Cross-Platform AI Memory: One Memory Layer for Claude, ChatGPT, and DeepSeek
If you use more than one AI assistant, you already know the problem. You worked out a plan with Claude last week, refined the numbers in ChatGPT yesterday, and asked DeepSeek a related question this morning. None of those tools knows what the others said. Every conversation starts from zero, and your context ends up scattered across three apps that will never talk to each other.
A cross-platform AI memory layer fixes that. Instead of leaving your knowledge locked inside each vendor's app, it pulls your conversations into a single index you can search and reuse anywhere. This article explains what that means, why it matters, and how Memex does it.
The problem with siloed AI memory
Each AI provider keeps its own memory inside its own walls. ChatGPT's memory does not reach Claude. Claude's projects do not see DeepSeek. The built-in memory features are real, but they are deliberately confined to one product, because the goal is to keep you inside that product.
For anyone who works across tools, that creates three recurring frustrations. The first is repetition: you re-explain the same background to every model, in every session. The second is lost context: a useful answer from two months ago sits buried in a chat history you cannot search well, often inside an app you were not even using for that task. The third is lock-in: the longer you use one assistant, the more your accumulated context ties you to it, not because it is better, but because your memory lives there.
The information that actually holds value, your decisions, your research, your reasoning over time, is not in any single conversation. It is spread across all of them. Right now, nothing connects them.
What a cross-platform AI memory layer does
A memory layer sits above the individual assistants rather than inside any one of them. It does three things.
- It ingests your conversations from multiple sources, including Claude, ChatGPT, and DeepSeek, into one place.
- It indexes them so the whole history is searchable by topic, date, and content, not just scrolled through.
- It exposes that memory through a simple interface, in Memex's case an HTTP API, so any AI tool or script can query it on demand.
The result is one durable memory that outlives any single chat session and is not owned by any single vendor. Ask a question in a fresh Claude conversation, and the relevant context from your past ChatGPT and DeepSeek sessions is ready to bring in. The memory follows you, not the app.
How Memex works
Memex is a personal AI memory layer built around exactly this idea. The flow is straightforward.
First, you connect your sources. Memex ingests exported conversations from Claude, ChatGPT, and DeepSeek. Next, the indexing runs automatically: each conversation is parsed, enriched with keywords and categories, summarized, and stored so it is retrievable later. Finally, you query by API. A clean read API lets you, or an AI assistant acting on your behalf, search across everything, filter by date, pull a specific conversation, or surface the threads most relevant to what you are working on now.
Because the index is unified, a question does not care which assistant originally produced the answer. The memory is cross-platform by design.
Why keeping your own data matters
There is a design principle worth calling out, because it is the part most memory products get wrong. Your raw conversations should stay yours.
The strongest privacy architectures do not centralize everyone's data into one giant pool. They keep the source data where it lives and share only the insight, what is relevant, what matches, what has been useful, rather than exposing the underlying content wholesale. Memex is built in that spirit: a memory layer you control, not a data grab dressed up as a feature. You decide what goes in, and the index works for you instead of feeding someone else's training pipeline.
That distinction matters more the more you put into your memory. A cross-platform layer only makes sense if the trust runs in the right direction.
Who this is for
A cross-platform AI memory layer is most useful if you use two or more AI assistants and switch between them depending on the task. It pays off when your work is ongoing, when research, writing, investing notes, or project planning depend on continuity across sessions. It helps if you want to search your own AI history the way you search your email, instead of scrolling forever. And it matters if you care about owning and porting your data rather than being locked into one vendor's ecosystem.
If every conversation you have is one-off and disposable, you do not need this. If your conversations build on each other over weeks and months, a unified memory is the difference between starting over and picking up where you left off.
Frequently asked questions
What is a cross-platform AI memory layer? It is a system that collects your conversations from multiple AI assistants, such as Claude, ChatGPT, and DeepSeek, into a single searchable index, so your context and history are not trapped inside any one app.
How is this different from ChatGPT's or Claude's built-in memory? Built-in memory only works within that one product. Claude cannot see your ChatGPT history, and the reverse is also true. A cross-platform layer like Memex unifies all of them so the same memory is available no matter which assistant you are using.
Does it work with DeepSeek? Yes. Memex ingests conversations from Claude, ChatGPT, and DeepSeek, and is designed to extend to other sources over time.
Do I keep control of my data? That is the point. Memex is a personal memory layer you control. You decide what is ingested, and the index exists to serve your retrieval, not to be pooled or repurposed.
Can an AI assistant query my Memex automatically? Yes. Because the memory is exposed through an HTTP API, an assistant can retrieve relevant past context on demand and bring it into a new conversation, on any platform.
Bring your AI memory together
Your best thinking should not be scattered across three apps that ignore each other. A cross-platform AI memory layer turns that scattered history into one searchable, portable memory you own, and it makes every assistant smarter by giving it access to everything you have already worked through.