chore: import upstream snapshot with attribution

python/samples/semantic-kernel-migration/chat_completion/01_basic_chat_completion.py

@@ -0,0 +1,64 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-openai",
+#     "semantic-kernel",
+# ]
+# ///
+# Run with any PEP 723 compatible runner, e.g.:
+#   uv run samples/semantic-kernel-migration/chat_completion/01_basic_chat_completion.py
+
+# Copyright (c) Microsoft. All rights reserved.
+"""Basic SK ChatCompletionAgent vs Agent Framework Agent.
+
+Both samples expect OpenAI-compatible environment variables (OPENAI_API_KEY or
+Azure OpenAI configuration). Update the prompts or client wiring to match your
+model of choice before running.
+"""
+
+import asyncio
+
+from agent_framework import Agent
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+async def run_semantic_kernel() -> None:
+    """Call SK's ChatCompletionAgent for a simple question."""
+
+    from semantic_kernel.agents import ChatCompletionAgent
+    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
+
+    # SK agent holds the thread state internally via ChatCompletionAgent.
+    agent = ChatCompletionAgent(
+        service=OpenAIChatCompletion(),
+        name="Support",
+        instructions="Answer in one sentence.",
+    )
+    response = await agent.get_response(messages="How do I reset my bike tire?")
+    print("[SK]", response.message.content)
+
+
+async def run_agent_framework() -> None:
+    """Call Agent Framework's Agent created from OpenAIChatClient."""
+    from agent_framework.openai import OpenAIChatClient
+
+    # AF constructs a lightweight Agent backed by OpenAIChatClient.
+    chat_agent = Agent(
+        client=OpenAIChatClient(),
+        name="Support",
+        instructions="Answer in one sentence.",
+    )
+    reply = await chat_agent.run("How do I reset my bike tire?")
+    print("[AF]", reply.text)
+
+
+async def main() -> None:
+    await run_semantic_kernel()
+    await run_agent_framework()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -0,0 +1,72 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-openai",
+#     "semantic-kernel",
+# ]
+# ///
+# Run with any PEP 723 compatible runner, e.g.:
+#   uv run samples/semantic-kernel-migration/chat_completion/02_chat_completion_with_tool.py
+
+# Copyright (c) Microsoft. All rights reserved.
+"""Demonstrate SK plugins vs Agent Framework tools with a chat agent.
+
+Configure your OpenAI or Azure OpenAI credentials before running. The example
+exposes a "specials" tool that both SDKs call during the conversation.
+"""
+
+import asyncio
+
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+async def run_semantic_kernel() -> None:
+    from semantic_kernel.agents import ChatCompletionAgent
+    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
+    from semantic_kernel.functions import kernel_function
+
+    class SpecialsPlugin:
+        @kernel_function(name="specials", description="List daily specials")
+        def specials(self) -> str:
+            return "Clam chowder, Cobb salad, Chai tea"
+
+    # SK advertises tools by attaching plugin instances at construction time.
+    agent = ChatCompletionAgent(
+        service=OpenAIChatCompletion(),
+        name="Host",
+        instructions="Answer menu questions accurately.",
+        plugins=[SpecialsPlugin()],
+    )
+    response = await agent.get_response("What soup can I order today?")
+    print("[SK]", response.message.content)
+
+
+async def run_agent_framework() -> None:
+    from agent_framework import Agent, tool
+    from agent_framework.openai import OpenAIChatClient
+
+    @tool(name="specials", description="List daily specials")
+    async def specials() -> str:
+        return "Clam chowder, Cobb salad, Chai tea"
+
+    # AF tools are provided as callables on each agent instance.
+    chat_agent = Agent(
+        client=OpenAIChatClient(),
+        name="Host",
+        instructions="Answer menu questions accurately.",
+        tools=[specials],
+    )
+    reply = await chat_agent.run("What soup can I order today?")
+    print("[AF]", reply.text)
+
+
+async def main() -> None:
+    await run_semantic_kernel()
+    await run_agent_framework()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/semantic-kernel-migration/chat_completion/03_chat_completion_thread_and_stream.py

@@ -0,0 +1,89 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-openai",
+#     "semantic-kernel",
+# ]
+# ///
+# Run with any PEP 723 compatible runner, e.g.:
+#   uv run samples/semantic-kernel-migration/chat_completion/03_chat_completion_thread_and_stream.py
+
+# Copyright (c) Microsoft. All rights reserved.
+"""Compare conversation threading and streaming responses for chat agents.
+
+Both implementations reuse a conversation thread across turns and stream output
+for the second turn.
+"""
+
+import asyncio
+
+from agent_framework import Agent
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+async def run_semantic_kernel() -> None:
+    from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
+    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
+
+    # SK thread object keeps the conversation history on the agent side.
+    agent = ChatCompletionAgent(
+        service=OpenAIChatCompletion(),
+        name="Writer",
+        instructions="Keep answers short and friendly.",
+    )
+    thread = ChatHistoryAgentThread()
+
+    first = await agent.get_response(
+        messages="Suggest a catchy headline for our product launch.",
+        thread=thread,
+    )
+    print("[SK]", first.message.content)
+
+    print("[SK][stream]", end=" ")
+    async for update in agent.invoke_stream(
+        messages="Draft a 2 sentence blurb.",
+        thread=thread,
+    ):
+        if update.message:
+            print(update.message.content, end="", flush=True)
+    print()
+
+
+async def run_agent_framework() -> None:
+    from agent_framework.openai import OpenAIChatClient
+
+    # AF session objects are requested explicitly from the agent.
+    chat_agent = Agent(
+        client=OpenAIChatClient(),
+        name="Writer",
+        instructions="Keep answers short and friendly.",
+    )
+    session = chat_agent.create_session()
+
+    first = await chat_agent.run(
+        "Suggest a catchy headline for our product launch.",
+        session=session,
+    )
+    print("[AF]", first.text)
+
+    print("[AF][stream]", end=" ")
+    async for chunk in chat_agent.run(
+        "Draft a 2 sentence blurb.",
+        session=session,
+        stream=True,
+    ):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print()
+
+
+async def main() -> None:
+    await run_semantic_kernel()
+    await run_agent_framework()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())