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Anki

Anki is a and open-source program on desktop (Windows, macOS, ) and platforms, with a paid version, designed to facilitate efficient memorization through active recall testing and algorithms, allowing users to focus more time on challenging material while minimizing reviews of familiar content. The software operates on the principle of active recall, where s present a question or prompt on one side and the answer on the other, requiring users to retrieve information from memory rather than passively rereading notes, which research shows strengthens neural pathways and improves retention. It incorporates , a method pioneered by and refined in algorithms like SM-2 (used in Anki) and the optional FSRS, which schedule card reviews at progressively longer intervals based on user performance—such as daily, then weekly or monthly—to combat the and optimize long-term learning. Developed and maintained by Ankitects Pty Ltd as a cross-platform tool available on Windows, macOS, , (free and open-source), and (paid app), Anki supports the creation of customizable decks that can include multimedia elements like images, audio clips, videos, and scientific markup (e.g., for equations). Users organize cards into hierarchical decks for targeted study sessions, rate their recall ease after each review (e.g., "Again," "Hard," "Good," or ""), and benefit from add-ons that extend functionality, such as advanced statistics or integration with external tools. Anki's synchronization service, AnkiWeb, enables seamless access to decks across devices, making it popular among students in fields like , where it aids in retaining vast amounts of complex knowledge for exams such as the USMLE.

Background and Development

History

Anki was first released on October 5, 2006, by Australian developer Damien Elmes as a desktop application for Windows, aimed at facilitating efficient memorization through techniques inspired by earlier software like . Early development focused on core functionality, with the project open-sourced under the GNU Affero General Public License (AGPL) in 2008, enabling community contributions that would later be hosted on . Version 2.0, released in October 2012, marked a significant evolution with major overhauls and the adoption of the framework for cross-platform compatibility. Subsequent updates built on this foundation; version 2.1, launched in 2018, introduced improvements to mobile syncing and quality-of-life enhancements like built-in MathJax support. In October 2023, version 23.10 integrated the FSRS (Free Scheduler) algorithm as an optional alternative to the default SM-2 scheduler, offering more personalized interval calculations based on user performance data. The latest stable release, 25.09.2 on September 17, 2025, includes performance enhancements such as improved async image decoding and polish, alongside bug fixes for custom colors and other minor issues.

Licensing and Development

Anki has been licensed under the GNU Affero General Public License version 3 (AGPLv3) since , a that ensures free redistribution, modification, and access to the source code for any distributed derivatives. The project is primarily developed by Damien Elmes, an Australian programmer, with support from a global volunteer community that contributes code, translations, and testing through the official repository at ankitects/anki. Development proceeds iteratively, with new versions released via AnkiWeb after testing to gather user feedback on features and stability, while issues and pull requests are tracked and resolved on . The project is funded primarily through proceeds from the paid app AnkiMobile, as direct donations are not accepted due to its operation as a business under Ankitects Pty Ltd. Cross-platform support for the desktop version relies on as the core language and the framework for the user interface, enabling builds for Windows, macOS, and ; mobile implementations, such as AnkiMobile for and the community-driven AnkiDroid for , are developed by separate teams to address platform-specific requirements.

Core Concepts

Spaced Repetition Algorithm

Anki's spaced repetition algorithm is derived from the SM-2 system originally developed for the software in the 1980s, with modifications to enhance user configurability and scheduling stability. Unlike the original SM-2, which uses fixed initial intervals of 1 day and 6 days, Anki allows customizable learning steps for new cards, employs four answer buttons (Again, Hard, Good, Easy) instead of six grades, and includes adjustments for late reviews by boosting subsequent intervals when cards are remembered despite delays. These changes prioritize ease of use while maintaining the core principle of extending review intervals based on successful recall. The relies on three primary parameters: the ease factor, which begins at 250% (or 2.5 in form) and can be adjusted in options with a minimum of 130%; the interval modifier, a multiplier defaulting to 100% that scales all ; and the starting ease, set to 250% for newly graduated . The next I for a is generally computed as: I = I_{\text{previous}} \times \frac{\text{ease}}{100} \times \text{interval modifier} with button-specific adjustments—such as multiplying by 1.2 for Hard or adding an easy bonus for —and ease factor modifications of ±15–20% based on the user's rating. To introduce variability and prevent card synchronization across users, a fuzz factor applies random adjustments of 0–5% to intervals exceeding 21 days for mature s. Notes and s serve as the input structures whose review ratings drive these calculations. In Anki version 23.10, released in November 2023, the Free Spaced Repetition Scheduler (FSRS) was integrated as an optional alternative to SM-2, leveraging to personalize intervals by analyzing a user's full review history. Subsequent updates include FSRS-5 in Anki 24.11 (November 2024), which introduces additional parameters to improve modeling of and integration with learning steps, enhancing scheduling accuracy. As of November 2025, FSRS remains optional, with discussions ongoing about making it the default. FSRS models memory using the three-component framework of retrievability, stability, and difficulty, deriving stability formulas from research to predict optimal review timing. A key parameter is the desired retention rate, defaulting to 90%, which users can adjust to balance review workload and recall probability—higher settings like 95% roughly halve intervals and double reviews. Custom scheduling enhances flexibility, allowing manual overrides through filtered decks or custom study sessions, and deck-specific configurations via option presets that tailor parameters like ease and intervals without affecting other decks. This enables users to adapt to individual needs while preserving the efficiency of .

Notes and Cards

In Anki, serve as the fundamental units of information storage, functioning as multi-field containers that organize content for learning. Each note typically includes fields such as "Front" and "Back" for question-and-answer pairs, along with additional customizable fields for supplementary details (e.g., a "source" field for referencing origins), and supports tags as a for . These fields support a variety of media types, including plain text, images embedded via file references, audio clips for practice, and for rendering mathematical equations and formulas. For instance, a note on might have a "Front" field with a word in the target language, a "Back" field with its definition and example sentence, an audio field for the , and tags like "" to aid . Cards are dynamically generated from notes using predefined or custom templates, transforming the structured note data into reviewable flashcards. Common template types include the basic template, which produces simple one-sided or two-sided cards displaying content from specified fields on the front (question) and back (answer); cloze deletion, which hides portions of text marked with {{c1::hidden text}} syntax to create multiple cards testing specific details; and image occlusion, which masks regions of an image (using shapes like rectangles or polygons) to quiz users on labeled parts. Card types vary by template configuration: one-sided cards show only the front for recognition tasks, two-sided cards reveal the back upon flipping, and reversed cards generate bidirectional pairs (e.g., English-to-Spanish and Spanish-to-English) from the same note to reinforce associations in both directions. This generation process allows a single note to produce multiple cards, enabling efficient coverage of related concepts without redundant data entry. Decks provide the organizational framework for notes and their derived cards, employing a hierarchical structure where subdecks are nested under decks using double colons (::) in naming conventions, such as "Language::Spanish::Vocabulary" to create a tree-like arrangement. This setup allows users to study at various levels—selecting a deck includes all subdeck cards, while subdecks enable focused sessions on subsets of material. Filtered decks extend this flexibility by creating temporary, custom study sessions that pull cards based on criteria like tags, due dates, or ratings, without permanently relocating them from their home decks; for example, a user might generate a filtered deck to cram cards tagged "" or those answered incorrectly in the past week. Management tools include suspending cards or to exclude them from reviews indefinitely (requiring manual reactivation) and burying them to postpone appearance until the next day, helping users skip duplicates or overwhelming content during sessions. The review process in Anki begins with new cards drawn from the selected , progressing through initial learning steps with short intervals—such as 1 minute followed by 10 minutes—based on user responses to gauge initial retention. As cards advance, they gain maturity through repeated successful , transitioning from "Learning" to "" status, where algorithms schedule subsequent appearances to optimize long-term recall. This structure ensures that notes and cards are reviewed in a targeted manner, adapting to the user's performance while maintaining the hierarchical organization of .

Key Features

Synchronization and Multi-Device Support

AnkiWeb serves as the primary cloud-based service for synchronizing Anki users' decks, files, and review history across multiple devices, enabling seamless access and updates without data loss. The service automatically merges compatible changes, such as added or reviewed cards, while prompting users to choose or for unmergeable conflicts like simultaneous edits to note fields or templates. This protocol ensures review progress and content modifications propagate reliably between devices, with initial setups requiring users to select whether to local data or from the . Synchronization occurs over encrypted connections to protect , though content stored on AnkiWeb is not encrypted at rest. Anki supports offline usage by maintaining local collections on devices, with manual or automatic sync triggers (defaulting to on app open/close) resuming when connectivity is restored. Integration spans desktop applications, mobile versions like AnkiDroid and AnkiMobile, and the web interface, allowing users to switch devices mid-session with minimal disruption after syncing. Storage limits apply to the collection file (containing decks, notes, and scheduling data) at 100 MB compressed or 250 MB uncompressed, while media files have no total size cap but are restricted to 100 MB per individual file. For privacy, AnkiWeb keeps user data private by default, storing only necessary elements like content, , and logs without sharing them publicly or with third parties except in cases of legal requests or investigations. Users can opt for local-only operation by disabling sync in preferences, avoiding uploads entirely, and export their data via AnkiWeb account settings or app tools in formats like .apkg for backups or transfers. Inactive accounts face data deletion after six months, underscoring the importance of regular exports for long-term retention.

Add-ons and Customization

Anki's extensibility is primarily facilitated through its add-on system, which allows users to enhance functionality by integrating community-developed plugins hosted on the AnkiWeb . As of mid-2025, the repository features nearly 2,000 add-ons, enabling modifications ranging from tweaks to advanced algorithmic adjustments. These add-ons are predominantly written in and installed by downloading code files—either as single .py scripts or zipped packages—directly through Anki's built-in add-on manager, which handles integration into the application's codebase. Among the most widely adopted add-ons are those that address specific user needs for and tracking. Image Occlusion Enhanced, for instance, permits users to mask portions of images or diagrams to create interactive cloze-style cards, streamlining the study of visual materials like illustrations. Review Heatmap visualizes daily review streaks and session intensities on a calendar-style graph, helping users monitor study habits over time. Additionally, FSRS Helper integrates the Free Scheduler (FSRS) , allowing fine-tuning of scheduling parameters to optimize retention rates beyond Anki's default system. Beyond add-ons, Anki supports direct of its and parameters without external . Users can themes using CSS in the card styling editor, adjusting colors, fonts, and layouts for better readability or personal aesthetics. Keyboard shortcuts are configurable via built-in preferences or dedicated add-ons, enabling remapping of actions like revealing answers or navigating cards to suit individual workflows. Deck options presets further allow batch application of settings such as interval modifiers or new card limits across multiple decks, with basic card templates providing foundational and CSS customization for front and back sides. However, the modular nature of add-ons can lead to conflicts, particularly when multiple plugins modify the same UI elements or database queries, resulting in crashes or unexpected behavior during reviews. Troubleshooting often involves the debug console, a built-in REPL accessible via Tools > Debug Console, where users can execute Python commands to inspect variables, reload modules, or disable problematic add-ons for isolation. The Anki community maintains guidelines for add-on development to ensure reliability and , emphasizing adherence to the AGPLv3 license for all contributions that extend the desktop application. Developers are encouraged to include version compatibility metadata in their add-on manifests, specifying supported Anki releases (e.g., 2.1.50+), and to test against upcoming updates using tools like compatibility checkers. This practice helps mitigate breakage from Anki's iterative releases, with the official add-on documentation providing templates for hooks, configuration screens, and error handling.

Language-Specific Tools

Anki includes built-in support for annotations, enabling the display of small superscript text such as or and above base characters in flashcards. Users enter annotations manually in note fields using the notation base text[annotation], for example, 日本語[にほんご] for or 你好[nǐ hǎo] for with tones. In card templates, the {{furigana:FieldName}} filter processes this input to render the ruby text correctly across platforms, while {{kana:FieldName}} extracts only the annotations and {{kanji:FieldName}} shows only the base text. For Chinese learners, Anki's Unicode compatibility handles tonal marks natively during text input and display, allowing seamless integration of or with tone diacritics like , , or ㄧ, ㄨ in notes. Users can incorporate animations or character decomposition (breaking hanzi into radicals and components) by embedding images or in fields, which templates then format for readings and visual aids during reviews. These features integrate directly with note fields, supporting customized card layouts without external dependencies. The desktop version of Anki supports interfaces in multiple languages, configurable via the preferences menu, while the mobile version (AnkiMobile) supports several languages, determined by device settings. Right-to-left scripts, such as , are handled through built-in field options and CSS styling in templates (e.g., direction: rtl), ensuring proper text flow and bidirectional mixing with left-to-right content like English. Despite these capabilities, Anki's language tools have limitations: ruby annotations require manual entry or user-supplied data for accuracy, with no built-in dictionaries for automatic generation of , , or decompositions. The system provides annotation and display only, without real-time translation or advanced processing like stroke recognition.

Platforms and Availability

Desktop Implementation

The desktop version of Anki is available for and later, macOS 12 and later, and recent Linux distributions from 2023 onwards, provided as standalone installers downloadable from the official AnkiWeb site. As of 2025, it is built using 3.13 and 6.9, enabling cross-platform compatibility while maintaining a native on each operating system. The core interface centers on the Decks screen, which functions as the deck browser, displaying a hierarchical list of decks with options to create, rename, or reorganize them, along with overview statistics like due cards and deck options presets. Users access the card via the Add screen, where they can input note fields, select note types, and preview rendered cards in real-time before saving. For advanced management, the window allows searching across cards and notes using flexible queries, bulk , tagging, and suspension, supporting efficient handling of extensive collections. Review tracking is facilitated through the Statistics screen, featuring graphs for daily reviews, retention rates, and long-term progress, customizable by time periods or deck subsets. Anki's system requirements are low, suitable for most modern hardware, though higher amounts of (at least 8 ) are recommended for smooth performance with large, media-rich decks. Initial requires around 200 MB of , which increases with added . Performance is optimized via database indexing and vacuuming tools, allowing smooth operation with large decks exceeding 100,000 cards on modern hardware with sufficient resources, provided regular maintenance like database checks is performed. Installation involves downloading the appropriate installer from AnkiWeb, running it to set up the application, with built-in prompts for updates upon new releases; portable versions are offered for Windows and , enabling USB-based use without system integration. This implementation supports seamless with AnkiWeb for multi-device continuity.

Mobile and Web Versions

AnkiMobile is the official iOS application for Anki, available exclusively through the Apple as a paid download priced at $24.99 (requires iOS 15.2 or later), with all proceeds funding the broader development of Anki. Released in and developed by Ankitects Pty Ltd, it provides full compatibility with Anki's system while adhering to Apple's app distribution policies that limit free third-party alternatives due to review requirements for unreviewed code. AnkiDroid serves as the Android counterpart, a free and open-source implementation licensed under GPLv3 and maintained by a volunteer community via GitHub (requires Android 8.0 or later). It is distributed through the Google Play Store and F-Droid, enabling easy installation on Android devices without cost. Both mobile apps feature touch-optimized user interfaces designed for portability, including gesture-based controls such as swipes and taps to rate card answers (e.g., left for "Again," right for "Good") and support for background audio playback during reviews to accommodate multitasking. However, they lack support for add-ons, a feature restricted on mobile platforms due to operating system limitations on third-party code execution, and some advanced editing capabilities are better suited to the desktop version. AnkiWeb functions as the free, browser-based reviewer accessible via any , primarily for conducting card reviews without the need for local installation. It facilitates syncing of review progress and across devices but prohibits deck editing or creation, and its online-only nature limits offline functionality, requiring an connection for all interactions. The and versions integrate with Anki's service to enable seamless cross-platform use.

Applications and Community

Educational and Professional Uses

Anki finds extensive application in educational contexts, particularly for language learning, where users employ cloze deletion cards to build through fill-in-the-blank prompts that reinforce contextual recall. This approach leverages to enhance retention of words and phrases, with studies demonstrating that spaced practice yields medium-to-large effects on outcomes, such as improved vocabulary knowledge in delayed tests. In , Anki is a staple tool for preparing for the (USMLE), with surveys indicating that approximately 70% of first-year medical students across U.S. institutions incorporate it into their study routines for subjects like and . Usage rates can reach 94% in specific cohorts, where Anki users spent an average of 73.86% of their study time using the app. Professionally, Anki supports certification preparation in fields like (IT) and , where it facilitates memorization of standards, protocols, and through customizable decks. For IT exams such as certifications, practitioners use Anki to drill principles and network concepts. In , pilots apply Anki for ongoing skill retention, creating cards for regulations, procedures, and emergency protocols to maintain proficiency amid irregular training schedules. A 2022 meta-analysis of systems underscores these benefits in second language learning, confirming superior long-term recall compared to massed practice. Anki integrates into formal curricula, such as massive open online courses (MOOCs) on platforms like , where instructors recommend it for reinforcing lecture material through supplemental reviews. Language platforms like also pair with Anki for extended practice, allowing users to export vocabulary into custom decks for targeted repetition beyond gamified lessons. Despite its efficacy, Anki presents challenges, including substantial initial setup time for creating or curating decks, which can deter . Over-reliance poses risks, such as superficial without deeper , potentially leading to if reviews exceed 200-300 cards daily without balancing active application. To mitigate these, effective deck creation emphasizes concise prompts focused on active , limiting new cards to 20-50 per day, and incorporating images or contexts to promote understanding over rote repetition.

Shared Resources and Ecosystem

AnkiWeb serves as the primary platform for sharing flashcards, hosting thousands of public decks categorized by topics including languages, sciences, , and . Users can easily upload their creations or existing ones, with features like user ratings, download counts, and comments enabling and quality assessment. As of 2025, this repository supports a diverse range of educational content, from basic vocabulary sets to advanced professional study materials, all accessible without cost. The Anki community fosters collaboration through dedicated forums on AnkiWeb and the subreddit r/Anki, where participants exchange tips on deck design, optimization, and ethical sharing practices. Guidelines emphasize creating original content and obtaining permissions for any third-party materials to avoid ; AnkiWeb implements automated checks and reporting mechanisms for violations, ensuring compliance with international laws. These discussions promote high standards, such as clear sourcing, minimal redundancy, and accessibility for diverse learners. The ecosystem has expanded via collaborative initiatives, notably medical projects like the AnKing decks, which aggregate and refine flashcards for exams such as the USMLE through crowdsourced contributions. Language-specific efforts thrive on platforms like , with repositories offering modular decks for languages including , , and , often incorporating audio and cultural notes. Integration with Anki's import functionality allows for efficient bulk sharing and adaptation, enabling groups to merge or customize resources seamlessly. Add-ons further support this by facilitating real-time collaboration on shared decks. Although the core sharing model on AnkiWeb is free and open, some creators monetize their work by offering premium decks—enhanced with exclusive content or professional curation—via independent marketplaces, while upholding the community's emphasis on accessible knowledge.

Comparisons

Algorithmic Differences

Anki's core scheduling relies on principles, with its algorithms evolving from the foundational SM-2 to the more advanced FSRS. The baseline SM-2 algorithm, implemented since Anki's inception, uses fixed multipliers to adjust intervals based on user ratings. For review cards, pressing "Hard" multiplies the previous interval by 120% while decreasing the ease factor by 15%; "Good" multiplies the interval by the current ease factor (default starting value of 250%) without altering ease; and "Easy" multiplies the interval by the easy bonus (default 130%) times the ease factor, increasing ease by 15%. In contrast, FSRS employs probabilistic modeling through three key parameters: difficulty (how hard the card is), stability (memory retention duration), and retrievability (recall probability at review time). These parameters are dynamically updated after each review using machine learning trained on the user's historical data to target a desired retention rate, typically 90%. As of 2025, FSRS has seen updates like version 5, with plans to become the default algorithm in late 2025 or early 2026. Prior to Anki version 23.10 in late , scheduling depended entirely on SM-2 with user-configurable tweaks such as interval modifiers and maximum intervals to fine-tune behavior across decks. Following integration, FSRS became available as an optional per-deck alternative, allowing hybrid setups where users can enable it selectively while retaining SM-2 for others, though full optimization requires consistent use across devices. Customization in both algorithms significantly influences scheduling, particularly through learning steps for new cards, which determine initial exposure intervals before to reviews. For instance, default steps of 1 minute and 10 minutes can be adjusted to 1 day and 10 days, extending early intervals and thereby reducing short-term review burden but potentially increasing long-term repetitions if retention suffers. Such changes propagate to base intervals in SM-2 by altering timing, while in FSRS, they feed into parameter training, affecting estimates and overall workload distribution. FSRS offers advantages over SM-2 by minimizing unnecessary reviews through data-driven predictions, with benchmarks indicating 20-30% fewer reviews needed to achieve equivalent retention levels, though it demands sufficient user data (ideally thousands of reviews) for effective optimization.

Alternatives to Anki

, developed by Piotr Wozniak since the 1980s, predates Anki and employs more intricate algorithms, such as SM-18, which uses advanced models to estimate changing item difficulty and optimize review schedules based on patterns and individual variability. Unlike Anki's free, open-source, and cross-platform availability, SuperMemo is a paid software primarily focused on Windows, offering advanced features like but with a steeper due to its complexity. Quizlet and Memrise represent more accessible, gamified alternatives that prioritize user engagement over deep customization. employs a basic form of within its cloud-based platform, emphasizing collaborative study sets, social sharing for classes, and interactive modes like to foster motivation, though it lacks Anki's granular control over card scheduling and open-source extensibility. Memrise similarly adopts a cloud-first approach with elements such as progress streaks and AI-driven feedback, incorporating social connections via native speaker videos for language learning, but provides less flexible customization compared to Anki's model. Mnemosyne offers a minimalist, , and open-source option that relies on the SM-2 algorithm—similar to early versions of Anki's system—for scheduling reviews based on user-rated difficulty, featuring a straightforward without support or add-on ecosystem. It supports cross-platform use but lacks built-in mobile applications or seamless syncing across devices, making it suitable for simple, research-oriented memorization rather than dynamic, multi-device workflows. In the broader market, Anki maintains a strong position through its extensive add-on ecosystem—hosting thousands of community-developed extensions on AnkiWeb—and widespread adoption in , where pre-made decks and integrations with resources like and AMBOSS aid in preparing for exams such as USMLE Step 1. However, Anki's technical setup can deter beginners, contrasting with Duolingo's integrated within gamified, lesson-based language courses that emphasize ease of entry and daily habits over standalone depth. Anki's FSRS implementation represents a modern algorithmic update enhancing its retention efficiency.

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