A fully-featured, highly customizable JavaScript data grid for building enterprise applications with React, Angular, Vue, or plain JavaScript.
AG Grid is a JavaScript data grid library for building high-performance, feature-rich tables in web applications. It solves the problem of displaying and interacting with large datasets in enterprise applications by providing a customizable grid with core features like sorting, filtering, and editing, plus advanced capabilities through its commercial version.
Frontend developers building data-intensive enterprise applications in React, Angular, Vue, or plain JavaScript who need a robust, performant grid component.
Developers choose AG Grid for its outstanding performance, extensive feature set, framework-agnostic design, and deep customization options, making it a leading alternative to building custom grid solutions from scratch.
The best JavaScript Data Table for building Enterprise Applications. Supports React / Angular / Vue / Plain JavaScript.
AG Grid handles large datasets efficiently with no third-party dependencies, as emphasized in the README for enterprise applications with thousands of rows.
It offers native integration with React, Angular, Vue, and plain JavaScript, providing dedicated packages and documentation for each, reducing framework lock-in.
The grid supports custom cell renderers, editors, filters, and tool panels, allowing developers to tailor virtually every aspect to specific use cases.
Even the free community version includes sorting, filtering, pagination, editing, theming, and server-side data support, covering core grid needs without extra cost.
Critical advanced features like integrated charting, Excel export, row grouping, and the AI toolkit are locked behind a commercial license, fragmenting functionality.
As a full-featured library, AG Grid adds significant weight to web applications, which can impact load times and performance on bandwidth-constrained networks.
Configuring advanced features requires navigating a dense API with numerous options, which can lead to steep learning curves and configuration errors for simpler implementations.
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