TanStack Table's core is framework agnostic, which means its API is the same regardless of the framework you're using. Adapters are provided to make working with the table core easier depending on your framework. See the Adapters menu for available adapters.
While TanStack Table is written in TypeScript, using TypeScript in your application is optional (but recommended as it comes with outstanding benefits to both you and your codebase)
If you use TypeScript, you will get top-notch type safety and editor autocomplete for all table APIs and state.
As it was mentioned extensively in the Intro section, TanStack Table is headless. This means that it doesn't render any DOM elements, and instead relies on you, the UI/UX developer to provide the table's markup and styles. This is a great way to build a table that can be used in any UI framework, including React, Vue, Solid, Svelte, Qwik, Angular, and even JS-to-native platforms like React Native!
Since TanStack Table is headless and runs on a vanilla JavaScript core, it is agnostic in a couple of ways:
The table core uses the following abstractions, commonly exposed by adapters:
TanStack Table will help you build just about any type of table you can imagine. It has built-in state and APIs for the following features:
These are just some of the capabilities that you can build with TanStack Table. There are many more features that are possible with TanStack Table that you can add along-side the built-in features.
Virtualization is an example of a feature that is not built-in to TanStack Table, but can be achieved by using another library (like TanStack Virtual) and adding it along-side your other table rendering logic.
TanStack Table also supports Custom Features (plugins) that you can use to modify the table instance to add your own custom logic to the table in a more integrated way.
And of course, you can just write your own state and hooks to add whatever other features you want for your table. The features from the TanStack Table core are just a solid foundation to build on, with a large focus on performance and DX.
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