fsrs4anki

FSRS4Anki ✨ A modern spaced-repetition scheduler for Anki based on the Free Spaced Repetition Scheduler algorithm ✨ Table of contents Introduction How to Get Started? Add-on Compatibility Contribute Contributors Developer Resources Stargazers Over Time Acknowledgements Introduction FSRS4Anki (Free Spaced Repetition Scheduler for Anki) consists of two main parts: the scheduler and the optimizer. The scheduler replaces Anki's built-in scheduler and schedules the cards according to the FSRS algorithm. The optimizer uses machine learning to learn your memory patterns and finds parameters that best fit your review history. For details about the working of the optimizer, please read the mechanism of optimization. Research on the FSRS algorithm follows the path pioneered by Maimemo: data-driven, balancing interpretability and verifiability. See also: Maimemo: A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling (free access) [中文版], and Maimemo: Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory (Google Scholar) [中文版]. FSRS Helper is an Anki add-on that complements the FSRS4Anki Scheduler. You can read about it here: https://github.com/open-spaced-repetition/fsrs4anki-helper How to Get Started? If you are using Anki 23.10 or newer, refer to this section of the Anki manual. If you are using an older version of Anki, refer to this tutorial. Note that setting up FSRS is much easier in Anki 23.10 or newer. Add-on Compatibility Some add-ons can cause conflicts with FSRS. As a general rule of thumb, if an add-on affects a card's intervals, it shouldn't be used with FSRS. Add-on Compatible? Comment Review Heatmap Yes ✅ Doesn't affect anything FSRS-related. Advanced Browser Yes ✅ Please use the latest version. Advanced Review Bottom Bar Yes ✅ Please use the latest version. The KING of Button Add-ons Yes ✅ Please use the latest version. Pass/Fail Yes ✅ Pass is the equivalent of Good, Fail is the equivalent of Again. AJT Card Management Yes ✅ Compatible with Anki 23.12 and newer. Incremental Reading v4.11.3 (unofficial clone) Unsure ❓ If you are using the standalone version of FSRS, it shows the interval given by Anki's built-in scheduler, not the custom scheduler. This add-on is technically compatible with built-in FSRS, but FSRS was not designed for incremental reading, and FSRS settings do not apply to IR cards because they work in a different way compared to other card types. Delay siblings No ❌ Delay siblings will modify the intervals given by FSRS. However, the FSRS Helper add-on has a similar feature that works better with FSRS. Please use the FSRS Helper add-on instead. Auto Ease Factor No ❌ The Ease Factor is no longer relevant when FSRS is enabled, therefore you won't benefit from using this add-on. autoLapseNewInterval No ❌ The New Interval setting is no longer relevant when FSRS is enabled, therefore you won't benefit from using this add-on. Straight Reward No ❌ The Ease Factor is no longer relevant when FSRS is enabled, therefore you won't benefit from using this add-on. Let me know via issues if you want me to check compatibility between FSRS and some add-on. Contribute You can contribute to FSRS4Anki by beta testing, submitting code, or sharing your data. If you want to share your data with me, please fill out this form: https://forms.gle/KaojsBbhMCytaA7h8 Contributors Expertium ⚠️ 📖 🔣 🤔 🐛 user1823 ⚠️ 📖 🔣 🤔 🐛 Christos Longros 🔣 🖋 Developer Resources If you're a developer considering using the FSRS algorithm in your own projects, we've curated some valuable resources for you. Check out the Awesome FSRS repository, where you'll find: FSRS implementations in various programming languages Related papers and research Example applications using FSRS Other algorithms and resources related to spaced repetition systems This carefully curated list will help you better understand FSRS and choose the right implementation for your project. We encourage you to explore these resources and consider contributing to the FSRS ecosystem. Research Resources For those new to spaced repetition algorithms, we recommend starting with our comprehensive guide: Spaced Repetition Algorithm: A Three-Day Journey from Novice to Expert Dive deeper into the academic foundations of FSRS and spaced repetition through our curated collection of Datasets, Code & Research Papers Explore our extensive collection of Research Notebooks documenting detailed analyses and experiments with FSRS and spaced repetition algorithms Stargazers Over Time Acknowledgements A special thanks to 墨墨背单词 (MaiMemo) for their support of FSRS development by allowing its research engineer, Jarrett Ye, to dedicate part of his working hours to this open-source project. This greatly helps in the continuous improvement and maintenance of FSRS for the benefit of the entire community. We would also like to extend our sincere gratitude to Damien Elmes of Ankitects for his invaluable technical support, and to the AnkiWeb users for the review history dataset. Without their collective contribution, FSRS would not have achieved its current level of popularity and influence.