---
id: fsrs
version: "6.3.2"
license: MIT License Copyright (c) 2022 Open Spaced Repetition Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# fsrs — Free Spaced Repetition Scheduler

License: permissive · Maintenance: active · Downloads: 129.3K/mo

## 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 above — verify before relying.

Implements the Free Spaced Repetition Scheduler algorithm to calculate optimal review intervals for flashcards and study materials based on memory retention science.

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

pip install fsrs
uv add fsrs
poetry add fsrs

## Installing fsrs

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.

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}")

Requires Python 3.10 or later.

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: MIT License Copyright (c) 2022 Open Spaced Repetition Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 129.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

spaced repetition scheduler, flashcard scheduling algorithm, FSRS implementation, memory retention scheduling, review interval calculator, adaptive learning scheduler, study interval optimization, spaced-repetition, learning-algorithm, study-tool

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