Extension Developer Guide


The extension developer API is not stable and will evolve in JupyterLab releases in the near future.

JupyterLab can be extended in four ways via:

  • application plugins (top level): Application plugins extend the functionality of JupyterLab itself.

  • mime renderer extensions (top level): Mime Renderer extensions are a convenience for creating an extension that can render mime data and potentially render files of a given type.

  • theme extensions (top level): Theme extensions allow you to customize the appearance of JupyterLab by adding your own fonts, CSS rules, and graphics to the application.

  • document widget extensions (lower level): Document widget extensions extend the functionality of document widgets added to the application, and we cover them in Documents.

See Let’s Make an xkcd JupyterLab Extension to learn how to make a simple JupyterLab extension.

A JupyterLab application is comprised of:

  • A core Application object

  • Plugins


A plugin adds a core functionality to the application:

  • A plugin can require other plugins for operation.

  • A plugin is activated when it is needed by other plugins, or when explicitly activated.

  • Plugins require and provide Token objects, which are used to provide a typed value to the plugin’s activate() method.

  • The module providing plugin(s) must meet the JupyterLab.IPluginModule interface, by exporting a plugin object or array of plugin objects as the default export.

    We provide two cookie cutters to create JuptyerLab plugin extensions in CommonJS and TypeScript.

The default plugins in the JupyterLab application include:

  • Terminal - Adds the ability to create command prompt terminals.

  • Shortcuts - Sets the default set of shortcuts for the application.

  • Images - Adds a widget factory for displaying image files.

  • Help - Adds a side bar widget for displaying external documentation.

  • File Browser - Creates the file browser and the document manager and the file browser to the side bar.

  • Editor - Add a widget factory for displaying editable source files.

  • Console - Adds the ability to launch Jupyter Console instances for interactive kernel console sessions.

A dependency graph for the core JupyterLab plugins (along with links to their source) is shown here: dependencies


Installing an extension allows for arbitrary code execution on the server, kernel, and in the client’s browser. You should therefore take steps to protect against malicious changes to your extension’s code. This includes ensuring strong authentication for your npm account.

Application Object

A Jupyter front-end application object is given to each plugin in its activate() function. The application object has:

  • commands - used to add and execute commands in the application.

  • keymap - used to add keyboard shortcuts to the application.

  • shell - a generic Jupyter front-end shell instance.

Jupyter Front-End Shell

The Jupyter front-end shell is used to add and interact with content in the application. The IShell interface provides an add() method for adding widgets to the application. In JupyterLab, the application shell consists of:

  • A top area for things like top level menus and toolbars.

  • left and right side bar areas for collapsible content.

  • A main work area for user activity.

  • A bottom area for things like status bars.

  • A header area for custom elements.


The Phosphor library is used as the underlying architecture of JupyterLab and provides many of the low level primitives and widget structure used in the application. Phosphor provides a rich set of widgets for developing desktop-like applications in the browser, as well as patterns and objects for writing clean, well-abstracted code. The widgets in the application are primarily Phosphor widgets, and Phosphor concepts, like message passing and signals, are used throughout. Phosphor messages are a many-to-one interaction that enables information like resize events to flow through the widget hierarchy in the application. Phosphor signals are a one-to-many interaction that enable listeners to react to changes in an observed object.

Extension Authoring

An Extension is a valid npm package that meets the following criteria:

  • Exports one or more JupyterLab plugins as the default export in its main file.

  • Has a jupyterlab key in its package.json which has "extension" metadata. The value can be true to use the main module of the package, or a string path to a specific module (e.g. "lib/foo").

  • It is also recommended to include the keyword jupyterlab-extension in the package.json, to aid with discovery (e.g. by the extension manager).

While authoring the extension, you can use the command:

npm install   # install npm package dependencies
npm run build  # optional build step if using TypeScript, babel, etc.
jupyter labextension install  # install the current directory as an extension

This causes the builder to re-install the source folder before building the application files. You can re-build at any time using jupyter lab build and it will reinstall these packages. You can also link other local npm packages that you are working on simultaneously using jupyter labextension link; they will be re-installed but not considered as extensions. Local extensions and linked packages are included in jupyter labextension list.

When using local extensions and linked packages, you can run the command

jupyter lab --watch

This will cause the application to incrementally rebuild when one of the linked packages changes. Note that only compiled JavaScript files (and the CSS files) are watched by the WebPack process. This means that if your extension is in TypeScript you’ll have to run a jlpm run build before the changes will be reflected in JupyterLab. To avoid this step you can also watch the TypeScript sources in your extension which is usually assigned to the tsc -w shortcut. If WebPack doesn’t seem to detect the changes, this can be related to the number of available watches.

Note that the application is built against released versions of the core JupyterLab extensions. If your extension depends on JupyterLab packages, it should be compatible with the dependencies in the jupyterlab/static/package.json file. Note that building will always use the latest JavaScript packages that meet the dependency requirements of JupyterLab itself and any installed extensions. If you wish to test against a specific patch release of one of the core JupyterLab packages you can temporarily pin that requirement to a specific version in your own dependencies.

If you must install a extension into a development branch of JupyterLab, you have to graft it into the source tree of JupyterLab itself. This may be done using the command

jlpm run add:sibling <path-or-url>

in the JupyterLab root directory, where <path-or-url> refers either to an extension npm package on the local file system, or a URL to a git repository for an extension npm package. This operation may be subsequently reversed by running

jlpm run remove:package <extension-dir-name>

This will remove the package metadata from the source tree and delete all of the package files.

The package should export EMCAScript 5 compatible JavaScript. It can import CSS using the syntax require('foo.css'). The CSS files can also import CSS from other packages using the syntax @import url('~foo/index.css'), where foo is the name of the package.

The following file types are also supported (both in JavaScript and CSS): json, html, jpg, png, gif, svg, js.map, woff2, ttf, eot.

If your package uses any other file type it must be converted to one of the above types or include a loader in the import statement. If you include a loader, the loader must be importable at build time, so if it is not already installed by JupyterLab, you must add it as a dependency of your extension.

If your JavaScript is written in any other dialect than EMCAScript 6 (2015) it should be converted using an appropriate tool. You can use Webpack to pre-build your extension to use any of it’s features not enabled in our build config. To build a compatible package set output.libraryTarget to "commonjs2" in your Webpack config. (see this example repo).

If you publish your extension on npm.org, users will be able to install it as simply jupyter labextension install <foo>, where <foo> is the name of the published npm package. You can alternatively provide a script that runs jupyter labextension install against a local folder path on the user’s machine or a provided tarball. Any valid npm install specifier can be used in jupyter labextension install (e.g. foo@latest, bar@, path/to/folder, and path/to/tar.gz).

There are a number of helper functions in testutils in this repo (which is a public npm package called @jupyterlab/testutils) that can be used when writing tests for an extension. See tests/test-application for an example of the infrastructure needed to run tests. There is a karma config file that points to the parent directory’s karma config, and a test runner, run-test.py that starts a Jupyter server.

Mime Renderer Extensions

Mime Renderer extensions are a convenience for creating an extension that can render mime data and potentially render files of a given type. We provide cookiecutters for Mime render extensions in JavaScript and TypeScript.

Mime renderer extensions are more declarative than standard extensions. The extension is treated the same from the command line perspective (jupyter labextension install ), but it does not directly create JupyterLab plugins. Instead it exports an interface given in the rendermime-interfaces package.

The JupyterLab repo has an example mime renderer extension for pdf files. It provides a mime renderer for pdf data and registers itself as a document renderer for pdf file types.

The rendermime-interfaces package is intended to be the only JupyterLab package needed to create a mime renderer extension (using the interfaces in TypeScript or as a form of documentation if using plain JavaScript).

The only other difference from a standard extension is that has a jupyterlab key in its package.json with "mimeExtension" metadata. The value can be true to use the main module of the package, or a string path to a specific module (e.g. "lib/foo").

The mime renderer can update its data by calling .setData() on the model it is given to render. This can be used for example to add a png representation of a dynamic figure, which will be picked up by a notebook model and added to the notebook document. When using IDocumentWidgetFactoryOptions, you can update the document model by calling .setData() with updated data for the rendered MIME type. The document can then be saved by the user in the usual manner.


A theme is a JupyterLab extension that uses a ThemeManager and can be loaded and unloaded dynamically. The package must include all static assets that are referenced by url() in its CSS files. Local URLs can be used to reference files relative to the location of the referring sibling CSS files. For example url('images/foo.png') or url('../foo/bar.css')can be used to refer local files in the theme. Absolute URLs (starting with a /) or external URLs (e.g. https:) can be used to refer to external assets. The path to the theme asset entry point is specified package.json under the "jupyterlab" key as "themePath". See the JupyterLab Light Theme for an example. Ensure that the theme files are included in the "files" metadata in package.json. Note that if you want to use SCSS, SASS, or LESS files, you must compile them to CSS and point JupyterLab to the CSS files.

To quickly create a theme based on the JupyterLab Light Theme, follow the instructions in the contributing guide and then run jlpm run create:theme from the repository root directory. Once you select a name, title and a description, a new theme folder will be created in the current directory. You can move that new folder to a location of your choice, and start making desired changes.

The theme extension is installed in the same way as a regular extension (see extension authoring).

Standard (General-Purpose) Extensions

JupyterLab’s modular architecture is based around the idea that all extensions are on equal footing, and that they interact with each other through typed interfaces that are provided by Token objects. An extension can provide a Token to the application, which other extensions can then request for their own use.

Core Tokens

The core packages of JupyterLab provide a set of tokens, which are listed here, along with short descriptions of when you might want to use them in your extensions.

  • @jupyterlab/application:ILayoutRestorer: An interface to the application layout restoration functionality. Use this to have your activities restored across page loads.

  • @jupyterlab/application:IMimeDocumentTracker: An instance tracker for documents rendered using a mime renderer extension. Use this if you want to list and interact with documents rendered by such extensions.

  • @jupyterlab/application:IRouter: The URL router used by the application. Use this to add custom URL-routing for your extension (e.g., to invoke a command if the user navigates to a sub-path).

  • @jupyterlab/apputils:ICommandPalette: An interface to the application command palette in the left panel. Use this to add commands to the palette.

  • @jupyterlab/apputils:ISplashScreen: An interface to the splash screen for the application. Use this if you want to show the splash screen for your own purposes.

  • @jupyterlab/apputils:IThemeManager: An interface to the theme manager for the application. Most extensions will not need to use this, as they can register a theme extension.

  • @jupyterlab/apputils:IWindowResolver: An interface to a window resolver for the application. JupyterLab workspaces are given a name, which are determined using the window resolver. Require this if you want to use the name of the current workspace.

  • @jupyterlab/codeeditor:IEditorServices: An interface to the text editor provider for the application. Use this to create new text editors and host them in your UI elements.

  • @jupyterlab/completer:ICompletionManager: An interface to the completion manager for the application. Use this to allow your extension to invoke a completer.

  • @jupyterlab/console:IConsoleTracker: An instance tracker for code consoles. Use this if you want to be able to iterate over and interact with code consoles created by the application.

  • @jupyterlab/console:IContentFactory: A factory object that creates new code consoles. Use this if you want to create and host code consoles in your own UI elements.

  • @jupyterlab/coreutils:ISettingRegistry: An interface to the JupyterLab settings system. Use this if you want to store settings for your application. See extension settings for more information.

  • @jupyterlab/coreutils:IStateDB: An interface to the JupyterLab state database. Use this if you want to store data that will persist across page loads. See state database for more information.

  • @jupyterlab/docmanager:IDocumentManager: An interface to the manager for all documents used by the application. Use this if you want to open and close documents, create and delete files, and otherwise interact with the file system.

  • @jupyterlab/filebrowser:IFileBrowserFactory: A factory object that creates file browsers. Use this if you want to create your own file browser (e.g., for a custom storage backend), or to interact with other file browsers that have been created by extensions.

  • @jupyterlab/fileeditor:IEditorTracker: An instance tracker for file editors. Use this if you want to be able to iterate over and interact with file editors created by the application.

  • @jupyterlab/imageviewer:IImageTracker: An instance tracker for images. Use this if you want to be able to iterate over and interact with images viewed by the application.

  • @jupyterlab/inspector:IInspector: An interface for adding variable inspectors to widgets. Use this to add the ability to hook into the variable inspector to your extension.

  • @jupyterlab/launcher:ILauncher: An interface to the application activity launcher. Use this to add your extension activities to the launcher panel.

  • @jupyterlab/mainmenu:IMainMenu: An interface to the main menu bar for the application. Use this if you want to add your own menu items.

  • @jupyterlab/notebook:ICellTools: An interface to the Cell Tools panel in the application left area. Use this to add your own functionality to the panel.

  • @jupyterlab/notebook:IContentFactory: A factory object that creates new notebooks. Use this if you want to create and host notebooks in your own UI elements.

  • @jupyterlab/notebook:INotebookTracker: An instance tracker for code consoles. Use this if you want to be able to iterate over and interact with notebooks created by the application.

  • @jupyterlab/rendermime:IRenderMimeRegistry: An interface to the rendermime registry for the application. Use this to create renderers for various mime-types in your extension. Most extensions will not need to use this, as they can register a mime renderer extension.

  • @jupyterlab/rendermime:ILatexTypesetter: An interface to the LaTeX typesetter for the application. Use this if you want to typeset math in your extension.

  • @jupyterlab/settingeditor:ISettingEditorTracker: An instance tracker for setting editors. Use this if you want to be able to iterate over and interact with setting editors created by the application.

  • @jupyterlab/terminal:ITerminalTracker: An instance tracker for terminals. Use this if you want to be able to iterate over and interact with terminals created by the application.

  • @jupyterlab/tooltip:ITooltipManager: An interface to the tooltip manager for the application. Use this to allow your extension to invoke a tooltip.

Standard Extension Example

For a concrete example of a standard extension, see How to extend the Notebook plugin. Notice that the mime renderer and themes extensions above use a limited, simplified interface to JupyterLab’s extension system. Modifying the notebook plugin requires the full, general-purpose interface to the extension system.

Storing Extension Data

In addition to the file system that is accessed by using the @jupyterlab/services package, JupyterLab offers two ways for extensions to store data: a client-side state database that is built on top of localStorage and a plugin settings system that provides for default setting values and user overrides.

Extension Settings

An extension can specify user settings using a JSON Schema. The schema definition should be in a file that resides in the schemaDir directory that is specified in the package.json file of the extension. The actual file name should use is the part that follows the package name of extension. So for example, the JupyterLab apputils-extension package hosts several plugins:

  • '@jupyterlab/apputils-extension:menu'

  • '@jupyterlab/apputils-extension:palette'

  • '@jupyterlab/apputils-extension:settings'

  • '@jupyterlab/apputils-extension:themes'

And in the package.json for @jupyterlab/apputils-extension, the schemaDir field is a directory called schema. Since the themes plugin requires a JSON schema, its schema file location is: schema/themes.json. The plugin’s name is used to automatically associate it with its settings file, so this naming convention is important. Ensure that the schema files are included in the "files" metadata in package.json.

See the fileeditor-extension for another example of an extension that uses settings.

Note: You can override default values of the extension settings by defining new default values in an overrides.json file in the application settings directory. So for example, if you would like to set the dark theme by default instead of the light one, an overrides.json file containing the following lines needs to be added in the application settings directory (by default this is the share/jupyter/lab/settings folder).

  "@jupyterlab/apputils-extension:themes": {
    "theme": "JupyterLab Dark"

State Database

The state database can be accessed by importing IStateDB from @jupyterlab/coreutils and adding it to the list of requires for a plugin:

const id = 'foo-extension:IFoo';

const IFoo = new Token<IFoo>(id);

interface IFoo {}

class Foo implements IFoo {}

const plugin: JupyterFrontEndPlugin<IFoo> = {
  requires: [IStateDB],
  provides: IFoo,
  activate: (app: JupyterFrontEnd, state: IStateDB): IFoo => {
    const foo = new Foo();
    const key = `${id}:some-attribute`;

    // Load the saved plugin state and apply it once the app
    // has finished restoring its former layout.
    Promise.all([state.fetch(key), app.restored])
      .then(([saved]) => { /* Update `foo` with `saved`. */ });

    // Fulfill the plugin contract by returning an `IFoo`.
    return foo;
  autoStart: true

Context Menus

JupyterLab has an application-wide context menu available as app.contextMenu. See the Phosphor docs for the item creation options. If you wish to preempt the application context menu, you can use a ‘contextmenu’ event listener and call event.stopPropagation to prevent the application context menu handler from being called (it is listening in the bubble phase on the document). At this point you could show your own Phosphor contextMenu, or simply stop propagation and let the system context menu be shown. This would look something like the following in a Widget subclass:

// In `onAfterAttach()`
this.node.addEventListener('contextmenu', this);

// In `handleEvent()`
case 'contextmenu':

Companion Packages

If your extensions depends on the presence of one or more packages in the kernel, or on a notebook server extension, you can add metadata to indicate this to the extension manager by adding metadata to your package.json file. The full options available are:

"jupyterlab": {
  "discovery": {
    "kernel": [
        "kernel_spec": {
          "language": "<regexp for matching kernel language>",
          "display_name": "<regexp for matching kernel display name>"   // optional
        "base": {
          "name": "<the name of the kernel package>"
        "overrides": {   // optional
          "<manager name, e.g. 'pip'>": {
            "name": "<name of kernel package on pip, if it differs from base name>"
        "managers": [   // list of package managers that have your kernel package
    "server": {
      "base": {
        "name": "<the name of the server extension package>"
      "overrides": {   // optional
        "<manager name, e.g. 'pip'>": {
          "name": "<name of server extension package on pip, if it differs from base name>"
      "managers": [   // list of package managers that have your server extension package

A typical setup for e.g. a jupyter-widget based package will then be:

"keywords": [
"jupyterlab": {
  "extension": true,
  "discovery": {
    "kernel": [
        "kernel_spec": {
          "language": "^python",
        "base": {
          "name": "myipywidgetspackage"
        "managers": [

Currently supported package managers are:

  • pip

  • conda