Python: Fix streamed workflow agent continuation context by finalizing AgentExecutor streams (#3882)

* Fix streamed workflow agent continuation context by finalizing AgentExecutor streams

* Fix stream handling

* Fixes

* Fix DevUI and tests

python/samples/03-workflows/orchestrations/handoff_with_tool_approval_checkpoint_resume.py

@@ -8,9 +8,11 @@ from typing import Any
 
 from agent_framework import (
     Agent,
+    AgentResponseUpdate,
     Content,
     FileCheckpointStorage,
     Workflow,
+    WorkflowEvent,
     tool,
 )
 from agent_framework.azure import AzureOpenAIResponsesClient
@@ -183,8 +185,16 @@ async def main() -> None:
     initial_request = "Hi, my order 12345 arrived damaged. I need a refund."
 
     # Phase 1: Initial run - workflow will pause when it needs user input
-    results = await workflow.run(message=initial_request)
-    request_events = results.get_request_info_events()
+    print("Running initial workflow...")
+    results = await workflow.run(message=initial_request, stream=True)
+
+    # Iterate through streamed events and collect request_info events
+    request_events: list[WorkflowEvent] = []
+    async for event in results:
+        event: WorkflowEvent
+        if event.type == "request_info":
+            request_events.append(event)
+
     if not request_events:
         print("Workflow completed without needing user input")
         return
@@ -224,8 +234,17 @@ async def main() -> None:
             raise RuntimeError("No checkpoints found.")
         checkpoint_id = checkpoint.checkpoint_id
 
-        results = await workflow.run(responses=responses, checkpoint_id=checkpoint_id)
-        request_events = results.get_request_info_events()
+        print("Resuming workflow from checkpoint...")
+        results = await workflow.run(responses=responses, checkpoint_id=checkpoint_id, stream=True)
+
+        # Iterate through streamed events and collect request_info events
+        request_events: list[WorkflowEvent] = []
+        async for event in results:
+            event: WorkflowEvent
+            if event.type == "request_info":
+                request_events.append(event)
+            elif event.type == "output" and isinstance(event.data, AgentResponseUpdate):
+                print(event.data.text, end="", flush=True)
 
     print("\n" + "=" * 60)
     print("DEMO COMPLETE")

python/samples/getting_started/orchestrations/handoff/handoff_with_code_interpreter_file.py

@@ -1,186 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""
-Handoff Workflow with Code Interpreter File Generation Sample
-
-This sample demonstrates retrieving file IDs from code interpreter output
-in a handoff workflow context. A triage agent routes to a code specialist
-that generates a text file, and we verify the file_id is captured correctly
-from the streaming workflow events.
-
-Verifies GitHub issue #2718: files generated by code interpreter in
-HandoffBuilder workflows can be properly retrieved.
-
-Prerequisites:
-    - AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-    - `az login` (Azure CLI authentication)
-    - AZURE_AI_MODEL_DEPLOYMENT_NAME
-"""
-
-import asyncio
-import os
-from collections.abc import AsyncIterable
-from typing import cast
-
-from agent_framework import (
-    AgentResponseUpdate,
-    Message,
-    WorkflowEvent,
-    WorkflowRunState,
-)
-from agent_framework.azure import AzureOpenAIResponsesClient
-from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
-from azure.identity import AzureCliCredential
-
-
-async def _drain(stream: AsyncIterable[WorkflowEvent]) -> list[WorkflowEvent]:
-    """Collect all events from an async stream."""
-    return [event async for event in stream]
-
-
-def _handle_events(events: list[WorkflowEvent]) -> tuple[list[WorkflowEvent[HandoffAgentUserRequest]], list[str]]:
-    """Process workflow events and extract file IDs and pending requests.
-
-    Returns:
-        Tuple of (pending_requests, file_ids_found)
-    """
-
-    requests: list[WorkflowEvent[HandoffAgentUserRequest]] = []
-    file_ids: list[str] = []
-
-    for event in events:
-        if event.type == "handoff_sent":
-            print(f"\n[Handoff from {event.data.source} to {event.data.target} initiated.]")
-        elif event.type == "status" and event.state in {
-            WorkflowRunState.IDLE,
-            WorkflowRunState.IDLE_WITH_PENDING_REQUESTS,
-        }:
-            print(f"[status] {event.state}")
-        elif event.type == "request_info" and isinstance(event.data, HandoffAgentUserRequest):
-            requests.append(cast(WorkflowEvent[HandoffAgentUserRequest], event))
-        elif event.type == "output":
-            data = event.data
-            if isinstance(data, AgentResponseUpdate):
-                for content in data.contents:
-                    if content.type == "hosted_file":
-                        file_ids.append(content.file_id)  # type: ignore
-                        print(f"[Found HostedFileContent: file_id={content.file_id}]")
-                    elif content.type == "text" and content.annotations:
-                        for annotation in content.annotations:
-                            file_id = annotation["file_id"]  # type: ignore
-                            file_ids.append(file_id)
-                            print(f"[Found file annotation: file_id={file_id}]")
-            elif isinstance(data, list):
-                conversation = cast(list[Message], data)
-                if isinstance(conversation, list):
-                    print("\n=== Final Conversation Snapshot ===")
-                    for message in conversation:
-                        speaker = message.author_name or message.role
-                        print(f"- {speaker}: {message.text or [content.type for content in message.contents]}")
-                    print("===================================")
-
-    return requests, file_ids
-
-
-async def main() -> None:
-    """Run a simple handoff workflow with code interpreter file generation."""
-    print("=== Handoff Workflow with Code Interpreter File Generation ===\n")
-
-    client = AzureOpenAIResponsesClient(
-        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
-        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        credential=AzureCliCredential(),
-    )
-
-    triage = client.as_agent(
-        name="triage_agent",
-        instructions=(
-            "You are a triage agent. Route code-related requests to the code_specialist. "
-            "When the user asks to create or generate files, hand off to code_specialist "
-            "by calling handoff_to_code_specialist."
-        ),
-    )
-
-    code_interpreter_tool = client.get_code_interpreter_tool()
-
-    code_specialist = client.as_agent(
-        name="code_specialist",
-        instructions=(
-            "You are a Python code specialist. Use the code interpreter to execute Python code "
-            "and create files when requested. Always save files to /mnt/data/ directory."
-        ),
-        tools=[code_interpreter_tool],
-    )
-
-    workflow = (
-        HandoffBuilder(
-            termination_condition=lambda conv: sum(1 for msg in conv if msg.role == "user") >= 2,
-        )
-        .participants([triage, code_specialist])
-        .with_start_agent(triage)
-        .build()
-    )
-
-    user_inputs = [
-        "Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.",
-        "exit",
-    ]
-    input_index = 0
-    all_file_ids: list[str] = []
-
-    print(f"User: {user_inputs[0]}")
-    events = await _drain(workflow.run(user_inputs[0], stream=True))
-    requests, file_ids = _handle_events(events)
-    all_file_ids.extend(file_ids)
-    input_index += 1
-
-    while requests:
-        request = requests[0]
-        if input_index >= len(user_inputs):
-            break
-        user_input = user_inputs[input_index]
-        print(f"\nUser: {user_input}")
-
-        responses = {request.request_id: HandoffAgentUserRequest.create_response(user_input)}
-        events = await _drain(workflow.run(stream=True, responses=responses))
-        requests, file_ids = _handle_events(events)
-        all_file_ids.extend(file_ids)
-        input_index += 1
-
-    print("\n" + "=" * 50)
-    if all_file_ids:
-        print(f"SUCCESS: Found {len(all_file_ids)} file ID(s) in handoff workflow:")
-        for fid in all_file_ids:
-            print(f"  - {fid}")
-    else:
-        print("WARNING: No file IDs captured from the handoff workflow.")
-    print("=" * 50)
-
-    """
-    Sample Output:
-
-    User: Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.
-    [Found HostedFileContent: file_id=assistant-JT1sA...]
-
-    === Conversation So Far ===
-    - user: Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.
-    - triage_agent: I am handing off your request to create the text file "hello.txt" with the specified content to the code specialist. They will assist you shortly.
-    - code_specialist: The file "hello.txt" has been created with the content "Hello from handoff workflow!". You can download it using the link below:
-
-    [hello.txt](sandbox:/mnt/data/hello.txt)
-    ===========================
-
-    [status] IDLE_WITH_PENDING_REQUESTS
-
-    User: exit
-    [status] IDLE
-
-    ==================================================
-    SUCCESS: Found 1 file ID(s) in handoff workflow:
-    - assistant-JT1sA...
-    ==================================================
-    """  # noqa: E501
-
-
-if __name__ == "__main__":
-    asyncio.run(main())