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You can give multiple AI assistants access to the same context, but there is no universal setting that automatically syncs memory across ChatGPT, Codex, Claude, and other tools. Each product’s built-in memory generally belongs to that product or runtime. For cross-product sharing, you need a common store and a supported way for every assistant in scope to read it—and, if needed, write to it.
The practical approach is to decide what should persist, use native memory for continuity inside one product, and connect a shared folder, repository, or service when you need context to travel between products. Confirm access and deletion behavior client by client before relying on it.
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What “shared memory” can mean
Memory features differ in where they keep information and which assistant can retrieve it. A saved preference in a consumer app, a local file used by a coding assistant, and a developer-managed store attached to an agent session are not automatically the same memory.
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|---|---|---|---|
| Product-managed memory | Retain personalization or relevant context within a product. | Usually the product and supported clients or environments; do not assume other assistants can see it. | Availability, controls, and deletion behavior for your plan, platform, region, and workspace. ChatGPT’s memory behavior and controls are described in its Memory FAQ. |
| Local memory files | Store notes in files used by a supported local client. | That client or other software given access to the same files. | Whether the client supports the feature, where data is stored, and how access is granted. ChatGPT web memory and local Codex memory are distinct; Codex local memory is documented at Codex memories. |
| Developer-managed shared store | Attach persistent documents or conversation state to agent runs or sessions. | Only the agents and applications configured to use the store. | API or platform requirements, scope, read/write permissions, version history, and retention. Examples are documented for Claude Managed Agents memory and the OpenAI Agents SDK sessions. |
These are different implementation patterns, not interchangeable switches. The official documentation for these products describes specific product and developer paths; it does not establish a single no-code consumer setting that connects every assistant.
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Choose what should persist
Keep shared memory short, useful, and easy to review. Good candidates are stable response preferences, project conventions, current decisions, and pointers to authoritative documents. For details that change often, store a link or file location rather than a stale copied summary.
- Write facts in plain language with enough context to interpret them later.
- Mark mutable facts with a date or status, and remove or update them when they change.
- Avoid retaining entire conversations by default; conversation logs can include user input, assistant responses, tool activity, and interruptions.
- Never place passwords, API keys, or other credentials in a memory file.
Set up memory within one product first
ChatGPT
For ChatGPT, open Settings > Personalization > Memory and review the controls available to your account. ChatGPT may draw on saved memories, chat history, custom instructions, and other available sources; availability and controls vary by plan, platform, region, and workspace. Saved memories are separate from chat history. Deleting a chat alone does not necessarily delete a saved memory. See the ChatGPT Memory FAQ for current controls and deletion details.
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Codex local memory
Codex local memory is separate from ChatGPT web memory. OpenAI’s Codex memories guide says local memories are off by default and stored under ~/.codex/memories/. Enable the feature in supported settings and use /memories in supported clients to inspect or manage it. Do not assume that these files sync to ChatGPT or another assistant.
Claude Managed Agents
Claude Managed Agents starts each session with fresh context by default. Developers can create a workspace-scoped memory store of text documents and attach it to sessions; the agent can read and write those files, and changes create immutable versions. This is a developer/platform feature, not automatic synchronization with ChatGPT or other assistants. See Anthropic’s memory documentation.
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Make one store available across products
If assistants in different products must use the same notes, create a common store—such as a shared folder, repository, or service—and verify that each intended client has a supported route to it. A file sitting in a folder is not shared with an assistant unless that assistant can access the file through its client or integration.
- Inventory the clients. List each consumer web or mobile app, local environment, and developer API you intend to use. Check the official documentation for each exact client; support in one app does not prove support in another.
- Create a concise source of truth. Use a small set of Markdown or text files for preferences, project conventions, and dated decisions. Keep authoritative project records elsewhere and link to them.
- Connect each client separately. Configure the supported file, repository, or service integration for that client. Confirm that it can actually retrieve the relevant note before assuming it shares the store.
- Set permissions deliberately. Decide which clients may read, which may write, and who can change the underlying files. Start with read-only access where possible; allow writes only when you have a way to inspect and recover changes.
- Keep a history and review stale entries. Use version history or backups so accidental edits can be reversed. Claude Managed Agents’ immutable versions are one documented example, not a guarantee that other stores offer the same protection.
- Test retrieval, edits, and removal. Add a harmless preference, ask every intended assistant to retrieve it, change or remove it, then check each client’s behavior. This verifies your actual connections without exposing sensitive information.
For developer-built agents, the OpenAI Agents SDK sessions documentation describes using a stable conversation identifier so multiple runs can contribute to one memory conversation, with memory layouts that can be shared or separated between agents. This is SDK behavior to configure in an application, not a consumer-account sync switch. The SDK also notes that saved conversation files can include user input, assistant and tool items, interruptions, and final outputs.
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Protect privacy and keep memory reliable
Treat memory as a convenience layer, not a definitive or complete record. OpenAI says ChatGPT may search for relevant context rather than use every detail, and a memory summary may omit information. For important or changing facts, point to the current source and review notes for conflicts or outdated entries.
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Before connecting a shared store, establish who can access it, which tools can write to it, where it is hosted, how long it is retained, and how copies will be removed. OpenAI warns that sensitive information can appear in ChatGPT Memory; the Agents SDK guidance also makes the contents of saved conversation files relevant to storage and retention decisions. Apply an appropriate policy for sensitive workloads.
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Deletion can involve more than one location. OpenAI notes that removing a saved ChatGPT memory may require deleting both the memory and the chat where it was first shared, as well as copies in files or connected apps. Deleting only the chat may not remove the saved memory. If you use a shared folder or service, separately remove the entry there and check any copies or backups according to that service’s controls.
What shared memory can—and cannot—promise
A shared store can reduce repeated context when each connected client can reach it, but it does not guarantee that every assistant will find, interpret, or update every note correctly. Compatibility, permissions, retention, and deletion are properties of the specific client and storage path, so verify each one rather than treating “memory” as a universal feature.
Anthropic’s April 23, 2026 company case study reports that Wisedocs used Claude Managed Agents cross-session memory in document verification and says verification was sped up 30%. That is a vendor-published result for that case, not an independent estimate of typical benefit or a prediction for personal workflows. See Anthropic’s case study.
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