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Python: Add support for the MagenticWorkflowBuilder (#496)
* magentic happy path - wip * Support workflow high-level magentic builder API. Add tests and samples. * Add sample docstring * Addressing PR feedback round 1 * Fix mypy errors * Callback improvements * Rename to MagenticBuilder * Improvements * Emit function calling deltas * PR feedback 2 * Clean up sample
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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 logging
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from agent_framework import ChatClientAgent, HostedCodeInterpreterTool
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from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
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from agent_framework_workflow import (
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MagenticAgentDeltaEvent,
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MagenticAgentMessageEvent,
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MagenticBuilder,
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MagenticCallbackEvent,
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MagenticCallbackMode,
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MagenticFinalResultEvent,
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MagenticOrchestratorMessageEvent,
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WorkflowCompletedEvent,
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)
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)
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"""
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Magentic Workflow (multi-agent) sample.
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This sample shows how to orchestrate multiple agents using the
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MagenticBuilder:
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- ResearcherAgent (ChatClientAgent backed by an OpenAI chat client) for
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finding information.
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- CoderAgent (ChatClientAgent backed by OpenAI Assistants with the hosted
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code interpreter tool) for analysis and computation.
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The workflow is configured with:
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- A Standard Magentic manager (uses a chat client for planning and progress).
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- Callbacks for final results, per-message agent responses, and streaming
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token updates.
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When run, the script builds the workflow, submits a task about estimating the
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energy efficiency and CO2 emissions of several ML models, streams intermediate
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events to the console, and prints the final aggregated answer at completion.
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"""
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async def main() -> None:
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researcher_agent = ChatClientAgent(
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name="ResearcherAgent",
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description="Specialist in research and information gathering",
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instructions=(
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"You are a Researcher. You find information without additional computation or quantitative analysis."
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),
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# This agent requires the gpt-4o-search-preview model to perform web searches.
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# Feel free to explore with other agents that support web search, for example,
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# the `OpenAIResponseAgent` or `AzureAIAgent` with bing grounding.
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chat_client=OpenAIChatClient(ai_model_id="gpt-4o-search-preview"),
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)
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coder_agent = ChatClientAgent(
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name="CoderAgent",
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description="A helpful assistant that writes and executes code to process and analyze data.",
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instructions="You solve questions using code. Please provide detailed analysis and computation process.",
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chat_client=OpenAIResponsesClient(),
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tools=HostedCodeInterpreterTool(),
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)
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# Unified callback
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async def on_event(event: MagenticCallbackEvent) -> None:
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"""
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The `on_event` callback processes events emitted by the workflow.
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Events include: orchestrator messages, agent delta updates, agent messages, and final result events.
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"""
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nonlocal last_stream_agent_id, stream_line_open
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if isinstance(event, MagenticOrchestratorMessageEvent):
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print(f"\n[ORCH:{event.kind}]\n\n{getattr(event.message, 'text', '')}\n{'-' * 26}")
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elif isinstance(event, MagenticAgentDeltaEvent):
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if last_stream_agent_id != event.agent_id or not stream_line_open:
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if stream_line_open:
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print()
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print(f"\n[STREAM:{event.agent_id}]: ", end="", flush=True)
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last_stream_agent_id = event.agent_id
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stream_line_open = True
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print(event.text, end="", flush=True)
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elif isinstance(event, MagenticAgentMessageEvent):
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if stream_line_open:
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print(" (final)")
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stream_line_open = False
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print()
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msg = event.message
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if msg is not None:
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response_text = (msg.text or "").replace("\n", " ")
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print(f"\n[AGENT:{event.agent_id}] {msg.role.value}\n\n{response_text}\n{'-' * 26}")
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elif isinstance(event, MagenticFinalResultEvent):
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print("\n" + "=" * 50)
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print("FINAL RESULT:")
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print("=" * 50)
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if event.message is not None:
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print(event.message.text)
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print("=" * 50)
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print("\nBuilding Magentic Workflow...")
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# State used by on_agent_stream callback
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last_stream_agent_id: str | None = None
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stream_line_open: bool = False
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workflow = (
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MagenticBuilder()
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.participants(researcher=researcher_agent, coder=coder_agent)
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.on_event(on_event, mode=MagenticCallbackMode.STREAMING)
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.with_standard_manager(
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chat_client=OpenAIChatClient(),
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max_round_count=10,
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max_stall_count=3,
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max_reset_count=2,
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)
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.build()
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)
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task = (
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"I am preparing a report on the energy efficiency of different machine learning model architectures. "
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"Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 "
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"on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). "
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"Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 "
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"VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model "
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"per task type (image classification, text classification, and text generation)."
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)
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print(f"\nTask: {task}")
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print("\nStarting workflow execution...")
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try:
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completion_event = None
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async for event in workflow.run_streaming(task):
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print(f"Event: {event}")
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if isinstance(event, WorkflowCompletedEvent):
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completion_event = event
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if completion_event is not None:
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data = getattr(completion_event, "data", None)
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preview = getattr(data, "text", None) or (str(data) if data is not None else "")
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print(f"Workflow completed with result:\n\n{preview}")
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except Exception as e:
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print(f"Workflow execution failed: {e}")
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,189 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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from typing import cast
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from agent_framework import ChatClientAgent, HostedCodeInterpreterTool
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from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
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from agent_framework_workflow import (
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MagenticAgentDeltaEvent,
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MagenticAgentMessageEvent,
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MagenticBuilder,
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MagenticCallbackEvent,
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MagenticCallbackMode,
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MagenticFinalResultEvent,
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MagenticOrchestratorMessageEvent,
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MagenticPlanReviewDecision,
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MagenticPlanReviewReply,
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MagenticPlanReviewRequest,
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RequestInfoEvent,
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WorkflowCompletedEvent,
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)
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)
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"""
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Magentic workflow with human-in-the-loop plan review and update.
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This sample builds a Magentic workflow with two cooperating agents and enables
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plan review so a human can approve or revise the plan before execution:
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- researcher: ChatClientAgent backed by OpenAIChatClient (web/search-capable model)
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- coder: ChatClientAgent backed by OpenAIAssistantsClient with the Hosted Code Interpreter tool
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Key behaviors demonstrated:
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- with_plan_review(): requests a PlanReviewRequest before coordination begins
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- Event loop that waits for RequestInfoEvent[PlanReviewRequest], prints the plan, then
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replies with PlanReviewReply (here we auto-approve, but you can edit/collect input)
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- Callbacks: on_agent_stream (incremental chunks), on_agent_response (final messages),
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on_result (final answer), and on_exception
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Prereqs: configure your OpenAI credentials in the environment so the Chat/Assistants
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clients can run. You can swap clients/models as needed.
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"""
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async def main() -> None:
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researcher_agent = ChatClientAgent(
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name="ResearcherAgent",
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description="Specialist in research and information gathering",
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instructions=(
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"You are a Researcher. You find information without additional computation or quantitative analysis."
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),
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# This agent requires the gpt-4o-search-preview model to perform web searches.
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# Feel free to explore with other agents that support web search, for example,
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# the `OpenAIResponseAgent` or `AzureAIAgent` with bing grounding.
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chat_client=OpenAIChatClient(ai_model_id="gpt-4o-search-preview"),
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)
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coder_agent = ChatClientAgent(
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name="CoderAgent",
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description="A helpful assistant that writes and executes code to process and analyze data.",
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instructions="You solve questions using code. Please provide detailed analysis and computation process.",
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chat_client=OpenAIResponsesClient(),
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tools=HostedCodeInterpreterTool(),
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)
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# Callbacks
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def on_exception(exception: Exception) -> None:
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print(f"Exception occurred: {exception}")
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logger.exception("Workflow exception", exc_info=exception)
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# Unified callback
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async def on_event(event: MagenticCallbackEvent) -> None:
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nonlocal last_stream_agent_id, stream_line_open
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if isinstance(event, MagenticOrchestratorMessageEvent):
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print(f"\n[ORCH:{event.kind}]\n\n{getattr(event.message, 'text', '')}\n{'-' * 26}")
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elif isinstance(event, MagenticAgentDeltaEvent):
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if last_stream_agent_id != event.agent_id or not stream_line_open:
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if stream_line_open:
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print()
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print(f"\n[STREAM:{event.agent_id}]: ", end="", flush=True)
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last_stream_agent_id = event.agent_id
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stream_line_open = True
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print(event.text, end="", flush=True)
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elif isinstance(event, MagenticAgentMessageEvent):
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if stream_line_open:
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print(" (final)")
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stream_line_open = False
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print()
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msg = event.message
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if msg is not None:
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response_text = (msg.text or "").replace("\n", " ")
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print(f"\n[AGENT:{event.agent_id}] {msg.role.value}\n\n{response_text}\n{'-' * 26}")
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elif isinstance(event, MagenticFinalResultEvent):
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print("\n" + "=" * 50)
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print("FINAL RESULT:")
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print("=" * 50)
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if event.message is not None:
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print(event.message.text)
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print("=" * 50)
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print("\nBuilding Magentic Workflow...")
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last_stream_agent_id: str | None = None
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stream_line_open: bool = False
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workflow = (
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MagenticBuilder()
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.participants(researcher=researcher_agent, coder=coder_agent)
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.on_exception(on_exception)
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.on_event(on_event, mode=MagenticCallbackMode.STREAMING)
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.with_standard_manager(
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chat_client=OpenAIChatClient(),
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max_round_count=10,
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max_stall_count=3,
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max_reset_count=2,
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)
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.with_plan_review()
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.build()
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)
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task = (
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"I am preparing a report on the energy efficiency of different machine learning model architectures. "
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"Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 "
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"on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). "
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"Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 "
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"VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model "
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"per task type (image classification, text classification, and text generation)."
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)
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print(f"\nTask: {task}")
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print("\nStarting workflow execution...")
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try:
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completion_event: WorkflowCompletedEvent | None = None
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pending_request: RequestInfoEvent | None = None
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while True:
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# Phase 1: run until either completion or a HIL request
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if pending_request is None:
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async for event in workflow.run_streaming(task):
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print(f"Event: {event}")
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if isinstance(event, WorkflowCompletedEvent):
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completion_event = event
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if isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest:
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pending_request = event
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review_req = cast(MagenticPlanReviewRequest, event.data)
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if review_req.plan_text:
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print(f"\n=== PLAN REVIEW REQUEST ===\n{review_req.plan_text}\n")
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# Break if completed
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if completion_event is not None:
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data = getattr(completion_event, "data", None)
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preview = getattr(data, "text", None) or (str(data) if data is not None else "")
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print(f"Workflow completed with result:\n\n{preview}")
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# Phase 2: respond to the pending plan review (HIL) request
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if pending_request is not None:
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# For demo purposes we approve as-is. Replace this with UI input
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# to collect a human decision/comments/edited plan.
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reply = MagenticPlanReviewReply(decision=MagenticPlanReviewDecision.APPROVE)
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async for event in workflow.send_responses_streaming({pending_request.request_id: reply}):
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print(f"Event: {event}")
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if isinstance(event, WorkflowCompletedEvent):
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completion_event = event
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if isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest:
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# Another review cycle requested; keep pending
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pending_request = event
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review_req = cast(MagenticPlanReviewRequest, event.data)
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if review_req.plan_text:
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print(f"\n=== PLAN REVIEW REQUEST ===\n{review_req.plan_text}\n")
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else:
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# Clear pending if no immediate new request
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pending_request = None
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except Exception as e:
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print(f"Workflow execution failed: {e}")
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on_exception(e)
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if __name__ == "__main__":
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asyncio.run(main())
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