Python: [BREAKING] changed AIFunction to FunctionTool and @ai_function to @tool (#3413)

* changed AIFunction to FunctionTool and @ai_function to @tool

* test and mypy fixes

* mypy fix

* switch function tool to always_require

* fix noop

* fix github copilot imports

* test fixes

* fix ollama test

* fixes for tests

* fix tests

* reverted change to always_require and extended timeout

* fix test

python/samples/getting_started/workflows/state-management/shared_states_with_agents.py

@@ -15,6 +15,7 @@ from agent_framework import (
     WorkflowBuilder,
     WorkflowContext,
     executor,
+    tool,
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

python/samples/getting_started/workflows/state-management/workflow_kwargs.py

@@ -4,20 +4,20 @@ import asyncio
 import json
 from typing import Annotated, Any
 
-from agent_framework import ChatMessage, SequentialBuilder, WorkflowOutputEvent, ai_function
+from agent_framework import ChatMessage, SequentialBuilder, WorkflowOutputEvent, tool
 from agent_framework.openai import OpenAIChatClient
 from pydantic import Field
 
 """
-Sample: Workflow kwargs Flow to @ai_function Tools
+Sample: Workflow kwargs Flow to @tool Tools
 
 This sample demonstrates how to flow custom context (skill data, user tokens, etc.)
-through any workflow pattern to @ai_function tools using the **kwargs pattern.
+through any workflow pattern to @tool functions using the **kwargs pattern.
 
 Key Concepts:
 - Pass custom context as kwargs when invoking workflow.run_stream() or workflow.run()
 - kwargs are stored in SharedState and passed to all agent invocations
-- @ai_function tools receive kwargs via **kwargs parameter
+- @tool functions receive kwargs via **kwargs parameter
 - Works with Sequential, Concurrent, GroupChat, Handoff, and Magentic patterns
 
 Prerequisites:
@@ -26,7 +26,8 @@ Prerequisites:
 
 
 # Define tools that accept custom context via **kwargs
-@ai_function
+# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
+@tool(approval_mode="never_require")
 def get_user_data(
     query: Annotated[str, Field(description="What user data to retrieve")],
     **kwargs: Any,
@@ -43,7 +44,7 @@ def get_user_data(
     return f"Retrieved data for user {user_name} with {access_level} access: {query}"
 
 
-@ai_function
+@tool(approval_mode="never_require")
 def call_api(
     endpoint_name: Annotated[str, Field(description="Name of the API endpoint to call")],
     **kwargs: Any,
@@ -86,7 +87,7 @@ async def main() -> None:
     # Build a simple sequential workflow
     workflow = SequentialBuilder().participants([agent]).build()
 
-    # Define custom context that will flow to ai_functions via kwargs
+    # Define custom context that will flow to tools via kwargs
     custom_data = {
         "api_config": {
             "base_url": "https://api.example.com",
@@ -110,7 +111,7 @@ async def main() -> None:
     print("Workflow Execution (watch for [tool_name] logs showing kwargs received):")
     print("-" * 70)
 
-    # Run workflow with kwargs - these will flow through to ai_functions
+    # Run workflow with kwargs - these will flow through to tools
     async for event in workflow.run_stream(
         "Please get my user data and then call the users API endpoint.",
         custom_data=custom_data,