Python: Fix migration samples (#5015)

* Fix migration samples

* Fix migration samples 2

* Fix formatting

* Comments

python/samples/semantic-kernel-migration/orchestrations/group_chat.py

@@ -16,8 +16,8 @@ import sys
 from collections.abc import Sequence
 from typing import Any, cast
 
-from agent_framework import Agent, Message
-from agent_framework.foundry import FoundryChatClient
+from agent_framework import Agent, AgentResponseUpdate, Message
+from agent_framework.openai import OpenAIChatCompletionClient
 from agent_framework.orchestrations import GroupChatBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -82,9 +82,6 @@ def build_semantic_kernel_agents() -> list[ChatCompletionAgent]:
 class ChatCompletionGroupChatManager(GroupChatManager):
     """Group chat manager that delegates orchestration decisions to an Azure OpenAI deployment."""
 
-    service: ChatCompletionClientBase
-    topic: str
-
     termination_prompt: str = (
         "You are coordinating a conversation about '{{$topic}}'. "
         "Decide if the discussion has produced a solid answer. "
@@ -104,8 +101,11 @@ class ChatCompletionGroupChatManager(GroupChatManager):
     )
 
     def __init__(self, *, topic: str, service: ChatCompletionClientBase, max_rounds: int | None = None) -> None:
-        super().__init__(topic=topic, service=service, max_rounds=max_rounds)
+        super().__init__(max_rounds=max_rounds)
+
         self._round_robin_index = 0
+        self._topic = topic
+        self._service = service
 
     async def _render_prompt(self, template: str, **kwargs: Any) -> str:
         prompt_template = KernelPromptTemplate(prompt_template_config=PromptTemplateConfig(template=template))
@@ -117,7 +117,7 @@ class ChatCompletionGroupChatManager(GroupChatManager):
 
     @override
     async def should_terminate(self, chat_history: ChatHistory) -> BooleanResult:
-        rendered_prompt = await self._render_prompt(self.termination_prompt, topic=self.topic)
+        rendered_prompt = await self._render_prompt(self.termination_prompt, topic=self._topic)
         chat_history.messages.insert(
             0,
             ChatMessageContent(role=AuthorRole.SYSTEM, content=rendered_prompt),
@@ -126,11 +126,11 @@ class ChatCompletionGroupChatManager(GroupChatManager):
             ChatMessageContent(role=AuthorRole.USER, content="Decide if the discussion is complete."),
         )
 
-        response = await self.service.get_chat_message_content(
+        response = await self._service.get_chat_message_content(
             chat_history,
             settings=PromptExecutionSettings(response_format=BooleanResult),
         )
-        return BooleanResult.model_validate_json(response.content)
+        return BooleanResult.model_validate_json(response.content)  # type: ignore
 
     @override
     async def select_next_agent(
@@ -140,7 +140,7 @@ class ChatCompletionGroupChatManager(GroupChatManager):
     ) -> StringResult:
         rendered_prompt = await self._render_prompt(
             self.selection_prompt,
-            topic=self.topic,
+            topic=self._topic,
             participants=", ".join(participant_descriptions.keys()),
         )
         chat_history.messages.insert(
@@ -151,18 +151,18 @@ class ChatCompletionGroupChatManager(GroupChatManager):
             ChatMessageContent(role=AuthorRole.USER, content="Pick the next participant to speak."),
         )
 
-        response = await self.service.get_chat_message_content(
+        response = await self._service.get_chat_message_content(
             chat_history,
             settings=PromptExecutionSettings(response_format=StringResult),
         )
-        result = StringResult.model_validate_json(response.content)
+        result = StringResult.model_validate_json(response.content)  # type: ignore
         if result.result not in participant_descriptions:
             raise RuntimeError(f"Unknown participant selected: {result.result}")
         return result
 
     @override
     async def filter_results(self, chat_history: ChatHistory) -> MessageResult:
-        rendered_prompt = await self._render_prompt(self.summary_prompt, topic=self.topic)
+        rendered_prompt = await self._render_prompt(self.summary_prompt, topic=self._topic)
         chat_history.messages.insert(
             0,
             ChatMessageContent(role=AuthorRole.SYSTEM, content=rendered_prompt),
@@ -171,11 +171,11 @@ class ChatCompletionGroupChatManager(GroupChatManager):
             ChatMessageContent(role=AuthorRole.USER, content="Summarize the plan."),
         )
 
-        response = await self.service.get_chat_message_content(
+        response = await self._service.get_chat_message_content(
             chat_history,
             settings=PromptExecutionSettings(response_format=StringResult),
         )
-        string_result = StringResult.model_validate_json(response.content)
+        string_result = StringResult.model_validate_json(response.content)  # type: ignore
         return MessageResult(
             result=ChatMessageContent(role=AuthorRole.ASSISTANT, content=string_result.result),
             reason=string_result.reason,
@@ -197,7 +197,7 @@ async def sk_agent_response_callback(message: ChatMessageContent | Sequence[Chat
 async def run_semantic_kernel_example(task: str) -> str:
     credential = AzureCliCredential()
     orchestration = GroupChatOrchestration(
-        members=build_semantic_kernel_agents(),
+        members=build_semantic_kernel_agents(),  # type: ignore
         manager=ChatCompletionGroupChatManager(
             topic=DISCUSSION_TOPIC,
             service=AzureChatCompletion(credential=credential),
@@ -225,7 +225,7 @@ async def run_semantic_kernel_example(task: str) -> str:
 
 
 async def run_agent_framework_example(task: str) -> str:
-    credential = AzureCliCredential()
+    client = OpenAIChatCompletionClient(credential=AzureCliCredential())
 
     researcher = Agent(
         name="Researcher",
@@ -234,32 +234,42 @@ async def run_agent_framework_example(task: str) -> str:
             "Gather concise facts or considerations that help plan a community hackathon. "
             "Keep your responses factual and scannable."
         ),
-        client=FoundryChatClient(credential=credential),
+        client=client,
     )
 
     planner = Agent(
         name="Planner",
         description="Turns the collected notes into a concrete action plan.",
         instructions=("Propose a structured action plan that accounts for logistics, roles, and timeline."),
-        client=FoundryChatClient(credential=credential),
+        client=client,
     )
 
     workflow = GroupChatBuilder(
         participants=[researcher, planner],
-        orchestrator_agent=Agent(client=FoundryChatClient(credential=credential)),
+        orchestrator_agent=Agent(client=client),
+        max_rounds=8,
+        intermediate_outputs=True,
     ).build()
 
-    final_response = ""
+    output_messages: list[Message] = []
+    last_message_id: str | None = None
     async for event in workflow.run(task, stream=True):
         if event.type == "output":
-            data = event.data
-            if isinstance(data, list) and len(data) > 0:
-                # Get the final message from the conversation
-                final_message = data[-1]
-                final_response = final_message.text or "" if isinstance(final_message, Message) else str(data)
+            if isinstance(event.data, AgentResponseUpdate):
+                if event.data.message_id != last_message_id:
+                    last_message_id = event.data.message_id
+                    print(f"{event.data.author_name}: {event.data.text}", end="")
+                else:
+                    print(event.data.text, end="")
             else:
-                final_response = str(data)
-    return final_response
+                output_messages.extend(cast(list[Message], event.data))
+                for message in output_messages:
+                    print(f"[{message.author_name}] {message.text}")
+
+    if output_messages:
+        return output_messages[-1].text
+
+    return ""
 
 
 async def main() -> None: