ChatGPT memory vs Claude Projects: what's the difference?

ChatGPT memory and Claude Projects solve different problems with similar names. This is a clear comparison of what each remembers, where each stops, and why neither travels between providers.

ChatGPT memory and Claude Projects both reduce repetition, but they are not the same feature. ChatGPT memory is a small, account-wide store of facts and preferences the assistant carries between unrelated chats. Claude Projects is a workspace that pins shared knowledge and instructions to one body of work. One follows you everywhere loosely; the other goes deep but stays in its box. Neither moves between providers.

If you use both tools — or both plus Cursor, Gemini, or Perplexity — the practical question is not which memory is better. It is what each actually covers, and what falls through the gap between them.

What is ChatGPT memory?

ChatGPT memory is a feature that lets the assistant retain facts about you across separate conversations. When you mention something stable — your stack, your tone preferences, your timezone, an ongoing project — ChatGPT may save it as a memory and recall it in future chats without you repeating it.

It works well for a specific kind of context:

• Durable personal facts ("I write in British English", "I work in TypeScript").
• Standing preferences ("be terse", "show code first, explain after").
• Lightweight continuity across otherwise unrelated chats.

Its limits are structural, not bugs. The store is small and curated by the model, not by you. It captures distilled facts, not the full reasoning of a working session. And it is scoped to your ChatGPT account, so it never reaches Claude, Cursor, or anything else. For why this kind of forgetting is built in, see Why AI forgets conversations.

What are Claude Projects?

A Claude Project is a dedicated workspace that bundles a set of chats with shared Project knowledge: documents, instructions, and reference material you attach once. Every conversation inside that Project starts with that knowledge already in context, which is why it feels like Claude "knows" the work.

Projects are strong where ChatGPT memory is thin:

• Deep, project-specific context — specs, schemas, style guides, long briefs.
• Explicit, inspectable knowledge you control directly rather than a model-curated list.
• Custom instructions scoped to one body of work instead of your whole account.

The trade-off is the inverse of ChatGPT memory. Projects are deep but bounded: context lives inside one Project, inside one provider. Open a different Project and it is gone. Open Cursor and it never existed.

ChatGPT memory vs Claude Projects: a direct comparison

• Scope — ChatGPT memory is account-wide and ambient. Claude Projects is workspace-scoped and explicit.
• Depth — ChatGPT memory holds distilled facts. Projects hold full documents and instructions.
• Control — ChatGPT decides what to save (you can view and delete). You decide what goes into a Project.
• Best for — ChatGPT memory for personal continuity; Projects for a single sustained piece of work.
• Portability — neither leaves its provider. This is the part that matters most and gets discussed least.

Why does neither solve the cross-tool problem?

Both features are real improvements, and both are deliberately provider-bound. ChatGPT memory makes ChatGPT more continuous. Projects make Claude more grounded. Neither was designed to share context with the other, because each is a retention strategy for one company's product.

That is fine until your workflow spans tools — which, for most builders, it already does. You design an API in Claude with a Project full of constraints, implement it in Cursor, then ask ChatGPT to draft the docs. Three tools, three memory models, zero shared context. Every switch is a reset, and resets are where constraints quietly get dropped. We cover that compounding cost in Why cross-AI memory matters.

What fills the gap?

The gap is not "ChatGPT memory should be deeper" or "Claude Projects should be broader". It is that memory is being implemented inside each model when it needs to live in a layer above all of them — captured once, retrievable from any tool.

That is the layer Vilix is built to be: try Vilix free for 7 days. It connects through a single one-time OAuth MCP connection and works across ChatGPT, Claude, Cursor, Perplexity, Gemini, and any MCP-compatible tool, capturing full conversation context with semantic, source-aware retrieval. It does not replace ChatGPT memory or Claude Projects — use those for what they are good at — it covers the cross-tool case they structurally cannot. You can export or delete your data at any time.

Which should you use?

Use ChatGPT memory for ambient personal continuity. Use Claude Projects when one piece of work needs deep, pinned context. Add a provider-independent memory layer when your real workflow crosses tools and you are tired of re-explaining the same project to a different assistant every day.

Frequently asked questions

Is Claude Projects the same as ChatGPT memory?

No. ChatGPT memory is an account-wide store of distilled facts the model carries between unrelated chats. Claude Projects is a scoped workspace that pins full documents and instructions to one body of work. Different scope, depth, and control model.

Does ChatGPT memory work across different chats?

Yes, within ChatGPT. Saved memories are account-wide, so a fact you mention in one chat can surface in another. It does not extend to Claude, Cursor, or other providers.

Can Claude Projects see my ChatGPT memory?

No. The two features are isolated to their own providers. Context from a Claude Project is not visible to ChatGPT, and ChatGPT memory is not visible inside Claude.

How do I keep context across both ChatGPT and Claude?

Use a memory layer that sits above both providers and is accessible from each tool. That way the same captured context — decisions, constraints, preferences — is available regardless of which assistant you open next.

Should I stop using built-in memory features?

No. They are useful for what they cover. The point is to add cross-tool memory on top, not to discard provider features that already work for single-tool continuity.