Leaderboard Deploy agents

Use structured logging with appropriate context and log levels to maximize the value of log messages. Include relevant metadata with each log message to facilitate filtering and debugging:

1. Use structured logging patterns that allow consistent inclusion of contextual information:

   # Instead of:
   logger.info("Processing item")
      
   # Do this:
   logger.info("Processing item", extra={"project_id": project.id, "item_type": item.type})
      
   # Or with Sentry SDK:
   sentry_sdk.set_context("operation_context", {"project_id": project.id, "item_type": item.type})
   

2. Choose log levels based on actionability:
   • ERROR: For actionable issues requiring immediate attention
   • WARNING: For potential problems that may need attention
   • INFO: For general operational information
   • DEBUG: For detailed troubleshooting information

   Instead of removing logging statements, consider lowering their level to DEBUG for infrequently needed information.

3. When logging exceptions:
   • Include both error type and message
   • Preserve stack traces using logger.exception() or exc_info=True
   • For lower severity issues, use appropriate level with context:

     try:
       # operation
     except ApiError as e:
       # For non-critical errors that don't need immediate action
       logger.warning("API operation failed", exc_info=True, extra={"operation": "fetch_data"})
       # Or with Sentry:
       sentry_sdk.set_context("operation_context", {"operation": "fetch_data"})
       sentry_sdk.capture_exception(error=e, level="warning")
     

4. Avoid duplicate logging. If using a framework that handles logging (like lifecycle), add context to it rather than creating separate log entries:

   # Instead of:
   lifecycle.record_halt(reason)
   logger.info("Operation halted", extra={"reason": reason})
      
   # Do this:
   lifecycle.add_extras({"project_id": project.id})
   lifecycle.record_halt(reason)
   

Following these practices improves troubleshooting efficiency, enables better log filtering, and ensures that logs provide maximum value for debugging production issues.