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Python: restructure: Python samples into progressive 01-05 layout (#3862)
* restructure: Python samples into progressive 01-05 layout - 01-get-started/: 6 numbered steps (hello agent → hosting) - 02-agents/: all agent concept samples (tools, middleware, providers, etc.) - 03-workflows/: ALL existing workflow samples preserved as-is - 04-hosting/: azure-functions, durabletask, a2a - 05-end-to-end/: demos, evaluation, hosted agents - Old files moved to _to_delete/ for review - Added AGENTS.md with structure documentation - autogen-migration/ and semantic-kernel-migration/ preserved at root * fix: switch to AzureOpenAI Foundry, fix CI failures - Switch all 01-get-started samples to AzureOpenAIResponsesClient with Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT + AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential) - Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes - Fix test paths in packages/ that referenced old getting_started/ dirs: durabletask conftest + streaming test, azurefunctions conftest, devui conftest + capture_messages + openai_sdk_integration - Fix workflow_as_agent_human_in_the_loop.py import (sibling import) - Update hosting READMEs and tool comment paths - Replace root README.md with new structure overview - Update AGENTS.md to document Azure OpenAI Foundry as default provider * cleanup: remove _to_delete folder, copy resource files to active dirs All files in _to_delete/ were either: - Exact duplicates of files in the new structure (240 files) - Same file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since active samples reference them. * fix: address PR review comments, centralize resources, remove root duplicates - Fix type annotation in 04_memory.py (string union -> proper types) - Fix old sample paths in observability files - Fix grammar/spelling in observability samples - Move sample_assets/ and resources/ to shared/ folder - Remove 8 duplicate observability files from 02-agents root - Update resource path references in multimodal_input and provider samples * fix: update broken links from old getting_started paths to new structure - Update relative paths in READMEs: getting_started/ → 01-get-started/, 02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/ - Fix absolute GitHub URLs in package READMEs - Fix broken link in ollama package README * fix: convert absolute GitHub URLs to relative paths for link checker Absolute URLs to python/samples/ on main branch 404 until PR merges. Converted to relative paths that linkspector can verify locally. * fix: update link for handoff sample moved to orchestrations/ * fix: update chatkit-integration README path from demos/ to 05-end-to-end/ * fix: update broken links in orchestrations README to match flat directory structure
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Eduard van Valkenburg
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# Function Tools Workflow
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This sample demonstrates an agent with function tools responding to user queries about a restaurant menu.
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## Overview
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The workflow showcases:
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- **Function Tools**: Agent equipped with tools to query menu data
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- **Real Azure OpenAI Agent**: Uses `AzureOpenAIChatClient` to create an agent with tools
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- **Agent Registration**: Shows how to register agents with the `WorkflowFactory`
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## Tools
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The MenuAgent has access to these function tools:
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| Tool | Description |
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|------|-------------|
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| `get_menu()` | Returns all menu items with category, name, and price |
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| `get_specials()` | Returns today's special items |
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| `get_item_price(name)` | Returns the price of a specific item |
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## Menu Data
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```
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Soups:
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- Clam Chowder - $4.95 (Special)
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- Tomato Soup - $4.95
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Salads:
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- Cobb Salad - $9.99
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- House Salad - $4.95
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Drinks:
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- Chai Tea - $2.95 (Special)
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- Soda - $1.95
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```
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## Prerequisites
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- Azure OpenAI configured with required environment variables
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- Authentication via azure-identity (run `az login` before executing)
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## Usage
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```bash
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python main.py
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```
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## Example Output
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```
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Loaded workflow: function-tools-workflow
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============================================================
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Restaurant Menu Assistant
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============================================================
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[Bot]: Welcome to the Restaurant Menu Assistant!
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[Bot]: Today's soup special is the Clam Chowder for $4.95!
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============================================================
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Session Complete
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============================================================
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```
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## How It Works
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1. Create an Azure OpenAI chat client
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2. Create an agent with instructions and function tools
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3. Register the agent with the workflow factory
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4. Load the workflow YAML and run it with `run()` and `stream=True`
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```python
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# Create the agent with tools
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client = AzureOpenAIChatClient(credential=AzureCliCredential())
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menu_agent = client.as_agent(
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name="MenuAgent",
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instructions="You are a helpful restaurant menu assistant...",
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tools=[get_menu, get_specials, get_item_price],
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)
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# Register with the workflow factory
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factory = WorkflowFactory(execution_mode="graph")
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factory.register_agent("MenuAgent", menu_agent)
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# Load and run the workflow
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workflow = factory.create_workflow_from_yaml_path(workflow_path)
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async for event in workflow.run(inputs={"userInput": "What is the soup of the day?"}, stream=True):
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...
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```
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# Copyright (c) Microsoft. All rights reserved.
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"""
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Demonstrate a workflow that responds to user input using an agent with
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function tools assigned. Exits the loop when the user enters "exit".
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"""
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import asyncio
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import os
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Annotated, Any
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from agent_framework import FileCheckpointStorage, tool
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework_declarative import ExternalInputRequest, ExternalInputResponse, WorkflowFactory
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from azure.identity import AzureCliCredential
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from pydantic import Field
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TEMP_DIR = Path(__file__).with_suffix("").parent / "tmp" / "checkpoints"
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TEMP_DIR.mkdir(parents=True, exist_ok=True)
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@dataclass
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class MenuItem:
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category: str
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name: str
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price: float
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is_special: bool = False
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MENU_ITEMS = [
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MenuItem(category="Soup", name="Clam Chowder", price=4.95, is_special=True),
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MenuItem(category="Soup", name="Tomato Soup", price=4.95, is_special=False),
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MenuItem(category="Salad", name="Cobb Salad", price=9.99, is_special=False),
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MenuItem(category="Salad", name="House Salad", price=4.95, is_special=False),
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MenuItem(category="Drink", name="Chai Tea", price=2.95, is_special=True),
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MenuItem(category="Drink", name="Soda", price=1.95, is_special=False),
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]
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# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_threads.py.
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@tool(approval_mode="never_require")
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def get_menu() -> list[dict[str, Any]]:
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"""Get all menu items."""
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return [{"category": i.category, "name": i.name, "price": i.price} for i in MENU_ITEMS]
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@tool(approval_mode="never_require")
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def get_specials() -> list[dict[str, Any]]:
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"""Get today's specials."""
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return [{"category": i.category, "name": i.name, "price": i.price} for i in MENU_ITEMS if i.is_special]
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@tool(approval_mode="never_require")
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def get_item_price(name: Annotated[str, Field(description="Menu item name")]) -> str:
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"""Get price of a menu item."""
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for item in MENU_ITEMS:
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if item.name.lower() == name.lower():
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return f"${item.price:.2f}"
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return f"Item '{name}' not found."
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async def main():
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# Create agent with tools
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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menu_agent = client.as_agent(
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name="MenuAgent",
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instructions="Answer questions about menu items, specials, and prices.",
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tools=[get_menu, get_specials, get_item_price],
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)
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# Clean up any existing checkpoints
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for file in TEMP_DIR.glob("*"):
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file.unlink()
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factory = WorkflowFactory(checkpoint_storage=FileCheckpointStorage(TEMP_DIR))
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factory.register_agent("MenuAgent", menu_agent)
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workflow = factory.create_workflow_from_yaml_path(Path(__file__).parent / "workflow.yaml")
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# Get initial input
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print("Restaurant Menu Assistant (type 'exit' to quit)\n")
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user_input = input("You: ").strip() # noqa: ASYNC250
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if not user_input:
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return
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# Run workflow with external loop handling
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pending_request_id: str | None = None
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first_response = True
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while True:
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if pending_request_id:
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response = ExternalInputResponse(user_input=user_input)
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stream = workflow.run(stream=True, responses={pending_request_id: response})
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else:
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stream = workflow.run({"userInput": user_input}, stream=True)
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pending_request_id = None
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first_response = True
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async for event in stream:
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if event.type == "output" and isinstance(event.data, str):
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if first_response:
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print("MenuAgent: ", end="")
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first_response = False
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print(event.data, end="", flush=True)
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elif event.type == "request_info" and isinstance(event.data, ExternalInputRequest):
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pending_request_id = event.request_id
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print()
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if not pending_request_id:
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break
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user_input = input("\nYou: ").strip()
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if not user_input:
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continue
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,22 @@
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# Function Tools Workflow - .NET-style
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#
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# This workflow demonstrates an agent with function tools in a loop
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# responding to user input, using the same minimal structure as .NET.
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#
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# Example input:
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# What is the soup of the day?
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#
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kind: Workflow
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trigger:
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kind: OnConversationStart
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id: workflow_demo
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actions:
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- kind: InvokeAzureAgent
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id: invoke_menu_agent
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agent:
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name: MenuAgent
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input:
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externalLoop:
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when: =Upper(System.LastMessage.Text) <> "EXIT"
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