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Eppo's open source Node SDK can be used for both feature flagging and experiment assignment:

Getting Started

Install the SDK

You can install the SDK with Yarn or NPM:

yarn add @eppo/node-server-sdk

Define an assignment logger

Eppo encourages centralizing application logging as much as possible. Accordingly, instead of implementing a new logging framework, Eppo's SDK integrates with your existing logging system via a logging callback function defined at SDK initialization. This logger takes an analytic event created by Eppo, assignment, and writes in to a table in the data warehouse (Snowflake, Databricks, BigQuery, or Redshift).

The code below illustrates an example implementation of a logging callback to the console and other event platforms. You could also use your own logging system, the only requirement is that the SDK receives a logAssignment function. Here we define an implementation of the Eppo IAssignmentLogger interface containing a single function named logAssignment:

// Import Eppo's assignment logger interface and client initializer
import { IAssignmentLogger, init } from "@eppo/node-server-sdk";

// Define logAssignment so that it logs events
const assignmentLogger: IAssignmentLogger = {
logAssignment(assignment) {

This example writes to your local machine and is useful for development in your local environment. In production, these logs will need to get written to a table in your data warehouse.

Deduplicating assignment logs

Eppo's SDK uses an internal cache to ensure that duplicate assignment events are not logged to the data warehouse. While Eppo's analytic engine will automatically deduplicate assignment records, this internal cache prevents firing unnecessary events and can help minimize costs associated with event logging.

Initialize the SDK

Initialize the SDK with an SDK key, which can be generated in the Eppo interface. Initialization should happen when your application starts up to generate a singleton client instance, once per application lifecycle:

import { init } from "@eppo/node-server-sdk";

await init({
apiKey: "<SDK_KEY>",

After initialization, the SDK begins polling Eppo’s API at regular intervals to retrieve the most recent experiment configurations such as variation values and traffic allocation. The SDK stores these configurations in memory so that assignments thereafter are effectively instant. For more information, see the architecture overview page.

Assign variations

Assign users to flags or experiments using get<Type>Assignment, depending on the type of the flag. For example, for a string-valued flag, use getStringAssignment:

import * as EppoSdk from "@eppo/node-server-sdk";

const eppoClient = EppoSdk.getInstance();
const variation = eppoClient.getStringAssignment(
{<SUBJECT-ATTRIBUTES>}, // Metadata used for targeting

The getStringAssignment function takes four inputs to assign a variation:

  • flagKey - The key for the flag you are evaluating. This key is available on the feature flag detail page (see below).
  • subjectKey - The entity ID that is being experimented on, typically represented by a UUID. This key is used to deterministically assign subjects to variants.
  • subjectAttributes - A map of metadata about the subject used for targeting. If targeting is not needed, pass in an empty object.
  • defaultValue - The value that will be returned if no allocation matches the subject, if the flag is not enabled, if getStringAssignment is invoked before the SDK has finished initializing, or if the SDK was not able to retrieve the flag configuration. Its type must match the get<Type>Assignment call.

Example flag key


See an end-to-end example below of setting up the Eppo Node client and logging events to the console.

// Import Eppo's assignment logger interface and client initializer
import { IAssignmentLogger, init } from "@eppo/node-server-sdk";

// Define logAssignment so that it logs events
const assignmentLogger: IAssignmentLogger = {
logAssignment(assignment) {

// Initialize the client
await init({
apiKey: "<SDK_KEY>",

// Then every call to getStringAssignment will also log the event
const user = {
userid: '1234567890',
attributes: { country: 'united states', subscription_status: 'gold' }

const eppoClient = EppoSdk.getInstance();
const variation = eppoClient.getStringAssignment(
// Output
allocation: 'allocation-2468',
experiment: 'new-user-onboarding-allocation-2468',
featureFlag: 'new-user-onboarding',
variation: 'treatment',
timestamp: '2024-03-21T18:58:23.176Z',
subject: '1234567890',
holdout: 'q1-holdout',
holdoutVariation: null,
subjectAttributes: { country: 'united states', subscription_status: 'gold' }

It may take up to 10 seconds for changes to Eppo experiments to be reflected by the SDK assignments.

Typed assignments

The following typed functions are available:


To read more about different flag types, see the page on Flag Variations.

Initialization options

How the SDK fetches experiment configurations is configurable via additional optional initialization options:

requestTimeoutMs (number)Timeout in milliseconds for HTTPS requests for the experiment configurations.5000
numInitialRequestRetries (number)Number of additional times the initial configurations request will be attempted if it fails. This is the request typically synchronously waited (via await) for completion. A small wait will be done between requests.1
pollAfterFailedInitialization (boolean)Poll for new configurations even if the initial configurations request failed.false
throwOnFailedInitialization (boolean)Throw an error (reject the promise) if unable to fetch initial configurations during initialization.true
numPollRequestRetries (number)If polling for updated configurations after initialization, the number of additional times a request will be attempted before giving up. Subsequent attempts are done using an exponential backoff.7

Assignment Logger schema

The SDK will invoke the logAssignment function with an assignment object that contains the following fields:

experiment (string)An Eppo experiment key"recommendation-algo-allocation-17"
subject (string)An identifier of the subject or user assigned to the experiment variationUUID
variation (string)The experiment variation the subject was assigned to"control"
timestamp (string)The time when the subject was assigned to the variation2021-06-22T17:35:12.000Z
subjectAttributes (Attributes)A free-form map of metadata about the subject. These attributes are only logged if passed to the SDK assignment function{ "country": "US" }
featureFlag (string)An Eppo feature flag key"recommendation-algo"
allocation (string)An Eppo allocation key"allocation-17"
holdout (string)An Eppo holdout group key"q1-holdout"
holdoutVariation (string)An Eppo holdout variation if experiment is eligible for analysis key"status_quo", "all_shipped_variations", or null

The Attributes type represents a mapping of an attribute name to its value, which could be a string, number or boolean (Record<string, string | number | boolean>).


More details about logging and examples (with Segment, Rudderstack, mParticle, Snowplow, Amplitude) can be found in the event logging page.

Usage with Contextual Multi-Armed Bandits

Eppo also supports contextual multi-armed bandits. You can read more about them in the high-level documentation. Bandit flag configuration--including setting up the flag key, status quo variation, bandit variation, and targeting rules--are configured within the Eppo application. However, available actions are supplied to the SDK in the code when querying the bandit.

To leverage bandits using the Node SDK, there are two additional steps over regular feature flags:

  1. Add a bandit action logger to the SDK client instance
  2. Query the bandit for an action

Define a bandit assignment logger

In order for the bandit to learn an optimized policy, we need to capture and log the bandit's actions. This requires defining a bandit logger in addition to an assignment logger.

// Import Eppo's logger interfaces and client initializer
import { IAssignmentLogger, IBanditLogger, init } from "@eppo/node-server-sdk";

// Define an assignment logger for recording variation assignments
const assignmentLogger: IAssignmentLogger = {
logAssignment(assignment: IAssignmentEvent) {
console.log("TODO: save assignment information to data warehouse", assignment);

// Define a bandit logger for recording bandit action assignments
const banditLogger: IBanditLogger = {
logBanditAction (banditEvent: IBanditEvent) {
console.log("TODO: save bandit action information to the data warehouse, ensuring the column names are as expected", banditEvent);

// Initialize the SDK with both loggers provided
await init({
apiKey: "<SDK_KEY>",

The SDK will invoke the logBanditAction() function with an IBanditEvent object that contains the following fields:

timestamp (string)The time when the action is taken in UTC as an ISO string"2024-03-22T14:26:55.000Z"
featureFlag (string)The key of the feature flag corresponding to the bandit"bandit-test-allocation-4"
bandit (string)The key (unique identifier) of the bandit"ad-bandit-1"
subject (string)An identifier of the subject or user assigned to the experiment variation"ed6f85019080"
subjectNumericAttributes (Attributes)Metadata about numeric attributes of the subject. Map of the name of attributes their provided values{"age": 30}
subjectCategoricalAttributes (Attributes)Metadata about non-numeric attributes of the subject. Map of the name of attributes their provided values{"loyalty_tier": "gold"}
action (string)The action assigned by the bandit"promo-20%-off"
actionNumericAttributes (Attributes)Metadata about numeric attributes of the assigned action. Map of the name of attributes their provided values{"discount": 0.1}
actionCategoricalAttributes (Attributes)Metadata about non-numeric attributes of the assigned action. Map of the name of attributes their provided values{"promoTextColor": "white"}
actionProbability (number)The weight between 0 and 1 the bandit valued the assigned action0.25
optimalityGap (number)The difference between the score of the selected action and the highest-scored action456
modelVersion (string)Unique identifier for the version (iteration) of the bandit parameters used to determine the action probability"v123"
metaData Record<string, unknown>Any additional freeform meta data, such as the version of the SDK{ "sdkLibVersion": "3.5.1" }

Querying the bandit for an action

To query the bandit for an action, you can use the getBanditAction() function. This function takes the following parameters:

  • flagKey (string): The key of the feature flag corresponding to the bandit
  • subjectKey (string): The key of the subject or user assigned to the experiment variation
  • subjectAttributes (Attributes | ContextAttributes): The context of the subject
  • actions (string[] | Record<string, Attributes | ContextAttributes>): Available actions, optionally mapped to their respective contexts
  • defaultValue (string): The default variation to return if the flag is not successfully evaluated

The ContextAttributes type represents attributes which have already been explicitly bucketed into categorical and numeric attributes ({ numericAttributes: Attributes, categoricalAttributes: Attributes }). There is more detail on this in the Subject Context section.

The following code queries the bandit for an action:

import { getInstance as getEppoSdkInstance } from "@eppo/node-server-sdk";
import { Attributes, BanditActions } from "@eppo/js-client-sdk-common";

const flagKey = "shoe-bandit";
const subjectKey = "user123";
const subjectAttributes: Attributes = { age: 25, country: "GB" };
const defaultValue = "default";
const actions: BanditActions = {
nike: {
numericAttributes: { brandAffinity: 2.3 },
categoricalAttributes: { imageAspectRatio: "16:9" }
adidas: {
numericAttributes: { brandAffinity: 0.2 },
categoricalAttributes: { imageAspectRatio: "16:9" }
const { variation, action } = getEppoSdkInstance().getBanditAction(

if (action) {
} else {

Subject context

The subject context contains contextual information about the subject that is independent of bandit actions. For example, the subject's age or country.

The subject context can be provided as Attributes, which will then assume anything that is number is a numeric attribute, and everything else is a categorical attribute.

You can also explicitly bucket the attribute types by providing the context as ContextAttributes. For example, you may have an attribute named priority, with possible values 0, 1, and 2 that you want to be treated categorically rather than numeric. ContextAttributes have two nested sets of attributes:

  • numericAttributes (Attributes): A mapping of attribute names to their numeric values (e.g., age: 30)
  • categoricalAttributes (Attributes): A mapping of attribute names to their categorical values (e.g., country)

Any non-numeric values explicitly passed in as values for numeric attributes will be ignored.

Attribute names and values are case-sensitive.


The subject context, passed in as the subjectAttributes parameter, is also still used for targeting rules for the feature flag, just like with non-bandit assignment methods.

Action contexts

The action context contains contextual information about each action. They can be provided as a mapping of attribute names to their contexts.

Similar to subject context, action contexts can be provided as Attributes--which will then assume anything that is number is a numeric attribute, and everything else is a categorical attribute--or as ContextAttributes, which have explicit bucketing into numericAttributes and categoricalAttributes.

Note that action contexts can contain two kinds of information:

  • Action-specific context (e.g., the image aspect ratio of image corresponding to this action)
  • Subject-action interaction context (e.g., there could be a "brand-affinity" model that computes brand affinities of users to brands,
    and scores of that model can be added to the action context to provide additional context for the bandit)

If there is no action context, an array of strings comprising only the actions names can also be passed in.

If the subject is assigned to the variation associated with the bandit, the bandit selects one of the supplied actions. All actions supplied are considered to be valid. If an action should not be available to a subject, do not include it for that call.

Like attributes, actions are case-sensitive.


getBanditAction() returns two fields:

  • variation (string): The variation that was assigned to the subject
  • action (string | null): The action that was assigned to the subject by the bandit, or null if the bandit was not assigned

The variation returns the feature flag variation. This can be the bandit itself, or the "status quo" variation if the subject is not assigned to the bandit.

If we are unable to generate a variation, for example when the flag is turned off, then the provided default variation is returned. In both of those cases, the returned action will be null, and you should use the status-quo algorithm to select an action (more on this below).

When action is not null, the bandit has selected an action for the subject.


If no actions are provided and the flag still has an active bandit, no assignments will be made and the default value will be returned.


If the flag no longer has any allocations with bandits, this function will behave the same as getStringAssignment(), with the provided actions being ignored and the assigned variation being returned along with a null action.

Status quo algorithm

In order to accurately measure the performance of the bandit, we need to compare it to the status quo algorithm using an experiment. This status quo algorithm could be a complicated algorithm to that selects an action according to a different model, or a simple baseline such as selecting a fixed or random action. When you create an analysis allocation for the bandit and the returned action is null, implement the desired status quo algorithm based on the variation value.


You may encounter a situation where a flag assignment produces a value that you did not expect. There are functions detailed here to help you understand how flags are assigned, which will allow you to take corrective action on potential configuration issues.