# Architecture Overview

## Core Components

### TaskExecutor

The `TaskExecutor` is the base class for all task implementations. It provides:

1. **Task Step Management**
   - Sequential step execution
   - State tracking
   - Checkpoint creation
   - Error handling

2. **LLM Integration**
   - Asynchronous API calls
   - Retry mechanisms
   - Response validation

### Configuration System

1. **Config Loader**
   - YAML configuration files
   - Environment variable support
   - Configuration validation

2. **Secure Configuration**
   - Encrypted storage for sensitive data
   - Key management
   - Secure API key handling

## Task Implementation

### Step Definition

Each task is defined as a series of steps:

```python
self.task_steps = [
    {
        "id": "step_id",
        "name": "Step Name",
        "required_info": ["required_data"],
        "instruction": "Step instruction for LLM"
    }
]
```

### Step Handlers

Step handlers are implemented as async methods:

```python
async def handle_step_id(self, step_input: dict) -> dict:
    # 1. Process input
    processed_data = self.preprocess(step_input)
    
    # 2. Call LLM if needed
    llm_response = await self.llm_call(
        instruction=step_input["instruction"],
        context=processed_data
    )
    
    # 3. Process response
    result = self.postprocess(llm_response)
    
    return {"result": result}
```

## Execution Flow

1. **Initialization**
   ```python
   executor = TaskExecutor(llm_model="model_name")
   ```

2. **Task Setup**
   ```python
   executor.task_steps = [...]
   ```

3. **Execution**
   ```python
   result = await executor.execute(input_data)
   ```

4. **Step Processing**
   - Validate input
   - Execute step handler
   - Create checkpoint
   - Handle errors
   - Move to next step

5. **Completion**
   - Return final result
   - Clean up resources

## Error Handling

1. **Retry Mechanism**
   - API call retries with exponential backoff
   - Configurable retry limits

2. **Error Types**
   - `TaskExecutionError`: General execution errors
   - `StepValidationError`: Input validation failures
   - `LLMError`: LLM API related errors

3. **Recovery**
   - Checkpoint-based recovery
   - State restoration
   - Partial results handling

## Best Practices

1. **Task Design**
   - Keep steps atomic and focused
   - Clear step instructions
   - Proper input validation
   - Comprehensive error handling

2. **LLM Usage**
   - Clear and specific prompts
   - Response validation
   - Handle token limits
   - Consider cost and latency

3. **Testing**
   - Unit tests for each step
   - Integration tests for full flow
   - Mock LLM calls in tests
   - Test error scenarios

4. **Security**
   - Secure API key handling
   - Input sanitization
   - Output validation
   - Access control

## Extension Points

1. **Custom Step Handlers**
   - Implement custom logic
   - Add new capabilities
   - Integrate external services

2. **LLM Providers**
   - Support multiple providers
   - Custom response parsing
   - Provider-specific optimizations

3. **Monitoring & Logging**
   - Custom metrics
   - Logging handlers
   - Performance monitoring