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Long-running AI work stays reliable when you preserve the right things in the right places: the active conversation or run state, the workspace needed to resume work, reusable lessons for later runs, and a human-reviewed project record. These are different kinds of continuity, not one universal “memory” feature. Choose a primary mechanism for continuing each conversation, define what must survive an interruption or restart, and test recovery before relying on it.
Why one kind of “memory” is not enough
A conversation transcript can help an agent answer its next turn, but it may not preserve files or environment state. A saved workspace can restore files without telling a later run why a decision was made. A reusable memory can carry preferences or lessons forward, but it should not silently replace the reviewed record of project facts.
The practical design is a continuity contract between the agent runtime, persistent storage, and the project’s human-reviewed artifacts. Decide what each layer owns, how it is recovered, and which one is authoritative when information conflicts.
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What should the protocol preserve?
Run or conversation state
This is the context needed to continue a conversation or resume an interrupted run. The OpenAI Agents documentation describes several approaches: application-managed history, stored sessions, server-managed Conversations API IDs, and response-ID continuation. They differ in who stores the history and how it is supplied to the next request.
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Pick one primary strategy per conversation unless you have an explicit reconciliation layer. The OpenAI documentation cautions: “In most applications, pick one strategy per conversation.” Combining provider-managed state with a separately replayed local transcript can duplicate context rather than improve continuity.
Workspace state
Workspace state includes the files, generated artifacts, and environment state required to continue work. OpenAI’s sandbox guidance treats compute and workspace state as distinct from the agent harness and from reusable memory. Decide whether a pause merely needs a later run to see saved files, or whether the implementation must resume a more complete workspace state.
Reusable memory
Memory is for information that can help future runs, such as a recurring preference, a correction, or a process lesson. It is not automatically the authoritative record of a project’s current status, evidence, or decisions. In its example, the OpenAI cookbook on memory and compaction distinguishes reusable memory from a reviewed memo: “The memo remains the human-reviewed artifact.”
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Keep decisions, current status, supporting evidence, unresolved questions, and the next action in a project-controlled artifact that a person can inspect and correct. The exact file layout depends on the project; the important design choice is that the record is reviewable and has a clear owner. Treat generated summaries or memories as inputs to that record, not as a substitute for human review.
Which continuity mechanism fits the failure you need to handle?
Choose a mechanism by the interruption it must survive. The options below are not interchangeable, and a system may use separate mechanisms for separate scopes.
| Need | Mechanism | What it is suited to preserve | Important boundary |
|---|---|---|---|
| Continue a conversation on the next turn | Application-managed history, a stored session, a server-managed conversation ID, or response-ID continuation | Conversation state, depending on the selected strategy | Choose a primary strategy per conversation; mixing managed and locally replayed state can duplicate context. See OpenAI’s running-agents guide. |
| Resume an interrupted agent run | A session mechanism that supports stored history and interruption resumption | Conversation history needed to continue work | Session history alone does not mean the workspace or every external side effect has been restored. See Agents SDK sessions documentation. |
| Recover graph state for an active thread | A LangGraph checkpointer | Thread-scoped graph state | An in-memory checkpointer does not survive a process restart; use persistent storage when restart recovery matters. See LangGraph persistence documentation. |
| Share application-defined information across threads | A LangGraph store | Cross-thread data such as reusable application facts or preferences | A store serves a different scope from a thread checkpoint; define what data belongs in each. See LangGraph persistence documentation. |
| Carry files and workspace state through a pause or restart | Resumable workspace state or snapshots | Workspace state, according to the sandbox implementation | Do not assume this also restores conversation history or the project’s reviewed record. See OpenAI’s sandbox guidance. |
How to assemble a continuity protocol
- Name the recovery target. State whether the system must handle the next turn, an interrupted run, a process restart, a handoff to another person, or some combination. “Save context” is too vague to test.
- Assign an owner to each state type. Record which runtime or service owns conversation history, where workspace state is persisted, where reusable memory lives, and which project artifact is the human-reviewed record.
- Select one primary conversation strategy. Choose application-managed history, stored sessions, server-managed conversation IDs, or response-ID continuation as appropriate to the runtime. Document how the next request resumes state and avoid sending the same history through a second layer without a deliberate reconciliation design. The OpenAI running-agents guide describes the options and the risk of duplicated state.
- Define what gets written to the project record. Capture decisions and their rationale, current status, evidence or artifact locations, open questions, and the next action. Have a person review changes that would otherwise turn a generated summary into an unchecked source of truth.
- Separate compaction from memory. Compaction helps the current run continue within a finite context window; reusable memory helps later runs; the reviewed artifact remains the project’s checked record. The OpenAI cookbook treats these as distinct roles rather than synonyms.
- Set persistence and retention rules. Choose what is retained, for how long, who can access it, and how it is backed up or removed. LangGraph notes that checkpoint accumulation can increase latency and storage costs, and recommends pruning or retention policies in its persistence documentation.
- Exercise recovery paths. Interrupt a run, resume it, restart the process, and verify that the expected conversation state, workspace files, and project record are available. Check for stale or duplicated context, missing artifacts, and unreviewed changes. A successful next-turn continuation does not by itself demonstrate restart recovery.
How to control context and storage growth
Keeping every detail available can make history harder to manage and can add context, latency, or storage overhead. Removing too much can erase the rationale needed to make the next decision. Treat those costs as an operational trade-off, not as a reason to call every summarization method “memory.”
- Replay history when the selected conversation strategy requires the application to provide prior messages. Decide whether all history or a filtered portion is necessary.
- Filter history when irrelevant messages need not be included in the next request. The Agents SDK sessions documentation covers filtering session history.
- Compact context when the current run needs a shorter working context. Keep the compacted context distinct from durable project facts and from reusable lessons.
- Retrieve memory progressively when a later run needs selected reusable information rather than a full transcript. Keep the human-reviewed project artifact as the authority for project decisions.
- Prune or expire persisted state under a documented retention policy. Preserve what recovery or audit needs require, and remove what no longer serves those purposes.
There is no universal cost figure or optimal retention period established by these product documents. Measure the effect in the implementation being operated, including storage growth and the amount of context actually supplied to runs.
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- Can a new run identify the current task and the next action from the project record?
- Are required workspace files present after the interruption or restart being tested?
- Does the continuation strategy restore history once, rather than combining overlapping state sources unintentionally?
- Can a person distinguish reviewed project facts from generated summaries or reusable memory?
- Are checkpoint retention, access, backup, and deletion behaviors defined for the chosen storage?
- Does the recovery test match the actual failure being claimed? An in-memory checkpoint, for example, is not proof of recovery after a process restart.
Managed persistence or self-managed storage?
With self-managed persistent storage, the application team controls storage choices and must account for retention, recovery, and maintenance. A hosted option may handle persistence as part of its service, but its behavior and operational responsibilities are product-specific. LangGraph’s documentation says Agent Server handles persistence automatically; that statement applies to that product choice, not to every agent deployment. Confirm what the selected service retains and how it recovers state before treating it as a continuity guarantee.
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