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python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from dataclasses import dataclass
+
+from agent_framework import (
+    AgentExecutorRequest,
+    AgentExecutorResponse,
+    ChatAgent,
+    ChatMessage,
+    Executor,
+    Role,
+    WorkflowBuilder,
+    WorkflowContext,
+    WorkflowViz,
+    handler,
+)
+from agent_framework.azure import AzureOpenAIChatClient
+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/ Azure OpenAI for `AzureOpenAIChatClient` 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 = ChatMessage(Role.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)
+
+
+def create_researcher_agent() -> ChatAgent:
+    """Creates a research domain expert agent."""
+    return AzureOpenAIChatClient(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",
+    )
+
+
+def create_marketer_agent() -> ChatAgent:
+    """Creates a marketing domain expert agent."""
+    return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
+        instructions=(
+            "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
+            " aligned to the prompt."
+        ),
+        name="marketer",
+    )
+
+
+def create_legal_agent() -> ChatAgent:
+    """Creates a legal domain expert agent."""
+    return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
+        instructions=(
+            "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
+            " based on the prompt."
+        ),
+        name="legal",
+    )
+
+
+async def main() -> None:
+    """Build and run the concurrent workflow with visualization."""
+
+    # Build a simple fan-out/fan-in workflow
+    workflow = (
+        WorkflowBuilder()
+        .register_agent(create_researcher_agent, name="researcher")
+        .register_agent(create_marketer_agent, name="marketer")
+        .register_agent(create_legal_agent, name="legal")
+        .register_executor(lambda: DispatchToExperts(id="dispatcher"), name="dispatcher")
+        .register_executor(lambda: AggregateInsights(id="aggregator"), name="aggregator")
+        .set_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())