Supertags vs Tags: Understanding Different Note-taking Paradigms (Logseq vs Tana)

Super tags in Tana are less about “labeling” and more about enforcing where information belongs—by attaching imported nodes to a predefined structure. That design choice changes how people should think about tags: in Logseq, tags mainly act as flexible pointers you can reuse and move around; in Tana, super tags behave like a schema that shapes the fields and templates attached to each imported item. The practical payoff is consistency—especially when building a large personal library or collaborating—while the tradeoff is extra setup and potential confusion between ordinary tags and super tags.

In Logseq, the workflow starts with conventional tags plus page properties. A book page imported via Readwise (for example, “The Myth of Sisyphus and Other Essays”) can include a Tags property that functions like a lightweight mapping from concepts to blocks. Highlights are then stored as blocks with metadata such as quote formatting and location. The creator uses tags to build a “library of blocks” that can be revisited later during writing: tagging “absurdism” or “nostalgia” helps retrieve relevant passages without forcing a rigid database model. Even though tags can be used for back-links (e.g., searching for a person name), the emphasis stays on flexibility—tags are treated as return points, not as strict categories.

Tana shifts the center of gravity. Imported Readwise items land in an inbox first (with an export setting that routes imports to the inbox rather than day notes), and then processed items move into day notes. Within the inbox, the highlights carry “super tags” that act like predefined templates. For instance, a highlight node includes a highlight super tag that signals the node’s type and enables access to a consistent structure, including metadata like Readwise location. The key limitation is conceptual: if someone tries to use a super tag like #suicide for a single morbid quote, the system effectively implies a whole “suicide database,” which may not match how the person wants to organize knowledge.

To handle that mismatch, the workflow uses a different field approach: instead of forcing a super tag for every concept, the creator uses a Concepts (tagged Concepts) field inside the template. Concepts like “absurdism” are then mapped per highlight, giving the benefits of structured retrieval without creating unnecessary top-level databases. This is where Tana’s strength shows up—consistent templates and inheritance-like structure—while Logseq’s strength shows up as low-friction, high-flexibility metadata.

Finally, the transcript outlines how to approximate super tag behavior in Logseq using page properties and a “type” property (e.g., setting a page type like “book”) plus input/output/event properties to mimic downstream structure. The creator also relies on templates and text expanders (e.g., book templates) to keep metadata consistent at creation time. The result is a looser system than Tana’s super tags, but one designed to support writing and knowledge management without the overhead of fully defining a rigid schema.