[BREAKING] Python: Add InvokeFunctionTool action for declarative workflows (#3716)

* add(declarative): Declarative workflow InvokeFunctionTool feature

* Cleanup

* Address PR feedback

* Remove InvokeTool kind, consolidate to InvokeFunctionTool

* Fix sample locations

* pin azure-ai-projects to 2.0.0b3 due to breaking changes

python/samples/03-workflows/README.md

@@ -127,16 +127,18 @@ Orchestration-focused samples (Sequential, Concurrent, Handoff, GroupChat, Magen
 
 YAML-based declarative workflows allow you to define multi-agent orchestration patterns without writing Python code. See the [declarative workflows README](./declarative/README.md) for more details on YAML workflow syntax and available actions.
 
-| Sample               | File                                                                     | Concepts                                                      |
-| -------------------- | ------------------------------------------------------------------------ | ------------------------------------------------------------- |
-| Conditional Workflow | [declarative/conditional_workflow/](./declarative/conditional_workflow/) | Nested conditional branching based on user input              |
-| Customer Support     | [declarative/customer_support/](./declarative/customer_support/)         | Multi-agent customer support with routing                     |
-| Deep Research        | [declarative/deep_research/](./declarative/deep_research/)               | Research workflow with planning, searching, and synthesis     |
-| Function Tools       | [declarative/function_tools/](./declarative/function_tools/)             | Invoking Python functions from declarative workflows          |
-| Human-in-Loop        | [declarative/human_in_loop/](./declarative/human_in_loop/)               | Interactive workflows that request user input                 |
-| Marketing            | [declarative/marketing/](./declarative/marketing/)                       | Marketing content generation workflow                         |
-| Simple Workflow      | [declarative/simple_workflow/](./declarative/simple_workflow/)           | Basic workflow with variable setting, conditionals, and loops |
-| Student Teacher      | [declarative/student_teacher/](./declarative/student_teacher/)           | Student-teacher interaction pattern                           |
+| Sample | File | Concepts |
+|---|---|---|
+| Agent to Function Tool | [declarative/agent_to_function_tool/](./declarative/agent_to_function_tool/) | Chain agent output to InvokeFunctionTool actions |
+| Conditional Workflow | [declarative/conditional_workflow/](./declarative/conditional_workflow/) | Nested conditional branching based on user input |
+| Customer Support | [declarative/customer_support/](./declarative/customer_support/) | Multi-agent customer support with routing |
+| Deep Research | [declarative/deep_research/](./declarative/deep_research/) | Research workflow with planning, searching, and synthesis |
+| Function Tools | [declarative/function_tools/](./declarative/function_tools/) | Invoking Python functions from declarative workflows |
+| Human-in-Loop | [declarative/human_in_loop/](./declarative/human_in_loop/) | Interactive workflows that request user input |
+| Invoke Function Tool | [declarative/invoke_function_tool/](./declarative/invoke_function_tool/) | Call registered Python functions with InvokeFunctionTool |
+| Marketing | [declarative/marketing/](./declarative/marketing/) | Marketing content generation workflow |
+| Simple Workflow | [declarative/simple_workflow/](./declarative/simple_workflow/) | Basic workflow with variable setting, conditionals, and loops |
+| Student Teacher | [declarative/student_teacher/](./declarative/student_teacher/) | Student-teacher interaction pattern |
 
 ### resources
 

python/samples/03-workflows/declarative/README.md

@@ -69,6 +69,9 @@ actions:
 - `InvokeAzureAgent` - Call an Azure AI agent
 - `InvokePromptAgent` - Call a local prompt agent
 
+### Tool Invocation
+- `InvokeFunctionTool` - Call a registered Python function
+
 ### Human-in-Loop
 - `Question` - Request user input
 - `WaitForInput` - Pause for external input

python/samples/03-workflows/declarative/agent_to_function_tool/main.py

@@ -0,0 +1,261 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Agent to Function Tool sample - demonstrates chaining agent output to function tools.
+
+This sample shows how to:
+1. Use InvokeAzureAgent to analyze user input with an AI model
+2. Pass the agent's structured output to InvokeFunctionTool actions
+3. Chain multiple function tools to process and transform data
+
+The workflow:
+1. Takes a user order request as input
+2. Uses an Azure agent to extract structured order data (item, quantity, details)
+3. Passes the extracted data to a function tool that calculates the order total
+4. Uses another function tool to format the final confirmation message
+
+Run with:
+    python -m samples.03-workflows.declarative.agent_to_function_tool.main
+"""
+
+import asyncio
+from pathlib import Path
+from typing import Any
+
+from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.declarative import WorkflowFactory
+from azure.identity import AzureCliCredential
+from pydantic import BaseModel, Field
+
+# Pricing data for the order calculation
+ITEM_PRICES = {
+    "pizza": {"small": 10.99, "medium": 14.99, "large": 18.99, "default": 14.99},
+    "burger": {"small": 6.99, "medium": 8.99, "large": 10.99, "default": 8.99},
+    "salad": {"small": 7.99, "medium": 9.99, "large": 11.99, "default": 9.99},
+    "sandwich": {"small": 6.99, "medium": 8.99, "large": 10.99, "default": 8.99},
+    "pasta": {"small": 11.99, "medium": 14.99, "large": 17.99, "default": 14.99},
+}
+
+EXTRAS_PRICES = {
+    "extra cheese": 2.00,
+    "bacon": 2.50,
+    "avocado": 1.50,
+    "mushrooms": 1.00,
+    "pepperoni": 2.00,
+}
+
+# Agent instructions for order analysis
+ORDER_ANALYSIS_INSTRUCTIONS = """You are an order analysis assistant. Analyze the customer's order request and extract:
+- item: what they want to order (e.g., "pizza", "burger", "salad")
+- quantity: how many (as a number, default to 1 if not specified)
+- details: any special requests, modifications, or size (e.g., "large", "extra cheese")
+- delivery_address: where to deliver (if mentioned, otherwise empty string)
+
+Always respond with valid JSON matching the required format."""
+
+
+# Pydantic model for structured agent output
+class OrderAnalysis(BaseModel):
+    """Structured output from the order analysis agent."""
+
+    item: str = Field(description="The food item being ordered (e.g., pizza, burger)")
+    quantity: int = Field(description="Number of items ordered", default=1)
+    details: str = Field(description="Special requests, size, or modifications")
+    delivery_address: str = Field(description="Delivery address if provided, empty string otherwise", default="")
+
+
+def calculate_order_total(order_data: dict[str, Any]) -> dict[str, Any]:
+    """Calculate the total cost of an order based on the agent's structured analysis.
+
+    Args:
+        order_data: Structured dict from the agent containing order analysis.
+
+    Returns:
+        Dictionary with pricing breakdown.
+    """
+    # Handle case where order_data might be None or invalid
+    if not order_data or not isinstance(order_data, dict):
+        return {
+            "error": f"Invalid order data: {order_data}",
+            "subtotal": 0.0,
+            "tax": 0.0,
+            "delivery_fee": 0.0,
+            "total": 0.0,
+        }
+
+    item = str(order_data.get("item", "")).lower()
+    quantity = int(order_data.get("quantity", 1))
+    details = str(order_data.get("details", "")).lower()
+    has_delivery = bool(order_data.get("delivery_address"))
+
+    # Determine size from details
+    size = "default"
+    for s in ["small", "medium", "large"]:
+        if s in details:
+            size = s
+            break
+
+    # Get base price for item
+    item_key = None
+    for key in ITEM_PRICES:
+        if key in item:
+            item_key = key
+            break
+
+    unit_price = ITEM_PRICES[item_key].get(size, ITEM_PRICES[item_key]["default"]) if item_key else 12.99
+
+    # Calculate extras
+    extras_total = 0.0
+    applied_extras: list[dict[str, Any]] = []
+    for extra, price in EXTRAS_PRICES.items():
+        if extra in details:
+            extras_total += price * quantity
+            applied_extras.append({"name": extra, "price": price})
+
+    # Calculate totals
+    subtotal = (unit_price * quantity) + extras_total
+    tax = round(subtotal * 0.08, 2)  # 8% tax
+    delivery_fee = 5.00 if has_delivery else 0.0
+    total = round(subtotal + tax + delivery_fee, 2)
+
+    return {
+        "item": item,
+        "quantity": quantity,
+        "size": size if size != "default" else "regular",
+        "unit_price": unit_price,
+        "extras": applied_extras,
+        "extras_total": extras_total,
+        "subtotal": round(subtotal, 2),
+        "tax": tax,
+        "delivery_fee": delivery_fee,
+        "total": total,
+        "has_delivery": has_delivery,
+    }
+
+
+def format_order_confirmation(order_data: dict[str, Any], order_calculation: dict[str, Any]) -> str:
+    """Format a human-readable order confirmation message.
+
+    Args:
+        order_data: Structured dict from the agent with order details.
+        order_calculation: Pricing calculation from calculate_order_total.
+
+    Returns:
+        Formatted confirmation message.
+    """
+    calc = order_calculation
+
+    # Handle error case
+    if "error" in calc:
+        return f"Sorry, we couldn't process your order: {calc['error']}"
+
+    # Build the confirmation message
+    qty = int(calc.get("quantity", 1))
+    size = calc.get("size", "regular").title()
+    item = calc.get("item", "item").title()
+    lines = [
+        "=" * 50,
+        "ORDER CONFIRMATION",
+        "=" * 50,
+        "",
+        f"Item: {qty}x {size} {item}",
+        f"Unit Price: ${calc.get('unit_price', 0):.2f}",
+    ]
+
+    # Add extras if any
+    extras = calc.get("extras", [])
+    if extras:
+        lines.append("\nExtras:")
+        for extra in extras:
+            lines.append(f"  + {extra['name'].title()}: ${extra['price']:.2f} each")
+        lines.append(f"  Extras Total: ${calc.get('extras_total', 0):.2f}")
+
+    lines.extend([
+        "",
+        "-" * 30,
+        f"Subtotal: ${calc.get('subtotal', 0):.2f}",
+        f"Tax (8%): ${calc.get('tax', 0):.2f}",
+    ])
+
+    if calc.get("has_delivery"):
+        delivery_address = order_data.get("delivery_address", "Address provided") if order_data else "Address provided"
+        lines.extend([
+            f"Delivery Fee: ${calc.get('delivery_fee', 0):.2f}",
+            f"Delivery To: {delivery_address}",
+        ])
+
+    lines.extend([
+        "-" * 30,
+        f"TOTAL: ${calc.get('total', 0):.2f}",
+        "=" * 50,
+        "",
+        "Thank you for your order!",
+    ])
+
+    return "\n".join(lines)
+
+
+async def main():
+    """Run the agent to function tool workflow."""
+    # Create Azure OpenAI client
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+
+    # Create the order analysis agent with structured output
+    order_analysis_agent = chat_client.as_agent(
+        name="OrderAnalysisAgent",
+        instructions=ORDER_ANALYSIS_INSTRUCTIONS,
+        default_options={"response_format": OrderAnalysis},
+    )
+
+    # Agent registry
+    agents = {
+        "OrderAnalysisAgent": order_analysis_agent,
+    }
+
+    # Get the path to the workflow YAML file
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+
+    # Create the workflow factory with agents and tools
+    factory = (
+        WorkflowFactory(agents=agents)
+        .register_tool("calculate_order_total", calculate_order_total)
+        .register_tool("format_order_confirmation", format_order_confirmation)
+    )
+
+    # Create the workflow from the YAML definition
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print("=" * 60)
+    print("Agent to Function Tool Workflow Demo")
+    print("=" * 60)
+    print()
+    print("This workflow demonstrates:")
+    print("  1. Using InvokeAzureAgent to analyze user input")
+    print("  2. Passing agent's structured output to InvokeFunctionTool")
+    print("  3. Chaining multiple function tools together")
+    print()
+
+    # Test with different order inputs
+    test_queries = [
+        "I want to order 3 large pizzas with extra cheese for delivery to 123 Main St",
+        "2 medium burgers with bacon please",
+        "Can I get a small salad with avocado and mushrooms, pick up",
+    ]
+
+    for query in test_queries:
+        print("-" * 60)
+        print(f"Input: {query}")
+        print("-" * 60)
+
+        # Run the workflow with streaming to capture output
+        try:
+            async for event in workflow.run(query, stream=True):
+                if event.type == "output" and isinstance(event.data, str):
+                    print(event.data, end="", flush=True)
+        except Exception as e:
+            print(f"\nWorkflow error: {type(e).__name__}: {e}")
+
+        print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/03-workflows/declarative/agent_to_function_tool/workflow.yaml

@@ -0,0 +1,56 @@
+# Agent to Function Tool Workflow
+#
+# This workflow demonstrates chaining an agent invocation with a function tool.
+# The agent analyzes user input, and the function tool processes the agent's output.
+#
+# Flow:
+# 1. Receive user query
+# 2. Invoke an Azure agent to analyze the query and extract structured data
+# 3. Pass the agent's structured output to a function tool for processing
+# 4. Return the final result
+#
+# Example input:
+# I want to order 3 large pizzas with extra cheese for delivery to 123 Main St
+
+kind: Workflow
+trigger:
+
+  kind: OnConversationStart
+  id: agent_to_function_tool_demo
+  actions:
+
+    # Invoke the order analysis agent to extract structured order data
+    - kind: InvokeAzureAgent
+      id: analyze_order
+      agent:
+        name: OrderAnalysisAgent
+      input:
+        messages: =Workflow.Inputs.input
+      output:
+        response: Local.agentResponse
+        responseObject: Local.orderData
+
+    # Invoke a function tool to calculate order total using the agent's output
+    - kind: InvokeFunctionTool
+      id: calculate_order
+      functionName: calculate_order_total
+      arguments:
+        order_data: =Local.orderData
+      output:
+        result: Local.orderCalculation
+
+    # Invoke another function tool to format the final confirmation
+    - kind: InvokeFunctionTool
+      id: format_confirmation
+      functionName: format_order_confirmation
+      arguments:
+        order_data: =Local.orderData
+        order_calculation: =Local.orderCalculation
+      output:
+        result: Local.confirmation
+
+    # Send the final confirmation to the user
+    - kind: SendActivity
+      id: send_confirmation
+      activity:
+        text: =Local.confirmation

python/samples/03-workflows/declarative/invoke_function_tool/main.py

@@ -0,0 +1,116 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Invoke Function Tool sample - demonstrates InvokeFunctionTool workflow actions.
+
+This sample shows how to:
+1. Define Python functions that can be called from workflows
+2. Register functions with WorkflowFactory.register_tool()
+3. Use the InvokeFunctionTool action in YAML to invoke registered functions
+4. Pass arguments using expression syntax (=Local.variable)
+5. Capture function output in workflow variables
+
+Run with:
+    python -m samples.03-workflows.declarative.invoke_function_tool.main
+"""
+
+import asyncio
+from pathlib import Path
+from typing import Any
+
+from agent_framework.declarative import WorkflowFactory
+
+
+# Define the function tools that will be registered with the workflow
+def get_weather(location: str, unit: str = "F") -> dict[str, Any]:
+    """Get weather information for a location.
+
+    This is a mock function that returns simulated weather data.
+    In a real application, this would call a weather API.
+
+    Args:
+        location: The city or location to get weather for.
+        unit: Temperature unit ("F" for Fahrenheit, "C" for Celsius).
+
+    Returns:
+        Dictionary with weather information.
+    """
+    # Simulated weather data
+    weather_data = {
+        "Seattle": {"temp": 55, "condition": "rainy"},
+        "New York": {"temp": 70, "condition": "partly cloudy"},
+        "Los Angeles": {"temp": 85, "condition": "sunny"},
+        "Chicago": {"temp": 60, "condition": "windy"},
+    }
+
+    data = weather_data.get(location, {"temp": 72, "condition": "unknown"})
+
+    # Convert to Celsius if requested
+    temp = data["temp"]
+    if unit.upper() == "C":
+        temp = round((temp - 32) * 5 / 9)  # type: ignore
+
+    return {
+        "location": location,
+        "temp": temp,
+        "unit": unit.upper(),
+        "condition": data["condition"],
+    }
+
+
+def format_message(template: str, data: dict[str, Any]) -> str:
+    """Format a message template with data.
+
+    Args:
+        template: A string template with {key} placeholders.
+        data: Dictionary of values to substitute.
+
+    Returns:
+        Formatted message string.
+    """
+    try:
+        return template.format(**data)
+    except KeyError as e:
+        return f"Error formatting message: missing key {e}"
+
+
+async def main():
+    """Run the invoke function tool workflow."""
+    # Get the path to the workflow YAML file
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+
+    # Create the workflow factory and register our tool functions
+    factory = (
+        WorkflowFactory().register_tool("get_weather", get_weather).register_tool("format_message", format_message)
+    )
+
+    # Create the workflow from the YAML definition
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print("=" * 60)
+    print("Invoke Function Tool Workflow Demo")
+    print("=" * 60)
+
+    # Test with different inputs - both location and unit must be provided
+    # as the workflow expects them in Workflow.Inputs
+    test_inputs = [
+        {"location": "Seattle", "unit": "F"},
+        {"location": "New York", "unit": "C"},
+        {"location": "Los Angeles", "unit": "F"},
+        {"location": "Chicago", "unit": "C"},
+    ]
+
+    for inputs in test_inputs:
+        print(f"\nInput: {inputs}")
+        print("-" * 40)
+
+        # Run the workflow
+        events = await workflow.run(inputs)
+
+        # Get the outputs
+        outputs = events.get_outputs()
+        for output in outputs:
+            print(f"Output: {output}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/03-workflows/declarative/invoke_function_tool/workflow.yaml

@@ -0,0 +1,51 @@
+# Invoke Function Tool Workflow
+
+name: invoke_function_tool_demo
+description: Demonstrates the InvokeFunctionTool action for invoking registered functions
+
+actions:
+  # Set up input location
+  - kind: SetValue
+    id: set_location
+    path: Local.location
+    value: =If(IsBlank(inputs.location), "Seattle", inputs.location)
+
+  # Set up temperature unit
+  - kind: SetValue
+    id: set_unit
+    path: Local.unit
+    value: =If(IsBlank(inputs.unit), "F", inputs.unit)
+
+  # Invoke the get_weather function tool
+  - kind: InvokeFunctionTool
+    id: invoke_weather
+    functionName: get_weather
+    arguments:
+      location: =Local.location
+      unit: =Local.unit
+    output:
+      messages: Local.weatherToolCallItems
+      result: Local.weatherInfo
+      autoSend: true
+
+  # Format a human-readable message using another function
+  - kind: InvokeFunctionTool
+    id: format_output
+    functionName: format_message
+    arguments:
+      template: "The weather in {location} is {temp}°{unit}"
+      data: =Local.weatherInfo
+    output:
+      result: Local.formattedMessage
+
+  # Output the result
+  - kind: SendActivity
+    id: send_weather
+    activity:
+      text: =Local.formattedMessage
+
+  # Store the result in workflow outputs
+  - kind: SetValue
+    id: set_output
+    path: Workflow.Outputs.weather
+    value: =Local.weatherInfo