a snapshot, or mounting persistent storage such as S3 to reuse configured memory directories. Choose based on whether you need the conversation, the workspace, reusable memory files, or some combination of them.
Restore GPT-Live sessions without repeating actions
For GPT-Live, the session management guide describes forking a completed stored recording on a new connection when storage is enabled, policy permits it, and finalization succeeds. If there is no completed stored recording to fork, start a new session with relevant saved text history.
Keep application task state and the session ID outside the conversation. Before retrying an unfinished action, check the backend’s status: the action may already have been submitted or completed even if the agent’s conversation does not show a confirmed result. Otherwise, a resumed agent can duplicate a side effect. The guide states that stored recordings are available for 30 days; with Zero Data Retention, storage is treated as false and forking is unavailable. These details are specific to GPT-Live and should be checked against its current documentation when implementing a workflow.
Quick Recap
Choose continuity by the state you need to recover
| State to preserve | Where it belongs | How continuation is keyed | What to watch for |
|---|---|---|---|
| Conversation history | SDK session storage | Same session instance, or matching session ID and backing store | History continuity does not by itself restore a paused run. |
| Approval-paused execution | Serialized run state, such as RunState |
Resume the saved run state after handling the interruption | Keep the associated session identity and backing store stable if sessions are enabled. |
| Codex Goal | The current thread | Pause and resume the Goal in that thread | Do not treat thread state as global memory or assume it transfers to a different thread. |
| Sandbox workspace and reusable memory | Live session, snapshot, files, or mounted persistent storage, depending on the need | Resume the session or restore the chosen workspace and storage | Workspace files, memory files, and message history are different assets. |
| GPT-Live conversation continuation | Completed stored recording or saved text history | Fork the stored session where available, or start a new session with relevant history | Check backend action status before retrying; storage and forking have API-specific conditions. |
A practical pause-and-resume checklist
- Identify the state to preserve. Decide whether the task needs messages, interrupted execution state, a thread objective, workspace data, application task status, or several of these.
- Persist state before the interruption. Save it in the session store, run-state serialization, thread, workspace snapshot or files, or application backend that owns it.
- Resume with the matching identity. Use the same session instance or matching session ID and backing store, the same Codex thread, or the saved source session where the product supports that flow.
- Reconcile external actions. Check the application or service that performs an action before asking the agent to retry it.
- Verify restored context. Confirm the intended history, run, goal, files, and task status are present before continuing work.
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