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
2026-06-16 21:04:09 +08:00 · 2026-01-28 15:53:53 +01:00
parent 15b43f2abe
commit a7d924a7d2
255 changed files with 1202 additions and 1290 deletions
@@ -14,6 +14,7 @@ from agent_framework import (
    WorkflowContext,
    WorkflowOutputEvent,
    executor,
+    tool,
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
@@ -22,6 +22,7 @@ from agent_framework import (
    WorkflowOutputEvent,
    handler,
    response_handler,
+    tool,
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
@@ -49,6 +50,8 @@ Prerequisites:
 - Authentication via azure-identity. Run `az login` before executing.
 """

+# 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 fetch_product_brief(
    product_name: Annotated[str, Field(description="Product name to look up.")],
@@ -65,6 +68,7 @@ def fetch_product_brief(
    }
    return briefs.get(product_name.lower(), f"No stored brief for '{product_name}'.")

+@tool(approval_mode="never_require")

 def get_brand_voice_profile(
    voice_name: Annotated[str, Field(description="Brand or campaign voice to emulate.")],
@@ -9,6 +9,7 @@ from agent_framework import (
    WorkflowBuilder,
    WorkflowContext,
    handler,
+    tool,
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
@@ -13,7 +13,7 @@ from agent_framework import (
    HandoffBuilder,
    Role,
    WorkflowAgent,
-    ai_function,
+    tool,
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
@@ -38,19 +38,20 @@ Key Concepts:
 """


-@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 process_refund(order_number: Annotated[str, "Order number to process refund for"]) -> str:
    """Simulated function to process a refund for a given order number."""
    return f"Refund processed successfully for order {order_number}."


-@ai_function
+@tool(approval_mode="never_require")
 def check_order_status(order_number: Annotated[str, "Order number to check status for"]) -> str:
    """Simulated function to check the status of a given order number."""
    return f"Order {order_number} is currently being processed and will ship in 2 business days."


-@ai_function
+@tool(approval_mode="never_require")
 def process_return(order_number: Annotated[str, "Order number to process return for"]) -> str:
    """Simulated function to process a return for a given order number."""
    return f"Return initiated successfully for order {order_number}. You will receive return instructions via email."
@@ -6,6 +6,7 @@ from agent_framework import (
    ChatAgent,
    HostedCodeInterpreterTool,
    MagenticBuilder,
+    tool,
 )
 from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient

@@ -11,6 +11,7 @@ from agent_framework import (
    WorkflowBuilder,
    WorkflowContext,
    handler,
+    tool,
 )
 from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
@@ -26,6 +26,7 @@ from agent_framework import (  # noqa: E402
    WorkflowContext,
    handler,
    response_handler,
+    tool,
 )
 from getting_started.workflows.agents.workflow_as_agent_reflection_pattern import (  # noqa: E402
    ReviewRequest,
@@ -4,22 +4,22 @@ import asyncio
 import json
 from typing import Annotated, Any

-from agent_framework import SequentialBuilder, ai_function
+from agent_framework import SequentialBuilder, tool
 from agent_framework.openai import OpenAIChatClient
 from pydantic import Field

 """
-Sample: Workflow as Agent with kwargs Propagation to @ai_function Tools
+Sample: Workflow as Agent with kwargs Propagation to @tool Tools

 This sample demonstrates how to flow custom context (skill data, user tokens, etc.)
-through a workflow exposed via .as_agent() to @ai_function tools using the **kwargs pattern.
+through a workflow exposed via .as_agent() to @tool functions using the **kwargs pattern.

 Key Concepts:
 - Build a workflow using SequentialBuilder (or any builder pattern)
 - Expose the workflow as a reusable agent via workflow.as_agent()
 - Pass custom context as kwargs when invoking workflow_agent.run() or run_stream()
 - kwargs are stored in SharedState and propagated to all agent invocations
- @ai_function tools receive kwargs via **kwargs parameter
+- @tool functions receive kwargs via **kwargs parameter

 When to use workflow.as_agent():
 - To treat an entire workflow orchestration as a single agent
@@ -32,7 +32,8 @@ 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,
@@ -49,7 +50,7 @@ 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,
@@ -95,7 +96,7 @@ async def main() -> None:
    # Expose the workflow as an agent using .as_agent()
    workflow_agent = workflow.as_agent(name="WorkflowAgent")

-    # 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",
@@ -119,7 +120,7 @@ async def main() -> None:
    print("Workflow Agent Execution (watch for [tool_name] logs showing kwargs received):")
    print("-" * 70)

-    # Run workflow agent with kwargs - these will flow through to ai_functions
+    # Run workflow agent with kwargs - these will flow through to tools
    # Note: kwargs are passed to workflow_agent.run_stream() just like workflow.run_stream()
    print("\n===== Streaming Response =====")
    async for update in workflow_agent.run_stream(
@@ -15,6 +15,7 @@ from agent_framework import (
    WorkflowBuilder,
    WorkflowContext,
    handler,
+    tool,
 )
 from agent_framework.openai import OpenAIChatClient
 from pydantic import BaseModel