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[BREAKING] Python: Refactor workflow events to unified discriminated union pattern (#3690)
* Refactor events * Merge main * Fixes * Cleanup * Update samples and tests * Remove unused imports * PR feedback * Merge main. Add properties for events to help typing * Formatting * Cleanup * use builtins.type to avoid shadowing by WorkflowEvent.type attribute * Final improvements
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Evan Mattson
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import json
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from typing import cast
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from agent_framework import (
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AgentRunUpdateEvent,
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ChatAgent,
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ChatMessage,
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MagenticBuilder,
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MagenticPlanReviewRequest,
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RequestInfoEvent,
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WorkflowOutputEvent,
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)
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from agent_framework.openai import OpenAIChatClient
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"""
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Sample: Magentic Orchestration with Human Plan Review
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This sample demonstrates how humans can review and provide feedback on plans
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generated by the Magentic workflow orchestrator. When plan review is enabled,
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the workflow requests human approval or revision before executing each plan.
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Key concepts:
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- with_plan_review(): Enables human review of generated plans
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- MagenticPlanReviewRequest: The event type for plan review requests
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- Human can choose to: approve the plan or provide revision feedback
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Plan review options:
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- approve(): Accept the proposed plan and continue execution
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- revise(feedback): Provide textual feedback to modify the plan
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Prerequisites:
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- OpenAI credentials configured for `OpenAIChatClient`.
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"""
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async def main() -> None:
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researcher_agent = ChatAgent(
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name="ResearcherAgent",
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description="Specialist in research and information gathering",
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instructions="You are a Researcher. You find information and gather facts.",
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chat_client=OpenAIChatClient(model_id="gpt-4o"),
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)
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analyst_agent = ChatAgent(
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name="AnalystAgent",
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description="Data analyst who processes and summarizes research findings",
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instructions="You are an Analyst. You analyze findings and create summaries.",
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chat_client=OpenAIChatClient(model_id="gpt-4o"),
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)
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manager_agent = ChatAgent(
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name="MagenticManager",
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description="Orchestrator that coordinates the workflow",
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instructions="You coordinate a team to complete tasks efficiently.",
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chat_client=OpenAIChatClient(model_id="gpt-4o"),
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)
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print("\nBuilding Magentic Workflow with Human Plan Review...")
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workflow = (
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MagenticBuilder()
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.participants([researcher_agent, analyst_agent])
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.with_manager(
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agent=manager_agent,
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max_round_count=10,
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max_stall_count=1,
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max_reset_count=2,
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)
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.with_plan_review() # Request human input for plan review
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.build()
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)
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task = "Research sustainable aviation fuel technology and summarize the findings."
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print(f"\nTask: {task}")
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print("\nStarting workflow execution...")
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print("=" * 60)
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pending_request: RequestInfoEvent | None = None
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pending_responses: dict[str, object] | None = None
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output_event: WorkflowOutputEvent | None = None
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while not output_event:
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if pending_responses is not None:
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stream = workflow.send_responses_streaming(pending_responses)
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else:
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stream = workflow.run(task, stream=True)
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last_message_id: str | None = None
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async for event in stream:
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if isinstance(event, AgentRunUpdateEvent):
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message_id = event.data.message_id
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if message_id != last_message_id:
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if last_message_id is not None:
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print("\n")
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print(f"- {event.executor_id}:", end=" ", flush=True)
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last_message_id = message_id
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print(event.data, end="", flush=True)
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elif isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest:
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pending_request = event
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elif isinstance(event, WorkflowOutputEvent):
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output_event = event
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pending_responses = None
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# Handle plan review request if any
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if pending_request is not None:
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event_data = cast(MagenticPlanReviewRequest, pending_request.data)
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print("\n\n[Magentic Plan Review Request]")
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if event_data.current_progress is not None:
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print("Current Progress Ledger:")
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print(json.dumps(event_data.current_progress.to_dict(), indent=2))
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print()
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print(f"Proposed Plan:\n{event_data.plan.text}\n")
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print("Please provide your feedback (press Enter to approve):")
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reply = await asyncio.get_event_loop().run_in_executor(None, input, "> ")
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if reply.strip() == "":
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print("Plan approved.\n")
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pending_responses = {pending_request.request_id: event_data.approve()}
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else:
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print("Plan revised by human.\n")
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pending_responses = {pending_request.request_id: event_data.revise(reply)}
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pending_request = None
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print("\n" + "=" * 60)
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print("WORKFLOW COMPLETED")
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print("=" * 60)
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print("Final Output:")
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# The output of the Magentic workflow is a list of ChatMessages with only one final message
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# generated by the orchestrator.
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output_messages = cast(list[ChatMessage], output_event.data)
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if output_messages:
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output = output_messages[-1].text
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print(output)
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if __name__ == "__main__":
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asyncio.run(main())
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