Shellf vs Bookmory: AI Recommendations vs Reading Timer (2026)

Bookmory is one of the most polished reading-habit apps on the stores. The core loop is about protecting time for reading: open the app, hit the timer, log your session. The calendar heatmap fills in. The streak grows. OCR lets you capture a passage with a camera snap and keep it searchable in notes. Everything is tuned to support a daily reading practice.

Shellf isn’t trying to do that job. There’s no reading timer, no session calendar, no OCR. The product is aimed at book discovery and library organisation: AI recommendations on a ~51k-book catalogue, per-trait rating tags that feed those recommendations, custom shelves with tag rules, LLM-generated reading insights.

Pick the one that matches the job-to-be-done. If reading more is the problem, Bookmory is purpose-built for that. If figuring out what to read next is the problem, Shellf is.

Bookmory is available on iOS and Android today, with 15-language localisation: Korean, Japanese, Arabic, German, Spanish, French, Indonesian, Italian, Polish, Portuguese, Russian, Turkish, Ukrainian, Chinese, and English. That’s a serious multilingual investment and it shows particularly in the Asian market where they’ve earned strong store ratings.

Shellf is Android today, iOS mid-2026, English-first. Localisation is planned but not shipped. If you read in a non-English language or on iOS, Bookmory is the more complete product right now — not close, honestly.

If you’re English-speaking on Android, the platform/language dimension probably isn’t your deciding factor, and the discussion moves to what each app actually does.

This is the main functional gap. Bookmory’s public marketing doesn’t pitch AI-powered discovery; the product is structured around tracking what you’re already reading. Shellf’s main bet is the opposite: V2.1 embeddings place each book into a high-dimensional taste space, your reading clusters into 4–7 taste centroids, and an LLM explains why a given suggestion fits.

Per-trait rating tags feed that engine. Readers mark a book’s individual traits (plot, dialogue, characters, pacing) as loved or hated. That gives the recommendation system richer signal than an overall star rating — the AI knows more about why you loved or disliked a given book, not just how many stars you gave it.

If AI-driven discovery is important to you, Shellf’s the right pick. If it’s not, Bookmory does its job well.

Bookmory’s note-taking is the cleverest piece of their product, in my opinion. OCR a page from a physical book, tag it, and the text is searchable later. Combined with the calendar view and timer, this creates a reading-log that feels closer to a journal than a spreadsheet. For readers who take lots of notes on paper books, it’s a real draw.

Shellf’s library leans a different direction: custom shelves with tag rules, any/all matching, DNF with reasons, half-star ratings plus per-trait rating tags. Voice dictation for notes rather than OCR. The shape is “organise a library around meaning” rather than “capture a reading session”.

Neither is strictly better — they optimise for different workflows. If you take physical notes and need searchable capture, Bookmory is the cleaner answer. If you want expressive library structure and fine-grained rating signal, Shellf is.

If you’re coming from Goodreads, Shellf’s WebView import is the fastest path I know of: sign in through the app and the library transfers in one tap. Bookmory’s import story is less prominent; you’d typically rebuild from your existing tracking or import a CSV if available.

Running both is straightforward: Bookmory for the actual reading sessions and OCR notes, Shellf for the library and discovery layer. CSV round-trip takes about two minutes if and when Bookmory exposes an export.

My practical suggestion: if you’re an Android reader and discovery matters to you, try Shellf alongside whatever habit tool you use today. If you love Bookmory’s timer and calendar and you don’t want to change that workflow, Shellf can live alongside it cleanly.