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Single Agent

This sample demonstrates how to create a worker-client setup that hosts a single AI agent and provides interactive conversation via the Durable Task Scheduler.

Key Concepts Demonstrated

  • Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
  • Registering durable agents with the worker and interacting with them via a client.
  • Conversation management (via sessions) for isolated interactions.
  • Worker-client architecture for distributed agent execution.

Environment Setup

See the 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.

Running the Sample

With the environment setup, you can run the sample using the combined approach or separate worker and client processes:

Option 1: Combined (Recommended for Testing)

cd samples/04-hosting/durabletask/01_single_agent
python sample.py

Option 2: Separate Processes

Start the worker in one terminal:

python worker.py

In a new terminal, run the client:

python client.py

The client will interact with the Joker agent:

Starting Durable Task Agent Client...
Using taskhub: default
Using endpoint: http://localhost:8080

Getting reference to Joker agent...
Created conversation session: a1b2c3d4-e5f6-7890-abcd-ef1234567890

User: Tell me a short joke about cloud computing.

Joker: Why did the cloud break up with the server?
Because it found someone more "uplifting"!

User: Now tell me one about Python programming.

Joker: Why do Python programmers prefer dark mode?
Because light attracts bugs!

Viewing Agent State

You can view the state of the agent in the Durable Task Scheduler dashboard:

  1. Open your browser and navigate to http://localhost:8082
  2. In the dashboard, you can view:
    • The state of the Joker agent entity (dafx-Joker)
    • Conversation history and current state
    • How the durable agents extension manages conversation context