Introduction | 简体中文 ｜はじめに

---

About

ts-fsrs is a versatile package written in TypeScript that supports ES modules, CommonJS, and UMD. It implements the Free Spaced Repetition Scheduler (FSRS) algorithm, enabling developers to integrate FSRS into their flashcard applications to enhance the user learning experience.

You can find the state transition diagram for cards here:

• google drive: ts-fsrs-workflow.drawio (You're free to leave comments)
• github: ts-fsrs-workflow.drawio

Development

For contributors and developers who want to work on ts-fsrs itself, we provide a Dev Container setup for a consistent development environment.

📖 Development Quickstart Guide - Get started with VS Code Dev Containers

Usage

ts-fsrs (scheduler)

ts-fsrs@3.x requires Node.js version 16.0.0 or higher. Starting with ts-fsrs@4.x, the minimum required Node.js version is 18.0.0. From version 3.5.6 onwards, ts-fsrs supports CommonJS, ESM, and UMD module systems.

npm install ts-fsrs
yarn add ts-fsrs
pnpm install ts-fsrs
bun add ts-fsrs

Example

import {createEmptyCard, formatDate, fsrs, generatorParameters, Rating, Grades} from 'ts-fsrs';

const params = generatorParameters({ enable_fuzz: true, enable_short_term: false });
const f = fsrs(params);
const card = createEmptyCard(new Date('2022-2-1 10:00:00'));// createEmptyCard();
const now = new Date('2022-2-2 10:00:00');// new Date();
const scheduling_cards = f.repeat(card, now);

for (const item of scheduling_cards) {
    // grades = [Rating.Again, Rating.Hard, Rating.Good, Rating.Easy]
    const grade = item.log.rating
    const { log, card } = item;
    console.group(`${Rating[grade]}`);
    console.table({
        [`card_${Rating[grade]}`]: {
            ...card,
            due: formatDate(card.due),
            last_review: formatDate(card.last_review as Date),
        },
    });
    console.table({
        [`log_${Rating[grade]}`]: {
            ...log,
            review: formatDate(log.review),
        },
    });
    console.groupEnd();
    console.log('----------------------------------------------------------------');
}

More resources:

• Docs - Github Pages
• Example.html - Github Pages
• Browser (ts-fsrs package using CDN)
• ts-fsrs-demo - Next.js+Hono.js+kysely
• spaced - Next.js+Drizzle+tRPC

@open-spaced-repetition/binding (optimizer)

⚠️ Beta Notice: This package is currently in beta. The API may change at any time.

For computationally intensive tasks like parameter optimization, we provide a high-performance optimizer based on fsrs-rs and napi-rs:

• Requirements: Node.js >= 20.0.0
• Use Cases: Parameter optimization, batch processing of large datasets
• Platform Support: Windows, macOS, Linux (x64/arm64), Android, WebAssembly
• ⚠️ Limitations:
  • Cannot run in edge-runtime environments (edge-runtime does not support WASI)
  • For WASM support, additional configuration is required

Installation

Basic Installation (Automatic platform detection):

npm install @open-spaced-repetition/binding
pnpm install @open-spaced-repetition/binding
yarn add @open-spaced-repetition/binding
bun add @open-spaced-repetition/binding

WebAssembly Installation:

To install the WASM version, configure your package manager:

• pnpm: Add to pnpm-workspace.yaml:

  supportedArchitectures:
    cpu: [current, wasm32]

• yarn: Requires yarn v4. Configure supportedArchitectures as detailed in the NAPI-RS documentation

• Force WASM mode: Set the environment variable:

  NAPI_RS_FORCE_WASI=1

Basic Example

See: packages/binding/tests/demo/simple.ts

import { computeParameters, convertCsvToFsrsItems } from '@open-spaced-repetition/binding';

// Optimize FSRS parameters from review history
const parameters = await computeParameters(fsrsItems, {
  enableShortTerm: true,
  timeout: 100,
  progress: (cur, total) => console.log(`${cur}/${total}`)
});

Note: The binding package provides native performance for parameter optimization. For regular card scheduling, use the ts-fsrs package above.

Basic Use

1. Initialization:

To begin, create an empty card instance and set the current date (default: current time from the system):

import { Card, createEmptyCard } from "ts-fsrs";
let card: Card = createEmptyCard();
// createEmptyCard(new Date('2022-2-1 10:00:00'));
// createEmptyCard(new Date(Date.UTC(2023, 9, 18, 14, 32, 3, 370)));
// createEmptyCard(new Date('2023-09-18T14:32:03.370Z'));

2. Parameter Configuration:

The library has multiple modifiable "SRS parameters" (settings, besides the weight/parameter values). Use generatorParameters to set these parameters for the SRS algorithm. Here's an example for setting a maximum interval:

import { Card, createEmptyCard, generatorParameters, FSRSParameters } from "ts-fsrs";
let card: Card = createEmptyCard();
const params: FSRSParameters = generatorParameters({ maximum_interval: 1000 });

3. Scheduling with FSRS:

The core functionality lies in the repeat function of the fsrs class. When invoked, it returns a set of cards scheduled based on different potential user ratings:

import {
  Card,
  createEmptyCard,
  generatorParameters,
  FSRSParameters,
  FSRS,
  RecordLog,
} from "ts-fsrs";

let card: Card = createEmptyCard();
const f: FSRS = new FSRS(); // or const f: FSRS = fsrs(params);
let scheduling_cards: RecordLog = f.repeat(card, new Date());
// if you want to specify the grade, you can use the following code: (ts-fsrs >=4.0.0)
// let scheduling_card: RecordLog = f.next(card, new Date(), Rating.Good);

4. Retrieving Scheduled Cards:

Once you have the scheduling_cards object, you can retrieve cards based on user ratings. For instance, to access the card scheduled for a 'Good' rating:

const good: RecordLogItem = scheduling_cards[Rating.Good];
const newCard: Card = good.card;

Get the new state of card for each rating:

scheduling_cards[Rating.Again].card
scheduling_cards[Rating.Again].log

scheduling_cards[Rating.Hard].card
scheduling_cards[Rating.Hard].log

scheduling_cards[Rating.Good].card
scheduling_cards[Rating.Good].log

scheduling_cards[Rating.Easy].card
scheduling_cards[Rating.Easy].log

5. Understanding Card Attributes:

Each Card object consists of various attributes that determine its status, scheduling, and other metrics:

type Card = {
  due: Date;             // Date when the card is next due for review
  stability: number;     // A measure of how well the information is retained
  difficulty: number;    // Reflects the inherent difficulty of the card content
  elapsed_days: number;  // Days since the card was last reviewed
  scheduled_days: number;// The interval of time in days between this review and the next one
  learning_steps: number;// Keeps track of the current step during the (re)learning stages
  reps: number;          // Total number of times the card has been reviewed
  lapses: number;        // Times the card was forgotten or remembered incorrectly
  state: State;          // The current state of the card (New, Learning, Review, Relearning)
  last_review?: Date;    // The most recent review date, if applicable
};

6. Understanding Log Attributes:

Each ReviewLog object contains various attributes that represent a review that was done on a card. Used for analysis, undoing the review, and optimization (WIP).

type ReviewLog = {
    rating: Rating; // Rating of the review (Again, Hard, Good, Easy)
    state: State; // State of the review (New, Learning, Review, Relearning)
    due: Date;  // Date of the last scheduling
    stability: number; // Stability of the card before the review
    difficulty: number; // Difficulty of the card before the review
    elapsed_days: number; // Number of days elapsed since the last review
    last_elapsed_days: number; // Number of days between the last two reviews
    scheduled_days: number; // Number of days until the next review
    learning_steps: number; // Keeps track of the current step during the (re)learning stages
    review: Date; // Date of the review
}