Quickstart for Agent

This brief quickstart describes how to run Agent, using two examples:

• To get started using Docker, see Running locally via Docker.

• To get started using example Node microservices, see the following video link.

Running locally via Node

Running locally via Docker

Follow these steps to deploy Optimizely Agent locally via Docker and access some of the common API endpoints.
If Docker is not installed then you can download it on Docker's official site.

First, pull the Docker image with:

docker pull optimizely/agent

Then start the service in the foreground with the following command:

docker run -p 8080:8080 --env OPTIMIZELY_LOG_PRETTY=true optimizely/agent

Note that we are enabling "pretty" logs which provide colorized and human-readable formatting.
The default log output format is structured JSON.

Evaluating REST APIs

The rest of the getting started guide will demonstrate the APIs' capabilities. For brevity, we have chosen to illustrate the API usage with Python.

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Note

The APIs are also defined via OpenAPI (Swagger) and can be found on localhost <http://localhost:8080/openapi.yaml> or online in our API reference documentation.

Start an http session

Each request made into Optimizely Agent is in the context of an Optimizely SDK Key. SDK Keys map API requests to a specific Optimizely Project and Environment. We can set up a global request header by using the requests.Session object.

import requests
s = requests.Session()
s.headers.update({'X-Optimizely-SDK-Key': '<<YOUR-SDK-KEY>>'})

To get your SDK key, navigate to the project settings of your Optimizely account.

Future examples will assume this session is being maintained.

Get current environment configuration

The /config endpoint returns a manifest of the current working environment.

resp = s.get('http://localhost:8080/v1/config')
env = resp.json()

for key in env['featuresMap']:
    print(key)

Activate Feature

The /activate?featureKey={key} endpoint activates the feature for a given user. In Optimizely, activation is in the context of a given user to make the relative bucketing decision. In this case we'll provide a userId via the request body. The userId will be used to determine how the feature will be evaluated. Features can either be part of a Feature Test in which variations of feature variables are being measured against one another or a feature rollout, which progressively make the feature available to the selected audience.

From an API standpoint the presence of a Feature Test or Rollout is abstracted away from the response and only the resulting variation or enabled feature is returned.

python
params = { "featureKey": "my-feature" }
payload = { "userId": "test-user" }
resp = s.post(url = 'http://localhost:8080/v1/activate', params=params, json=payload)

print(resp.json())

The activate API is a POST to signal to the caller that there are side-effects. Namely, activation results in a "decision" event sent to Optimizely analytics for the purpose of analyzing Feature Test results. A "decision" will NOT be sent if the feature is simply part of a rollout.