open-spaced-repetition/fsrs4anki

What it solves

It replaces Anki's default scheduling system with a more modern, data-driven approach to spaced repetition. This helps users optimize when they review cards to better match their individual memory patterns and improve learning efficiency.

How it works

The project consists of two primary components:

1. The Scheduler: Implements the Free Spaced Repetition Scheduler (FSRS) algorithm to determine the timing of card reviews.
2. The Optimizer: Uses machine learning to analyze a user's specific review history and calculate the optimal parameters that fit their memory performance.

Who it’s for

Anki users who want a more personalized and efficient scheduling algorithm than the built-in one, specifically those using Anki 23.10 or newer (where integration is simplified).

Highlights

• Data-driven approach based on the FSRS algorithm.
• Includes a machine learning optimizer to personalize scheduling based on user history.
• Compatible with several popular Anki add-ons like Review Heatmap and Advanced Browser.
• Built on research pioneered by Maimemo regarding stochastic shortest path algorithms for memory dynamics.