Python: Fixed SK migration samples (#4046)

* Fixed sk migration provider samples

* Fixes to SK migration samples

python/samples/semantic-kernel-migration/azure_ai_agent/01_basic_azure_ai_agent.py

@@ -25,7 +25,7 @@ async def run_semantic_kernel() -> None:
     async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client:
         settings = AzureAIAgentSettings()  # Reads env vars for region/deployment.
         # SK builds the remote agent definition then wraps it with AzureAIAgent.
-        definition = await client.agents.as_agent(
+        definition = await client.agents.create_agent(
             model=settings.model_deployment_name,
             name="Support",
             instructions="Answer customer questions in one paragraph.",

python/samples/semantic-kernel-migration/azure_ai_agent/02_azure_ai_agent_with_code_interpreter.py

@@ -25,7 +25,7 @@ async def run_semantic_kernel() -> None:
     async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client:
         settings = AzureAIAgentSettings()
         # Register the hosted code interpreter tool with the remote agent.
-        definition = await client.agents.as_agent(
+        definition = await client.agents.create_agent(
             model=settings.model_deployment_name,
             name="Analyst",
             instructions="Use the code interpreter for numeric work.",
@@ -46,9 +46,7 @@ async def run_agent_framework() -> None:
         AzureCliCredential() as credential,
         AzureAIAgentsProvider(credential=credential) as provider,
     ):
-        # Create a client to access hosted tool factory methods
-        client = AzureAIAgentClient(agents_client=provider._agents_client)
-        code_interpreter_tool = client.get_code_interpreter_tool()
+        code_interpreter_tool = AzureAIAgentClient.get_code_interpreter_tool()
 
         agent = await provider.create_agent(
             name="Analyst",

python/samples/semantic-kernel-migration/azure_ai_agent/03_azure_ai_agent_threads_and_followups.py

@@ -19,7 +19,7 @@ async def run_semantic_kernel() -> None:
 
     async with AzureCliCredential() as credential, AzureAIAgent.create_client(credential=credential) as client:
         settings = AzureAIAgentSettings()
-        definition = await client.agents.as_agent(
+        definition = await client.agents.create_agent(
             model=settings.model_deployment_name,
             name="Planner",
             instructions="Track follow-up questions within the same thread.",

python/samples/semantic-kernel-migration/chat_completion/02_chat_completion_with_tool.py

@@ -43,7 +43,7 @@ async def run_semantic_kernel() -> None:
 
 
 async def run_agent_framework() -> None:
-    from agent_framework._tools import tool
+    from agent_framework import tool
     from agent_framework.openai import OpenAIChatClient
 
     @tool(name="specials", description="List daily specials")

python/samples/semantic-kernel-migration/openai_assistant/01_basic_openai_assistant.py

@@ -46,11 +46,12 @@ async def run_agent_framework() -> None:
         instructions="Answer questions in one concise paragraph.",
         model=ASSISTANT_MODEL,
     ) as assistant_agent:
-        reply = await assistant_agent.run("What is the capital of Denmark?")
+        session = assistant_agent.create_session()
+        reply = await assistant_agent.run("What is the capital of Denmark?", session=session)
         print("[AF]", reply.text)
         follow_up = await assistant_agent.run(
             "How many residents live there?",
-            session=assistant_agent.create_session(),
+            session=session,
         )
         print("[AF][follow-up]", follow_up.text)
 

python/samples/semantic-kernel-migration/openai_assistant/02_openai_assistant_with_code_interpreter.py

@@ -23,7 +23,7 @@ async def run_semantic_kernel() -> None:
 
     # Enable the hosted code interpreter tool on the assistant definition.
     definition = await client.beta.assistants.create(
-        model=OpenAISettings().chat_deployment_name,
+        model=OpenAISettings().chat_model_id,
         name="CodeRunner",
         instructions="Run the provided request as code and return the result.",
         tools=code_interpreter_tool,

python/samples/semantic-kernel-migration/openai_assistant/03_openai_assistant_function_tool.py

@@ -34,7 +34,7 @@ async def run_semantic_kernel() -> None:
 
     class WeatherPlugin:
         @kernel_function(name="get_forecast", description="Look up the forecast for a city and day.")
-        async def fake_weather_lookup(city: str, day: str) -> dict[str, Any]:
+        async def fake_weather_lookup(self, city: str, day: str) -> dict[str, Any]:
             """Pretend to call a weather service."""
             return {
                 "city": city,
@@ -50,9 +50,8 @@ async def run_semantic_kernel() -> None:
         model=ASSISTANT_MODEL,
         name="WeatherHelper",
         instructions="Call get_forecast to fetch weather details.",
-        plugins=[WeatherPlugin()],
     )
-    agent = OpenAIAssistantAgent(client=client, definition=definition)
+    agent = OpenAIAssistantAgent(client=client, definition=definition, plugins=[WeatherPlugin()])
 
     thread: AssistantAgentThread | None = None
     response = await agent.get_response(
@@ -64,7 +63,7 @@ async def run_semantic_kernel() -> None:
 
 
 async def run_agent_framework() -> None:
-    from agent_framework._tools import tool
+    from agent_framework import tool
     from agent_framework.openai import OpenAIAssistantsClient
 
     @tool(

python/samples/semantic-kernel-migration/openai_responses/01_basic_responses_agent.py

@@ -14,24 +14,19 @@ import asyncio
 
 
 async def run_semantic_kernel() -> None:
-    from azure.identity import AzureCliCredential
-    from semantic_kernel.agents import AzureResponsesAgent
-    from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
+    from semantic_kernel.agents import OpenAIResponsesAgent
+    from semantic_kernel.connectors.ai.open_ai import OpenAISettings
 
-    credential = AzureCliCredential()
-    try:
-        client = AzureResponsesAgent.create_client(credential=credential)
-        # SK response agents wrap Azure OpenAI's hosted Responses API.
-        agent = AzureResponsesAgent(
-            ai_model_id=AzureOpenAISettings().responses_deployment_name,
-            client=client,
-            instructions="Answer in one concise sentence.",
-            name="Expert",
-        )
-        response = await agent.get_response("Why is the sky blue?")
-        print("[SK]", response.message.content)
-    finally:
-        await credential.close()
+    client = OpenAIResponsesAgent.create_client()
+    # SK response agents wrap OpenAI's hosted Responses API.
+    agent = OpenAIResponsesAgent(
+        ai_model_id=OpenAISettings().responses_model_id,
+        client=client,
+        instructions="Answer in one concise sentence.",
+        name="Expert",
+    )
+    response = await agent.get_response("Why is the sky blue?")
+    print("[SK]", response.message.content)
 
 
 async def run_agent_framework() -> None:

python/samples/semantic-kernel-migration/openai_responses/02_responses_agent_with_tool.py

@@ -14,9 +14,8 @@ import asyncio
 
 
 async def run_semantic_kernel() -> None:
-    from azure.identity import AzureCliCredential
-    from semantic_kernel.agents import AzureResponsesAgent
-    from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
+    from semantic_kernel.agents import OpenAIResponsesAgent
+    from semantic_kernel.connectors.ai.open_ai import OpenAISettings
     from semantic_kernel.functions import kernel_function
 
     class MathPlugin:
@@ -24,26 +23,22 @@ async def run_semantic_kernel() -> None:
         def add(self, a: float, b: float) -> float:
             return a + b
 
-    credential = AzureCliCredential()
-    try:
-        client = AzureResponsesAgent.create_client(credential=credential)
-        # Plugins advertise callable tools to the Responses agent.
-        agent = AzureResponsesAgent(
-            ai_model_id=AzureOpenAISettings().responses_deployment_name,
-            client=client,
-            instructions="Use the add tool when math is required.",
-            name="MathExpert",
-            plugins=[MathPlugin()],
-        )
-        response = await agent.get_response("Use add(41, 1) and explain the result.")
-        print("[SK]", response.message.content)
-    finally:
-        await credential.close()
+    client = OpenAIResponsesAgent.create_client()
+    # Plugins advertise callable tools to the Responses agent.
+    agent = OpenAIResponsesAgent(
+        ai_model_id=OpenAISettings().responses_model_id,
+        client=client,
+        instructions="Use the add tool when math is required.",
+        name="MathExpert",
+        plugins=[MathPlugin()],
+    )
+    response = await agent.get_response("Use add(41, 1) and explain the result.")
+    print("[SK]", response.message.content)
 
 
 async def run_agent_framework() -> None:
     from agent_framework import Agent
-    from agent_framework._tools import tool
+    from agent_framework import tool
     from agent_framework.openai import OpenAIResponsesClient
 
     @tool(name="add", description="Add two numbers")

python/samples/semantic-kernel-migration/openai_responses/03_responses_agent_structured_output.py

@@ -22,28 +22,22 @@ class ReleaseBrief(BaseModel):
 
 
 async def run_semantic_kernel() -> None:
-    from azure.identity import AzureCliCredential
-    from semantic_kernel.agents import AzureResponsesAgent
-    from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
+    from semantic_kernel.agents import OpenAIResponsesAgent
+    from semantic_kernel.connectors.ai.open_ai import OpenAISettings
 
-    credential = AzureCliCredential()
-    try:
-        client = AzureResponsesAgent.create_client(credential=credential)
-        # response_format requests schema-constrained output from the model.
-        agent = AzureResponsesAgent(
-            ai_model_id=AzureOpenAISettings().responses_deployment_name,
-            client=client,
-            instructions="Return launch briefs as structured JSON.",
-            name="ProductMarketer",
-            text=AzureResponsesAgent.configure_response_format(ReleaseBrief),
-        )
-        response = await agent.get_response(
-            "Draft a launch brief for the Contoso Note app.",
-            response_format=ReleaseBrief,
-        )
-        print("[SK]", response.message.content)
-    finally:
-        await credential.close()
+    client = OpenAIResponsesAgent.create_client()
+    # response_format requests schema-constrained output from the model.
+    agent = OpenAIResponsesAgent(
+        ai_model_id=OpenAISettings().responses_model_id,
+        client=client,
+        instructions="Return launch briefs as structured JSON.",
+        name="ProductMarketer",
+        text=OpenAIResponsesAgent.configure_response_format(ReleaseBrief),
+    )
+    response = await agent.get_response(
+        "Draft a launch brief for the Contoso Note app.",
+    )
+    print("[SK]", response.message.content)
 
 
 async def run_agent_framework() -> None:

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

@@ -32,7 +32,7 @@ PROMPT = "Explain the concept of temperature from multiple scientific perspectiv
 ######################################################################
 
 
-def build_semantic_kernel_agents() -> list[Agent]:
+def build_semantic_kernel_agents() -> list[ChatCompletionAgent]:
     credential = AzureCliCredential()
 
     physics_agent = ChatCompletionAgent(

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

@@ -20,7 +20,7 @@ from agent_framework import Agent, Message
 from agent_framework.azure import AzureOpenAIChatClient, AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatBuilder
 from azure.identity import AzureCliCredential
-from semantic_kernel.agents import Agent, ChatCompletionAgent, GroupChatOrchestration
+from semantic_kernel.agents import ChatCompletionAgent, GroupChatOrchestration
 from semantic_kernel.agents.orchestration.group_chat import (
     BooleanResult,
     GroupChatManager,
@@ -50,7 +50,7 @@ DISCUSSION_TOPIC = "What are the essential steps for launching a community hacka
 ######################################################################
 
 
-def build_semantic_kernel_agents() -> list[Agent]:
+def build_semantic_kernel_agents() -> list[ChatCompletionAgent]:
     credential = AzureCliCredential()
 
     researcher = ChatCompletionAgent(
@@ -82,25 +82,25 @@ class ChatCompletionGroupChatManager(GroupChatManager):
     topic: str
 
     termination_prompt: str = (
-        "You are coordinating a conversation about '{{topic}}'. "
+        "You are coordinating a conversation about '{{$topic}}'. "
         "Decide if the discussion has produced a solid answer. "
         'Respond using JSON: {"result": true|false, "reason": "..."}.'
     )
 
     selection_prompt: str = (
-        "You are coordinating a conversation about '{{topic}}'. "
+        "You are coordinating a conversation about '{{$topic}}'. "
         "Choose the next participant by returning JSON with keys (result, reason). "
-        "The result must match one of: {{participants}}."
+        "The result must match one of: {{$participants}}."
     )
 
     summary_prompt: str = (
-        "You have just finished a discussion about '{{topic}}'. "
+        "You have just finished a discussion about '{{$topic}}'. "
         "Summarize the plan and highlight key takeaways. Return JSON with keys (result, reason) where "
         "result is the final response text."
     )
 
-    def __init__(self, *, topic: str, service: ChatCompletionClientBase) -> None:
-        super().__init__(topic=topic, service=service)
+    def __init__(self, *, topic: str, service: ChatCompletionClientBase, max_rounds: int | None = None) -> None:
+        super().__init__(topic=topic, service=service, max_rounds=max_rounds)
         self._round_robin_index = 0
 
     async def _render_prompt(self, template: str, **kwargs: Any) -> str:

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

@@ -20,7 +20,7 @@ from agent_framework import (
     WorkflowEvent,
 )
 from agent_framework.azure import AzureOpenAIChatClient
-from agent_framework.orchestrations import HandoffBuilder, HandoffUserInputRequest
+from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
 from azure.identity import AzureCliCredential
 from semantic_kernel.agents import Agent, ChatCompletionAgent, HandoffOrchestration, OrchestrationHandoffs
 from semantic_kernel.agents.runtime import InProcessRuntime
@@ -223,7 +223,7 @@ async def _drain_events(stream: AsyncIterable[WorkflowEvent]) -> list[WorkflowEv
 def _collect_handoff_requests(events: list[WorkflowEvent]) -> list[WorkflowEvent]:
     requests: list[WorkflowEvent] = []
     for event in events:
-        if event.type == "request_info" and isinstance(event.data, HandoffUserInputRequest):
+        if event.type == "request_info" and isinstance(event.data, HandoffAgentUserRequest):
             requests.append(event)
     return requests
 
@@ -241,12 +241,16 @@ async def run_agent_framework_example(initial_task: str, scripted_responses: Seq
     triage, refund, status, returns = _create_af_agents(client)
 
     workflow = (
-        HandoffBuilder(name="sk_af_handoff_migration", participants=[triage, refund, status, returns])
-        .set_coordinator(triage)
+        HandoffBuilder(
+            name="sk_af_handoff_migration",
+            participants=[triage, refund, status, returns],
+            termination_condition=lambda conv: sum(1 for m in conv if m.role == "user") >= 4,
+        )
+        .with_start_agent(triage)
         .add_handoff(triage, [refund, status, returns])
         .add_handoff(refund, [status, triage])
         .add_handoff(status, [refund, triage])
-        .add_handoff(returns, triage)
+        .add_handoff(returns, [triage])
         .build()
     )
 
@@ -260,7 +264,7 @@ async def run_agent_framework_example(initial_task: str, scripted_responses: Seq
             user_reply = next(scripted_iter)
         except StopIteration:
             user_reply = "Thanks, that's all."
-        responses = {request.request_id: user_reply for request in pending}
+        responses = {request.request_id: [Message(role="user", text=user_reply)] for request in pending}
         final_events = await _drain_events(workflow.run(stream=True, responses=responses))
         pending = _collect_handoff_requests(final_events)
 

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

@@ -19,7 +19,6 @@ from agent_framework import Agent
 from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
 from agent_framework.orchestrations import MagenticBuilder
 from semantic_kernel.agents import (
-    Agent,
     ChatCompletionAgent,
     MagenticOrchestration,
     OpenAIAssistantAgent,
@@ -44,7 +43,7 @@ PROMPT = (
 ######################################################################
 
 
-async def build_semantic_kernel_agents() -> list[Agent]:
+async def build_semantic_kernel_agents() -> list:
     research_agent = ChatCompletionAgent(
         name="ResearchAgent",
         description="A helpful assistant with access to web search. Ask it to perform web searches.",
@@ -135,19 +134,19 @@ async def run_agent_framework_example(prompt: str) -> str | None:
         instructions=(
             "You are a Researcher. You find information without additional computation or quantitative analysis."
         ),
-        client=OpenAIChatClient(ai_model_id="gpt-4o-search-preview"),
+        client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
     )
 
-    # Create code interpreter tool using instance method
+    # Create code interpreter tool using static method
     coder_client = OpenAIResponsesClient()
-    code_interpreter_tool = coder_client.get_code_interpreter_tool()
+    code_interpreter_tool = OpenAIResponsesClient.get_code_interpreter_tool()
 
     coder = Agent(
         name="CoderAgent",
         description="A helpful assistant that writes and executes code to process and analyze data.",
         instructions="You solve questions using code. Please provide detailed analysis and computation process.",
         client=coder_client,
-        tools=code_interpreter_tool,
+        tools=[code_interpreter_tool],
     )
 
     # Create a manager agent for orchestration
@@ -158,12 +157,22 @@ async def run_agent_framework_example(prompt: str) -> str | None:
         client=OpenAIChatClient(),
     )
 
-    workflow = MagenticBuilder(participants=[researcher, coder], manager_agent=manager_agent).build()
+    workflow = MagenticBuilder(
+        participants=[researcher, coder], manager_agent=manager_agent
+    ).build()
 
     final_text: str | None = None
     async for event in workflow.run(prompt, stream=True):
         if event.type == "output":
-            final_text = cast(str, event.data)
+            data = event.data
+            if isinstance(data, str):
+                final_text = data
+            elif isinstance(data, list):
+                # Extract text from the last assistant message
+                for msg in reversed(data):
+                    if hasattr(msg, "text") and msg.text:
+                        final_text = msg.text
+                        break
 
     return final_text
 

python/samples/semantic-kernel-migration/processes/fan_out_fan_in_process.py

@@ -153,7 +153,7 @@ async def run_semantic_kernel_process_example() -> None:
         kernel=kernel,
         initial_event=KernelProcessEvent(id=CommonEvents.START_PROCESS.value, data="Initial"),
     ) as process_context:
-        process_state = await process_context.get_executor_state()
+        process_state = await process_context.get_state()
         c_step_state: KernelProcessStepState[CStepState] | None = next(
             (s.state for s in process_state.steps if s.state.name == "CStep"),
             None,

python/samples/semantic-kernel-migration/processes/nested_process.py

@@ -144,7 +144,7 @@ async def run_semantic_kernel_nested_process() -> None:
         initial_event=ProcessEvents.START_PROCESS.value,
         data="Test",
     )
-    process_info = await process_handle.get_executor_state()
+    process_info = await process_handle.get_state()
 
     inner_process: KernelProcess | None = next(
         (s for s in process_info.steps if s.state.name == "Inner"),