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agent-framework/python/samples/03-workflows/declarative/function_tools
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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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---------

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b05fc9e849 ยท 2026-02-19 10:55:13 +00:00
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Function Tools Workflow

This sample demonstrates an agent with function tools responding to user queries about a restaurant menu.

Overview

The workflow showcases:

  • Function Tools: Agent equipped with tools to query menu data
  • Real Azure OpenAI Agent: Uses AzureOpenAIChatClient to create an agent with tools
  • Agent Registration: Shows how to register agents with the WorkflowFactory

Tools

The MenuAgent has access to these function tools:

Tool Description
get_menu() Returns all menu items with category, name, and price
get_specials() Returns today's special items
get_item_price(name) Returns the price of a specific item

Menu Data

Soups:
  - Clam Chowder - $4.95 (Special)
  - Tomato Soup - $4.95

Salads:
  - Cobb Salad - $9.99
  - House Salad - $4.95

Drinks:
  - Chai Tea - $2.95 (Special)
  - Soda - $1.95

Prerequisites

  • Azure OpenAI configured with required environment variables
  • Authentication via azure-identity (run az login before executing)

Usage

python main.py

Example Output

Loaded workflow: function-tools-workflow
============================================================
Restaurant Menu Assistant
============================================================

[Bot]: Welcome to the Restaurant Menu Assistant!

[Bot]: Today's soup special is the Clam Chowder for $4.95!

============================================================
Session Complete
============================================================

How It Works

  1. Create an Azure OpenAI chat client
  2. Create an agent with instructions and function tools
  3. Register the agent with the workflow factory
  4. Load the workflow YAML and run it with run() and stream=True
# Create the agent with tools
client = AzureOpenAIChatClient(credential=AzureCliCredential())
menu_agent = client.as_agent(
    name="MenuAgent",
    instructions="You are a helpful restaurant menu assistant...",
    tools=[get_menu, get_specials, get_item_price],
)

# Register with the workflow factory
factory = WorkflowFactory(execution_mode="graph")
factory.register_agent("MenuAgent", menu_agent)

# Load and run the workflow
workflow = factory.create_workflow_from_yaml_path(workflow_path)
async for event in workflow.run(inputs={"userInput": "What is the soup of the day?"}, stream=True):
    ...