chore: import upstream snapshot with attribution

python/samples/03-workflows/functional/naive_group_chat.py

@@ -0,0 +1,82 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Naive group chat using the functional workflow API.
+
+A simple round-robin group chat where agents take turns responding.
+Because it's just a function, you control the loop, the turn order,
+and the termination condition with plain Python — no framework abstractions.
+
+Compare this with the graph-based GroupChat orchestration to see how the
+functional API lets you start simple and add complexity only when needed.
+"""
+
+import asyncio
+
+from agent_framework import Agent, Message, workflow
+from agent_framework.foundry import FoundryChatClient
+from azure.identity import AzureCliCredential
+
+# ---------------------------------------------------------------------------
+# Create agents
+# ---------------------------------------------------------------------------
+
+client = FoundryChatClient(credential=AzureCliCredential())
+
+expert = Agent(
+    name="PythonExpert",
+    instructions=(
+        "You are a Python expert in a group discussion. "
+        "Answer questions about Python and refine your answer based on feedback. "
+        "Keep responses concise (2-3 sentences)."
+    ),
+    client=client,
+)
+
+critic = Agent(
+    name="Critic",
+    instructions=(
+        "You are a constructive critic in a group discussion. "
+        "Point out edge cases, gotchas, or missing nuances in the previous answer. "
+        "If the answer is solid, say so briefly."
+    ),
+    client=client,
+)
+
+summarizer = Agent(
+    name="Summarizer",
+    instructions=(
+        "You are a summarizer in a group discussion. "
+        "After the discussion, provide a final concise summary that incorporates "
+        "the expert's answer and the critic's feedback. Keep it to 2-3 sentences."
+    ),
+    client=client,
+)
+
+# ---------------------------------------------------------------------------
+# A naive group chat is just a loop — no special framework needed
+# ---------------------------------------------------------------------------
+
+
+@workflow
+async def group_chat(question: str) -> str:
+    """Round-robin group chat: expert answers, critic reviews, summarizer wraps up."""
+    participants = [expert, critic, summarizer]
+    # Passing list[Message] keeps roles/authorship intact between turns,
+    # instead of stringifying everything into a single prompt.
+    conversation: list[Message] = [Message("user", [question])]
+
+    # Simple round-robin: each agent sees the full conversation so far
+    for agent in participants:
+        response = await agent.run(conversation)
+        conversation.extend(response.messages)
+
+    return "\n\n".join(f"{m.author_name or m.role}: {m.text}" for m in conversation)
+
+
+async def main():
+    result = await group_chat.run("What's the difference between a list and a tuple in Python?")
+    print(result.get_outputs()[0])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())