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Eduard van Valkenburg b1b528e4a8 Python: [BREAKING] Remove deprecated kwargs compatibility paths (#4858)
* [BREAKING] Remove deprecated kwargs compatibility paths

Remove the deprecated kwargs compatibility shims across core agents, clients, tools, middleware, and telemetry.

Keep workflow kwargs behavior intact in this branch and follow up separately in #4850.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix PR CI fallout for kwargs removal

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address PR review feedback

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* updates

* Fix Azure AI CI fallout

Remove the stale _get_current_conversation_id override from the Azure AI client after the OpenAI base helper was deleted.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fixed new classes

* Fix Assistants deprecated import gating

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix integration replay regressions

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Switch multi-agent hosting samples to Azure chat completions

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Simplify Azure multi-agent sample config

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
b1b528e4a8 · 2026-03-27 21:00:12 +00:00
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Multi-Agent

This sample demonstrates how to host multiple AI agents with different tools in a single worker-client setup using the Durable Task Scheduler.

Key Concepts Demonstrated

  • Hosting multiple agents (WeatherAgent and MathAgent) in a single worker process.
  • Each agent with its own specialized tools and instructions.
  • Interacting with different agents using separate conversation sessions.
  • Worker-client architecture for multi-agent systems.

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/02_multi_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 both agents:

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

================================================================================
Testing WeatherAgent
================================================================================

Created weather conversation session: <guid>
User: What is the weather in Seattle?

🔧 [TOOL CALLED] get_weather(location=Seattle)
✓ [TOOL RESULT] {'location': 'Seattle', 'temperature': 72, 'conditions': 'Sunny', 'humidity': 45}

WeatherAgent: The current weather in Seattle is sunny with a temperature of 72°F and 45% humidity.

================================================================================
Testing MathAgent
================================================================================

Created math conversation session: <guid>
User: Calculate a 20% tip on a $50 bill

🔧 [TOOL CALLED] calculate_tip(bill_amount=50.0, tip_percentage=20.0)
✓ [TOOL RESULT] {'bill_amount': 50.0, 'tip_percentage': 20.0, 'tip_amount': 10.0, 'total': 60.0}

MathAgent: For a $50 bill with a 20% tip, the tip amount is $10.00 and the total is $60.00.

Viewing Agent State

You can view the state of both agents 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 both WeatherAgent and MathAgent entities (dafx-WeatherAgent, dafx-MathAgent)
    • Each agent's conversation state across multiple interactions