chore: import upstream snapshot with attribution

python/samples/03-workflows/orchestrations/magentic.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import json
+import logging
+import os
+from typing import cast
+
+from agent_framework import (
+    Agent,
+    AgentResponseUpdate,
+    Message,
+    WorkflowEvent,
+)
+from agent_framework.foundry import FoundryChatClient
+from agent_framework.orchestrations import GroupChatRequestSentEvent, MagenticBuilder, MagenticProgressLedger
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+logging.basicConfig(level=logging.WARNING)
+logger = logging.getLogger(__name__)
+
+
+"""
+Sample: Magentic Orchestration (multi-agent)
+
+What it does:
+- Orchestrates multiple agents using `MagenticBuilder` with streaming callbacks.
+
+- ResearcherAgent (Agent backed by an OpenAI chat client) for
+    finding information.
+- CoderAgent (Agent backed by OpenAI Assistants with the hosted
+    code interpreter tool) for analysis and computation.
+
+The workflow is configured with:
+- A Standard Magentic manager (uses a chat client for planning and progress).
+- Callbacks for final results, per-message agent responses, and streaming
+    token updates.
+
+When run, the script builds the workflow, submits a task about estimating the
+energy efficiency and CO2 emissions of several ML models, streams intermediate
+events, and prints the final answer. The workflow completes when idle.
+
+Prerequisites:
+- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
+"""
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+async def main() -> None:
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
+        credential=AzureCliCredential(),
+    )
+
+    researcher_agent = Agent(
+        name="ResearcherAgent",
+        description="Specialist in research and information gathering",
+        instructions=(
+            "You are a Researcher. You find information without additional computation or quantitative analysis."
+        ),
+        client=client,
+    )
+
+    # Create code interpreter tool using instance method
+    code_interpreter_tool = client.get_code_interpreter_tool()
+
+    coder_agent = 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=client,
+        tools=code_interpreter_tool,
+    )
+
+    # Create a manager agent for orchestration
+    manager_agent = Agent(
+        name="MagenticManager",
+        description="Orchestrator that coordinates the research and coding workflow",
+        instructions="You coordinate a team to complete complex tasks efficiently.",
+        client=client,
+    )
+
+    print("\nBuilding Magentic Workflow...")
+
+    # Mark participant responses as intermediate so the stream shows the
+    # conversation as it unfolds while the manager's final answer remains the
+    # terminal workflow output.
+    workflow = MagenticBuilder(
+        participants=[researcher_agent, coder_agent],
+        intermediate_output_from=[researcher_agent, coder_agent],
+        manager_agent=manager_agent,
+        max_round_count=10,
+        max_stall_count=3,
+        max_reset_count=2,
+    ).build()
+
+    task = (
+        "I am preparing a report on the energy efficiency of different machine learning model architectures. "
+        "Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 "
+        "on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). "
+        "Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 "
+        "VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model "
+        "per task type (image classification, text classification, and text generation)."
+    )
+
+    print(f"\nTask: {task}")
+    print("\nStarting workflow execution...")
+
+    # Keep track of the last executor to format output nicely in streaming mode
+    last_response_id: str | None = None
+    output_event: WorkflowEvent | None = None
+    async for event in workflow.run(task, stream=True):
+        if event.type in ("intermediate", "output") and isinstance(event.data, AgentResponseUpdate):
+            response_id = event.data.response_id
+            if response_id != last_response_id:
+                if last_response_id is not None:
+                    print("\n")
+                print(f"- {event.executor_id}:", end=" ", flush=True)
+                last_response_id = response_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 == "group_chat" and isinstance(event.data, GroupChatRequestSentEvent):
+            print(f"\n[REQUEST SENT ({event.data.round_index})] to agent: {event.data.participant_name}")
+
+        elif event.type == "output":
+            output_event = event
+
+    if output_event:
+        # The output of the magentic workflow is a collection of chat messages from all participants
+        outputs = cast(list[Message], output_event.data)
+        print("\n" + "=" * 80)
+        print("\nFinal Conversation Transcript:\n")
+        for message in outputs:
+            print(f"{message.author_name or message.role}: {message.text}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())