Research any topic before you write.
Find related topics. | Discover entities. | See connections. | Build a topical map.
Kivy is a free and open source Python framework for developing mobile apps and other multitouch application software with a natural user interface (NUI). It is distributed under the terms of the MIT License, and can run on Android, iOS, Linux, macOS, and Windows.
Related projects, Overview & Code example
Explore the main themes, entities and connections around Kivy (framework). Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Kivy (framework) | Developer | Kivy organization | 1.00 | infobox |
| Kivy (framework) | License | MIT (Free software) | 1.00 | infobox |
| Kivy (framework) | Operating system | Cross-platform | 1.00 | infobox |
| Kivy (framework) | Release | 1 February 2011; 15 years ago (2011-02-01) | 1.00 | infobox |
| Kivy (framework) | Repository | github.com/kivy/kivy | 1.00 | infobox |
| Kivy (framework) | Stable release | 2.3.1 / 26 December 2024; 19 months ago (2024-12-26) | 1.00 | infobox |
| Kivy (framework) | Type | Application framework | 1.00 | infobox |
| Kivy (framework) | Website | kivy.org | 1.00 | infobox |
| Kivy (framework) | Written in | Python, Cython | 1.00 | infobox |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.