Fix: Respect reasoning_effort config for GPT-5 models

[Tyler-Rak]

User description

Fixes #2120

Problem

GPT-5 models were hardcoded to use reasoning_effort='minimal', ignoring the user's config.reasoning_effort setting.

Solution

Modified litellm_ai_handler.py to:

• Read reasoning_effort from configuration instead of hardcoding
• Support values: 'none', 'low', 'medium', 'high'
• Default to 'none' for non-thinking models, 'low' for thinking models
• Add logging to show which reasoning effort level is being used

Testing

Verified with GPT-5 model that:

• Config setting reasoning_effort = "medium" is now respected
• Logs confirm: "Using reasoning_effort=medium for GPT-5 model (from config)"
• Model correctly uses medium reasoning effort in API calls

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PR Type

Bug fix

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Description

• Respect user's reasoning_effort config setting for GPT-5 models

• Replace hardcoded 'minimal'/'low' values with configurable defaults

• Support reasoning effort values: 'none', 'low', 'medium', 'high'

• Add logging to show which reasoning effort level is being used

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Diagram Walkthrough

flowchart LR
  A["GPT-5 Model Request"] --> B{"Model Type?"}
  B -->|"Thinking Model"| C["Use config or default 'low'"]
  B -->|"Non-thinking Model"| D["Use config or default 'none'"]
  C --> E["Set reasoning_effort parameter"]
  D --> E
  E --> F["Log effort level used"]

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File Walkthrough

	Relevant files
Bug fix	litellm_ai_handler.py Make GPT-5 reasoning_effort configurable with smart defaults pr_agent/algo/ai_handlers/litellm_ai_handler.py Read reasoning_effort from config instead of hardcoding values Use 'low' as default for thinking models, 'none' for non-thinking models Validate config value against supported efforts list Add info logging to display which reasoning effort is being used +17/-8

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[qodo-merge-for-open-source]

PR Compliance Guide 🔍

Below is a summary of compliance checks for this PR:

Security Compliance
🟢	No security concerns identified No security vulnerabilities detected by AI analysis. Human verification advised for critical code.
Ticket Compliance
🟡	🎫 #2120 🟢 Fix the root cause in litellm_ai_handler.py where reasoning_effort is hardcoded Respect the user's reasoning_effort configuration setting instead of hardcoding values Allow users to configure reasoning_effort in .pr_agent.toml and have it properly applied 🔴 Fix the issue where gpt-5.1 and gpt-5.1-codex always receive reasoning_effort='minimal', causing API failures Support the correct reasoning_effort values for gpt-5.1-codex ('low', 'medium', 'high') and avoid using 'minimal' ⚪ Verify that the fix works correctly with actual OpenAI API calls for gpt-5.1-codex model Confirm that the default 'none' value works for gpt-5.1 non-thinking models Test that gpt-5.1-codex properly accepts 'low', 'medium', and 'high' values from config
Codebase Duplication Compliance
⚪	Codebase context is not defined Follow the guide to enable codebase context checks.
Custom Compliance
🟢	Consistent Naming Conventions Objective: All new variables, functions, and classes must follow the project's established naming standards Status: Passed
	No Dead or Commented-Out Code Objective: Keep the codebase clean by ensuring all submitted code is active and necessary Status: Passed
	Robust Error Handling Objective: Ensure potential errors and edge cases are anticipated and handled gracefully throughout the code Status: Passed
	Single Responsibility for Functions Objective: Each function should have a single, well-defined responsibility Status: Passed
	When relevant, utilize early return Objective: In a code snippet containing multiple logic conditions (such as 'if-else'), prefer an early return on edge cases than deep nesting Status: Passed
Update

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🟡 - Partial Compliant
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⚪ - Requires Further Human Verification
🏷️ - Compliance label

[qodo-merge-for-open-source]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
General	✅ Refactor logic to improve readability Suggestion Impact: The commit implements the exact refactoring suggested. It stores the validation result in `is_config_valid` variable, adds a `source` variable to track whether config or default is used, restructures the conditional logic to eliminate redundant checks, adds a warning log for invalid configuration values, and updates the info log to use the `source` variable with quoted effort value. code diff: - - if model.endswith('_thinking'): - # For thinking models, use config value or default to 'low' - effort = config_effort if config_effort in supported_efforts else 'low' + is_config_valid = config_effort in supported_efforts + source = "config" + + if is_config_valid: + effort = config_effort else: - # For non-thinking models, use config value or default to 'none' - # If 'none' fails for specific models (e.g., codex), they should set config to 'low' - effort = config_effort if config_effort in supported_efforts else 'none' + source = "default" + if config_effort is not None: + get_logger().warning( + f"Invalid reasoning_effort '{config_effort}' in config. " + f"Using default. Supported values: {supported_efforts}" + ) + if model.endswith('_thinking'): + effort = 'low' + else: + effort = 'none' thinking_kwargs_gpt5 = { "reasoning_effort": effort, "allowed_openai_params": ["reasoning_effort"], } - get_logger().info(f"Using reasoning_effort={effort} for GPT-5 model (from {'config' if config_effort in supported_efforts else 'default'})") + get_logger().info(f"Using reasoning_effort='{effort}' for GPT-5 model (from {source})") Refactor the reasoning_effort logic to remove redundant checks by storing the validation result in a variable, and add a warning log for invalid configuration values. pr_agent/algo/ai_handlers/litellm_ai_handler.py [295-313] # Respect user's reasoning_effort config setting # Supported values: 'none', 'low', 'medium', 'high' # Note: gpt-5.1 supports 'none', but gpt-5.1-codex does not config_effort = get_settings().config.reasoning_effort supported_efforts = ['none', 'low', 'medium', 'high'] +is_config_valid = config_effort in supported_efforts +source = "config" -if model.endswith('_thinking'): - # For thinking models, use config value or default to 'low' - effort = config_effort if config_effort in supported_efforts else 'low' +if is_config_valid: + effort = config_effort else: - # For non-thinking models, use config value or default to 'none' - # If 'none' fails for specific models (e.g., codex), they should set config to 'low' - effort = config_effort if config_effort in supported_efforts else 'none' + source = "default" + if config_effort is not None: + get_logger().warning( + f"Invalid reasoning_effort '{config_effort}' in config. " + f"Using default. Supported values: {supported_efforts}" + ) + if model.endswith('_thinking'): + effort = 'low' + else: + effort = 'none' thinking_kwargs_gpt5 = { "reasoning_effort": effort, "allowed_openai_params": ["reasoning_effort"], } -get_logger().info(f"Using reasoning_effort={effort} for GPT-5 model (from {'config' if config_effort in supported_efforts else 'default'})") +get_logger().info(f"Using reasoning_effort='{effort}' for GPT-5 model (from {source})") [Suggestion processed] Suggestion importance[1-10]: 6 __ Why: The suggestion correctly identifies redundant checks and proposes a refactoring that improves readability. It also enhances usability by adding a warning log for invalid configuration values, which is a valuable improvement.	Low
Learned best practice	Use safe configuration attribute access Use safe attribute access with getattr() to handle cases where config.reasoning_effort may not exist. This prevents AttributeError if the configuration attribute is missing or None. pr_agent/algo/ai_handlers/litellm_ai_handler.py [298-307] -config_effort = get_settings().config.reasoning_effort +config = getattr(get_settings(), 'config', None) +config_effort = getattr(config, 'reasoning_effort', None) if config else None supported_efforts = ['none', 'low', 'medium', 'high'] if model.endswith('_thinking'): # For thinking models, use config value or default to 'low' effort = config_effort if config_effort in supported_efforts else 'low' else: # For non-thinking models, use config value or default to 'none' # If 'none' fails for specific models (e.g., codex), they should set config to 'low' effort = config_effort if config_effort in supported_efforts else 'none' Apply / Chat Suggestion importance[1-10]: 6 __ Why: Relevant best practice - Use safe dictionary access methods like .get() with default values when accessing configuration attributes that may not exist to prevent AttributeError exceptions at runtime.	Low
Update

• Author self-review: I have reviewed the PR code suggestions, and addressed the relevant ones.

[Tyler-Rak]

Code review suggestions addressed:

✅ Suggestion 1 (Refactor logic): Implemented in commit 956f366

• Improved readability by storing validation result in variable
• Added warning log for invalid configuration values

❌ Suggestion 2 (getattr defensive coding): Not implementing

• get_settings() always returns a valid Dynaconf object
• config.reasoning_effort is always defined with default value "medium" in configuration.toml
• Existing validation already handles None/invalid values
• Adding getattr() would add unnecessary complexity

All relevant suggestions have been addressed.