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* [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> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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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:
- Open your browser and navigate to
http://localhost:8082 - 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