Javascript

Supports both browser and NodeJS environments.

Installation

yarn add @growthbook/growthbook

or

npm i --save @growthbook/growthbook

or use directly in your HTML without installing first:

<script type="module">
  import { GrowthBook } from "https://unpkg.com/@growthbook/growthbook/dist/bundles/esm.min.js";
  //...
</script>

Quick Usage

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

// Create a GrowthBook context
const growthbook = new GrowthBook();

// Load feature definitions (from API, database, etc.)
fetch("https://cdn.growthbook.io/api/features/<your api key>")
  .then((res) => res.json())
  .then((parsed) => {
    growthbook.setFeatures(parsed.features);
  });

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

// Get the value of a non-boolean feature with a fallback
const color = growthbook.getFeatureValue("button-color", "blue");

The GrowthBook Context

The GrowthBook constructor takes a number of optional settings.

Features

If you already have features loaded as a JSON object, you can pass them into the constructor with the features field:

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

If you need to load feature definitions from a remote source like an API or database, you can update the context at any time with setFeatures() (seen above in the Quick Start). Note - if you try to use a feature before it is loaded, it will always evaluate to null.

If you use the GrowthBook App to manage your features, you don't need to build this JSON file yourself - it will auto-generate one for you and make it available via an API endpoint.

If you prefer to build this file by hand or you want to know how it works under the hood, check out the detailed Feature Definitions section below.

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 comonly 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",
  },
});

If you need to set or update attributes asynchronously, you can do so with setAttributes(). This will completely overwrite the attributes object with whatever you pass in. Also, be aware that changing attributes may change the assigned feature values. This can be disorienting to users if not handled carefully.

Tracking Callback

Any time an experiment is run to determine the value of a feature, we call a function so you can record the assigned value in your event tracking or analytics system of choice.

new GrowthBook({
  trackingCallback: (experiment, result) => {
    // Example using Segment.io
    analytics.track("Experiment Viewed", {
      experimentId: experiment.key,
      variationId: result.variationId,
    });
  },
});

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

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 growthbook.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.

Using Features

Every feature has a "value" which is assigned to a user. This value can be any JSON data type. If a feature doesn't exist, the value will be null.

There are 4 main methods for evaluating features:

if (growthbook.isOn("my-feature")) {
  // Value is truthy
}

if (growthbook.isOff("my-feature")) {
  // Value is falsy (null, 0, "", or false)
}

// Get the value with a fallback for when it's null
const value = growthbook.getFeatureValue("my-feature", 123);

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

The evalFeature method returns a FeatureResult object with more info about why the feature was assigned to the user. It has the following properties:

• value - The value of the feature (or null if not defined)
• source - Why the value was assigned to the user. One of override, unknownFeature, defaultValue, force, or experiment
• ruleId - The string id of the rule (if any) which was used to assign the value to the user
• experiment - Information about the experiment (if any) which was used to assign the value to the user
• experimentResult - The result of the experiment (if any) which was used to assign the value to the user

Feature Definitions

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 coverage value for your rule, which is a number between 0 and 1 and represents 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).

This is useful for gradually rolling out features to users (start coverage at 0 and slowly increase towards 1 as you watch metrics).

// 20% of users will get the new feature
{
  "new-feature": {
    defaultValue: false,
    rules: [
      {
        force: true,
        coverage: 0.2
      }
    ]
  }
}

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:

// 20% of companies will get the new feature
// Users in the same company will always get the same value (either true or false)
{
  "new-feature": {
    defaultValue: false,
    rules: [
      {
        force: true,
        coverage: 0.2,
        hashAttribute: "company"
      }
    ]
  }
}

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"]
      }
    ]
  }
}

Weights

You can use the weights setting to control what percent of users get assigned to each variation. Weights determine the traffic split between variations and must add to 1.

{
  "results-per-page": {
    rules: [
      {
        variations: ["small", "medium", "large"],
        // 50% of users will get "small" (index 0)
        // 30% will get "medium" (index 1)
        // 20% will get "large" (index 2)
        weights: [0.5, 0.3, 0.2]
      }
    ]
  }
}

Tracking Key

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:

{
  "feature-1": {
    rules: [
      {
        // Use "my-experiment" as the key instead of "feature-1"
        key: "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 growthbook = 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",
        },
      ],
    },
  },
});

Coverage

You can use the coverage setting to introduce sampling and reduce the percent of users who are included in your experiment. Coverage must be between 0 and 1 and defaults to 1 (everyone included). This feature uses deterministic hashing to ensure consistent sampling.

{
  "my-feature": {
    rules: [
      // 80% of users will be included in the experiment
      {
        variations: [false, true],
        coverage: 0.8
      },
      // The remaining 20% will fall through to this next matching rule
      {
        force: false
      }
    ]
  }
}

Namespaces

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

We do this using deterministic hashing to assign users a value between 0 and 1 for each namespace. Experiments can specify which namespace it is in and what part of the range [0,1] it should include. If the ranges for two experiments in a namespace don't overlap, they will be mutually exclusive.

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

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

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 growthbook.run method:

const { value } = growthbook.run({
  key: "my-experiment",
  variations: ["red", "blue", "green"],
});

All of the other settings (weights, hashAttribute, coverage, namespace, condition) are supported when using inline experiments.

In addition, there are a few other settings that only really make sense for inline experiments:

• force can be set to one of the variation array indexes. Everyone will be immediately assigned the specified value.
• active can be set to false to disable the experiment and return the control for everyone

Inline Experiment Return Value

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

const {
  inExperiment,
  hashUsed,
  variationId,
  value,
  hashAttribute,
  hashValue,
} = growthbook.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"

// 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.

Typescript

When using getFeatureValue, the type of the feature is inferred from the fallback value you provide.

// color will be type "string"
const color = growthbook.getFeatureValue("button-color", "blue");

When using evalFeature, the value has type any by default, but you can specify a more restrictive type:

// result.value will be type "number" now
const result = growthbook.evalFeature<number>("button-size");

When using inline experiments, the returned value is inferred from the variations you pass in:

// result.value will be type "string"
const result = growthbook.run({
  key: "my-test",
  variations: ["blue", "green"],
});

There are a number of types you can import as well if needed:

import type {
  Context,
  ConditionInterface,
  Experiment,
  FeatureDefinition,
  FeatureResult,
  ExperimentResult,
} from "@growthbook/growthbook";