fsrs
Free Spaced Repetition Scheduler
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries a permissive MIT license, and solves a well-defined problem (spaced repetition scheduling) with a proven algorithm. It's suitable for anyone building a study or flashcard system. Install it if you need scheduling logic; skip it if you're using a pre-built flashcard app.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction install with a single lightweight runtime dependency (typing-extensions).
- Active maintenance with a recent release (5 days old) and steady repository activity.
License · maintenance · safety
permissive license (permissive) — MIT License permits free use, modification, and distribution with minimal restrictions—suitable for commercial and open-source projects alike.
last release 2026-08-09 (5 days) · last repo commit 2026-08-09 · 470 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 129,257 downloads/mo, #11,677 on PyPI
Alternatives
Verify before relying
pip install fsrs
from fsrs import Scheduler, Card, Rating
scheduler = Scheduler()
card = Card()
card, review_log = scheduler.review_card(card, Rating.Good)
print(f"Next review due: {card.due}")- Whether the optimizer extra (fsrs[optimizer]) adds significant dependencies or install friction beyond the base package.
- Performance characteristics when scheduling large numbers of cards or handling high-frequency review operations.
- How closely the computed optimal parameters match real-world study outcomes compared to other SRS implementations.
What it is and what it does
Py-FSRS is a Python implementation of the Free Spaced Repetition Scheduler algorithm, which calculates when to review flashcards or study materials to maximize long-term retention. It models memory decay and uses a set of 21 tunable parameters to predict the optimal time to show each card again—balancing review frequency against forgetting probability. The package provides core classes (Scheduler, Card, Rating, ReviewLog) for building custom spaced repetition systems, with JSON serialization for persistence and network use.
The scheduler tracks three card states (Learning, Review, Relearning) and four rating levels (Again, Hard, Good, Easy), adjusting intervals based on your desired retention rate (default 90%). It supports customizable learning steps, relearning intervals for lapsed cards, and optional parameter optimization if you have historical review logs. All timestamps use UTC, and the package includes retrievability calculation to estimate the current probability of recalling a card.
Use it for
- Build a custom flashcard app or study tool that automatically schedules reviews based on memory science rather than fixed intervals.
- Optimize FSRS parameters for your own review history to improve scheduling accuracy for future study sessions.
- Integrate spaced repetition scheduling into language learning, exam prep, or professional certification platforms.
- Reschedule existing cards after parameter optimization to apply improved scheduling to your current deck.
- Calculate and monitor card retrievability (recall probability) to decide when to prioritize urgent reviews.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries a permissive MIT license, and solves a well-defined problem (spaced repetition scheduling) with a proven algorithm. It's suitable for anyone building a study or flashcard system. Install it if you need scheduling logic; skip it if you're using a pre-built flashcard app.
Install
fsrs on PyPI
Before you install
Low friction install with a single lightweight runtime dependency (typing-extensions). Active maintenance with a recent release (5 days old) and steady repository activity.
Requires Python 3.10 or later.
License in practice
MIT License permits free use, modification, and distribution with minimal restrictions—suitable for commercial and open-source projects alike.
Quickstart
pip install fsrs
from fsrs import Scheduler, Card, Rating
scheduler = Scheduler()
card = Card()
card, review_log = scheduler.review_card(card, Rating.Good)
print(f"Next review due: {card.due}")Verify before relying
- Whether the optimizer extra (fsrs[optimizer]) adds significant dependencies or install friction beyond the base package.
- Performance characteristics when scheduling large numbers of cards or handling high-frequency review operations.
- How closely the computed optimal parameters match real-world study outcomes compared to other SRS implementations.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetyping-extensions |
| Maintenance | Actively maintained 5 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 129,257 / month, #11,677 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3 |
Evidence: fsrs-6.3.2-py3-none-any.whl
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