Refactor and improve reasoning effort handling in Python OpenAI Responses Agent #5
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Overview
This PR addresses code quality and design issues in the Python OpenAI Responses Agent reasoning implementation by simplifying complex logic, adding proper validation, and aligning with the C# implementation approach.
Issues Fixed
1. Overly Complex Reasoning Priority Logic
The
_generate_options()
method contained complex nested logic for handling reasoning effort priority that was difficult to understand and maintain:Before:
After:
2. Missing Parameter Validation
Added comprehensive validation for reasoning effort parameters at both construction and invocation time:
Invalid values now properly raise
AgentInitializationException
orAgentInvokeException
with clear error messages.3. Improved Code Organization
Extracted complex logic into focused helper methods:
_resolve_reasoning_effort()
- Handles priority hierarchy clearly_is_o_series_model()
- Clean O-series model detection_get_default_reasoning_for_model()
- Model-specific defaults_validate_reasoning_effort_parameter()
- Invoke-time validationKey Improvements
Clear Priority Hierarchy
The reasoning effort resolution now follows a clear, documented priority:
reasoning
parameter in invoke callsreasoning_effort
settingEnhanced Edge Case Handling
Properly handles the case where
reasoning_effort=None
is explicitly passed to disable automatic reasoning for O-series models.Better Documentation
Added comprehensive docstrings explaining the priority hierarchy, validation rules, and expected behavior.
Backward Compatibility
✅ All existing functionality is preserved
✅ All 19 existing tests continue to pass
✅ API remains unchanged for existing users
✅ No breaking changes introduced
Testing
Enhanced test coverage with proper validation testing:
The implementation now follows the C# pattern more closely with cleaner, more maintainable code while preserving all existing functionality.
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