A powerful, interactive charting and data visualization library for browser


Apache ECharts


Apache ECharts is a free, powerful charting and visualization library offering an easy way of adding intuitive, interactive, and highly customizable charts to your commercial products. It is written in pure JavaScript and based on zrender, which is a whole new lightweight canvas library.

License Latest npm release NPM downloads Contributors
Build Status

Get Apache ECharts

You may choose one of the following methods:

+ Download from the official website
+ npm install echarts --save



Get Help

+ GitHub Issues for bug report and feature requests
+ Email for general questions
+ Subscribe to the mailing list to get updated with the project


Build echarts source code:

Execute the instructions in the root directory of the echarts:
(Node.js is required)

  1. ``` sh
  2. # Install the dependencies from NPM:
  3. npm install

  4. # Rebuild source code immediately in watch mode when changing the source code.
  5. # It opens the `./test` directory and you may open `-cases.html` to get the list
  6. # of all test cases.
  7. # If you wish to create a test case, run `npm run mktest:help` to learn more.
  8. npm run dev

  9. # Check correctness of TypeScript code.
  10. npm run checktype

  11. # If intending to build and get all types of the "production" files:
  12. npm run release
  13. ```

Then the "production" files are generated in the dist directory.


If you wish to debug locally or make pull requests, please refer to the contributing document.


Awesome ECharts


+ ECharts GL An extension pack of ECharts, which provides 3D plots, globe visualization, and WebGL acceleration.

+ Extension for Baidu Map 百度地图扩展 An extension provides a wrapper of Baidu Map Service SDK.

+ vue-echarts ECharts component for Vue.js

+ echarts-stat Statistics tool for ECharts


ECharts is available under the Apache License V2.

Code of Conduct

Please refer to Apache Code of Conduct.


Deqing Li, Honghui Mei, Yi Shen, Shuang Su, Wenli Zhang, Junting Wang, Ming Zu, Wei Chen.
Visual Informatics, 2018.