# recall vs innernet > recall remembers what you consumed. innernet remembers what you decided. by innernet · compare · checked 2026-09-03 https://innernet.live/company/compare/recall-vs-innernet --- ## the short version recall remembers what you consumed. innernet remembers what you decided. recall is a very good AI knowledge base for the things you read, watch and listen to — save from anywhere, get a summary, get tagged automatically, chat with it across several models, and get quizzed so it sticks. Hundreds of thousands of people use it and the execution is strong. But a library of what you took in from the world is a different object from a record of your own judgment. The first tells an AI what you've been exposed to. Only the second tells it how you think. ## what recall is building *"Save, summarize, and chat with your articles, videos, podcasts, PDFs, and notes — with ChatGPT, Claude, or Gemini."* One-click saving from 15+ sources including YouTube and Spotify, automatic AI summaries with audio playback, smart tagging that organises without you, a multi-model chat interface, spaced-repetition quizzes for retention, and a connection map that links related ideas across everything you've saved. Browser extensions across every major browser plus iOS and Android. Free tier, premium above it. The knowledge-graph-of-what-you-consume idea is well realised, and the retention layer on top is a smart addition most competitors skip. ## what innernet is building A personal memory layer that the AI tools you already use read from. `npx innernet` connects Claude Code, Cursor, Codex, Windsurf, Gemini CLI, Claude Desktop, VS Code, Cline, Zed and Continue to a single memory, with an MCP endpoint for everything else. netti — one per person — files what accumulates into Context Maps: dimensions that read as living documents, nodes for the people and decisions that recur, commits and branches you can work inside. The material is your own reasoning, not the world's. ## where they diverge ### 1. inbound vs. outbound memory recall's corpus arrives from outside: articles, videos, podcasts. innernet's comes from inside: what you concluded, why, and what changed it. Both are memory. Only one of them can answer *"why did we go with this architecture"* — and that question comes up far more often than *"what did that podcast say."* ### 2. connections between sources vs. conclusions about you recall's connection map links ideas across the things you saved — genuinely clever, and useful for research. But a link between two articles is a fact about the articles. innernet's dimensions are statements about your project and your thinking, with sources attached and a history showing when each one changed. The graph is about a different subject. ### 3. remembering it yourself vs. your tools remembering The spaced-repetition layer says something about recall's goal: it wants the knowledge in *your* head. innernet has no interest in your recall — it wants your tools to arrive already informed so you don't have to hold it at all. Opposite theories of where memory should live, and both are defensible. ## side by side | | recall | innernet | |---|---|---| | Material | content you consume | reasoning you produce | | Structure | auto-tags and a connection map across sources | dimensions and nodes about your work | | Where memory should live | partly in your head, via spaced repetition | in your tools, so it doesn't have to | | AI's role | chat over what you saved | writes and updates conclusions | | Reachable by Claude Code / Cursor | not the surface it's built for | yes — one command | | Change over time | the library grows | positions versioned, branchable, roll-back-able | | Best question it answers | "what did that source say?" | "why did I decide that, and when did I change my mind?" | ## where recall is genuinely better - **Capture breadth.** YouTube, Spotify, podcasts, PDFs, articles, from any browser. We're much narrower. - **Summarisation and audio playback.** Good at turning long material into something usable quickly. - **The connection map.** Surfacing links between things you saved months apart is a real feature and a pleasant one. - **Spaced repetition.** Almost nobody else bothers, and for actual learning it works. - **Scale and polish.** A large user base and a mature product across every platform. ## where innernet is stronger - **It holds decisions and reasoning**, which is the memory that changes what an AI does next. - **It's read inside the tools you work in**, without you opening anything. - **Change is first-class.** Positions are versioned and branchable; the view you abandoned stays readable. - **Per-fact disclosure.** Every fact carries how far it may travel, enforced when a tool reads. - **netti forms a view.** Not a tidy library of what you saved, but a statement of what it adds up to. ## which one you want **Use recall if** you consume a lot and want it saved, summarised, connected and retained. **Use innernet if** you want the AI tools you work in to understand your projects and your decisions without being told again each session. **Both is sensible.** One is your reading; the other is your reasoning. ## the question we actually get > "Recall already builds a knowledge graph of everything I've saved. Isn't yours just the same idea?" Same technique, different subject. Their graph connects sources to each other. Ours connects facts to *you* — what you decided, on what evidence, and which earlier position it replaced. You could read every node in a content graph and still not know how the person using it thinks, which is exactly the thing an AI needs in order to be useful to them. --- *Accurate as of 3 September 2026, based on recall's public site. If we've described their product incorrectly, tell us and we'll correct it.*