# Prefect Adapter

The Prefect adapter provides integration with Prefect for general workflow orchestration.

**Current Status**: Stub implementation (143 lines) - returns simulated results

## Overview

Prefect is ideal for production workflows with scheduling requirements, state management, and deployment pipelines.

## Configuration

```yaml
prefect:
  enabled: true
```

## Usage

**Note**: Actual functionality not yet implemented - this is a stub adapter.

```bash
# Not yet functional - adapter is stub implementation
mahavishnu workflow trigger --name etl_pipeline --adapter prefect
```

## Planned Features

- Dynamic flow creation from task specifications
- Hybrid execution support (local, cloud, containers)
- State management and checkpointing
- Flow coordination and deployment pipelines
- Batch processing workflows

## Current Implementation

**Status**: Stub implementation (143 lines)

The adapter currently returns simulated results. Real orchestration functionality requires:

- Prefect flow construction
- State management implementation
- Checkpointing integration
- LLM integration for dynamic workflows
- Progress tracking and streaming

## Migration from Airflow

Prefect is the modern replacement for Airflow, offering:

- Pure Python (no YAML DAG definitions)
- No scheduler infrastructure required
- 60-70% cost savings
- Better type safety
- Easier testing

**Example migration**:

```python
# Before (Airflow)
with DAG('my_dag', start_date=datetime(2025, 1, 1)) as dag:
    task1 = PythonOperator(task_id='task1', python_callable=my_func)

# After (Prefect)
@flow
def my_flow():
    my_task()
```

## Estimated Completion Effort

2 weeks for full implementation including:

- Flow construction logic
- State management
- LLM integration
- Progress tracking
- Checkpointing

## Next Steps

1. Implement Prefect flow construction
1. Add LLM integration for dynamic workflows
1. Implement state management
1. Add progress tracking and streaming
1. Implement checkpointing
