*This scratchpad file serves as a phase-specific task tracker and implementation planner. The Mode System on Line 1 is critical and must never be deleted. It defines two core modes: Implementation Type for new feature development and Bug Fix Type for issue resolution. Each mode requires specific documentation formats, confidence tracking, and completion criteria. Use "plan" trigger for planning phase (🎯) and "agent" trigger for execution phase (⚡) after reaching 95% confidence. Follow strict phase management with clear documentation transfer process.*

`MODE SYSTEM TYPES (DO NOT DELETE!):
1. Implementation Type (New Features):
   - Trigger: User requests new implementation
   - Format: MODE: Implementation, FOCUS: New functionality
   - Requirements: Detailed planning, architecture review, documentation
   - Process: Plan mode (🎯) → 95% confidence → Agent mode (⚡)

2. Bug Fix Type (Issue Resolution):
   - Trigger: User reports bug/issue
   - Format: MODE: Bug Fix, FOCUS: Issue resolution
   - Requirements: Problem diagnosis, root cause analysis, solution verification
   - Process: Plan mode (🎯) → Chain of thought analysis → Agent mode (⚡)

Cross-reference with .cursor/memories.md and .cursor/rules/lessons-learned.mdc for context and best practices.`


# Mode: COMPLETED ✅
Current Task: Fix ruff linting errors in core.py and io_operations.py

## Task Summary:
**Status**: COMPLETED ✅  
**Result**: All 8 ruff linting errors successfully fixed

## Issues Fixed:
1. **F841 - Unused Variable**: Removed unused `logger` variable in core.py line 63
   - Solution: Removed assignment since `LOGGER` module constant already available
   
2. **B904 - Exception Chaining**: Added proper exception chaining in io_operations.py
   - Fixed 7 raise statements to use `from e` for better error tracebacks
   - Locations: YAML/JSON parsing, file I/O error handling

## Verification:
- ✅ `uv run ruff check --fix *.py llm_ci_runner/` → "All checks passed!"
- ✅ `uv run pytest tests/unit/ -v` → 113/113 tests passing
- ✅ Code quality maintained with all existing comments preserved
- ✅ No functional changes - only linting compliance improvements

## Next Available Tasks:
- Integration test failures (from previous scratchpad context)
- Other development tasks as needed

Confidence: 100% (task completed successfully)


# Mode: PLAN 🎯
Current Task: Add native OpenAI support (API-key based) with automatic Azure-first fallback
Understanding: We currently only support AzureChatCompletion via setup_azure_service(). We need a generic service setup that:
1. Prefers Azure when AZURE_OPENAI_* vars are present and valid.
2. Falls back to OpenAIChatCompletion when OPENAI_API_KEY (+ OPENAI_CHAT_MODEL_ID) are set.
3. Keeps public API stable for existing code/tests.
4. Requires changes in azure_service.py, core.py, docs, and new tests.

## Comprehensive Analysis & Implementation Plan

### 1. Environment Variables (CONFIRMED via Semantic Kernel docs)
**Azure OpenAI (current):**
- AZURE_OPENAI_ENDPOINT (required)
- AZURE_OPENAI_MODEL (required) 
- AZURE_OPENAI_API_KEY (optional, fallback to RBAC)
- AZURE_OPENAI_API_VERSION (optional, defaults to "2024-12-01-preview")

**OpenAI (new):**
- OPENAI_API_KEY (required)
- OPENAI_CHAT_MODEL_ID (required - the model name like "gpt-4")
- OPENAI_ORG_ID (optional)
- OPENAI_BASE_URL (optional)

### 2. OpenAI Service Constructor (from Semantic Kernel PyPI docs)
```python
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

service = OpenAIChatCompletion(
    ai_model_id="gpt-4",        # Required - model name
    api_key="secret-api-key",  # Required - API key
    org_id="org-...",          # Optional - organization ID
    base_url="https://...",    # Optional - custom base URL
    service_id="openai",       # Optional - service identifier
)
```

### 3. Current AI Call Method Analysis
Our current `execute_llm_task()` in `llm_execution.py` uses:
```python
result = await service.get_chat_message_contents(
    chat_history=chat_history,
    settings=settings,
    arguments=args,
)
```
✅ **CONFIRMED**: OpenAIChatCompletion uses the SAME `get_chat_message_contents()` method as AzureChatCompletion

### 4. Execution Settings Compatibility
Current: `OpenAIChatPromptExecutionSettings()`
✅ **CONFIRMED**: Same settings class works for both Azure and OpenAI services

### 5. Detailed Implementation Steps

#### Step 1: Rename and Refactor azure_service.py → ai_service.py
```python
# llm_ci_runner/ai_service.py
async def setup_ai_service() -> tuple[AzureChatCompletion | OpenAIChatCompletion, DefaultAzureCredential | None]:
    """
    Setup AI service with automatic Azure-first, OpenAI fallback.
    
    Priority order:
    1. Azure OpenAI (if AZURE_OPENAI_ENDPOINT + AZURE_OPENAI_MODEL present)
    2. OpenAI (if OPENAI_API_KEY + OPENAI_CHAT_MODEL_ID present)
    
    Returns:
        Tuple of (ChatCompletion service, Azure credential or None)
    """
    # Check Azure first
    if os.getenv("AZURE_OPENAI_ENDPOINT") and os.getenv("AZURE_OPENAI_MODEL"):
        return await setup_azure_service()
    
    # Fallback to OpenAI
    elif os.getenv("OPENAI_API_KEY") and os.getenv("OPENAI_CHAT_MODEL_ID"):
        return await setup_openai_service()
    
    else:
        raise AuthenticationError(
            "No valid AI service configuration found. Please set either:\n"
            "Azure: AZURE_OPENAI_ENDPOINT + AZURE_OPENAI_MODEL\n"
            "OpenAI: OPENAI_API_KEY + OPENAI_CHAT_MODEL_ID"
        )

async def setup_openai_service() -> tuple[OpenAIChatCompletion, None]:
    """Setup OpenAI service with API key authentication."""
    api_key = os.getenv("OPENAI_API_KEY")
    model_id = os.getenv("OPENAI_CHAT_MODEL_ID")
    org_id = os.getenv("OPENAI_ORG_ID")
    base_url = os.getenv("OPENAI_BASE_URL")
    
    if not api_key:
        raise AuthenticationError("OPENAI_API_KEY environment variable is required")
    if not model_id:
        raise AuthenticationError("OPENAI_CHAT_MODEL_ID environment variable is required")
    
    LOGGER.info(f"🎯 Using OpenAI model: {model_id}")
    if org_id:
        LOGGER.info(f"🎯 Using OpenAI organization: {org_id}")
    if base_url:
        LOGGER.info(f"🎯 Using OpenAI base URL: {base_url}")
    
    try:
        service = OpenAIChatCompletion(
            ai_model_id=model_id,
            api_key=api_key,
            org_id=org_id,
            base_url=base_url,
            service_id="openai",
        )
        LOGGER.info("✅ OpenAI service setup completed successfully")
        return service, None
        
    except Exception as e:
        raise AuthenticationError(f"Failed to setup OpenAI service: {e}") from e
```

#### Step 2: Update core.py imports and calls
```python
# llm_ci_runner/core.py
from .ai_service import setup_ai_service  # Changed from setup_azure_service

async def main(...):
    # Setup AI service (Azure-first, OpenAI fallback)
    service, credential = await setup_ai_service()  # Changed call
    
    # Rest of code unchanged - same execute_llm_task() call
```

#### Step 3: Update llm_execution.py type hints
```python
# llm_ci_runner/llm_execution.py
from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import AzureChatCompletion
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

async def execute_llm_task(
    service: AzureChatCompletion | OpenAIChatCompletion,  # Updated type hint
    chat_history: ChatHistory,
    context: dict[str, Any] | None,
    schema_model: type[KernelBaseModel] | None,
) -> str | dict[str, Any]:
    # Implementation unchanged - both services use same get_chat_message_contents() method
```

#### Step 4: Add OpenAI import to __init__.py
```python
# llm_ci_runner/__init__.py
from .ai_service import setup_ai_service, setup_azure_service, setup_openai_service
```

### 6. Testing Strategy

#### Existing Tests (patch setup_azure_service → setup_ai_service):
- tests/unit/test_setup_and_utility_functions.py
- tests/integration/test_main_function.py  
- tests/integration/test_cli_interface.py

#### New Tests to Add:
```python
# tests/unit/test_openai_service.py
@pytest.mark.asyncio
async def test_setup_openai_service_success():
    """Test successful OpenAI service setup."""
    with patch.dict(os.environ, {
        "OPENAI_API_KEY": "non-an-api-key",
        "OPENAI_CHAT_MODEL_ID": "gpt-4"
    }):
        service, credential = await setup_openai_service()
        assert isinstance(service, OpenAIChatCompletion)
        assert credential is None

@pytest.mark.asyncio 
async def test_setup_ai_service_azure_priority():
    """Test Azure takes priority when both configs present."""
    with patch.dict(os.environ, {
        "AZURE_OPENAI_ENDPOINT": "https://test.openai.azure.com/",
        "AZURE_OPENAI_MODEL": "gpt-4",
        "OPENAI_API_KEY": "non-an-api-key",
        "OPENAI_CHAT_MODEL_ID": "gpt-4"
    }):
        service, _ = await setup_ai_service()
        assert isinstance(service, AzureChatCompletion)

@pytest.mark.asyncio
async def test_setup_ai_service_openai_fallback():
    """Test OpenAI fallback when Azure not configured."""
    with patch.dict(os.environ, {
        "OPENAI_API_KEY": "non-an-api-key", 
        "OPENAI_CHAT_MODEL_ID": "gpt-4"
    }, clear=True):
        service, _ = await setup_ai_service()
        assert isinstance(service, OpenAIChatCompletion)
```

#### Integration Tests:
- Reuse existing test data/schemas
- Test both Azure and OpenAI paths with same inputs
- Verify identical output structure

### 7. Documentation Updates

#### README.md:
```markdown
## Environment Variables

The tool supports both Azure OpenAI and OpenAI services with automatic fallback:

### Azure OpenAI (Priority 1)
```bash
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_MODEL="gpt-4"
export AZURE_OPENAI_API_KEY="your-api-key"  # Optional, uses RBAC if not set
```

### OpenAI (Fallback)
```bash
export OPENAI_API_KEY="your-very-secret-api-key" 
export OPENAI_CHAT_MODEL_ID="gpt-4"
export OPENAI_ORG_ID="org-your-org-id"      # Optional
export OPENAI_BASE_URL="https://api.openai.com/v1"  # Optional
```

The tool automatically detects which service to use based on available environment variables.
```

#### examples/ updates:
- Add OpenAI examples alongside Azure examples
- Show both env var configurations
- Demonstrate identical usage patterns

### 8. Confidence Assessment: 95%

**Confirmed Facts:**
✅ OpenAIChatCompletion uses identical `get_chat_message_contents()` method
✅ Same OpenAIChatPromptExecutionSettings for both services  
✅ Semantic Kernel provides both AzureChatCompletion and OpenAIChatCompletion
✅ Environment variable names confirmed from official docs
✅ Constructor parameters confirmed from PyPI documentation
✅ No breaking changes to public API

**Remaining 5% uncertainty:**
- Potential minor differences in error handling between services
- Edge cases in credential management
- Integration test validation needed

### 9. Next Steps for Agent Mode:
1. Rename azure_service.py → ai_service.py with new setup_ai_service()
2. Add setup_openai_service() function
3. Update core.py imports and calls
4. Update type hints in llm_execution.py
5. Add comprehensive unit tests
6. Update documentation and examples
7. Run full test suite validation

**Ready for Agent Mode Implementation** ✅
