Dev guideAPI Reference
Dev guideAPI ReferenceUser GuideGitHubNuGetDev CommunitySubmit a ticketLog In
GitHubNuGetDev CommunitySubmit a ticket

OptimizelyUserContext for the Python SDK

Describes the OptimizelyUserContext object, which allows you to make flag decisions and track events for a user context for the Optimizely Feature Experimentation Python SDK.

The OptimizelyUserContext object allows you to make flag decisions and track events for a user context you have already created using the Create User Context method.

Additionally, if you have the Advanced Audience Targeting integration enabled, you can evaluate if your user would qualify for a real-time audience segment from Optimizely Data Platform.

OptimizelyUserContext minimum SDK minimum SDK version

OptimizelyUserContext is supported on SDK v3.8.0 and higher.

Forced decision methods minimum SDK minimum SDK version

set_forced_decision(), get_forced_decision(), remove_forced_decision() and remove_all_forced_decision() methods are supported on 4.0.0 and higher.

Advanced Audience Targeting minimum SDK version

fetch_qualified_segments() and is_qualified_for() methods are supported on version 5.0.0 and higher.



Advanced Audience Targeting is in beta. Apply on the Optimizely beta signup page or contact your Customer Success Manager.

OptimizelyUserContext definition

The following code shows the object definition for OptimizelyUserContext:

class OptimizelyUserContext(object):
  # set an attribute for the user
  def set_attribute(self, attribute_key, attribute_value):
  # get attributes for the user
  def get_user_attributes(self):

  # make a decision about which flag variation the user buckets into for the flag key 
  def decide(self, key, options=None):

  # make decisions about which flag variations the user buckets into for flag keys 
  def decide_for_keys(self, keys, options=None):

  # make decisions about which flag variations the user buckets into for all flags 
  def decide_all(self, options=None):

  # track user event
  def track_event(self, event_key, event_tags=None):
  # OptimizelyDecisionContext
  class OptimizelyDecisionContext(object):
    def __init__(self, flag_key, rule_key):
  # OptimizelyForcedDecision
  class OptimizelyForcedDecision(object):
    def __init__(self, variation_key):

  # Sets the forced decision (variation_key) for a given decision context
  def set_forced_decision(self, OptimizelyDecisionContext, OptimizelyForcedDecision):

  # Returns the forced decision for a given decision context
  def get_forced_decision(self, OptimizelyDecisionContext):

  # Removes the forced decision for a given decision context
  def remove_forced_decision(self, OptimizelyDecisionContext):

  # Removes all forced decisions bound to this user context
  def remove_all_forced_decisions(self):
  # The following methods require the Advanced Audience targeting integration enabled. 
  # See note following this code sample.

  # Return the saved results of **fetch_qualified_segments()**. 
  # Can be None if not properly updated with fetch_qualified_segments().  
  def get_qualified_segments(self):
  # Overwrite the qualified segments array. 
  # This allows for bypassing the remote fetching process from ODP 
  # or for utilizing your own fetching service.  
  def set_qualified_segments(self, segments):
  # Fetch all qualified segments for the user context.
  # If no callback is provided, this method will fetch the qualified segments
  # and return a boolean signifying success.
  # If a callback is provided, the method will fetch segments in a separate thread, 
  # invoke the provided callback when results are available, and return the thread handle.
  def fetch_qualified_segments(callback=None, options=None):
  # Check is the user qualified for the given segment. 
  def is_qualified_for(self, segment):



You must first enable the Advanced Audience Targeting integration to be able to call the get_qualified_segments(), set_qualified_segments(), fetch_qualified_segments(), and is_qualified_for() methods.


The following table shows attributes for the OptimizelyUserContext object:

user_idStringThe ID of the user
(optional) attributesDictA dictionary of custom key-value pairs specifying attributes for the user that are used for audience targeting. You can pass the dictionary with the user ID when you create the user.


The following table shows methods for the OptimizelyUserContext object:

set_attributePass a custom user attribute as a key-value pair to the user context.
decideReturns a decision result for a flag key for a user. The method returns the decision result in an OptimizelyDecision object, which contains all data required to deliver the flag rule.
See Decide methods
decide_for_keysReturns a dictionary of flag decisions for specified flag keys.
See Decide methods
decide_allReturns decisions for all active (unarchived) flags for a user.
See Decide methods
track_eventTracks a conversion event for a user (an action a user takes) and logs an error message if the specified event key does not match any existing events.
See Track Event
set_forced_decisionForces a user into a specific variation.
See Set Forced Decision
get_forced_decisionReturns what variation the user was forced into.
See Get Forced Decision
remove_forced_decisionRemoves a user from a specific forced variation.
See Remove Forced Decision
remove_all_forced_decisionsRemoves a user from all forced variations.
See Remove All Forced Decisions
fetch_qualified_segments **Fetch all Optimizely Data Platform (ODP) real-time segments that the user context qualified for. Has a synchronous and asynchronous implementation. See Advanced Audience Targeting segment qualification methods.
is_qualified_for **Checks if the user context qualified for a given ODP real-time segment. See Advanced Audience Targeting segment qualification methods.

** Requires the Advanced Audience Targeting integration.

See Also

Create User Context

Source files

The language and platform source files containing the implementation for Python are available on GitHub.