1. BrainTube — save once, query anywhere
What it is: BrainTube compiles what you watch and read — YouTube videos, podcasts, articles, PDFs — into a knowledge base every AI can query. Chrome extension for one-click capture, automatic transcription, a Postgres-backed knowledge graph, FSRS spaced repetition for what you want to remember, and a native MCP server.
How memory actually works: MCP server. Connect it once to Claude.ai (one-click OAuth), Claude Desktop, ChatGPT via MCP connectors, Cursor, or any MCP-compatible client, and the same corpus is queryable from all of them. Answers cite back to the exact timestamp or paragraph in the source. Per-client setup steps: how to give Claude or ChatGPT long-term memory using MCP.
Best for: People whose knowledge mostly comes in as long-form video, podcasts and articles, and who want that library usable from every AI tool they touch. Positioning: save once, query anywhere.
Honest limits: Younger product than Notion or Recall; smaller plugin surface. If your day already runs inside a Notion or Obsidian workspace, keep it — BrainTube is a memory layer for AI, not a workspace replacement. And it's a knowledge-base memory, not conversation memory: it doesn't record what you said to ChatGPT yesterday.
2. Supermemory — memory primitives with an MCP server on top
What it is: A memory layer positioned at developers building AI products, with a newer personal app and an MCP server exposed on top. Its core is open source under the MIT licence (supermemoryai/supermemory, roughly 28,900 GitHub stars as of 17 August 2026).
How memory actually works: Developer API/SDK is the primary surface; the MCP server lets MCP-compatible clients read from the same store.
Best for: Developers embedding long-term memory into their own AI product — with a personal app option if you also want to use it directly.
Honest limits: Developer-first shape. The consumer experience — auto-capturing YouTube, podcasts and articles you actually consume — is not the center of the product. Compare directly: BrainTube vs Supermemory.
3. Mem0 — open-source memory layer for AI agents
What it is: An open-source memory framework for AI applications, with a hosted platform available on top. The core is Apache-2.0 licensed and is the most-starred project in this roundup — roughly 63,000 GitHub stars as of 17 August 2026.
How memory actually works: SDK integration inside your AI app. You call the memory layer explicitly — store this, retrieve that — as part of building an agent.
Best for: Engineering teams building AI agents that need long-term recall of user facts and interactions, and who want the option to self-host.
Honest limits: Not a consumer product. There's no "install and start saving" flow — you're wiring memory into code you're writing. Compare directly: BrainTube vs Mem0.
4. Zep — long-term memory for agent applications
What it is: An open-source memory server for AI apps, focused on long-term memory of user conversations and structured facts extracted from them. Apache-2.0 licensed, with roughly 4,800 GitHub stars as of 17 August 2026 — a smaller community than Mem0's but the same self-host option.
How memory actually works: Runs as a service your AI app talks to. Conversation history goes in; a graph-based memory representation comes out and gets fed back to the model as context.
Best for: Developers building conversational AI products that need durable per-user memory with structured extraction, and want an open-source core.
Honest limits: Like Mem0, this is infrastructure, not a personal app. If you want to use AI memory rather than build with it, this isn't the shape.
5. Recall — save-and-chat for articles, videos and podcasts
What it is: A capture-and-chat app for articles, YouTube, podcasts and PDFs, with strong in-app summarization. Recall states on its own site that it is used by more than 500,000 people. Paid plans are Plus at $10 per month billed yearly and Max at $38 per month billed yearly; the free plan includes 10 AI summaries per month (Recall pricing page).
How memory actually works: Chat happens inside Recall, and Recall now also exposes API and MCP access — listed on its own pricing page as a Free-tier feature — so MCP clients can query a Recall library too (see the Recall MCP docs).
Best for: People who want a single, self-contained "save and ask" app and are happy chatting with their content in one place.
Honest limits: Recall's centre of gravity is still its own in-app assistant rather than feeding external ones, and its AI summaries are capped at 10 per month on the free plan. But the earlier version of this page said Recall was not designed to feed Claude, ChatGPT or Cursor — that is no longer accurate, and we have corrected it. Compare directly: BrainTube vs Recall.
6. Notion AI — memory of your workspace, inside Notion
What it is: Notion's built-in assistant, with access to the pages, databases and wikis inside your Notion workspace.
How memory actually works: Retrieval over your Notion content, from inside Notion — and, via Notion's official MCP server, from Claude, Cursor and ChatGPT as well. What it holds is still only what lives in your workspace.
Best for: Teams and individuals who already live in Notion and want a competent assistant that can read across their pages.
Honest limits: Only your Notion content. Nothing you watched on YouTube, listened to as a podcast, or read on the open web is in it unless you manually copied it into Notion.
7. ChatGPT built-in memory
What it is: ChatGPT can remember facts about you across chats, and (in supported plans) reference your prior conversations.
How memory actually works: Native to ChatGPT. Managed inside the ChatGPT app; you can view and delete individual memories.
Best for: People whose AI usage is mostly one assistant — ChatGPT — and who want it to feel less amnesiac between sessions.
Honest limits: Locked to ChatGPT. Claude cannot read it. Cursor cannot read it. A future model you switch to next year cannot read it. This is exactly the fragmentation MCP-based memory is designed to fix.
8. Claude built-in memory
What it is: Anthropic has shipped memory features that let Claude retain context about you and your prior conversations, managed inside Claude.
How memory actually works: Native to Claude. Same shape as ChatGPT's memory in the ways that matter here: it's per-assistant.
Best for: Heavy single-assistant Claude users who want continuity inside Claude and don't need those memories to reach any other tool.
Honest limits: Same as ChatGPT's — locked to one assistant. Notably, if you also want your Claude to reason over YouTube videos and articles you've saved, that's not what built-in memory is for; that's what an MCP server (BrainTube, Supermemory) is for.
A 3-question decision guide
1. Do you use more than one AI assistant? If yes, native memory (ChatGPT's, Claude's, Notion AI's) will keep fragmenting your context. Look at MCP-based tools: BrainTube, Supermemory, Mem0, Zep — and see the step-by-step MCP memory setup guide for the config in each client. For which clients share one memory today, see one memory across ChatGPT, Claude and Cursor.
2. Do you need your own saved sources — or just recall of your conversations? Saved sources (YouTube, podcasts, articles, PDFs): BrainTube is the consumer-shaped answer. Conversation recall inside one assistant: the built-in memories cover that — for how ChatGPT, Claude and Gemini compare on their own memory, see which AI has the best memory. Building your own AI app with long-term memory: Mem0, Zep, Supermemory.
3. Do you want it to work in your editor too? If Cursor is part of your day, prefer tools that ship an MCP server — a single connection lets Cursor read the same knowledge your Claude and ChatGPT do. See the developer overview for how the BrainTube MCP endpoint plugs into MCP clients, and the wider category context in best second brain apps in 2026. If you are specifically trying to extend Claude — including Claude Code — see Claude memory alternatives.
How these figures were checked
Every external figure on this page — licences, GitHub star counts and Recall's pricing — was read directly from the vendor's own site or repository on 17 August 2026, and each is linked above so you can re-check it. Star counts move; treat them as an order-of-magnitude signal, not a live number. Where we could not verify a vendor's own documentation, we have left the claim unsourced rather than guess — that applies to the built-in memory features in ChatGPT and Claude.