Create Alfred workflows with ease

Usage no npm install needed!

<script type="module">
  import alfy from '';



Create Alfred workflows with ease

Build Status


  • Easy input↔output.
  • Config and cache handling built-in.
  • Fetching remote files with optional caching.
  • Publish your workflow to npm.
  • Automatic update notifications.
  • Easily testable workflows.
  • Finds the node binary.
  • Support for top-level await.
  • Presents uncaught exceptions and unhandled Promise rejections to the user.
    No need to manually .catch() top-level promises.


You need Node.js 8+ and Alfred 3 or 4 with the paid Powerpack upgrade.


$ npm install alfy


  1. Create a new blank Alfred workflow.

  2. Add a Script Filter (right-click the canvas → InputsScript Filter), set Language to /bin/bash, and add the following script:

./node_modules/.bin/run-node index.js "$1"

We can't call node directly as GUI apps on macOS doesn't inherit the $PATH.

Tip: You can use generator-alfred to scaffold out an alfy based workflow. If so, you can skip the rest of the steps, go straight to the index.js and do your thing.

  1. Set the Keyword by which you want to invoke your workflow.

  2. Go to your new workflow directory (right-click on the workflow in the sidebar → Open in Finder).

  3. Initialize a repo with npm init.

  4. Install Alfy with npm install alfy.

  5. In the workflow directory, create a index.js file, import alfy, and do your thing.


Here we fetch some JSON from a placeholder API and present matching items to the user:

const alfy = require('alfy');

const data = await alfy.fetch('');

const items = alfy
    .inputMatches(data, 'title')
    .map(element => ({
        title: element.title,
        subtitle: element.body,


Some example usage in the wild: alfred-npms, alfred-emoj, alfred-ng.

Update notifications

Alfy uses alfred-notifier in the background to show a notification when an update for your workflow is available.


Alfy offers the possibility of caching data, either with the fetch or directly through the cache object.

An important thing to note is that the cached data gets invalidated automatically when you update your workflow. This offers the flexibility for developers to change the structure of the cached data between workflows without having to worry about invalid older data.

Publish to npm

By adding alfy-init as postinstall and alfy-cleanup as preuninstall script, you can publish your package to npm instead of to Packal. This way, your packages are only one simple npm install command away.

    "name": "alfred-unicorn",
    "version": "1.0.0",
    "description": "My awesome unicorn workflow",
    "author": {
        "name": "Sindre Sorhus",
        "email": "",
        "url": ""
    "scripts": {
        "postinstall": "alfy-init",
        "preuninstall": "alfy-cleanup"
    "dependencies": {
        "alfy": "*"

Tip: Prefix your workflow with alfred- to make them easy searchable through npm.

You can remove these properties from your info.plist file as they are being added automatically at install time.

After publishing your workflow to npm, your users can easily install or update the workflow.

$ npm install --global alfred-unicorn

Tip: instead of manually updating every workflow yourself, use the alfred-updater workflow to do that for you.


Workflows can easily be tested with alfy-test. Here is a small example.

import test from 'ava';
import alfyTest from 'alfy-test';

test('main', async t => {
    const alfy = alfyTest();

    const result = await alfy('workflow input');

    t.deepEqual(result, [
            title: 'foo',
            subtitle: 'bar'


When developing your workflow it can be useful to be able to debug it when something is not working. This is when the workflow debugger comes in handy. You can find it in your workflow view in Alfred. Press the insect icon to open it. It will show you the plain text output of alfy.output() and anything you log with alfy.log():

const unicorn = getUnicorn();

Environment variables

Alfred lets users set environment variables for a workflow which can then be used by that workflow. This can be useful if you, for example, need the user to specify an API token for a service. You can access the workflow environment variables from process.env. For example process.env.apiToken.




Type: string

Input from Alfred. What the user wrote in the input box.

output(list, options?)

Return output to Alfred.


Type: object[]

List of object with any of the supported properties.


        title: 'Unicorn'
        title: 'Rainbow'

Type: object


Type: number (seconds)
Values: 0.1...0.5

A script can be set to re-run automatically after some interval. The script will only be re-run if the script filter is still active and the user hasn't changed the state of the filter by typing and triggering a re-run. More info.

For example, it could be used to update the progress of a particular task:

            title: 'Downloading Unicorns…',
            subtitle: `${progress}%`,
        // Re-run and update progress every 3 seconds.
        rerunInterval: 3 


Log value to the Alfred workflow debugger.

matches(input, list, item?)

Returns an string[] of items in list that case-insensitively contains input.

alfy.matches('Corn', ['foo', 'unicorn']);
//=> ['unicorn']

Type: string

Text to match against the list items.


Type: string[]

List to be matched against.


Type: string | Function

By default, it will match against the list items.

Specify a string to match against an object property:

const list = [
        title: 'foo'
        title: 'unicorn'

alfy.matches('Unicorn', list, 'title');
//=> [{title: 'unicorn'}]

Or nested property:

const list = [
        name: {
            first: 'John',
            last: 'Doe'
        name: {
            first: 'Sindre',
            last: 'Sorhus'

alfy.matches('sindre', list, 'name.first');
//=> [{name: {first: 'Sindre', last: 'Sorhus'}}]

Specify a function to handle the matching yourself. The function receives the list item and input, both lowercased, as arguments, and is expected to return a boolean of whether it matches:

const list = ['foo', 'unicorn'];

// Here we do an exact match.
// `Foo` matches the item since it's lowercased for you.
alfy.matches('Foo', list, (item, input) => item === input);
//=> ['foo']

inputMatches(list, item?)

Same as matches(), but with alfy.input as input.


Display an error or error message in Alfred.

Note: You don't need to .catch() top-level promises. Alfy handles that for you.


Type: Error | string

Error or error message to be displayed.

fetch(url, options?)

Returns a Promise that returns the body of the response.


Type: string

URL to fetch.


Type: object

Any of the got options.


Type: boolean
Default: true

Parse response body with JSON.parse and set accept header to application/json.


Type: number

Number of milliseconds this request should be cached.


Type: Function

Transform the response before it gets cached.

await alfy.fetch('', {
    transform: body => { = 'bar';
        return body;

You can also return a Promise.

const xml2js = require('xmls2js');
const pify = require('pify');

const parseString = pify(xml2js.parseString);

await alfy.fetch('', {
    transform: body => parseString(body)


Type: object

Persist config data.

Exports a conf instance with the correct config path set.


alfy.config.set('unicorn', '🦄');

//=> '🦄'


Type: object

Persist cache data.

Exports a modified conf instance with the correct cache path set.


alfy.cache.set('unicorn', '🦄');

//=> '🦄'

The set method of this instance accepts an optional third argument where you can provide a maxAge option. maxAge is the number of milliseconds the value is valid in the cache.


const delay = require('delay');

alfy.cache.set('foo', 'bar', {maxAge: 5000});

//=> 'bar'

// Wait 5 seconds
await delay(5000);

//=> undefined


Type: boolean

Whether the user currently has the workflow debugger open.


Type: object
Keys: info warning error alert like delete

Get various default system icons.

The most useful ones are included as keys. The rest you can get with icon.get(). Go to /System/Library/CoreServices/CoreTypes.bundle/Contents/Resources in Finder to see them all.


//=> '/System/Library/CoreServices/CoreTypes.bundle/Contents/Resources/AlertStopIcon.icns'

//=> '/System/Library/CoreServices/CoreTypes.bundle/Contents/Resources/Clock.icns'


Type: object


    name: 'Emoj',
    version: '0.2.5',
    uid: 'user.workflow.B0AC54EC-601C-479A-9428-01F9FD732959',
    bundleId: 'com.sindresorhus.emoj'


Type: object

Alfred metadata.


Example: '3.0.2'

Find out which version the user is currently running. This may be useful if your workflow depends on a particular Alfred version's features.


Example: 'alfred.theme.yosemite'

Current theme used.


Example: 'rgba(255,255,255,0.98)'

If you're creating icons on the fly, this allows you to find out the color of the theme background.


Example: 'rgba(255,255,255,0.98)'

The color of the selected result.


Example: 3

Find out what subtext mode the user has selected in the Appearance preferences.

Usability note: This is available so developers can tweak the result text based on the user's selected mode, but a workflow's result text should not be bloated unnecessarily based on this, as the main reason users generally hide the subtext is to make Alfred look cleaner.


Example: '/Users/sindresorhus/Library/Application Support/Alfred/Workflow Data/com.sindresorhus.npms'

Recommended location for non-volatile data. Just use which uses this path.


Example: '/Users/sindresorhus/Library/Caches/com.runningwithcrayons.Alfred/Workflow Data/com.sindresorhus.npms'

Recommended location for volatile data. Just use alfy.cache which uses this path.


Example: '/Users/sindresorhus/Dropbox/Alfred/Alfred.alfredpreferences'

This is the location of the Alfred.alfredpreferences. If a user has synced their settings, this will allow you to find out where their settings are regardless of sync state.


Example: 'adbd4f66bc3ae8493832af61a41ee609b20d8705'

Non-synced local preferences are stored within Alfred.alfredpreferences under …/preferences/local/${preferencesLocalHash}/.


Alfred workflows using Alfy