test

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

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+# Copyright (c) Microsoft. All rights reserved.
+"""AutoGen MagenticOneGroupChat vs Agent Framework MagenticBuilder.
+
+Demonstrates orchestrated multi-agent workflows with a central coordinator
+managing specialized agents for complex tasks.
+"""
+
+import asyncio
+
+
+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 import (
+        MagenticAgentDeltaEvent,
+        MagenticAgentMessageEvent,
+        MagenticBuilder,
+        MagenticFinalResultEvent,
+        MagenticOrchestratorMessageEvent,
+    )
+    from agent_framework.openai import OpenAIChatClient
+
+    client = OpenAIChatClient(model_id="gpt-4.1-mini")
+
+    # Create specialized agents
+    researcher = client.as_agent(
+        name="researcher",
+        instructions="You are a research analyst. Gather and analyze information.",
+        description="Research analyst for data gathering",
+    )
+
+    coder = client.as_agent(
+        name="coder",
+        instructions="You are a programmer. Write code based on requirements.",
+        description="Software developer for implementation",
+    )
+
+    reviewer = client.as_agent(
+        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=researcher, coder=coder, reviewer=reviewer)
+        .with_standard_manager(
+            chat_client=client,
+            max_round_count=20,
+            max_stall_count=3,
+            max_reset_count=1,
+        )
+        .build()
+    )
+
+    # Run complex task
+    print("[Agent Framework] Magentic conversation:")
+    last_stream_agent_id: str | None = None
+    stream_line_open: bool = False
+
+    async for event in workflow.run_stream("Research Python async patterns and write a simple example"):
+        if isinstance(event, MagenticOrchestratorMessageEvent):
+            if stream_line_open:
+                print()
+                stream_line_open = False
+            print(f"---------- Orchestrator:{event.kind} ----------")
+            print(getattr(event.message, "text", ""))
+        elif isinstance(event, MagenticAgentDeltaEvent):
+            if last_stream_agent_id != event.agent_id or not stream_line_open:
+                if stream_line_open:
+                    print()
+                print(f"---------- {event.agent_id} ----------")
+                last_stream_agent_id = event.agent_id
+                stream_line_open = True
+            if event.text:
+                print(event.text, end="", flush=True)
+        elif isinstance(event, MagenticAgentMessageEvent):
+            if stream_line_open:
+                print()
+                stream_line_open = False
+        elif isinstance(event, MagenticFinalResultEvent):
+            if stream_line_open:
+                print()
+                stream_line_open = False
+            print("---------- Final Result ----------")
+            if event.message is not None:
+                print(event.message.text)
+
+    if stream_line_open:
+        print()
+    print()  # Final newline after conversation
+
+
+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())