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The GrowthBook PHP SDK requires PHP version 8.0 or greater.

Installation

GrowthBook is available on Composer:

Quick Usage

Some of the feature flags you evaluate might be running an A/B test behind the scenes which you’ll want to track in your analytics system. At the end of the request, you can loop through all experiments and track them however you want to:

Loading Features

There are 2 ways to load features into the SDK. You can use initialize with a Client Key and API Host. Or, you can manually fetch and cache feature flags and pass them in with the withFeatures method.

initialize method

The initialize method can fetch features from the GrowthBook API for you. By default, there is no caching enabled. You can enable it by passing any PSR16-compatible instance into the withCache method. Caching is required for production usage
To load features, we require a PSR-17 (HttpClient) and PSR-18 (RequestFactoryInterface) compatible library like Guzzle to be installed. We will auto-discover most HTTP libraries without any configuration required, but if you prefer to specify it explicitly, you can use the withHttpClient method. Note - you’ll need to specify both an HttpClient and a RequestFactoryInterface implementation. The initialize method takes 3 arguments:
  • $clientKey (required) - Get this from your SDK Connection in GrowthBook.
  • $apiHost (optional) - Defaults to https://cdn.growthbook.io. If self-hosting GrowthBook, set this to your API host.
  • $decryptionKey (optional) - Only required if you’ve enabled encryption for your SDK Connection.

withFeatures method

If you prefer to have full control over the fetching/caching behavior, you can use the withFeatures method instead to pass an associative array of features into the SDK.

withSavedGroups method

If you’re using saved groups and not using the initialize method, you’ll need to call withSavedGroups to provide the saved groups data to the SDK.

The Growthbook Class

The Growthbook class has a number of properties. These can be set using a Fluent interface or can be passed into a constructor using an associative array. Every property also has a getter method if needed. Here’s an example:
Note: you can also use the fluent methods (e.g. withFeatures) at any point to update properties.

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)
Attributes can be any JSON data type - boolean, integer, float, string, or array.
If you want to update attributes later, please note that the withAttributes method completely overwrites the attributes object. You can use array_merge if you only want to update a subset of fields:

Tracking Experiments

Any time an experiment is run to determine the value of a feature, you want to track that event in your analytics system. You can either do this via a callback function:
Or track all events at the end of the request by looping through an array:
Or, you can pass the impressions onto your front-end and fire analytics events from there. To do this, simply add a block to your template (shown here in plain PHP, but similar idea for Twig, Blade, etc.).
Below are examples for a few popular front-end tracking libraries:

Google Analytics

Segment

Mixpanel

Logging

GrowthBook can output log messages to help you debug your feature flags and experiments. We support any PSR-3 compatible logger. We implement a fluent interface (withLogger) as well as the standard LoggerAware interface (setLogger).

Using Features

There are 3 main methods for interacting with features.
  • $growthbook->isOn("feature-key") returns true if the feature is on
  • $growthbook->isOff("feature-key") returns false if the feature is on
  • $growthbook->getValue("feature-key", "default") returns the value of the feature with a fallback
In addition, you can use $growthbook->getFeature("feature-key") to get back a FeatureResult object with the following properties:
  • value - The JSON-decoded value of the feature (or null if not defined)
  • on and off - The JSON-decoded value cast to booleans
  • source - Why the value was assigned to the user. One of unknownFeature, defaultValue, force, or experiment
  • 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

Sticky Bucketing

By default GrowthBook does not persist assigned experiment variations for a user. We rely on deterministic hashing to ensure that the same user attributes always map to the same experiment variation. However, there are cases where this isn’t good enough. For example, if you change targeting conditions in the middle of an experiment, users may stop being shown a variation even if they were previously bucketed into it. Sticky Bucketing is a solution to these issues. You can provide a Sticky Bucket Service to the GrowthBook instance to persist previously seen variations and ensure that the user experience remains consistent for your users. A sample InMemoryStickyBucketService implementation is provided for reference, but in production you will definitely want to implement your own version using a database, cookies, or similar for persistence. Sticky Bucket documents contain three fields
  • attributeName - The name of the attribute used to identify the user (e.g. id, cookie_id, etc.)
  • attributeValue - The value of the attribute (e.g. 123)
  • assignments - A dictionary of persisted experiment assignments. For example: {"exp1__0":"control"}
The attributeName/attributeValue combo is the primary key. Here’s an example implementation using a theoretical db object:

Inline Experiments

Instead of declaring all features up-front and referencing them by ids in your code, you can also just run an experiment directly. This is done with the $growthbook->runInlineExperiment method:
As you can see, there are 2 required parameters for experiments, a string key, and an array of variations. Variations can be any data type, not just strings. There are a number of additional settings to control the experiment behavior. The methods are all chainable. Here’s an example that shows all of the possible settings:

Inline Experiment Return Value

A call to runInlineExperiment returns an ExperimentResult object with a few useful properties:
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.

Supported Features