Does this work with Claude and ChatGPT at the same time?

Yes. That's the entire point of an MCP-connected memory layer. Point Claude Desktop and ChatGPT (via its MCP connector) at the same endpoint and both get the same view of your knowledge. Cursor, Gemini and any other MCP-capable client can join the same store.

What happens when I switch AI tools next year?

You reconnect the new tool to the same memory endpoint. Your library, your transcripts, your highlights and your graph edges stay put — only the client changes. This is why 'AI-agnostic' matters more than which model is best this quarter.

Is my data locked in?

It shouldn't be, and this is the test to apply to any memory layer you consider. BrainTube ships full JSON export on every plan — source URLs, transcripts, highlights, notes — so you can walk away with a re-indexable corpus. Ask the same question of every alternative.

Model Context Protocol — the open standard Anthropic released in late 2024 that lets any AI client read any tool or data source through one uniform interface. Full explainer at /learn/what-is-mcp.

Do I have to run a server myself?

Not with a hosted memory layer like BrainTube — you paste a URL and an API key into the client's MCP config, and this works identically on every plan including Free. If you prefer to self-host retrieval yourself, DIY RAG (Option 3 above) is the path — and because BrainTube's JSON export is available on every plan, you can take your full corpus into any self-hosted setup.

Do I need this if I only use ChatGPT?

Probably not today. ChatGPT Memory plus custom GPTs will cover most single-app workflows. Revisit when you find yourself opening Claude or Cursor for jobs ChatGPT isn't the best fit for — that's the moment the silo problem starts to cost you.

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AI memory

How to Give Your AI Persistent Memory Across Every Tool You Use

How to give your AI persistent memory that works across every tool you use — the four options ranked, honest setup, and how to keep your data portable.

Updated July 2026

Persistent memory across tools means a single store of your knowledge that Claude, ChatGPT, Cursor, Gemini and whatever ships next quarter can all read from. Without it, every AI starts from zero and everything you told the last one is stranded inside that app. With it, your context compounds instead of being retyped every session — and the store survives every model migration you're going to make over the next five years.

The problem: every AI starts from zero, and your knowledge is trapped per-app

You've spent the last year explaining yourself to ChatGPT — your projects, your writing voice, your customer, the acronyms nobody outside your company understands. Then Claude 4 arrives, it's obviously better for the thing you're doing this week, and you start over. Every fact ChatGPT knew about you is stranded on the wrong side of the browser tab. Six months later a smaller, faster model shows up and the cycle repeats. Meanwhile the highlights you saved in Readwise never reach Cursor, the transcripts you paid to have made never reach Claude, and the AI-notes app you tried in March holds three months of your thinking that no other tool can see. This is the default state of using AI in 2026, and the shape of the fix is a memory layer that lives outside every app.

Option 1 — copy-paste (why it fails)

The naïve fix is to keep a master document — a running brief, a background dump, a system-prompt file — and paste it into every new chat. It works for a week. Then the doc gets long, then it gets stale, then you forget which chat has the up-to-date version, then you paste the wrong one into an important conversation and the AI confidently reasons from year-old assumptions. Copy-paste is a workaround, not a solution. It also doesn't scale to knowledge you didn't type yourself — the two-hour podcast, the fifty-page PDF, the six months of highlights.

Option 2 — per-app memory features (silos)

Every major AI now ships its own memory: ChatGPT Memory, Claude Projects, custom GPTs, Gemini's saved-info. Each is genuinely useful inside that one app. None of them talks to any of the others. If your workflow is single-app and you're happy to stay there forever, this is fine. If you rotate — Claude for reasoning, ChatGPT for search, Cursor in the editor — you now maintain N independent memories and none of them agree. And when you switch off an app, its memory goes with it.

Option 3 — RAG your own notes (real, but maintenance-heavy)

Roll your own retrieval-augmented generation over a folder of markdown, a Notion export or an Obsidian vault. This works and is the honest choice for engineers who enjoy owning the plumbing. The cost is that you now maintain a chunker, an embedding pipeline, a vector store, a re-ranker, an API layer and — critically — a connector for every AI client you use. It's a real second job. Most people who start here migrate to a hosted memory layer within a year not because DIY doesn't work but because the maintenance never ends.

Option 4 — MCP-connected memory layer (portable)

The fourth option is a hosted memory layer that exposes your knowledge over Model Context Protocol — the open standard Claude, Cursor, ChatGPT-via-connectors and Gemini already speak. Connect once per client, and every future conversation in every client can query the same store. When you swap models next quarter you swap the client, not the memory. This is the shape most people converge on once they've tried the other three, because it's the only one where the effort you put in this year keeps compounding next year in tools that don't exist yet.

Setup walkthrough: BrainTube as a worked example (our product)

BrainTube is our product, treated here as a labelled worked example. Sign up on the free plan (30 capture credits/month, no card). Install the Chrome extension and save whatever you're already watching, listening to and reading — YouTube videos, podcast episodes via RSS, articles, PDFs. BrainTube transcribes audio, timestamps every sentence, and builds a knowledge graph across your saves. Copy the MCP endpoint from Settings and paste it into Claude Desktop's config, Claude.ai's connectors, Cursor's MCP list, or any other MCP-capable client. From that moment on, every AI you connect can answer questions grounded in your library — with a click-through citation back to the exact minute in the source. Paid plans (Starter $9/250 credits, Pro $19/1,000, Pro+ $49/5,000) exist for higher capture volumes; the MCP connection itself works identically on every tier.

Migration and export: the safety valve

The single most important test of any memory layer is whether you can leave with your data. Portability isn't just an ethical nicety — it's the reason you're building a memory layer in the first place, and if the layer itself locks you in it defeats the purpose. BrainTube ships full JSON export on every plan, including free: source URLs, transcripts, highlights, notes and graph edges. That means if BrainTube disappears, is acquired badly, or ships a decision you disagree with, you walk out with a re-indexable corpus. Apply the same test to every alternative you evaluate: if export is gated behind the paid tier, absent, or partial, treat the corpus as impermanent no matter what the marketing says.

When persistent memory across tools is overkill

If you only ever use one AI app, and you're confident you'll still be using it in two years, per-app memory is enough — a memory layer is extra plumbing you don't need. If you're a solo user with fewer than a hundred saves, copy-paste plus a discipline of pasting a short brief into each new chat will get you 80% of the way. Persistent, portable memory earns its keep when your library is big enough that retrieval matters, or when you use two or more AI tools regularly, or when you'd like the effort you're putting in today to still be paying off after the next model migration. Those are the conditions where an MCP-connected memory layer stops being nice-to-have and starts being the point.

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