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Python: [BREAKING] Python: Intro group chat and refactor orchestrations. Fix as_agent(). Standardize orchestration start msg types. (#1538)
* Intro group chat and refactor magentic. Fix as_agent() * Cleanup and improvements * Add as_agent docstring clarification * Standardize orchestration messages to use agent-style inputs. * Simplify group chat constructs * Further cleanup * Add sk to af group chat migration sample. Update README. * Improvements and simplifications * consolidating shared orchestration logic * Further clean up * Add group chat sample * Improve typing * Fix test imports * Fix readme links * Cleanup per PR Feedback
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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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from agent_framework import ConcurrentBuilder
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from agent_framework.azure import AzureOpenAIChatClient
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from azure.identity import AzureCliCredential
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"""
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Sample: Build a concurrent workflow orchestration and wrap it as an agent.
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This script wires up a fan-out/fan-in workflow using `ConcurrentBuilder`, and then
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invokes the entire orchestration through the `workflow.as_agent(...)` interface so
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downstream coordinators can reuse the orchestration as a single agent.
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Demonstrates:
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- Fan-out to multiple agents, fan-in aggregation of final ChatMessages.
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- Reusing the orchestrated workflow as an agent entry point with `workflow.as_agent(...)`.
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- Workflow completion when idle with no pending work
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Prerequisites:
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- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
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- Familiarity with Workflow events (AgentRunEvent, WorkflowOutputEvent)
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"""
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async def main() -> None:
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# 1) Create three domain agents using AzureOpenAIChatClient
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chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
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researcher = chat_client.create_agent(
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instructions=(
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"You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
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" opportunities, and risks."
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),
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name="researcher",
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)
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marketer = chat_client.create_agent(
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instructions=(
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"You're a creative marketing strategist. Craft compelling value propositions and target messaging"
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" aligned to the prompt."
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),
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name="marketer",
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)
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legal = chat_client.create_agent(
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instructions=(
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"You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
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" based on the prompt."
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),
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name="legal",
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)
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# 2) Build a concurrent workflow
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workflow = ConcurrentBuilder().participants([researcher, marketer, legal]).build()
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# 3) Expose the concurrent workflow as an agent for easy reuse
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agent = workflow.as_agent(name="ConcurrentWorkflowAgent")
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prompt = "We are launching a new budget-friendly electric bike for urban commuters."
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agent_response = await agent.run(prompt)
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if agent_response.messages:
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print("\n===== Aggregated Messages =====")
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for i, msg in enumerate(agent_response.messages, start=1):
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role = getattr(msg.role, "value", msg.role)
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name = msg.author_name if msg.author_name else role
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print(f"{'-' * 60}\n\n{i:02d} [{name}]:\n{msg.text}")
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"""
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Sample Output:
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===== Aggregated Messages =====
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------------------------------------------------------------
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01 [user]:
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We are launching a new budget-friendly electric bike for urban commuters.
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------------------------------------------------------------
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02 [researcher]:
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**Insights:**
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- **Target Demographic:** Urban commuters seeking affordable, eco-friendly transport;
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likely to include students, young professionals, and price-sensitive urban residents.
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- **Market Trends:** E-bike sales are growing globally, with increasing urbanization,
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higher fuel costs, and sustainability concerns driving adoption.
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- **Competitive Landscape:** Key competitors include brands like Rad Power Bikes, Aventon,
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Lectric, and domestic budget-focused manufacturers in North America, Europe, and Asia.
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- **Feature Expectations:** Customers expect reliability, ease-of-use, theft protection,
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lightweight design, sufficient battery range for daily city commutes (typically 25-40 miles),
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and low-maintenance components.
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**Opportunities:**
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- **First-time Buyers:** Capture newcomers to e-biking by emphasizing affordability, ease of
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operation, and cost savings vs. public transit/car ownership.
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...
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------------------------------------------------------------
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03 [marketer]:
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**Value Proposition:**
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"Empowering your city commute: Our new electric bike combines affordability, reliability, and
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sustainable design—helping you conquer urban journeys without breaking the bank."
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**Target Messaging:**
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*For Young Professionals:*
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...
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------------------------------------------------------------
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04 [legal]:
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**Constraints, Disclaimers, & Policy Concerns for Launching a Budget-Friendly Electric Bike for Urban Commuters:**
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**1. Regulatory Compliance**
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- Verify that the electric bike meets all applicable federal, state, and local regulations
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regarding e-bike classification, speed limits, power output, and safety features.
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- Ensure necessary certifications (e.g., UL certification for batteries, CE markings if sold internationally) are obtained.
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**2. Product Safety**
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- Include consumer safety warnings regarding use, battery handling, charging protocols, and age restrictions.
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...
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""" # noqa: E501
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if __name__ == "__main__":
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asyncio.run(main())
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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 ChatAgent, GroupChatBuilder
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from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
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logging.basicConfig(level=logging.INFO)
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"""
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Sample: Group Chat Orchestration (manager-directed)
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What it does:
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- Demonstrates the generic GroupChatBuilder with a language-model manager directing two agents.
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- The manager coordinates a researcher (chat completions) and a writer (responses API) to solve a task.
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- Uses the default group chat orchestration pipeline shared with Magentic.
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Prerequisites:
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- OpenAI environment variables configured for `OpenAIChatClient` and `OpenAIResponsesClient`.
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"""
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async def main() -> None:
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researcher = ChatAgent(
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name="Researcher",
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description="Collects relevant background information.",
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instructions="Gather concise facts that help a teammate answer the question.",
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chat_client=OpenAIChatClient(model_id="gpt-4o-mini"),
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)
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writer = ChatAgent(
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name="Writer",
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description="Synthesizes a polished answer using the gathered notes.",
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instructions="Compose clear and structured answers using any notes provided.",
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chat_client=OpenAIResponsesClient(),
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)
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workflow = (
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GroupChatBuilder()
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.set_prompt_based_manager(chat_client=OpenAIChatClient(), display_name="Coordinator")
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.participants(researcher=researcher, writer=writer)
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.build()
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)
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task = "Outline the core considerations for planning a community hackathon, and finish with a concise action plan."
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print("\nStarting Group Chat Workflow...\n")
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print(f"Input: {task}\n")
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try:
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workflow_agent = workflow.as_agent(name="GroupChatWorkflowAgent")
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agent_result = await workflow_agent.run(task)
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if agent_result.messages:
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print("\n===== as_agent() Transcript =====")
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for i, msg in enumerate(agent_result.messages, start=1):
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role_value = getattr(msg.role, "value", msg.role)
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speaker = msg.author_name or role_value
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print(f"{'-' * 50}\n{i:02d} [{speaker}]\n{msg.text}")
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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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# 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 (
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ChatAgent,
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HostedCodeInterpreterTool,
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MagenticAgentDeltaEvent,
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MagenticAgentMessageEvent,
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MagenticBuilder,
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MagenticFinalResultEvent,
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MagenticOrchestratorMessageEvent,
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WorkflowOutputEvent,
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)
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from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
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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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Sample: Build a Magentic orchestration and wrap it as an agent.
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The script configures a Magentic workflow with streaming callbacks, then invokes the
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orchestration through `workflow.as_agent(...)` so the entire Magentic loop can be reused
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like any other agent while still emitting callback telemetry.
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Prerequisites:
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- OpenAI credentials configured for `OpenAIChatClient` and `OpenAIResponsesClient`.
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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=(
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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 `AzureAgentProtocol` with bing grounding.
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chat_client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
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)
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coder_agent = ChatAgent(
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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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print("\nBuilding Magentic Workflow...")
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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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.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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last_stream_agent_id: str | None = None
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stream_line_open: bool = False
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final_output: str | None = None
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async for event in workflow.run_stream(task):
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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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if event.text:
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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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elif isinstance(event, WorkflowOutputEvent):
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final_output = str(event.data) if event.data is not None else None
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if stream_line_open:
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print()
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stream_line_open = False
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if final_output is not None:
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print(f"\nWorkflow completed with result:\n\n{final_output}\n")
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# Wrap the workflow as an agent for composition scenarios
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workflow_agent = workflow.as_agent(name="MagenticWorkflowAgent")
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agent_result = await workflow_agent.run(task)
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if agent_result.messages:
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print("\n===== as_agent() Transcript =====")
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for i, msg in enumerate(agent_result.messages, start=1):
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role_value = getattr(msg.role, "value", msg.role)
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speaker = msg.author_name or role_value
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print(f"{'-' * 50}\n{i:02d} [{speaker}]\n{msg.text}")
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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 +1,87 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import Role, SequentialBuilder
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from agent_framework.azure import AzureOpenAIChatClient
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from azure.identity import AzureCliCredential
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"""
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Sample: Build a sequential workflow orchestration and wrap it as an agent.
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The script assembles a sequential conversation flow with `SequentialBuilder`, then
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invokes the entire orchestration through the `workflow.as_agent(...)` interface so
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other coordinators can reuse the chain as a single participant.
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Note on internal adapters:
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- Sequential orchestration includes small adapter nodes for input normalization
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("input-conversation"), agent-response conversion ("to-conversation:<participant>"),
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and completion ("complete"). These may appear as ExecutorInvoke/Completed events in
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the stream—similar to how concurrent orchestration includes a dispatcher/aggregator.
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You can safely ignore them when focusing on agent progress.
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Prerequisites:
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- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
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"""
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async def main() -> None:
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# 1) Create agents
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chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
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writer = chat_client.create_agent(
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instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
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name="writer",
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)
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reviewer = chat_client.create_agent(
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instructions=("You are a thoughtful reviewer. Give brief feedback on the previous assistant message."),
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name="reviewer",
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)
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# 2) Build sequential workflow: writer -> reviewer
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workflow = SequentialBuilder().participants([writer, reviewer]).build()
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# 3) Treat the workflow itself as an agent for follow-up invocations
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agent = workflow.as_agent(name="SequentialWorkflowAgent")
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prompt = "Write a tagline for a budget-friendly eBike."
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agent_response = await agent.run(prompt)
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if agent_response.messages:
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print("\n===== Conversation =====")
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for i, msg in enumerate(agent_response.messages, start=1):
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role_value = getattr(msg.role, "value", msg.role)
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normalized_role = str(role_value).lower() if role_value is not None else "assistant"
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name = msg.author_name or ("assistant" if normalized_role == Role.ASSISTANT.value else "user")
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print(f"{'-' * 60}\n{i:02d} [{name}]\n{msg.text}")
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"""
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Sample Output:
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===== Final Conversation =====
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------------------------------------------------------------
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01 [user]
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Write a tagline for a budget-friendly eBike.
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------------------------------------------------------------
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02 [writer]
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Ride farther, spend less—your affordable eBike adventure starts here.
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------------------------------------------------------------
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03 [reviewer]
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This tagline clearly communicates affordability and the benefit of extended travel, making it
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appealing to budget-conscious consumers. It has a friendly and motivating tone, though it could
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be slightly shorter for more punch. Overall, a strong and effective suggestion!
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===== as_agent() Conversation =====
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------------------------------------------------------------
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01 [writer]
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Go electric, save big—your affordable ride awaits!
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------------------------------------------------------------
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02 [reviewer]
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Catchy and straightforward! The tagline clearly emphasizes both the electric aspect and the affordability of the
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eBike. It's inviting and actionable. For even more impact, consider making it slightly shorter:
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"Go electric, save big." Overall, this is an effective and appealing suggestion for a budget-friendly eBike.
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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