[BREAKING] Python: Move orchestrations to dedicated package (#3685)

* Move orchestrations to dedicated package * Merge main * Fix markdown links * Fix links
2026-06-16 21:04:09 +08:00 · 2026-02-05 03:05:13 +00:00
parent 10afb86213
commit 0daa7700c6
47 changed files with 1197 additions and 712 deletions
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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import json
+import logging
+from typing import cast
+
+from agent_framework import (
+    AgentResponseUpdate,
+    ChatAgent,
+    ChatMessage,
+    GroupChatRequestSentEvent,
+    HostedCodeInterpreterTool,
+    WorkflowOutputEvent,
+)
+from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
+from agent_framework.orchestrations import MagenticBuilder, MagenticOrchestratorEvent, MagenticProgressLedger
+
+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 (ChatAgent backed by an OpenAI chat client) for
+    finding information.
+- CoderAgent (ChatAgent 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:
+- OpenAI credentials configured for `OpenAIChatClient` and `OpenAIResponsesClient`.
+"""
+
+
+async def main() -> None:
+    researcher_agent = ChatAgent(
+        name="ResearcherAgent",
+        description="Specialist in research and information gathering",
+        instructions=(
+            "You are a Researcher. You find information without additional computation or quantitative analysis."
+        ),
+        # This agent requires the gpt-4o-search-preview model to perform web searches.
+        # Feel free to explore with other agents that support web search, for example,
+        # the `OpenAIResponseAgent` or `AzureAgentProtocol` with bing grounding.
+        chat_client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
+    )
+
+    coder_agent = ChatAgent(
+        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.",
+        chat_client=OpenAIResponsesClient(),
+        tools=HostedCodeInterpreterTool(),
+    )
+
+    # Create a manager agent for orchestration
+    manager_agent = ChatAgent(
+        name="MagenticManager",
+        description="Orchestrator that coordinates the research and coding workflow",
+        instructions="You coordinate a team to complete complex tasks efficiently.",
+        chat_client=OpenAIChatClient(),
+    )
+
+    print("\nBuilding Magentic Workflow...")
+
+    workflow = (
+        MagenticBuilder()
+        .participants([researcher_agent, coder_agent])
+        .with_manager(
+            agent=manager_agent,
+            max_round_count=10,
+            max_stall_count=3,
+            max_reset_count=2,
+        )
+        # Enable intermediate outputs to observe the conversation as it unfolds
+        # Intermediate outputs will be emitted as WorkflowOutputEvent events
+        .with_intermediate_outputs()
+        .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
+    async for event in workflow.run_stream(task):
+        if isinstance(event, MagenticOrchestratorEvent):
+            print(f"\n[Magentic Orchestrator Event] Type: {event.event_type.name}")
+            if isinstance(event.data, ChatMessage):
+                print(f"Please review the plan:\n{event.data.text}")
+            elif isinstance(event.data, MagenticProgressLedger):
+                print(f"Please review progress ledger:\n{json.dumps(event.data.to_dict(), indent=2)}")
+            else:
+                print(f"Unknown data type in MagenticOrchestratorEvent: {type(event.data)}")
+
+            # 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 isinstance(event, GroupChatRequestSentEvent):
+            print(f"\n[REQUEST SENT ({event.round_index})] to agent: {event.participant_name}")
+
+        elif isinstance(event, WorkflowOutputEvent):
+            data = event.data
+            if isinstance(data, AgentResponseUpdate):
+                response_id = 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)
+            else:
+                # The output of the magentic workflow is a collection of chat messages from all participants
+                outputs = cast(list[ChatMessage], 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())