AnkiWeb addon 172454001
Creates interactive drag-and-drop flashcards where you label images or sort items into categories, with instant feedback, shuffling, theme and layout options, across Anki Desktop, AnkiDroid and AnkiMobile.
Open on AnkiWeb GitHub Find alternatives
active
| Min Anki | Max Anki | Updated |
|---|---|---|
| 23.09 | 25.07+ | 2026-10-02 |
Loading…
Builds sentences by arranging interactive word tiles instead of typing answers, with customizable card layout, optional answer check, and cross-platform Anki support.
Widens the editor's first field and adds focus-independent shortcuts for previewing next or previous notes and opening an image-search link, speeding intensive image drag-and-drop.
Randomizes image-occlusion answer-box placement each review and draws an arrow to the target, supporting forward/reverse/typing modes, context labels, and single-card cycling.
Automatically resizes and aligns images pasted or drag-dropped into Anki's editor using configurable strategies, with deck-specific defaults and toolbar controls.
Creates interactive medical flowcharts and mind maps inside Anki, with drag-and-drop editing, auto-cloze cards, JSON/AI import, alignment tools, and click-to-reveal review.
Adds visual Browser layouts, tabs, zoom, explorer-style navigation, drag-and-drop organization, folder colours, and a grid-based note-type manager with counters, quick editors, nested folders, and JSON import/export.
Drag Drop turns your Anki cards into interactive drag-and-drop flashcards. Instead of only looking at an image or reading a question, you can actively recall the answer by dragging labels to the correct place or sorting items into the right categories.
It is especially useful for diagrams, anatomy, maps, charts, processes, illustrations, and category-based questions, but you can use it for many different subjects and types of study.
The add-on is designed to make visual learning and active recall more interactive while keeping the card simple and easy to use.
Drag labels to the correct places on an image
Create visual image-labeling cards
Drag labels to the correct places on an image
Sort items into the correct categories
Interactive cards with instant feedback
Image Labeling
Place labels on an image and create drop zones for the correct answers. This works well for anatomy, biology, geography, diagrams, charts, maps, objects, and other visual subjects.
Category Sorting
Create categories and drag answers into the correct category buckets. You can also add incorrect answers or distractors to make the exercise more challenging.
Open Add in Anki and choose the Drag_Drop note type.
For image labeling, use the Visual Placement Studio to place and resize the correct drop zones directly on your image.
For category sorting, use the Easy Card Creator to create categories, answers, and optional distractors.
Open Tools → Drag Drop to customize the add-on.
DragDrop is made for active recall. Instead of simply recognizing the answer on a card, you have to place, move, and sort the information yourself.
I created this add-on because I wanted a more visual and interactive way to study certain topics in Anki. I hope it can also be useful for other subjects and study methods.
If you use it in a way I didn't think of, or find something that could make it better, feel free to share it.
Report an issue or suggest a feature on GitHub
A companion add-on for: FocusFlow.
A companion add-on for: Sequential Cloze.
A companion add-on for: Mouse Buttons as Review Shortcuts.
Interactive visual drag-and-drop flashcards for Anki Desktop (Windows, macOS, Linux), AnkiDroid, and AnkiMobile.
Drag Drop turns static diagrams, anatomical charts, process maps, labeled illustrations, and multi-category taxonomies into engaging interactive learning cards. Test your active recall by dragging labels to their designated drop zones or sorting items into category buckets.
Ctrl+Shift+A / Cmd+Shift+A).drag_drop.ankiaddon.A).Open Tools → Drag Drop (or Tools → Add-ons → select Drag Drop → Config) to access the clean settings panel:
This project is licensed under the MIT License — see the LICENSE file for details.
Copyright (c) 2026 Adel Aitah