[BREAKING] Python: Merge send_responses into run method (#3720)

* Streamline workflow run api with send responses in one method * Fixes * Address copilot feedback
2026-06-16 21:04:09 +08:00 · 2026-02-07 07:32:38 +09:00
parent 15256bb616
commit a17f13598b
39 changed files with 561 additions and 335 deletions
@@ -0,0 +1,144 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import json
+from typing import cast
+
+from agent_framework import (
+    AgentRunUpdateEvent,
+    ChatAgent,
+    ChatMessage,
+    MagenticBuilder,
+    MagenticPlanReviewRequest,
+    WorkflowEvent,
+)
+from agent_framework.openai import OpenAIChatClient
+
+"""
+Sample: Magentic Orchestration with Human Plan Review
+
+This sample demonstrates how humans can review and provide feedback on plans
+generated by the Magentic workflow orchestrator. When plan review is enabled,
+the workflow requests human approval or revision before executing each plan.
+
+Key concepts:
+- with_plan_review(): Enables human review of generated plans
+- MagenticPlanReviewRequest: The event type for plan review requests
+- Human can choose to: approve the plan or provide revision feedback
+
+Plan review options:
+- approve(): Accept the proposed plan and continue execution
+- revise(feedback): Provide textual feedback to modify the plan
+
+Prerequisites:
+- OpenAI credentials configured for `OpenAIChatClient`.
+"""
+
+
+async def main() -> None:
+    researcher_agent = ChatAgent(
+        name="ResearcherAgent",
+        description="Specialist in research and information gathering",
+        instructions="You are a Researcher. You find information and gather facts.",
+        chat_client=OpenAIChatClient(model_id="gpt-4o"),
+    )
+
+    analyst_agent = ChatAgent(
+        name="AnalystAgent",
+        description="Data analyst who processes and summarizes research findings",
+        instructions="You are an Analyst. You analyze findings and create summaries.",
+        chat_client=OpenAIChatClient(model_id="gpt-4o"),
+    )
+
+    manager_agent = ChatAgent(
+        name="MagenticManager",
+        description="Orchestrator that coordinates the workflow",
+        instructions="You coordinate a team to complete tasks efficiently.",
+        chat_client=OpenAIChatClient(model_id="gpt-4o"),
+    )
+
+    print("\nBuilding Magentic Workflow with Human Plan Review...")
+
+    workflow = (
+        MagenticBuilder()
+        .participants([researcher_agent, analyst_agent])
+        .with_manager(
+            agent=manager_agent,
+            max_round_count=10,
+            max_stall_count=1,
+            max_reset_count=2,
+        )
+        .with_plan_review()  # Request human input for plan review
+        .build()
+    )
+
+    task = "Research sustainable aviation fuel technology and summarize the findings."
+
+    print(f"\nTask: {task}")
+    print("\nStarting workflow execution...")
+    print("=" * 60)
+
+    pending_request: WorkflowEvent | None = None
+    pending_responses: dict[str, object] | None = None
+    output_event: WorkflowEvent | None = None
+
+    while not output_event:
+        if pending_responses is not None:
+            stream = workflow.run(stream=True, responses=pending_responses)
+        else:
+            stream = workflow.run(task, stream=True)
+
+        last_message_id: str | None = None
+        async for event in stream:
+            if isinstance(event, AgentRunUpdateEvent):
+                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 == "request_info" and event.request_type is MagenticPlanReviewRequest:
+                pending_request = event
+
+            elif event.type == "output":
+                output_event = event
+
+        pending_responses = None
+
+        # Handle plan review request if any
+        if pending_request is not None:
+            event_data = cast(MagenticPlanReviewRequest, pending_request.data)
+
+            print("\n\n[Magentic Plan Review Request]")
+            if event_data.current_progress is not None:
+                print("Current Progress Ledger:")
+                print(json.dumps(event_data.current_progress.to_dict(), indent=2))
+                print()
+            print(f"Proposed Plan:\n{event_data.plan.text}\n")
+            print("Please provide your feedback (press Enter to approve):")
+
+            reply = await asyncio.get_event_loop().run_in_executor(None, input, "> ")
+            if reply.strip() == "":
+                print("Plan approved.\n")
+                pending_responses = {pending_request.request_id: event_data.approve()}
+            else:
+                print("Plan revised by human.\n")
+                pending_responses = {pending_request.request_id: event_data.revise(reply)}
+            pending_request = None
+
+    print("\n" + "=" * 60)
+    print("WORKFLOW COMPLETED")
+    print("=" * 60)
+    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[ChatMessage], output_event.data)
+    if output_messages:
+        output = output_messages[-1].text
+        print(output)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())