ol-social-auth 0.2.2

An Open edX plugin implementing MIT social auth backend

An Open edX plugin implementing MIT social auth backend

Version Compatibility

Compatible with all edx releases

Installing The Plugin

For detailed installation instructions, please refer to the plugin installation guide.

Installation required in:

• LMS

Configurations

This section outlines the steps for integrating your application with ol-social-auth for various deployment scenarios. Please refer to the corresponding documentation for detailed instructions.

• Devstack: To configure ol-social-auth with an edx-platform instance provisioned using devstack, follow the instructions here

• Tutor: To configure ol-social-auth with an edx-platform instance provisioned using tutor, follow the instructions here also

How to use

Make sure to properly configure the plugin following the links in the above “Configurations” section before use.

• Install the plugin in the lms following the installation steps above.

• Verify that you are not logged in on edx-platform.

• Create a new user in your MIT application and verify that a corresponding user is successfully created on the edX platform.

Expired Token Cleanup

This plugin includes a scheduled Celery task (ol_clear_expired_tokens) that automatically removes expired OAuth2 access tokens, refresh tokens, and grant tokens from the database.

Behavior:

• Runs every Monday at 9:00 AM (server time) via Celery Beat by default. The schedule can be customized by overriding the ol_clear_expired_tokens entry in CELERYBEAT_SCHEDULE.

• Uses django-oauth-toolkit’s clear_expired() to delete tokens that have exceeded the configured expiration threshold.

• Sets REFRESH_TOKEN_EXPIRE_SECONDS to 30 days (overriding the edx-platform default of 90 days). Tokens revoked or expired longer than 30 days ago will be cleaned up.

Note: If running this plugin for the first time on a database with a large backlog of expired tokens (millions of rows), consider running the edx_clear_expired_tokens management command manually first to reduce the initial volume before relying on the scheduled task.