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Javascript

Supports both browser and NodeJS environments.

Installation​

Install with a package manager

npm install --save @growthbook/growthbook

or use directly in your HTML without installing first:

<!-- Creates `window.growthbook` with all of the exported classes -->
<script src="https://cdn.jsdelivr.net/npm/@growthbook/growthbook/dist/bundles/index.js"></script>

Quick Usage​

Step 1: Configure your app​

import { GrowthBook } from "@growthbook/growthbook";

// Create a GrowthBook instance
const gb = new GrowthBook({
  apiHost: "https://cdn.growthbook.io",
  clientKey: "sdk-abc123",
  // Enable easier debugging during development
  enableDevMode: true,
  // Targeting attributes
  attributes: {
    id: "123",
    country: "US"
  },
  // Only required for A/B testing
  // Called every time a user is put into an experiment
  trackingCallback: (experiment, result) => {
    console.log("Experiment Viewed", {
      experimentId: experiment.key,
      variationId: result.key,
    });
  },
});

// Wait for features to be available
await gb.loadFeatures({ autoRefresh: true });

Step 2: Start Feature Flagging!​

There are 2 main methods for evaluating features: isOn and getFeatureValue:

// Simple boolean (on/off) feature flag
if (gb.isOn("my-feature")) {
  console.log("Feature enabled!");
}

// Get the value of a string/JSON/number feature with a fallback
const color = gb.getFeatureValue("button-color", "blue");

Node.js​

If using this SDK in a server-side environment, you may need to configure some polyfills for missing browser APIs.

const { setPolyfills } = require("@growthbook/growthbook");

setPolyfills({
  // Required when using built-in feature loading and Node 17 or lower
  fetch: require("cross-fetch"),
  // Required when using encrypted feature flags and Node 18 or lower
  SubtleCrypto: require("node:crypto").webcrypto.subtle,
  // Optional, can make feature rollouts faster
  EventSource: require("eventsource"),
  // Optional, can reduce startup times by persisting cached feature flags
  localStorage: {
    // Example using Redis
    getItem: (key) => redisClient.get(key),
    setItem: (key, value) => redisClient.set(key, value),
  }
})

Create a separate GrowthBook instance for every incoming request. This is easiest if you use a middleware:

// Example using Express
app.use(function(req, res, next) {
  // Create a GrowthBook instance and store in the request
  req.growthbook = new GrowthBook({
    apiHost: "https://cdn.growthbook.io",
    clientKey: "sdk-abc123",
    enableDevMode: true,
  });

  // Clean up at the end of the request
  res.on('close', () => req.growthbook.destroy());

  // Wait for features to load (will be cached in-memory for future requests)
  req.growthbook.loadFeatures()
    .then(() => next())
    .catch((e) => {
      console.error("Failed to load features from GrowthBook", e);
      next();
    })
})

Then, you can access the GrowthBook instance from any route:

app.get("/", (req, res) => {
  const gb = req.growthbook;
  // ...
})

Loading Features​

In order for the GrowthBook SDK to work, it needs to have feature definitions from the GrowthBook API. There are 2 ways to get this data into the SDK.

Built-in Fetching and Caching​

If you pass an apiHost and clientKey into the GrowthBook constructor, it will handle the network requests, caching, retry logic, etc. for you automatically. If your feature payload is encrypted, you can also pass in a decryptionKey.

const gb = new GrowthBook({
  apiHost: "https://cdn.growthbook.io",
  clientKey: "sdk-abc123",
  decryptionKey: "key_abc123", // Only if you have feature encryption turned on
});

// Wait for features to be downloaded
await gb.loadFeatures({
  // When features change, update the GrowthBook instance automatically
  // Default: `false`
  autoRefresh: true,
  // If the network request takes longer than this (in milliseconds), continue
  // Default: `0` (no timeout)
  timeout: 2000,
});

Until features are loaded, all features will evaluate to null. If you're ok with a potential flicker in your application (features going from null to their real value), you can call loadFeatures without awaiting the result.

If you want to refresh the features at any time (e.g. when a navigation event occurs), you can call gb.refreshFeatures().

Custom Integration​

If you prefer to handle the network and caching logic yourself, you can instead pass in a features JSON object directly. For example, you might store features in Postgres and send it down to your front-end as part of your app's initial bootstrap API call.

const gb = new GrowthBook({
  features: {
    "feature-1": {...},
    "feature-2": {...},
    "another-feature": {...},
  }
})

Note that you don't have to call gb.loadFeatures(). There's nothing to load - everything required is already passed in.

You can update features at any time by calling gb.setFeatures() with a new JSON object.

Re-rendering When Features Change​

When features change (e.g. by calling gb.refreshFeatures()), you need to re-render your app so that all of your feature flag checks can be re-evaluated. You can specify your own custom rendering function for this purpose:

// Callback to re-render your app when feature flag values change
gb.setRenderer(() => {
  // TODO: re-render your app
});

Experimentation (A/B Testing)​

In order to run A/B tests, you need to set up a tracking callback function. This is called every time a user is put into an experiment and can be used to track the exposure event in your analytics system (Segment, Mixpanel, GA, etc.).

const gb = new GrowthBook({
  apiHost: "https://cdn.growthbook.io",
  clientKey: "sdk-abc123",
  trackingCallback: (experiment, result) => {
    // Example using Segment
    analytics.track("Experiment Viewed", {
      experimentId: experiment.key,
      variationId: result.key,
    });
  },
});

This same tracking callback is used for both feature flag experiments and Visual Editor experiments.

Feature Flag Experiments​

There is nothing special you have to do for feature flag experiments. Just evaluate the feature flag like you would normally do. If the user is put into an experiment as part of the feature flag, it will call the trackingCallback automatically in the background.

// If this has an active experiment and the user is included,
// it will call trackingCallback automatically
const newLogin = gb.isOn("new-signup-form");

If the experiment came from a feature rule, result.featureId in the trackingCallback will contain the feature id, which may be useful for tracking/logging purposes.

Visual Editor Experiments​

Experiments created through the GrowthBook Visual Editor will run automatically as soon as their targeting conditions are met.

Note: Visual Editor experiments are only supported in a web browser environment. They will not run in Node.js, Mobile apps, or Desktop apps.

If you are using this SDK in a Single Page App (SPA), you will need to let the GrowthBook instance know when the URL changes so the active experiments can update accordingly.

// Call this every time a navigation event happens in your SPA
function onRouteChange() {
  gb.setURL(window.location.href);
}

TypeScript​

When used in a TypeScript project, GrowthBook includes basic type inference out of the box:

// Type will be `string` based on the fallback provided ("blue")
const color = gb.getFeatureValue("button-color", "blue");

// You can manually specify types as well
// feature.value will be type `number`
const feature = gb.evalFeature<number>("font-size");
console.log(feature.value);

// Experiments will use the variations to infer the return value
// result.value will be type "string"
const result = gb.run({
  key: "my-test",
  variations: ["blue", "green"],
});

Strict Typing​

If you want to enforce stricter types in your application, you can do that when creating the GrowthBook instance:

// Define all your feature flags and types here
interface AppFeatures {
  "button-color": string;
  "font-size": number;
  "newForm": boolean;
}

// Pass into the GrowthBook instance
const gb = new GrowthBook<AppFeatures>({
  ...
});

Now, all feature flag methods will be strictly typed.

// feature.value will by type `number`
const feature = gb.evalFeature("font-size");
console.log(feature.value);

// Typos will cause compile-time errors
gb.isOn("buton-color"); // "buton" instead of "button"

Instead of defining the AppFeatures interface manually like above, you can auto-generate it from your GrowthBook account using the GrowthBook CLI.

GrowthBook Instance (reference)​

Attributes​

You can specify attributes about the current user and request. These are used for two things:

  1. Feature targeting (e.g. paid users get one value, free users get another)
  2. Assigning persistent variations in A/B tests (e.g. user id "123" always gets variation B)

The following are some commonly used attributes, but use whatever makes sense for your application.

new GrowthBook({
  attributes: {
    id: "123",
    loggedIn: true,
    deviceId: "abc123def456",
    company: "acme",
    paid: false,
    url: "/pricing",
    browser: "chrome",
    mobile: false,
    country: "US",
  },
});

Updating Attributes​

If attributes change, you can call setAttributes() to update. This will completely overwrite any existing attributes. To do a partial update, use the following pattern:

gb.setAttributes({
  // Only update the `url` attribute, keep the rest the same
  ...gb.getAttributes(),
  url: "/new-page"
})

Secure Attributes​

When secure attribute hashing is enabled, all targeting conditions in the SDK payload referencing attributes with datatype secureString or secureString[] will be anonymized via SHA-256 hashing. This allows you to safely target users based on sensitive attributes. You must enable this feature in your SDK Connection for it to take effect.

If your SDK Connection has secure attribute hashing enabled, you will need to manually hash any secureString or secureString[] attributes that you pass into the GrowthBook SDK.

To hash an attribute, use a cryptographic library with SHA-256 support, and compute the SHA-256 hashed value of your attribute plus your organization's secure attribute salt.

const salt = "f09jq3fij"; // Your organization's secure attribute salt (see Organization Settings)

// hashing a secureString attribute
const userEmail = sha256(user.email + salt);

// hashing an secureString[] attribute
const userTags = user.tags.map(tag => sha256(tag + salt));

gb.setAttributes({
  id: user.id,
  loggedIn: true,
  email: userEmail,
  tags: userTags,
});

await gb.loadFeatures({ autoRefresh: true });

// In this example, we are using Node.js's built-in crypto library
function sha256(str) {
  return crypto.createHash("sha256").update(str).digest("hex");
}

Note that in a browser context, we will not be able to natively access the Node.js crypto library. In modern browsers window.crypto.subtle is available, although calls are asynchronous. You would need to await all attribute hashing to complete before calling gb.setAttributes().

async function sha256(str) {
  const buffer = await crypto.subtle.digest("SHA-256", new TextEncoder("utf-8").encode(str));
  const hashArray = Array.from(new Uint8Array(buffer));
  return hashArray.map(byte => byte.toString(16).padStart(2, "0")).join("");
}

Alternatively, CryptoJS (https://www.npmjs.com/package/crypto-js) provides a synchronous API:

import sha256 from 'crypto-js/sha256';

const userEmail = sha256(user.email + salt);

Feature Usage Callback​

GrowthBook can fire a callback whenever a feature is evaluated for a user. This can be useful to update 3rd party tools like NewRelic or DataDog.

new GrowthBook({
  onFeatureUsage: (featureKey, result) => {
    console.log("feature", featureKey, "has value", result.value);
  },
});

The result argument is the same thing returned from gb.evalFeature.

Note: If you evaluate the same feature multiple times (and the value doesn't change), the callback will only be fired the first time.

Dev Mode​

There is a GrowthBook Chrome DevTools Extension that can help you debug and test your feature flags in development.

In order for this to work, you must explicitly enable dev mode when creating your GrowthBook instance:

const gb = new GrowthBook({
  enableDevMode: true,
});

To avoid exposing all of your internal feature flags and experiments to users, we recommend setting this to false in production in most cases.

evalFeature​

In addition to the isOn and getFeatureValue helper methods, there is the evalFeature method that gives you more detailed information about why the value was assigned to the user.

// Get detailed information about the feature evaluation
const result = gb.evalFeature("my-feature");

// The value of the feature (or `null` if not defined)
console.log(result.value);

// Why the value was assigned to the user
// One of: `override`, `unknownFeature`, `defaultValue`, `force`, or `experiment`
console.log(result.source);

// The string id of the rule (if any) which was used
console.log(result.ruleId);

// Information about the experiment (if any) which was used
console.log(result.experiment);

// The result of the experiment (or `undefined`)
console.log(result.experimentResult);

Inline Experiments​

Instead of declaring all features up-front in the context and referencing them by ids in your code, you can also just run an experiment directly. This is done with the gb.run method:

// These are the only required options
const { value } = gb.run({
  key: "my-experiment",
  variations: ["red", "blue", "green"],
});

Customizing the Traffic Split​

By default, this will include all traffic and do an even split between all variations. There are 2 ways to customize this behavior:

// Option 1: Using weights and coverage
gb.run({
  key: "my-experiment",
  variations: ["red", "blue", "green"],
  // Only include 10% of traffic
  coverage: 0.1,
  // Split the included traffic 50/25/25 instead of the default 33/33/33
  weights: [0.5, 0.25, 0.25],
});

// Option 2: Specifying ranges
gb.run({
  key: "my-experiment",
  variations: ["red", "blue", "green"],
  // Identical to the above
  // 5% of traffic in A, 2.5% each in B and C
  ranges: [
    [0, 0.05],
    [0.5, 0.525],
    [0.75, 0.775],
  ],
});

Hashing​

We use deterministic hashing to assign a variation to a user. We hash together the user's id and experiment key, which produces a number between 0 and 1. Each variation is assigned a range of numbers, and whichever one the user's hash value falls into will be assigned.

You can customize this hashing behavior:

gb.run({
  key: "my-experiment",
  variations: ["A", "B"],

  // Which hashing algorithm to use
  // Version 2 is the latest and the one we recommend
  hashVersion: 2,

  // Use a different seed instead of the experiment key
  seed: "abcdef123456",

  // Use a different user attribute (default is `id`)
  hashAttribute: "device_id",
});

Note: For backwards compatibility, if no hashVersion is specified, it will fall back to using version 1, which is deprecated. In the future, version 2 will become the default. We recommend specifying version 2 now for all new experiments to avoid migration issues down the line.

Meta Info​

You can also define meta info for the experiment and/or variations. These do not affect the behavior, but they are passed through to the trackingCallback, so they can be used to annotate events.

gb.run({
  key: "results-per-page",
  variations: [10, 20],

  // Experiment meta info
  name: "Results per Page",
  phase: "full-traffic"

  // Variation meta info
  meta: [
    {
      key: "control",
      name: "10 Results per Page",
    },
    {
      key: "variation",
      name: "20 Results per Page",
    },
  ]
})

Mutual Exclusion​

Sometimes you want to run multiple conflicting experiments at the same time. You can use the filters setting to run mutually exclusive experiments.

We do this using deterministic hashing to assign users a value between 0 and 1 for each filter.

// Will include 60% of users - ones with a hash between 0 and 0.6
gb.run({
  key: "experiment-1",
  variation: [0, 1],
  filters: [
    {
      seed: "pricing",
      attribute: "id",
      ranges: [[0, 0.6]]
    }
  ]
});


// Will include the other 40% of users - ones with a hash between 0.6 and 1
gb.run({
  key: "experiment-2",
  variation: [0, 1],
  filters: [
    {
      seed: "pricing",
      attribute: "id",
      ranges: [[0.6, 1.0]]
    }
  ]
});

Note - If a user is excluded from an experiment due to a filter, the rule will be skipped and the next matching rule will be used instead.

Holdout Groups​

To use global holdout groups, use a nested experiment design:

// The value will be `true` if in the holdout group, otherwise `false`
const holdout = gb.run({
  key: "holdout",
  variations: [true, false],
  // 10% of users in the holdout group
  weights: [0.1, 0.9]
});

// Only run your main experiment if the user is NOT in the holdout
if (!holdout.value) {
  const res = gb.run({
    key: "my-experiment",
    variations: ["A", "B"]
  })
}

Targeting Conditions​

You can also define targeting conditions that limit which users are included in the experiment. These conditions are evaluated against the attributes passed into the GrowthBook context. The syntax for conditions is based on the MongoDB query syntax and is straightforward to read and write.

For example, if the attributes are:

{
  "id": "123",
  "browser": {
    "vendor": "firefox",
    "version": 94
  },
  "country": "CA"
}

The following condition would evaluate to true and the user would be included in the experiment:

gb.run({
  key: "my-experiment",
  variation: [0, 1],
  condition: {
    "browser.vendor": "firefox",
    "country": {
      "$in": ["US", "CA", "IN"]
    }
  }
})

Inline Experiment Return Value​

A call to gb.run(experiment) returns an object with a few useful properties:

const {
  value,
  key,
  name,
  variationId,
  inExperiment,
  hashUsed,
  hashAttribute,
  hashValue,
} = gb.run({
  key: "my-experiment",
  variations: ["A", "B"],
});

// If user is included in the experiment
console.log(inExperiment); // true or false

// The index of the assigned variation
console.log(variationId); // 0 or 1

// The value of the assigned variation
console.log(value); // "A" or "B"

// The key and name of the assigned variation (if specified in `meta`)
console.log(key); // "0" or "1"
console.log(name); // ""

// If the variation was randomly assigned by hashing
console.log(hashUsed);

// The user attribute that was hashed
console.log(hashAttribute); // "id"

// The value of that attribute
console.log(hashValue); // e.g. "123"

The inExperiment flag will be false if the user was excluded from being part of the experiment for any reason (e.g. failed targeting conditions).

The hashUsed flag will only be true if the user was randomly assigned a variation. If the user was forced into a specific variation instead, this flag will be false.

Feature Definitions (reference)​

The feature definition JSON file contains information about all of the features in your application.

Each feature consists of a unique key, a list of possible values, and rules for how to assign those values to users.

{
  "feature-1": {...},
  "feature-2": {...},
  "another-feature": {...},
}

Basic Feature​

An empty feature always has the value null:

{
  "my-feature": {}
}

Default Values​

You can change the default assigned value with the defaultValue property:

{
  "my-feature": {
    defaultValue: "green"
  }
}

Override Rules​

You can override the default value with rules.

Rules give you fine-grained control over how feature values are assigned to users. There are 2 types of feature rules: force and experiment. Force rules give the same value to everyone. Experiment rules assign values to users randomly.

Rule Ids​

Rules can specify a unique identifier with the id property. This can help with debugging and QA by letting you see exactly why a specific value was assigned to a user.

Rule Conditions​

Rules can optionally define targeting conditions that limit which users the rule applies to. These conditions are evaluated against the attributes passed into the GrowthBook context. The syntax for conditions is based on the MongoDB query syntax and is straightforward to read and write.

For example, if the attributes are:

{
  "id": "123",
  "browser": {
    "vendor": "firefox",
    "version": 94
  },
  "country": "CA"
}

The following condition would evaluate to true:

{
  "browser.vendor": "firefox",
  "country": {
    "$in": ["US", "CA", "IN"]
  }
}

If a condition evaluates to false, the rule will be skipped. This means you can chain rules together with different conditions to support even the most complex use cases.

Force Rules​

Force rules do what you'd expect - force a specific value for the feature

// Firefox users in the US or Canada get "green"
// Everyone else gets the default "blue"
{
  "button-color": {
    defaultValue: "blue",
    rules: [
      {
        id: "rule-123",
        condition: {
          browser: "firefox",
          country: {
            $in: ["US", "CA"]
          }
        },
        force: "green"
      }
    ],
  }
}
Gradual Rollouts​

You can specify a range for your rule, which determines what percent of users will get the rule applied to them. Users who do not get the rule applied will fall through to the next matching rule (or default value). You can also specify a seed that will be used for hashing.

In order to figure out if a user is included or not, we use deterministic hashing. By default, we use the user attribute id for this, but you can override this by specifying hashAttribute for the rule:

This is useful for gradually rolling out features to users (start with a small range and slowly increase).

{
  "new-feature": {
    defaultValue: false,
    rules: [
      {
        force: true,
        hashAttribute: "device-id",
        seed: 'new-feature-rollout-abcdef123',
        // 20% of users
        range: [0, 0.2]
        // Increase to 40%:
        // range: [0, 0.4]
      }
    ]
  }
}

Experiment Rules​

Experiment rules let you adjust the percent of users who get randomly assigned to each variation. This can either be used for hypothesis-driven A/B tests or to simply mitigate risk by gradually rolling out new features to your users.

// Each variation gets assigned to a random 1/3rd of users
{
  "image-size": {
    rules: [
      {
        variations: ["small", "medium", "large"]
      }
    ]
  }
}
Customizing the Traffic Split​

By default, an experiment rule will include all traffic and do an even split between all variations. There are 2 ways to customize this behavior:

// Option 1: Using weights and coverage
{
  variations: ["red", "blue", "green"],
  // Only include 10% of traffic
  coverage: 0.1,
  // Split the included traffic 50/25/25 instead of the default 33/33/33
  weights: [0.5, 0.25, 0.25]
}

// Option 2: Specifying ranges
{
  variations: ["red", "blue", "green"],
  // Identical to the above
  // 5% of traffic in A, 2.5% each in B and C
  ranges: [
    [0, 0.05],
    [0.5, 0.525],
    [0.75, 0.775]
  ]
}

A user is assigned a number from 0 to 1 and whichever variation's range includes their number will be assigned to them.

Variation Meta Info​

You can use the meta setting to provide additional info about the variations such as name.

{
  "image-size": {
    rules: [
      {
        variations: ["sm", "md", "lg"],
        ranges: [
          [0, 0.5],
          [0.5, 0.75],
          [0.75, 1.0]
        ],
        meta: [
          {
            key: "control",
            name: "Small",
          },
          {
            key: "v1",
            name: "Medium",
          },
          {
            key: "v2",
            name: "Large",
          }
        ]
      }
    ]
  }
}
Tracking Key and Name​

When a user is assigned a variation, we call the trackingCallback function so you can record the exposure with your analytics event tracking system. By default, we use the feature id to identify the experiment, but this can be overridden if needed with the key setting. You can also optionally provide a human-readable name.

{
  "feature-1": {
    rules: [
      {
        // Use "my-experiment" as the key instead of "feature-1"
        key: "my-experiment",
        name: "My Experiment",
        variations: ["A", "B"]
      }
    ]
  },
}
Hash Attribute​

We use deterministic hashing to make sure the same user always gets assigned the same value. By default, we use the attribute id, but this can be overridden with the hashAttribute setting:

const gb = new GrowthBook({
  attributes: {
    id: "123",
    company: "acme",
  },
  features: {
    "my-feature": {
      rules: [
        // All users with the same "company" value
        // will be assigned the same variation
        {
          variations: ["A", "B"],
          hashAttribute: "company",
        },
        // If "company" is empty for the user (e.g. if they are logged out)
        // The experiment will be skipped and fall through to this next rule
        {
          force: "A",
        },
      ],
    },
  },
});
Filters​

Sometimes you want to run multiple conflicting experiments at the same time. You can use the filters setting to run mutually exclusive experiments.

We do this using deterministic hashing to assign users a value between 0 and 1 for each filter.

{
  "feature1": {
    rules: [
      // Will include 60% of users - ones with a hash between 0 and 0.6
      {
        variations: [false, true],
        filters: [
          {
            seed: "pricing",
            attribute: "id",
            ranges: [[0, 0.6]]
          }
        ]
      }
    ]
  },
  "feature2": {
    rules: [
      // Will include the other 40% of users - ones with a hash between 0.6 and 1
      {
        variations: [false, true],
        filters: [
          {
            seed: "pricing",
            attribute: "id",
            ranges: [[0.6, 1.0]]
          }
        ]
      },
    ]
  }
}

Note - If a user is excluded from an experiment due to a filter, the rule will be skipped and the next matching rule will be used instead.

Examples​