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chore: import upstream snapshot with attribution
2026-07-13 13:39:25 +08:00

52 lines
1.9 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
"""Multi-Agent Sample - Durable Task Integration (Combined Worker + Client)
This sample demonstrates running both the worker and client in a single process
for multiple agents with different tools. The worker registers two agents
(WeatherAgent and MathAgent), each with their own specialized capabilities.
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
- Sign in with Azure CLI for AzureCliCredential authentication
- Durable Task Scheduler must be running (e.g., using Docker)
To run this sample:
python sample.py
"""
import logging
# Import helper functions from worker and client modules
from client import get_client, run_client # pyrefly: ignore[missing-import]
from dotenv import load_dotenv
from worker import get_worker, setup_worker # pyrefly: ignore[missing-import]
# Configure logging
logging.basicConfig(level=logging.INFO, force=True)
logger = logging.getLogger(__name__)
def main():
"""Main entry point - runs both worker and client in single process."""
logger.debug("Starting Durable Task Multi-Agent Sample (Combined Worker + Client)...")
silent_handler = logging.NullHandler()
# Create and start the worker using helper function and context manager
dts_worker = get_worker(log_handler=silent_handler)
with dts_worker:
# Register agents using helper function
setup_worker(dts_worker)
# Start the worker
dts_worker.start()
logger.debug("Worker started and listening for requests...")
# Create the client using helper function
agent_client = get_client(log_handler=silent_handler)
try:
# Run client interactions using helper function
run_client(agent_client)
except Exception as e:
logger.exception(f"Error during agent interaction: {e}")
logger.debug("Sample completed. Worker shutting down...")
if __name__ == "__main__":
load_dotenv()
main()