agent-framework/python/samples/03-workflows/visualization/concurrent_with_visualization.py

# Copyright (c) Microsoft. All rights reserved.

import asyncio
import os
from dataclasses import dataclass

from agent_framework import (
    AgentExecutor,
    AgentExecutorRequest,
    AgentExecutorResponse,
    Executor,
    Message,
    WorkflowBuilder,
    WorkflowContext,
    WorkflowViz,
    handler,
)
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identity import AzureCliCredential
from typing_extensions import Never

"""
Sample: Concurrent (Fan-out/Fan-in) with Agents + Visualization

What it does:
- Fan-out: dispatch the same prompt to multiple domain agents (research, marketing, legal).
- Fan-in: aggregate their responses into one consolidated output.
- Visualization: generate Mermaid and GraphViz representations via `WorkflowViz` and optionally export SVG.

Prerequisites:
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
- Azure AI/ Azure OpenAI for `AzureOpenAIResponsesClient` agents.
- Authentication via `azure-identity` — uses `AzureCliCredential()` (run `az login`).
- For visualization export: `pip install graphviz>=0.20.0` and install GraphViz binaries.
"""


class DispatchToExperts(Executor):
    """Dispatches the incoming prompt to all expert agent executors (fan-out)."""

    @handler
    async def dispatch(self, prompt: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
        # Wrap the incoming prompt as a user message for each expert and request a response.
        initial_message = Message("user", text=prompt)
        await ctx.send_message(AgentExecutorRequest(messages=[initial_message], should_respond=True))


@dataclass
class AggregatedInsights:
    """Structured output from the aggregator."""

    research: str
    marketing: str
    legal: str


class AggregateInsights(Executor):
    """Aggregates expert agent responses into a single consolidated result (fan-in)."""

    @handler
    async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
        # Map responses to text by executor id for a simple, predictable demo.
        by_id: dict[str, str] = {}
        for r in results:
            # AgentExecutorResponse.agent_response.text contains concatenated assistant text
            by_id[r.executor_id] = r.agent_response.text

        research_text = by_id.get("researcher", "")
        marketing_text = by_id.get("marketer", "")
        legal_text = by_id.get("legal", "")

        aggregated = AggregatedInsights(
            research=research_text,
            marketing=marketing_text,
            legal=legal_text,
        )

        # Provide a readable, consolidated string as the final workflow result.
        consolidated = (
            "Consolidated Insights\n"
            "====================\n\n"
            f"Research Findings:\n{aggregated.research}\n\n"
            f"Marketing Angle:\n{aggregated.marketing}\n\n"
            f"Legal/Compliance Notes:\n{aggregated.legal}\n"
        )

        await ctx.yield_output(consolidated)


async def main() -> None:
    """Build and run the concurrent workflow with visualization."""

    # Create agent instances
    researcher = AgentExecutor(
        AzureOpenAIResponsesClient(
            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
            credential=AzureCliCredential(),
        ).as_agent(
            instructions=(
                "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
                " opportunities, and risks."
            ),
            name="researcher",
        )
    )

    marketer = AgentExecutor(
        AzureOpenAIResponsesClient(
            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
            credential=AzureCliCredential(),
        ).as_agent(
            instructions=(
                "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
                " aligned to the prompt."
            ),
            name="marketer",
        )
    )

    legal = AgentExecutor(
        AzureOpenAIResponsesClient(
            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
            credential=AzureCliCredential(),
        ).as_agent(
            instructions=(
                "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
                " based on the prompt."
            ),
            name="legal",
        )
    )

    # Create executor instances
    dispatcher = DispatchToExperts(id="dispatcher")
    aggregator = AggregateInsights(id="aggregator")

    # Build a simple fan-out/fan-in workflow
    workflow = (
        WorkflowBuilder(start_executor=dispatcher)
        .add_fan_out_edges(dispatcher, [researcher, marketer, legal])
        .add_fan_in_edges([researcher, marketer, legal], aggregator)
        .build()
    )

    # Generate workflow visualization
    print("Generating workflow visualization...")
    viz = WorkflowViz(workflow)
    # Print out the mermaid string.
    print("Mermaid string: \n=======")
    print(viz.to_mermaid())
    print("=======")
    # Print out the DiGraph string with internal executors.
    print("DiGraph string: \n=======")
    print(viz.to_digraph(include_internal_executors=True))
    print("=======")

    # Export the DiGraph visualization as SVG.
    svg_file = viz.export(format="svg")
    print(f"SVG file saved to: {svg_file}")


if __name__ == "__main__":
    asyncio.run(main())