Python: Clean left-over WorkflowCompletedEvent (#884)

* Clean left-over WorkflowCompletedEvent

* Improve comments

* Fix type check error

python/samples/getting_started/observability/04-workflow.py

@@ -6,9 +6,8 @@ from typing import Any
 from agent_framework import (
     Executor,
     WorkflowBuilder,
-    WorkflowCompletedEvent,
-    WorkflowOutputEvent,
     WorkflowContext,
+    WorkflowOutputEvent,
     handler,
 )
 from agent_framework.observability import get_tracer, setup_observability
@@ -49,9 +48,8 @@ class ReverseTextExecutor(Executor):
         result = text[::-1]
         print(f"ReverseTextExecutor: Result '{result}'")
 
-        # Yield the output and signal workflow completion.
+        # Yield the output.
         await ctx.yield_output(result)
-        await ctx.add_event(WorkflowCompletedEvent())
 
 
 async def run_sequential_workflow() -> None:

python/samples/getting_started/workflow/README.md

@@ -113,7 +113,7 @@ Notes
 Sequential orchestration uses a few small adapter nodes for plumbing:
 - "input-conversation" normalizes input to `list[ChatMessage]`
 - "to-conversation:<participant>" converts agent responses into the shared conversation
-- "complete" publishes the final `WorkflowCompletedEvent`
+- "complete" publishes the final `WorkflowOutputEvent`
 These may appear in event streams (ExecutorInvoke/Completed). They’re analogous to
 concurrent’s dispatcher and aggregator and can be ignored if you only care about agent activity.
 

python/samples/getting_started/workflow/checkpoint/checkpoint_with_resume.py

@@ -39,7 +39,7 @@ Pipeline:
 2) ReverseTextExecutor reverses the string.
 3) SubmitToLowerAgent prepares an AgentExecutorRequest for the lowercasing agent.
 4) lower_agent (AgentExecutor) converts text to lowercase via Azure OpenAI.
-5) FinalizeFromAgent emits a WorkflowCompletedEvent with the final result.
+5) FinalizeFromAgent yields the final result.
 
 What you learn:
 - How to persist executor state using ctx.get_state and ctx.set_state.

python/samples/getting_started/workflow/orchestration/concurrent_agents.py

@@ -22,7 +22,7 @@ Demonstrates:
 
 Prerequisites:
 - Azure OpenAI access configured for AzureChatClient (use az login + env vars)
-- Familiarity with Workflow events (AgentRunEvent, WorkflowCompletedEvent)
+- Familiarity with Workflow events (AgentRunEvent, WorkflowOutputEvent)
 """
 
 

python/samples/getting_started/workflow/parallelism/aggregate_results_of_different_types.py

@@ -3,7 +3,8 @@
 import asyncio
 import random
 
-from agent_framework import Executor, WorkflowBuilder, WorkflowCompletedEvent, WorkflowContext, handler
+from agent_framework import Executor, WorkflowBuilder, WorkflowContext, WorkflowOutputEvent, handler
+from typing_extensions import Never
 
 """
 Sample: Concurrent fan out and fan in with two different tasks that output results of different types.
@@ -52,10 +53,10 @@ class Sum(Executor):
 
 
 class Aggregator(Executor):
-    """Aggregate the results from the different tasks and emit the `WorkflowCompletedEvent`."""
+    """Aggregate the results from the different tasks and yield the final output."""
 
     @handler
-    async def handle(self, results: list[int | float], ctx: WorkflowContext[None]):
+    async def handle(self, results: list[int | float], ctx: WorkflowContext[Never, list[int | float]]):
         """Receive the results from the source executors.
 
         The framework will automatically collect messages from the source executors
@@ -65,9 +66,9 @@ class Aggregator(Executor):
             results (list[int | float]): execution results from upstream executors.
                 The type annotation must be a list of union types that the upstream
                 executors will produce.
-            cts (WorkflowContext[None]): A workflow context.
+            ctx (WorkflowContext[Never, list[int | float]]): A workflow context that can yield the final output.
         """
-        await ctx.add_event(WorkflowCompletedEvent(data=results))
+        await ctx.yield_output(results)
 
 
 async def main() -> None:
@@ -87,13 +88,13 @@ async def main() -> None:
     )
 
     # 3) Run the workflow
-    completion: WorkflowCompletedEvent | None = None
+    output: list[int | float] | None = None
     async for event in workflow.run_stream([random.randint(1, 100) for _ in range(10)]):
-        if isinstance(event, WorkflowCompletedEvent):
-            completion = event
+        if isinstance(event, WorkflowOutputEvent):
+            output = event.data
 
-    if completion is not None:
-        print(completion.data)
+    if output is not None:
+        print(output)
 
 
 if __name__ == "__main__":