feature-flags-python

To evaluate PostHog feature flags in Python, install the posthog library, initialize the client with your project token and host, then call feature_enabled for boolean flags or get_feature_flag for multivariate variants. This approach works across Django, Flask, and data pipelines.

Yes, you can retrieve custom payloads from PostHog feature flags in Python by using the get_feature_flag_payload method after initializing the posthog client. This allows your server-side application to access flag-specific data for guarded feature rollouts and data pipelines.

Yes, the PostHog Python library supports multivariate flag evaluation through the get_feature_flag method. This allows Python applications to retrieve and react to specific variants beyond simple boolean checks, enabling complex guarded rollouts in back-end services.

To include feature flag data in captured PostHog events from Python, enable the send_feature_flags option in your capture calls. This attaches the active flag information to the event, allowing you to analyze flag impact directly within your PostHog analytics.

The best way to manage server-side feature rollouts in Python applications is using PostHog's API to evaluate flags without hardcoding keys. By initializing the client and using methods like feature_enabled, you achieve safe, decoupled flag evaluation across Django, Flask, and data pipelines.

Using PostHog for feature flag evaluation prevents tight coupling and hardcoded keys in Python applications. It provides a straightforward API to safely evaluate boolean and multivariate flags, retrieve payloads, and include flag information in captures for controlled feature rollouts.