chore: import upstream snapshot with attribution

python/samples/autogen-migration/orchestrations/04_magentic_one.py

@@ -0,0 +1,172 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import json
+from typing import cast
+
+from agent_framework import (
+    Agent,
+    AgentResponseUpdate,
+    Message,
+    WorkflowEvent,
+)
+from agent_framework.orchestrations import MagenticProgressLedger
+from dotenv import load_dotenv
+
+"""AutoGen MagenticOneGroupChat vs Agent Framework MagenticBuilder.
+
+Demonstrates orchestrated multi-agent workflows with a central coordinator
+managing specialized agents for complex tasks.
+"""
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+async def run_autogen() -> None:
+    """AutoGen's MagenticOneGroupChat for orchestrated collaboration."""
+
+    from autogen_agentchat.agents import AssistantAgent
+    from autogen_agentchat.teams import MagenticOneGroupChat
+    from autogen_agentchat.ui import Console
+    from autogen_ext.models.openai import OpenAIChatCompletionClient
+
+    client = OpenAIChatCompletionClient(model="gpt-4.1-mini")
+
+    # Create specialized agents
+    researcher = AssistantAgent(
+        name="researcher",
+        model_client=client,
+        system_message="You are a research analyst. Gather and analyze information.",
+        description="Research analyst for data gathering",
+        model_client_stream=True,
+    )
+
+    coder = AssistantAgent(
+        name="coder",
+        model_client=client,
+        system_message="You are a programmer. Write code based on requirements.",
+        description="Software developer for implementation",
+        model_client_stream=True,
+    )
+
+    reviewer = AssistantAgent(
+        name="reviewer",
+        model_client=client,
+        system_message="You are a code reviewer. Review code for quality and correctness.",
+        description="Code reviewer for quality assurance",
+        model_client_stream=True,
+    )
+
+    # Create MagenticOne team with coordinator
+    team = MagenticOneGroupChat(
+        participants=[researcher, coder, reviewer],
+        model_client=client,  # Coordinator uses this client
+        max_turns=20,
+        max_stalls=3,
+    )
+
+    # Run complex task and display the conversation
+    print("[AutoGen] Magentic One conversation:")
+    await Console(team.run_stream(task="Research Python async patterns and write a simple example"))
+
+
+async def run_agent_framework() -> None:
+    """Agent Framework's MagenticBuilder for orchestrated collaboration."""
+    from agent_framework.openai import OpenAIChatClient
+    from agent_framework.orchestrations import MagenticBuilder
+
+    client = OpenAIChatClient(model="gpt-4.1-mini")
+
+    # Create specialized agents
+    researcher = Agent(
+        client=client,
+        name="researcher",
+        instructions="You are a research analyst. Gather and analyze information.",
+        description="Research analyst for data gathering",
+    )
+
+    coder = Agent(
+        client=client,
+        name="coder",
+        instructions="You are a programmer. Write code based on requirements.",
+        description="Software developer for implementation",
+    )
+
+    reviewer = Agent(
+        client=client,
+        name="reviewer",
+        instructions="You are a code reviewer. Review code for quality and correctness.",
+        description="Code reviewer for quality assurance",
+    )
+
+    # Create Magentic workflow
+    workflow = MagenticBuilder(
+        participants=[researcher, coder, reviewer],
+        manager_agent=Agent(
+            client=client,
+            name="magentic_manager",
+            instructions="You coordinate a team to complete complex tasks efficiently.",
+            description="Orchestrator for team coordination",
+        ),
+        max_round_count=20,
+        max_stall_count=3,
+        max_reset_count=1,
+    ).build()
+
+    # Run complex task
+    last_message_id: str | None = None
+    output_event: WorkflowEvent | None = None
+    print("[Agent Framework] Magentic conversation:")
+    async for event in workflow.run("Research Python async patterns and write a simple example", stream=True):
+        if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
+            message_id = event.data.message_id
+            if message_id != last_message_id:
+                if last_message_id is not None:
+                    print("\n")
+                print(f"- {event.executor_id}:", end=" ", flush=True)
+                last_message_id = message_id
+            print(event.data, end="", flush=True)
+
+        elif event.type == "magentic_orchestrator":
+            print(f"\n[Magentic Orchestrator Event] Type: {event.data.event_type.name}")
+            if isinstance(event.data.content, Message):
+                print(f"Please review the plan:\n{event.data.content.text}")
+            elif isinstance(event.data.content, MagenticProgressLedger):
+                print(f"Please review progress ledger:\n{json.dumps(event.data.content.to_dict(), indent=2)}")
+            else:
+                print(f"Unknown data type in MagenticOrchestratorEvent: {type(event.data.content)}")
+
+            # Block to allow user to read the plan/progress before continuing
+            # Note: this is for demonstration only and is not the recommended way to handle human interaction.
+            # Please refer to `with_plan_review` for proper human interaction during planning phases.
+            await asyncio.get_event_loop().run_in_executor(None, input, "Press Enter to continue...")
+
+        elif event.type == "output":
+            output_event = event
+
+    if not output_event:
+        raise RuntimeError("Workflow did not produce a final output event.")
+    print("\n\nWorkflow completed!")
+    print("Final Output:")
+    # The output of the Magentic workflow is a list of ChatMessages with only one final message
+    # generated by the orchestrator.
+    output_messages = cast(list[Message], output_event.data)
+    if output_messages:
+        output = output_messages[-1].text
+        print(output)
+
+
+async def main() -> None:
+    print("=" * 60)
+    print("Magentic One Orchestration Comparison")
+    print("=" * 60)
+    print("AutoGen: MagenticOneGroupChat")
+    print("Agent Framework: MagenticBuilder\n")
+    await run_autogen()
+    print()
+    await run_agent_framework()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())