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To make an AI agent remember after a restart, first decide what must continue: the conversation, an interrupted run, the workspace, or selected facts for future runs. Save that state outside the process that will stop, then restore it using the same session or memory identity and compatible framework configuration. A transcript alone may not restore the agent’s execution state or files.
What does “memory” need to mean for your agent?
These four state types solve different continuity problems. Identify the one you need before choosing a persistence mechanism.
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| State | What it preserves | Use it when |
|---|---|---|
| Conversation or session history | The user and agent turns needed to continue a conversation. An SDK session can load prior items and persist new turn items. | A later turn should continue the same conversation. |
| Resumable execution state | A checkpoint or run-state object for work interrupted mid-run. | You need to resume an interrupted execution, rather than merely begin another turn with remembered context. |
| Workspace state | Files and other artifacts the agent used or created. | The next runtime must reopen the same working materials. |
| Distilled long-term memory | Selected facts or summaries intended to inform later runs without replaying the full transcript. | Useful context should carry into a new run or conversation. |
These categories can overlap, but they are not interchangeable. OpenAI’s Agents SDK session guide describes conversation history and resumable run state as separate concerns; its sandbox memory guide distinguishes memory files from session history and workspace restoration. Microsoft likewise advises restoring the full framework session object, not just message text or a history key, in its Agent Framework session guidance.
Restart checklist
- Name the continuity goal. Decide whether you are continuing a conversation, resuming an interrupted run, reopening files, or carrying useful knowledge into a new run.
- Find the authoritative state and its owner. Establish whether the application, framework, hosted service, or workspace owns the state. Avoid two competing sources of truth for the same conversation.
- Persist across the restart boundary. Process-local memory disappears when that process ends. Store framework session state in durable storage, or preserve the workspace or configured memory directory when those are required.
- Restore the right identity. Reuse the session, conversation, or memory identifier that points to the saved state, and scope it to the intended user or tenant. A saved object that cannot be found under the restored identity will not provide continuity.
- Restore compatible configuration. Load state through the framework and provider configuration that created it. For framework-managed sessions, use the supported restore path rather than reconstructing a session from visible messages alone.
- Validate before taking action. Check the current task status, files, credentials, permissions, and external facts. Treat recalled notes as guidance, not proof that conditions remain unchanged.
- Make persistence recoverable. Decide how your implementation handles interrupted writes, duplicate updates, and incomplete turns. Atomic writes and idempotent updates are useful design goals, but guarantees depend on the storage backend and framework contract.
- Set retention and deletion rules. Determine what is kept, for how long, and how a user or tenant can clear it. Check the selected service’s documented behavior; retention is not universal.
- Exercise the restart path. Save state, stop the process or sandbox, start a fresh runtime, restore the intended session or workspace, and verify that the agent sees the required history and artifacts—and not unrelated state.
Choose a persistence pattern
SDK-managed conversation sessions
The OpenAI Agents JavaScript SDK provides a Session interface through which the runner can load prior conversation items and persist new turn items. The guide describes a built-in Conversations API session, a local-development MemorySession, and custom backends that implement the interface. A local-development session should not be assumed to survive a fresh process; use a backend with persistence across the restart boundary when that is a requirement. See the SDK session documentation for the applicable interface and persistence guidance.
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Full framework session serialization
Microsoft Agent Framework supports local session state and service-managed storage. Its guidance is to save and restore the full session object in durable storage, since framework state may include more than the visible messages. Restore it through compatible agent and provider configuration; the session is an opaque framework state object, not merely a transcript. Consult the session documentation for the framework’s supported storage path.
Workspace snapshots and file-based memory
OpenAI sandbox memory is stored as files in a configured memories directory in the workspace. A later run can use those files only if the relevant workspace state is preserved—for example, by keeping or resuming the sandbox session or snapshot, or by using persistent storage. A fresh, empty sandbox does not contain the previous memory files. The feature uses progressive disclosure: a short summary is injected first, the agent can search an index when relevant, and it can open detailed rollout summaries as needed. This avoids treating a complete transcript as the only way to carry context, but generated memories can be stale. See the sandbox memory documentation.
Managed service memory
Hosted memory services can handle storage and retrieval while requiring the application to maintain the correct identity and scope. Amazon Bedrock associates session summaries with a memory identifier, making prior-session summaries available to later requests using that identifier. AWS says retention for this agent memory feature is configurable from 1 to 365 days; that range applies to this service, not to agent memory generally. See Amazon Bedrock agent memory documentation.
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Cloudflare Agent Memory describes scoped profiles for users, agents, teams, tenants, or application entities, with automatic or explicit ingestion and retrieval of durable context absent from the current prompt, conversation, or execution. This is a managed-service pattern; see Cloudflare Agent Memory documentation for its described scope and behavior.
Questions to settle before choosing
- State coverage: Does the approach preserve conversation history, interrupted execution, workspace files, extracted memory, or only some of these?
- Persistence boundary: Is state kept only in a process, in an application-owned durable store, in a workspace snapshot, or by a hosted service?
- Restore requirements: Which session or memory identifiers, provider configuration, and workspace must be restored?
- Isolation: How is state scoped across users, tenants, agents, teams, or conversations?
- Freshness and correction: Can old memories be updated or disregarded when current files or external conditions disagree?
- Retention and deletion: What is the lifetime of stored state, and how can the application or user clear it?
- Failure handling: Who writes and restores state, and what happens if a save or restore is partial? Check the selected backend’s guarantees rather than assuming all implementations are atomic or recoverable.
Verify the behavior, not just the save call
A successful write does not prove that a new runtime can recover the state the agent needs. Test each continuity goal separately: confirm the expected turns load for a continuing conversation; confirm an interrupted run uses its resumable state; confirm required files appear in the restored workspace; and confirm a new run retrieves only the intended durable memories. Also test missing or expired state and a failed restore, so the agent can stop safely instead of acting on a false assumption of continuity.
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