[BREAKING] Python: Refactor workflow events to unified discriminated union pattern (#3690)

* Refactor events

* Merge main

* Fixes

* Cleanup

* Update samples and tests

* Remove unused imports

* PR feedback

* Merge main. Add properties for events to help typing

* Formatting

* Cleanup

* use builtins.type to avoid shadowing by WorkflowEvent.type attribute

* Final improvements

python/samples/getting_started/workflows/declarative/customer_support/main.py

@@ -26,7 +26,6 @@ import logging
 import uuid
 from pathlib import Path
 
-from agent_framework import RequestInfoEvent, WorkflowOutputEvent
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework.declarative import (
     AgentExternalInputRequest,
@@ -259,7 +258,7 @@ async def main() -> None:
             stream = workflow.run(user_input, stream=True)
 
         async for event in stream:
-            if isinstance(event, WorkflowOutputEvent):
+            if event.type == "output":
                 data = event.data
                 source_id = getattr(event, "source_executor_id", "")
 
@@ -286,7 +285,7 @@ async def main() -> None:
                     else:
                         accumulated_response += str(data)
 
-            elif isinstance(event, RequestInfoEvent) and isinstance(event.data, AgentExternalInputRequest):
+            elif event.type == "request_info" and isinstance(event.data, AgentExternalInputRequest):
                 request = event.data
 
                 # The agent_response from the request contains the structured response

python/samples/getting_started/workflows/declarative/deep_research/main.py

@@ -24,7 +24,6 @@ Usage:
 import asyncio
 from pathlib import Path
 
-from agent_framework import WorkflowOutputEvent
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework.declarative import WorkflowFactory
 from azure.identity import AzureCliCredential
@@ -193,7 +192,7 @@ async def main() -> None:
     task = "What is the weather like in Seattle and how does it compare to the average for this time of year?"
 
     async for event in workflow.run(task, stream=True):
-        if isinstance(event, WorkflowOutputEvent):
+        if event.type == "output":
             print(f"{event.data}", end="", flush=True)
 
     print("\n" + "=" * 60)

python/samples/getting_started/workflows/declarative/function_tools/main.py

@@ -10,7 +10,7 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Annotated, Any
 
-from agent_framework import FileCheckpointStorage, RequestInfoEvent, WorkflowOutputEvent, tool
+from agent_framework import FileCheckpointStorage, tool
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework_declarative import ExternalInputRequest, ExternalInputResponse, WorkflowFactory
 from azure.identity import AzureCliCredential
@@ -98,12 +98,12 @@ async def main():
         first_response = True
 
         async for event in stream:
-            if isinstance(event, WorkflowOutputEvent) and isinstance(event.data, str):
+            if event.type == "output" and isinstance(event.data, str):
                 if first_response:
                     print("MenuAgent: ", end="")
                     first_response = False
                 print(event.data, end="", flush=True)
-            elif isinstance(event, RequestInfoEvent) and isinstance(event.data, ExternalInputRequest):
+            elif event.type == "request_info" and isinstance(event.data, ExternalInputRequest):
                 pending_request_id = event.request_id
 
         print()

python/samples/getting_started/workflows/declarative/human_in_loop/main.py

@@ -15,7 +15,7 @@ In a production scenario, you would integrate with a real UI or chat interface.
 import asyncio
 from pathlib import Path
 
-from agent_framework import Workflow, WorkflowOutputEvent
+from agent_framework import Workflow
 from agent_framework.declarative import ExternalInputRequest, WorkflowFactory
 from agent_framework_declarative._workflows._handlers import TextOutputEvent
 
@@ -27,7 +27,7 @@ async def run_with_streaming(workflow: Workflow) -> None:
 
     async for event in workflow.run({}, stream=True):
         # WorkflowOutputEvent wraps the actual output data
-        if isinstance(event, WorkflowOutputEvent):
+        if event.type == "output":
             data = event.data
             if isinstance(data, TextOutputEvent):
                 print(f"[Bot]: {data.text}")

python/samples/getting_started/workflows/declarative/marketing/main.py

@@ -15,7 +15,6 @@ Demonstrates sequential multi-agent pipeline:
 import asyncio
 from pathlib import Path
 
-from agent_framework import WorkflowOutputEvent
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework.declarative import WorkflowFactory
 from azure.identity import AzureCliCredential
@@ -85,7 +84,7 @@ async def main() -> None:
     product = "An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours."
 
     async for event in workflow.run(product, stream=True):
-        if isinstance(event, WorkflowOutputEvent):
+        if event.type == "output":
             print(f"{event.data}", end="", flush=True)
 
     print("\n" + "=" * 60)

python/samples/getting_started/workflows/declarative/student_teacher/main.py

@@ -22,7 +22,6 @@ Prerequisites:
 import asyncio
 from pathlib import Path
 
-from agent_framework import WorkflowOutputEvent
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework.declarative import WorkflowFactory
 from azure.identity import AzureCliCredential
@@ -82,7 +81,7 @@ async def main() -> None:
     print("=" * 50)
 
     async for event in workflow.run("How would you compute the value of PI?", stream=True):
-        if isinstance(event, WorkflowOutputEvent):
+        if event.type == "output":
             print(f"{event.data}", flush=True, end="")
 
     print("\n" + "=" * 50)