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To make an AI agent remember after a restart, save the right state outside the process that is stopping, then restore it with the same session or memory identity and compatible configuration. First decide what must continue: a conversation, an interrupted run, a workspace with files, or useful context for a later run. These are different kinds of state; saving chat messages alone may not restore the rest.
Choose what needs to survive
“Memory” can refer to several separate artifacts. Pick the one that matches the continuity you need before choosing a storage method.
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- Conversation history: the turns that let an agent continue the same conversation. The OpenAI Agents SDK Session interface loads stored items for a turn and persists new user input and assistant output after the run.
- Resumable execution state: a checkpoint or run-state object that lets an interrupted execution continue. This is not necessarily the same as starting a new run with long-term memory.
- Workspace state: files and other artifacts the agent needs. A restored conversation does not necessarily restore the workspace that contained them.
- Distilled long-term memory: selected or summarized information intended to help a later run without replaying an entire transcript.
These distinctions matter in practice: OpenAI’s sandbox documentation treats workspace snapshots and reusable memory as separate concerns, while Microsoft Agent Framework advises saving a full session object rather than only message text or a history key.
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- Name the continuity goal. Decide whether the agent must resume an interrupted run, continue a conversation, recall useful context in a new run, or reopen a workspace with files.
- Identify who owns the state. Determine whether it is client-managed, held by a framework session, stored in a workspace, or managed by a service. Avoid competing stores for the same conversation history.
- Write it to durable storage. Process-local memory disappears when its process ends. Persist the full supported session object when the framework requires it; for file-based memory, preserve the configured memory directory or a resumable session or snapshot.
- Restore the right identity. Reuse the session, conversation, or memory identifier associated with the state, and scope it to the intended user or tenant. A different identifier can leave the restored run with no access to the old context.
- Restore compatible configuration. Load state through the framework or provider configuration that created it. A serialized session is not a substitute for compatible agent and provider settings.
- Validate before acting. Check current files, task status, external facts, credentials, and permissions. Treat remembered notes as guidance, not authority over the current environment.
- Make writes recoverable. Consider atomic writes, idempotent updates, and a defined path for incomplete turns. Guarantees vary by backend; consult the contract for the persistence implementation you use.
- Set retention and deletion rules. Decide what history or extracted memories persist, for how long, and how a user or tenant can clear them. Confirm the selected service’s actual retention behavior.
- 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 before permitting consequential actions.
Choose a persistence pattern
| Approach | What it can preserve | What restoration depends on |
|---|---|---|
| SDK-managed conversation session | Conversation items loaded for a turn and new user and assistant items persisted after a run. | A session implementation that persists beyond the process. The OpenAI Agents JavaScript SDK includes a Conversations API session and a local-development MemorySession; a process-local session alone does not survive a fresh process. |
| Full framework session serialization | Framework session state, which can include more than visible message text. | Persisting and restoring the full supported session object through compatible agent and provider configuration. Microsoft Agent Framework supports local session state and service-managed storage. |
| Workspace snapshot and file-based memory | Workspace files and, when present, memory files in the configured directory. | Keeping the sandbox session, resuming persisted session state or a snapshot, or using persistent storage. A fresh empty sandbox starts without the previous memory files. |
| Managed service memory | Service-generated summaries or durable context retrieved in later requests. | The service’s identifiers, configuration, scoping, and retention rules. |
SDK sessions for conversation continuity
The OpenAI Agents JavaScript SDK session guide describes an interface through which the runner fetches prior conversation items and persists new turn items. Its local-development MemorySession is not durable across a fresh process by itself. Choose a persistent backend if a process restart must not end conversation continuity, and check the selected backend’s guarantees for atomic and idempotent writes.
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Full session objects for framework state
For Microsoft Agent Framework, save and restore the complete supported session object, not just the visible messages or a history key. Its agent memory guidance treats session state as an opaque object to be restored through the framework. Keep the agent and provider configuration compatible with the state being loaded.
Snapshots and file-based memory for workspace continuity
OpenAI sandbox memory is stored in a configured memories directory in the workspace. Preserve that directory by keeping the sandbox session, resuming persisted session state or a snapshot, or using persistent storage. The OpenAI sandbox and Agents SDK documentation describes progressive disclosure: a short summary is injected at the beginning, relevant memory is searched when needed, and detailed rollout summaries are opened as appropriate. This avoids replaying every log, but a memory file may be stale and should not overrule current evidence.
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Amazon Bedrock agent memory associates sessions with a memory identifier and makes summaries from prior sessions available to later requests using that identifier. AWS documents configurable retention from 1 to 365 days for this feature; that range applies to Bedrock agent memory, not to agent memory in general. See the Amazon Bedrock agent memory documentation.
Cloudflare Agent Memory describes a managed approach with profiles scoped to users, agents, teams, tenants, or application entities, and automatic or explicit ingestion. This can provide durable context absent from a current prompt, conversation, or execution. As with any managed service, check its current configuration, isolation, retention, and deletion behavior against your requirements.
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Check these failure points before enabling actions
- State was saved in the wrong place: process-local memory or files in a discarded sandbox will not cross that restart boundary.
- Only transcript text was saved: framework-specific state may be missing if the supported session object was not persisted.
- The identifier changed: the restored runtime may point to a new conversation or memory rather than the stored one.
- Configuration is incompatible: a session may not be restorable under a different framework or provider setup.
- A partial write left an incomplete turn: recovery depends on the selected backend; define how to detect and retry or discard incomplete state.
- Memory is stale or mis-scoped: verify that retrieved context belongs to the intended user or tenant and agrees with current files and permissions.
Decide what to compare when choosing a backend
Do not compare persistence options by the label “memory” alone. Check the state coverage, where the state lives, what identifiers and configuration restoration requires, how users or tenants are isolated, how stale information can be corrected, and how retention and deletion work. Also establish operational ownership: which component writes state, which restores it, and what happens after a failed or partial write. Those recovery guarantees are implementation-specific, not universal properties of agent memory.
Documentation and service behavior can change. Confirm current details for the framework and service version you deploy, and make the restart test part of your own release or operations checks.
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