Orchestrate Cross-Framework Supply Agents with Google ADK and CrewAI

                      
                     
"""
Production implementation for orchestrating cross-framework supply agents with Google ADK and CrewAI.
Provides secure, scalable operations for handling agent workflows.
"""
from typing import Dict, Any, List
import os
import logging
import requests
import json
import time

# Setup logging for tracking execution flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

    def validate(self) -> None:
        """
        Validate critical configuration settings.
        Raises:
            ValueError: If configuration is invalid.
        """
        if not self.database_url:
            raise ValueError('DATABASE_URL is not set.')
        if not self.api_url:
            raise ValueError('API_URL is not set.')

# Initialize configuration and validate
config = Config()
config.validate()

# Logger setup for tracking API requests and results
logger.info('Configuration validated successfully.')

async def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate request data.
    Args:
        data: Input to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'agent_id' not in data:
        raise ValueError('Missing agent_id')  # Must have agent_id
    if not isinstance(data.get('agent_id'), str):
        raise ValueError('agent_id must be a string.')  # Type check
    return True  # Data is valid

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input data fields.
    Args:
        data: Input data to sanitize.
    Returns:
        Sanitized data.
    """
    return {key: str(value).strip() for key, value in data.items()}  # Strip whitespace

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """
    Fetch data from a given API endpoint.
    Args:
        endpoint: API endpoint to call.
    Returns:
        Parsed JSON response.
    Raises:
        ConnectionError: If the request fails.
    """
    try:
        response = requests.get(endpoint)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()  # Return parsed JSON
    except requests.RequestException as e:
        logger.error(f'Error fetching data from {endpoint}: {e}')
        raise ConnectionError('Failed to fetch data.')  # Handle connection errors

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Transform records to the desired format.
    Args:
        records: List of records to transform.
    Returns:
        Transformed list of records.
    """
    return [{"id": record['agent_id'], "status": "processed"} for record in records]  # Example transformation

async def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save data to the database.
    Args:
        data: Data to save.
    Raises:
        Exception: If saving fails.
    """
    logger.info('Saving data to the database...')
    # Implementation of saving data to the database goes here.
    # e.g., use a connection pool and execute an insert statement.

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """
    Process a batch of data records.
    Args:
        data: List of records to process.
    """
    for record in data:
        await save_to_db(record)  # Save each record to the database

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, float]:
    """
    Aggregate metrics from the processed data.
    Args:
        data: List of records to aggregate.
    Returns:
        Dictionary of aggregated metrics.
    """
    # Example aggregation logic
    return {"total": len(data), "success": sum(1 for record in data if record['status'] == 'processed')}

async def call_api(data: Dict[str, Any]) -> None:
    """
    Call an external API with the provided data.
    Args:
        data: Data to send.
    Raises:
        Exception: If API call fails.
    """
    try:
        response = requests.post(config.api_url, json=data)
        response.raise_for_status()  # Check for errors in the response
        logger.info('API call successful.')
    except requests.RequestException as e:
        logger.error(f'API call failed: {e}')  # Log API errors
        raise  # Re-raise the exception for handling

async def handle_errors(func, *args, retries: int = 3, backoff: int = 2) -> Any:
    """
    Handle errors with retries.
    Args:
        func: Function to execute.
        args: Arguments for the function.
        retries: Number of retries.
        backoff: Backoff factor for delay.
    Returns:
        Result of the function call.
    Raises:
        Exception: If all retries fail.
    """
    for attempt in range(retries):
        try:
            return await func(*args)  # Attempt to call the function
        except Exception as e:
            logger.warning(f'Attempt {attempt + 1} failed: {e}')
            if attempt < retries - 1:
                time.sleep(backoff ** attempt)  # Exponential backoff
            else:
                logger.error('All attempts failed.')
                raise  # Raise the exception if all attempts fail

class SupplyAgentOrchestrator:
    """
    Main orchestrator class for handling supply agent workflows.
    """
    async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute the main workflow.
        Args:
            input_data: Input data for the workflow.
        Returns:
            Aggregated metrics from processing.
        """
        await validate_input(input_data)  # Validate input
        sanitized_data = await sanitize_fields(input_data)  # Sanitize fields
        raw_records = await fetch_data(config.api_url)  # Fetch records
        transformed_records = await transform_records(raw_records)  # Transform records
        await process_batch(transformed_records)  # Process batch
        metrics = await aggregate_metrics(transformed_records)  # Aggregate metrics
        return metrics  # Return aggregated metrics

if __name__ == '__main__':
    # Example usage of the orchestrator
    orchestrator = SupplyAgentOrchestrator()
    sample_input = {'agent_id': 'Agent001'}  # Sample input data
    try:
        result = orchestrator.execute(sample_input)  # Execute the workflow
        logger.info(f'Workflow completed successfully with result: {result}')  # Log success
    except Exception as e:
        logger.error(f'Workflow failed: {e}')  # Log failure