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Python: Improve the workflow getting started samples (#570)
* Wip: samples * wip - samples * Updates to workflow getting started samples * Checkpointing enhancements * Cleanup * PR feedback * Updates * Sample updates * Updates * Revamp samples, improve doc strings and code comments * Cleanup unused comment * Formatting cleanup * wip * Further work on samples. Allow agent to be specified as edge. * Cleanup * Typing cleanup * Sample updates --------- Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from dataclasses import dataclass
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from typing import Any
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from agent_framework import ChatMessage, Role
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from agent_framework.azure import AzureChatClient
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from agent_framework.workflow import (
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AgentExecutor,
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AgentExecutorRequest,
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AgentExecutorResponse,
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AgentRunEvent,
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Executor,
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WorkflowBuilder,
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WorkflowCompletedEvent,
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WorkflowContext,
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WorkflowViz,
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handler,
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)
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from azure.identity import AzureCliCredential
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"""
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Sample: Concurrent (Fan-out/Fan-in) with Agents + Visualization
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What it does:
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- Fan-out: dispatch the same prompt to multiple domain agents (research, marketing, legal).
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- Fan-in: aggregate their responses into one consolidated output.
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- Visualization: generate Mermaid and GraphViz representations via `WorkflowViz` and optionally export SVG.
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Prerequisites:
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- Azure AI/ Azure OpenAI for `AzureChatClient` agents.
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- Authentication via `azure-identity` — uses `AzureCliCredential()` (run `az login`).
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- For visualization export: `pip install agent-framework-workflow[viz]` and install GraphViz binaries.
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"""
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class DispatchToExperts(Executor):
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"""Dispatches the incoming prompt to all expert agent executors (fan-out)."""
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def __init__(self, expert_ids: list[str], id: str | None = None):
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super().__init__(id)
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self._expert_ids = expert_ids
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@handler
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async def dispatch(self, prompt: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
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# Wrap the incoming prompt as a user message for each expert and request a response.
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initial_message = ChatMessage(Role.USER, text=prompt)
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for expert_id in self._expert_ids:
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await ctx.send_message(
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AgentExecutorRequest(messages=[initial_message], should_respond=True),
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target_id=expert_id,
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)
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@dataclass
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class AggregatedInsights:
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"""Structured output from the aggregator."""
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research: str
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marketing: str
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legal: str
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class AggregateInsights(Executor):
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"""Aggregates expert agent responses into a single consolidated result (fan-in)."""
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def __init__(self, expert_ids: list[str], id: str | None = None):
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super().__init__(id)
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self._expert_ids = expert_ids
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@handler
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async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext[Any]) -> None:
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# Map responses to text by executor id for a simple, predictable demo.
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by_id: dict[str, str] = {}
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for r in results:
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# AgentExecutorResponse.agent_run_response.text contains concatenated assistant text
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by_id[r.executor_id] = r.agent_run_response.text
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research_text = by_id.get("researcher", "")
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marketing_text = by_id.get("marketer", "")
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legal_text = by_id.get("legal", "")
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aggregated = AggregatedInsights(
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research=research_text,
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marketing=marketing_text,
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legal=legal_text,
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)
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# Provide a readable, consolidated string as the final workflow result.
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consolidated = (
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"Consolidated Insights\n"
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"====================\n\n"
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f"Research Findings:\n{aggregated.research}\n\n"
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f"Marketing Angle:\n{aggregated.marketing}\n\n"
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f"Legal/Compliance Notes:\n{aggregated.legal}\n"
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)
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await ctx.add_event(WorkflowCompletedEvent(data=consolidated))
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async def main() -> None:
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# 1) Create agent executors for domain experts
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chat_client = AzureChatClient(credential=AzureCliCredential())
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researcher = AgentExecutor(
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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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),
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id="researcher",
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)
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marketer = AgentExecutor(
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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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),
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id="marketer",
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)
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legal = AgentExecutor(
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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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),
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id="legal",
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)
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expert_ids = [researcher.id, marketer.id, legal.id]
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dispatcher = DispatchToExperts(expert_ids=expert_ids, id="dispatcher")
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aggregator = AggregateInsights(expert_ids=expert_ids, id="aggregator")
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# 2) Build a simple fan-out/fan-in workflow
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workflow = (
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WorkflowBuilder()
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.set_start_executor(dispatcher)
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.add_fan_out_edges(dispatcher, [researcher, marketer, legal])
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.add_fan_in_edges([researcher, marketer, legal], aggregator)
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.build()
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)
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# 2.5) Generate workflow visualization
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print("Generating workflow visualization...")
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viz = WorkflowViz(workflow)
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# Print out the mermaid string.
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print("Mermaid string: \n=======")
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print(viz.to_mermaid())
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print("=======")
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# Print out the DiGraph string.
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print("DiGraph string: \n=======")
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print(viz.to_digraph())
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print("=======")
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try:
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# Export the DiGraph visualization as SVG.
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svg_file = viz.export(format="svg")
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print(f"SVG file saved to: {svg_file}")
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except ImportError:
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print("Tip: Install 'viz' extra to export workflow visualization: pip install agent-framework-workflow[viz]")
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# 3) Run with a single prompt
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completion: WorkflowCompletedEvent | None = None
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async for event in workflow.run_stream("We are launching a new budget-friendly electric bike for urban commuters."):
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if isinstance(event, AgentRunEvent):
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# Show which agent ran and what step completed.
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print(event)
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if isinstance(event, WorkflowCompletedEvent):
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completion = event
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if completion:
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print("===== Final Aggregated Output =====")
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print(completion.data)
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if __name__ == "__main__":
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asyncio.run(main())
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