AI Memory Extensions Compared: Which One Follows You Across Every Chatbot?

Every AI memory extension makes the same promise: tell an AI something once, and every AI you use will know it. It is a great promise. It is also, in most cases, only partly true. The honest question is not "does it have memory" but "where does that memory actually reach?"

There is a simple test that cuts through the marketing. It takes thirty seconds to state: tell one chatbot a fact, then open a different chatbot and ask whether it knows the fact. ChatGPT to Claude. Claude to Gemini. Gemini back to ChatGPT. If the fact survives the trip, the extension does what it says. If it does not, you have found its boundary.

Let us walk the three main approaches through that test and watch where each one breaks. No lab coat required, just an understanding of how each approach is built.

Contestant 1: built-in memory

ChatGPT has memory. Claude has memory features. Gemini keeps saved info. These are the defaults most people meet first, and against a single-provider version of the test they pass easily: tell ChatGPT your working hours on Monday, ask it on Friday, and it remembers.

Now run the real test. Tell ChatGPT your working hours, then open Claude and ask. Nothing. Claude never heard of you. This is not a bug or a missing feature; it is the business model. Provider memory is a retention feature, and retention features do not export you to a competitor.

Where it breaks: at the vendor boundary, which is exactly the boundary the test is about. It is also the only approach where you cannot see the machinery: you get whatever retention, ranking, and deletion behavior the provider chose, with no alternative.

Verdict: passes the test only if your test never leaves one provider. For the multi-chatbot life, it is a non-answer.

Contestant 2: browser extensions

This is the biggest category: tools like Supermemory, Memory Plugin, myNeutron, and AI Context Flow that live in your browser and inject saved context into the web UIs of ChatGPT, Claude, Gemini, and others. Run the test inside Chrome and most of them pass: save a fact while chatting with ChatGPT, open Claude in another tab, and there it is.

So where is the boundary? It is the browser window itself. The extension is a guest inside Chrome's house. The moment your workflow steps outside, into a desktop app, a phone app, a terminal running Claude Code, or an IDE like Cursor, the extension is not there and the memory does not follow. Some of these tools paper over this with companion pieces: Supermemory and AI Context Flow both ship MCP servers, and Memory Plugin offers an API and custom GPTs. But the browser extension alone, which is what most people install, only covers the browser.

There is a second, quieter failure mode: capture scope. Tools that watch everything you do in the browser to build context automatically are convenient, and they also carry the widest privacy surface of the three approaches. Tools that only save what you explicitly store are narrower but safer. Either way, you should know which deal you signed before you hand over your browsing life.

There is a third one worth knowing: extensions depend on the chat UIs staying readable to them. When ChatGPT or Claude redesigns their web app, injection can break until the extension ships an update. You are coupled to someone else's frontend.

Where it breaks: outside the browser, at the edges of what the extension may observe, and whenever a chatbot redesigns its UI.

Verdict: passes the test as long as your whole AI life happens in browser tabs. The moment it does not, you need something else.

Contestant 3: MCP memory layers

The third approach skips the browser entirely. Tools like Vilix AI, Supermemory's MCP connector, and MemSync's API expose memory as a service that AI tools reach over MCP (Model Context Protocol). The memory is its own layer: any compatible client can read from it and write to it, so a fact saved from Cursor is retrievable from Claude Code, and nothing depends on which tab is open.

Run the test here and it looks like the first clean pass: the fact genuinely lives outside every chatbot, so it follows you across all the tools that speak MCP. Cross-device comes along for free, since the memory is server-side rather than living in one browser profile.

Now the failure modes, because there are two and they matter. First, MCP is not universal. The ChatGPT website and the Gemini website do not speak MCP, so a pure MCP layer cannot reach the two most popular chat UIs directly. If half your day is in chatgpt.com tabs, an MCP-only tool leaves that half uncovered. Second, the model decides when to call the memory tools. In practice this works well: the client exposes search and save tools, and the model uses them. But it is a decision the model makes each turn, not a guarantee, so occasionally a fact you expected to be recalled is not, and you have to ask for it explicitly.

Where it breaks: at the boundary of MCP support, and on the occasional turn where the model does not reach for the tools.

Verdict: the only approach that passes the test across developer tools and devices, but it does not cover the big web chat UIs. A clean pass on a subset of the test, not the whole thing.

The scorecard

No approach passes the full version of the test today. Here is the honest map:

• Built-in memory passes inside one provider and fails the moment you switch.
• Browser extensions pass across web chatbots inside the browser and fail everywhere else.
• MCP layers pass across MCP-compatible tools and devices, and fail inside the big web chat UIs.

Anyone selling you a universal pass is selling you one of these three with the boundaries cropped out of the screenshot.

So what actually works

Stop shopping for "the best" and match the approach to the boundary you hit most often:

• If you live in ChatGPT, Claude, and Gemini web tabs and rarely leave the browser, a browser extension is the pragmatic pick. Supermemory and AI Context Flow are the strongest here because both add an MCP server for the day you wander into developer tools.
• If you live in Cursor, Claude Code, Codex, or terminal agents, an MCP memory layer is the right shape. Vilix AI is the option I know best: it auto-saves conversation turns, retrieves them with semantic search, keeps one memory across all your devices, and lets you list, update, delete, or export everything from any connected tool or the dashboard. Setup is about ten minutes per tool, there is a free tier, and the 7-day Pro trial does not ask for a credit card. The honest caveats: it only works with MCP-compatible clients, and the model occasionally needs a nudge to call the memory tools.
• If you build your own agents, an API-first option like MemSync gives you the most control, at the cost of writing the integration yourself.
• If you never leave one provider, skip all of this and use the built-in memory. It is free and already there.

And whatever you pick, run the test yourself before you commit: tell one tool a fact, open another, and check. Thirty seconds, no marketing required.