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Python: Complete durableagent package (#3058)
* Add worker and clients * Clean code and refactor common code * Implement sample * Add sample * Update readmes * Fix tests * Fix tests * Update requirements * Fix typo * Address comments * use response.text
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Laveesh Rohra
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# Single Agent Sample
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This sample demonstrates how to use the durable agents extension to create a worker-client setup that hosts a single AI agent and provides interactive conversation via the Durable Task Scheduler.
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## Key Concepts Demonstrated
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- Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
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- Registering durable agents with the worker and interacting with them via a client.
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- Conversation management (via threads) for isolated interactions.
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- Worker-client architecture for distributed agent execution.
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## Environment Setup
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See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
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## Running the Sample
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With the environment setup, you can run the sample using separate worker and client processes:
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**Start the worker:**
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```bash
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cd samples/getting_started/durabletask/01_single_agent
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python worker.py
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```
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The worker will register the Joker agent and listen for requests.
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**In a new terminal, run the client:**
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```bash
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python client.py
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```
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The client will interact with the Joker agent:
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```
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Starting Durable Task Agent Client...
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Using taskhub: default
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Using endpoint: http://localhost:8080
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Getting reference to Joker agent...
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Created conversation thread: a1b2c3d4-e5f6-7890-abcd-ef1234567890
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User: Tell me a short joke about cloud computing.
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Joker: Why did the cloud break up with the server?
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Because it found someone more "uplifting"!
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User: Now tell me one about Python programming.
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Joker: Why do Python programmers prefer dark mode?
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Because light attracts bugs!
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```
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## Viewing Agent State
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You can view the state of the agent in the Durable Task Scheduler dashboard:
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1. Open your browser and navigate to `http://localhost:8082`
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2. In the dashboard, you can view the state of the Joker agent, including its conversation history and current state
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The agent maintains conversation state across multiple interactions, and you can inspect this state in the dashboard to understand how the durable agents extension manages conversation context.
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"""Client application for interacting with a Durable Task hosted agent.
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This client connects to the Durable Task Scheduler and sends requests to
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registered agents, demonstrating how to interact with agents from external processes.
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Prerequisites:
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- The worker must be running with the agent registered
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Durable Task Scheduler must be running
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"""
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import asyncio
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import logging
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import os
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from agent_framework_durabletask import DurableAIAgentClient
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from azure.identity import DefaultAzureCredential
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from durabletask.azuremanaged.client import DurableTaskSchedulerClient
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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async def main() -> None:
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"""Main entry point for the client application."""
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logger.info("Starting Durable Task Agent Client...")
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# Get environment variables for taskhub and endpoint with defaults
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taskhub_name = os.getenv("TASKHUB", "default")
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endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
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logger.info(f"Using taskhub: {taskhub_name}")
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logger.info(f"Using endpoint: {endpoint}")
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logger.info("")
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# Set credential to None for emulator, or DefaultAzureCredential for Azure
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credential = None if endpoint == "http://localhost:8080" else DefaultAzureCredential()
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# Create a client using Azure Managed Durable Task
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client = DurableTaskSchedulerClient(
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host_address=endpoint,
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secure_channel=endpoint != "http://localhost:8080",
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taskhub=taskhub_name,
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token_credential=credential
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)
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# Wrap it with the agent client
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agent_client = DurableAIAgentClient(client)
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# Get a reference to the Joker agent
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logger.info("Getting reference to Joker agent...")
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joker = agent_client.get_agent("Joker")
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# Create a new thread for the conversation
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thread = joker.get_new_thread()
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logger.info(f"Created conversation thread: {thread.session_id}")
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logger.info("")
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try:
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# First message
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message1 = "Tell me a short joke about cloud computing."
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logger.info(f"User: {message1}")
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logger.info("")
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# Run the agent - this blocks until the response is ready
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response1 = joker.run(message1, thread=thread)
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logger.info(f"Agent: {response1.text}")
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logger.info("")
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# Second message - continuing the conversation
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message2 = "Now tell me one about Python programming."
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logger.info(f"User: {message2}")
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logger.info("")
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response2 = joker.run(message2, thread=thread)
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logger.info(f"Agent: {response2.text}")
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logger.info("")
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logger.info(f"Conversation completed successfully!")
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logger.info(f"Thread ID: {thread.session_id}")
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except Exception as e:
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logger.exception(f"Error during agent interaction: {e}")
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finally:
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logger.info("Client shutting down")
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if __name__ == "__main__":
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asyncio.run(main())
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# Agent Framework packages (installing from local package until a package is published)
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-e ../../../../
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-e ../../../../packages/durabletask
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# Azure authentication
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azure-identity
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"""Single Agent Sample - Durable Task Integration (Combined Worker + Client)
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This sample demonstrates running both the worker and client in a single process.
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The worker is started first to register the agent, then client operations are
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performed against the running worker.
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Prerequisites:
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Durable Task Scheduler must be running (e.g., using Docker)
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To run this sample:
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python sample.py
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"""
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import logging
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import os
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework_durabletask import DurableAIAgentClient, DurableAIAgentWorker
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from azure.identity import AzureCliCredential, DefaultAzureCredential
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from dotenv import load_dotenv
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from durabletask.azuremanaged.client import DurableTaskSchedulerClient
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from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def create_joker_agent():
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"""Create the Joker agent using Azure OpenAI.
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Returns:
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AgentProtocol: The configured Joker agent
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"""
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return AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
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name="Joker",
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instructions="You are good at telling jokes.",
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)
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def main():
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"""Main entry point - runs both worker and client in single process."""
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logger.info("Starting Durable Task Agent Sample (Combined Worker + Client)...")
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# Get environment variables for taskhub and endpoint with defaults
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taskhub_name = os.getenv("TASKHUB", "default")
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endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
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logger.info(f"Using taskhub: {taskhub_name}")
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logger.info(f"Using endpoint: {endpoint}")
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logger.info("")
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# Set credential to None for emulator, or DefaultAzureCredential for Azure
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credential = None if endpoint == "http://localhost:8080" else DefaultAzureCredential()
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secure_channel = endpoint != "http://localhost:8080"
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# Create and start the worker using a context manager
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with DurableTaskSchedulerWorker(
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host_address=endpoint,
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secure_channel=secure_channel,
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taskhub=taskhub_name,
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token_credential=credential
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) as worker:
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# Wrap with the agent worker
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agent_worker = DurableAIAgentWorker(worker)
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# Create and register the Joker agent
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logger.info("Creating and registering Joker agent...")
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joker_agent = create_joker_agent()
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agent_worker.add_agent(joker_agent)
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logger.info(f"✓ Registered agent: {joker_agent.name}")
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logger.info(f" Entity name: dafx-{joker_agent.name}")
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logger.info("")
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# Start the worker
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worker.start()
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logger.info("Worker started and listening for requests...")
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logger.info("")
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# Create the client
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client = DurableTaskSchedulerClient(
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host_address=endpoint,
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secure_channel=secure_channel,
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taskhub=taskhub_name,
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token_credential=credential
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)
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# Wrap it with the agent client
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agent_client = DurableAIAgentClient(client)
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# Get a reference to the Joker agent
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logger.info("Getting reference to Joker agent...")
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joker = agent_client.get_agent("Joker")
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# Create a new thread for the conversation
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thread = joker.get_new_thread()
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logger.info(f"Created conversation thread: {thread.session_id}")
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logger.info("")
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try:
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# First message
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message1 = "Tell me a short joke about cloud computing."
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logger.info(f"User: {message1}")
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logger.info("")
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# Run the agent - this blocks until the response is ready
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response1 = joker.run(message1, thread=thread)
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logger.info(f"Agent: {response1.text}; {response1}")
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logger.info("")
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# Second message - continuing the conversation
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message2 = "Now tell me one about Python programming."
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logger.info(f"User: {message2}")
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logger.info("")
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response2 = joker.run(message2, thread=thread)
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logger.info(f"Agent: {response2.text}; {response2}")
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logger.info("")
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logger.info(f"Conversation completed successfully!")
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logger.info(f"Thread ID: {thread.session_id}")
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except Exception as e:
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logger.exception(f"Error during agent interaction: {e}")
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logger.info("")
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logger.info("Sample completed. Worker shutting down...")
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if __name__ == "__main__":
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load_dotenv()
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main()
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"""Worker process for hosting a single Azure OpenAI-powered agent using Durable Task.
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This worker registers agents as durable entities and continuously listens for requests.
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The worker should run as a background service, processing incoming agent requests.
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Prerequisites:
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Start a Durable Task Scheduler (e.g., using Docker)
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"""
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import asyncio
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import logging
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import os
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework_durabletask import DurableAIAgentWorker
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from azure.identity import AzureCliCredential, DefaultAzureCredential
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from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def create_joker_agent():
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"""Create the Joker agent using Azure OpenAI.
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Returns:
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AgentProtocol: The configured Joker agent
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"""
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return AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
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name="Joker",
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instructions="You are good at telling jokes.",
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)
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async def main():
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"""Main entry point for the worker process."""
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logger.info("Starting Durable Task Agent Worker...")
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# Get environment variables for taskhub and endpoint with defaults
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taskhub_name = os.getenv("TASKHUB", "default")
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endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
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logger.info(f"Using taskhub: {taskhub_name}")
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logger.info(f"Using endpoint: {endpoint}")
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# Set credential to None for emulator, or DefaultAzureCredential for Azure
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credential = None if endpoint == "http://localhost:8080" else DefaultAzureCredential()
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# Create a worker using Azure Managed Durable Task
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worker = DurableTaskSchedulerWorker(
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host_address=endpoint,
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secure_channel=endpoint != "http://localhost:8080",
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taskhub=taskhub_name,
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token_credential=credential
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)
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# Wrap it with the agent worker
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agent_worker = DurableAIAgentWorker(worker)
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# Create and register the Joker agent
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logger.info("Creating and registering Joker agent...")
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joker_agent = create_joker_agent()
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agent_worker.add_agent(joker_agent)
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logger.info(f"✓ Registered agent: {joker_agent.name}")
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logger.info(f" Entity name: dafx-{joker_agent.name}")
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logger.info("")
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logger.info("Worker is ready and listening for requests...")
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logger.info("Press Ctrl+C to stop.")
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logger.info("")
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try:
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# Start the worker (this blocks until stopped)
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worker.start()
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# Keep the worker running
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while True:
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await asyncio.sleep(1)
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except KeyboardInterrupt:
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logger.info("Worker shutdown initiated")
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logger.info("Worker stopped")
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if __name__ == "__main__":
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asyncio.run(main())
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