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A support agent can remember useful context by saving conversation history or selected customer facts in application-managed storage, then retrieving the right information for that authenticated customer on a later turn. The model does not remember between calls on its own: your application must choose what to save, keep each customer’s data separate, and provide a way to correct or delete it.
Decide what “remember” means for your support agent
There are two related but different ways to preserve context. A session stores conversation items so a later model call can continue from earlier turns. Curated memory stores a smaller set of selected facts, summaries, or lessons that can be retrieved when relevant. The OpenAI Agents SDK session guide describes stored conversation items and custom session implementations; Anthropic’s memory-tool documentation describes an application-managed memory-file pattern.
| Approach | What you store | Best suited to | Main trade-off |
|---|---|---|---|
| Conversation history | Prior turns or conversation items | Continuity within an ongoing support issue | History can grow, become irrelevant, or include outdated details if you feed it back indiscriminately. |
| Curated memory | Selected facts, summaries, or lessons | Useful context that should carry across separate conversations | Your application needs rules for selection, freshness, correction, and deletion. |
You can combine the approaches: keep a session for the active issue and retrieve a few relevant saved facts from earlier interactions. Do not treat a memory file, summary, or old conversation as authoritative account data. For current order, refund, or subscription status, retrieve the value from the system of record when needed.
Build the memory flow around authenticated identity
Every read and write needs a customer or account scope established by your application’s authentication layer. A session API or storage product does not, by itself, verify a customer’s identity or prove that one tenant cannot access another tenant’s records.
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- Authenticate the customer. Resolve the signed-in user to a stable internal customer or account identifier. Do not use a customer-supplied identifier as proof of identity.
- Load narrowly. Retrieve the active conversation and only the saved context relevant to the current issue. Apply the authenticated customer or tenant scope in the application or storage layer on every lookup.
- Call the agent. Provide the selected context to the model alongside the current request. Treat retrieved memories as potentially stale, incomplete, or wrong.
- Save deliberately. Persist the new conversation items and, if appropriate, update a compact memory. Avoid automatically converting every message into permanent customer facts.
- Check access boundaries. Test that a user in one account cannot read, update, or delete another account’s session or memory, including through guessed identifiers and concurrent requests.
A conceptual record might contain an internal customer or tenant key, a session or memory identifier, the stored content, timestamps, and any retention metadata your application needs. This is a design sketch, not a prescribed schema: choose fields and indexing based on your datastore and access controls.
Choose storage that matches your persistence needs
The OpenAI Agents SDK guide presents an OpenAI conversations session, a local MemorySession, and custom session implementations. A session can fetch earlier conversation items before a run and persist new user and assistant items afterward. Custom implementations can use a datastore such as Redis, DynamoDB, or SQLite and can add controls such as retention policies, encryption, or per-turn metadata.
Local development
MemorySession stores state in the running process. It resets when that process exits, and the SDK documentation positions it for local development, demos, or tests—not production durability. It is useful for trying the conversation flow, but a restart or a different process should not be mistaken for a persistent customer memory system.
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For a durable deployment, select a storage approach that fits your expected availability, operational capacity, access-control model, retention needs, and recovery plan. The SDK examples establish that several backends can be used; they do not establish that one is best for every support system. Keep storage and model orchestration behind application code so you can enforce identity scope and lifecycle rules consistently.
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Retrieve only context that can help with this turn
Sending all past conversations on every request can add noise as well as useful information. A practical retrieval policy can prioritize the current issue’s session, then fetch a small set of relevant saved details. Include enough context for the agent to understand why a fact matters, and distinguish customer-provided information from values verified in a system of record.
- Use conversation history when the agent needs the sequence of events in an active issue.
- Use curated memory for a small number of durable preferences or recurring context, with a way to update or remove it.
- Use live application tools for facts that can change, such as order status or subscription state.
- Give the agent a clear path to ask the customer when retrieved context is missing or conflicts with what the customer says now.
These retrieval choices are engineering recommendations, not guarantees provided by an SDK’s session feature or a memory tool. Anthropic describes its memory tool as a mechanism for an application to store and retrieve information in a memory-file directory; that mechanism alone does not supply customer identity isolation or a deletion policy.
Make correction, retention, and deletion part of the design
Customers need a path to review or correct remembered information and request its removal. OpenAI’s consumer memory documentation describes review and deletion controls, while the Agents SDK session interface includes a clearing operation. For a custom support application, implement controls for the actual records your system owns rather than assuming one clear action erases every copy.
- Identify the records and derived summaries tied to a customer, including any copies your application creates.
- Define how long sessions and curated memories remain useful, and how they expire or are removed.
- Ensure correction updates the stored value used for future retrieval, not only the current conversation.
- Make deletion account-scoped and verify that it covers the relevant application-managed records and logs.
- Tell customers what the agent uses as remembered context and how they can manage it.
Clearing a session in one layer does not automatically erase data stored in another layer or copies retained in logs. Map the data flow before promising deletion behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand provider data handling separately from application storage
OpenAI’s API data-controls documentation states that API data is not used to train or improve models unless the customer opts in. It separately describes abuse-monitoring logs and application state: abuse-monitoring logs may be retained for up to 30 days by default, subject to exceptions, while state controls and eligibility for zero data retention vary by endpoint, feature, and settings. This is not equivalent to saying that a provider stores nothing.
Those statements describe OpenAI API handling, not every model provider or every feature. Anthropic’s memory-tool pattern and vendor support-platform claims have their own scope and terms. Confirm the current behavior for the exact service, endpoint, feature, region, and configuration you use. OpenAI’s documentation was accessed October 7, 2026; retention and feature details can change.
Test whether memory helps without overstating results
Persistent context is a design choice, not proof that support outcomes improve. Evaluate it against a baseline using representative support cases and measure outcomes that matter to your service, such as whether the agent requests information the customer already provided, whether it uses stale facts, and whether the customer’s issue is resolved correctly. Do not count a response as successful merely because the agent recalled a detail.
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- Include cases with missing, conflicting, and outdated memories.
- Test cross-customer and cross-tenant isolation, including deletion and correction flows.
- Check that the agent retrieves live account state rather than relying on an old summary.
- Compare outcomes with and without retrieved memory under the same evaluation conditions.
No verified efficacy result establishes a particular lift in resolution rate, satisfaction, or repeat-contact reduction from persistent memory. Measure those outcomes in your own deployment before claiming improvement.
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