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agent-framework/python/samples/04-hosting/durabletask/02_multi_agent
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Copilot b05fc9e849 Python: Add load_dotenv() to samples for .env file support (#4043)
* Initial plan

* Add load_dotenv() to 303 Python samples for environment variable loading

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* Update SAMPLE_GUIDELINES.md to document load_dotenv() requirement

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* Update samples README.md to document .env file usage

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* Run ruff format on all changed sample files

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* Clarify load_dotenv() usage in README - local dev vs production

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* Remove deprecated getting_started folder as requested

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* Document env_file_path parameter for per-client configuration

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* Merge main branch to resolve conflicts

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* Fix run_evaluation.py file that was empty in merge commit

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* Remove dotnet changes from merge - out of scope for this PR

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* Remove package and test changes from merge - only sample changes needed

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* Remove test_func_utils.py - only sample changes needed

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* Revert sample files not in original changeset - keep only load_dotenv additions

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* Move load_dotenv() outside snippet tag in 06_host_your_agent.py

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* Fix comment placement - move load_dotenv before code comments

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* Fix load_dotenv() placement across all samples - after docstring, before code comments

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* Merge latest main branch with load_dotenv changes

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* Remove non-sample changes from merge - keep only load_dotenv additions

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* Revert non-load_dotenv sample changes from merge

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* Fix run_evaluation.py - use main's improved version (file already had load_dotenv)

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* Manual update

* Manual update 2

* Fix Role usage and load_dotenv placement per PR review feedback

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* Fix Role usage - use string literals not enum attributes

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* Fix SAMPLE_GUIDELINES.md example - load_dotenv before docstring per guidance

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* Move load_dotenv() before docstrings in all samples per SAMPLE_GUIDELINES ordering

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* Address PR review: rename files, fix placement, add session usage, remove note

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* Update Redis README to reference renamed file redis_history_provider.py

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b05fc9e849 · 2026-02-19 10:55:13 +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