chore: import upstream snapshot with attribution

python/samples/03-workflows/orchestrations/concurrent_agents.py

@@ -0,0 +1,144 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from typing import Any
+
+from agent_framework import Agent, Message
+from agent_framework.foundry import FoundryChatClient
+from agent_framework.orchestrations import ConcurrentBuilder
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+Sample: Concurrent fan-out/fan-in (agent-only API) with default aggregator
+
+Build a high-level concurrent workflow using ConcurrentBuilder and three domain agents.
+The default dispatcher fans out the same user prompt to all agents in parallel.
+The default aggregator fans in their results and yields output containing
+a list[Message] representing the concatenated conversations from all agents.
+
+Demonstrates:
+- Minimal wiring with ConcurrentBuilder(participants=[...]).build()
+- Fan-out to multiple agents, fan-in aggregation of final ChatMessages
+- Workflow completion when idle with no pending work
+
+Prerequisites:
+- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
+- Familiarity with Workflow events (WorkflowEvent)
+"""
+
+
+async def main() -> None:
+    # 1) Create three domain agents using FoundryChatClient
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
+        credential=AzureCliCredential(),
+    )
+
+    researcher = Agent(
+        client=client,
+        instructions=(
+            "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
+            " opportunities, and risks."
+        ),
+        name="researcher",
+    )
+
+    marketer = Agent(
+        client=client,
+        instructions=(
+            "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
+            " aligned to the prompt."
+        ),
+        name="marketer",
+    )
+
+    legal = Agent(
+        client=client,
+        instructions=(
+            "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
+            " based on the prompt."
+        ),
+        name="legal",
+    )
+
+    # 2) Build a concurrent workflow
+    # Participants are either Agents (type of SupportsAgentRun) or Executors
+    workflow = ConcurrentBuilder(participants=[researcher, marketer, legal]).build()
+
+    # 3) Run with a single prompt and pretty-print the final combined messages
+    events = await workflow.run("We are launching a new budget-friendly electric bike for urban commuters.")
+    outputs = events.get_outputs()
+
+    if outputs:
+        print("===== Final Aggregated Conversation (messages) =====")
+        for output in outputs:
+            messages: list[Message] | Any = output
+            for i, msg in enumerate(messages, start=1):
+                name = msg.author_name if msg.author_name else "user"
+                print(f"{'-' * 60}\n\n{i:02d} [{name}]:\n{msg.text}")
+
+    """
+    Sample Output:
+
+    ===== Final Aggregated Conversation (messages) =====
+    ------------------------------------------------------------
+
+    01 [user]:
+    We are launching a new budget-friendly electric bike for urban commuters.
+    ------------------------------------------------------------
+
+    02 [researcher]:
+    **Insights:**
+
+    - **Target Demographic:** Urban commuters seeking affordable, eco-friendly transport;
+        likely to include students, young professionals, and price-sensitive urban residents.
+    - **Market Trends:** E-bike sales are growing globally, with increasing urbanization,
+        higher fuel costs, and sustainability concerns driving adoption.
+    - **Competitive Landscape:** Key competitors include brands like Rad Power Bikes, Aventon,
+        Lectric, and domestic budget-focused manufacturers in North America, Europe, and Asia.
+    - **Feature Expectations:** Customers expect reliability, ease-of-use, theft protection,
+        lightweight design, sufficient battery range for daily city commutes (typically 25-40 miles),
+        and low-maintenance components.
+
+    **Opportunities:**
+
+    - **First-time Buyers:** Capture newcomers to e-biking by emphasizing affordability, ease of
+        operation, and cost savings vs. public transit/car ownership.
+    ...
+    ------------------------------------------------------------
+
+    03 [marketer]:
+    **Value Proposition:**
+    "Empowering your city commute: Our new electric bike combines affordability, reliability, and
+        sustainable design—helping you conquer urban journeys without breaking the bank."
+
+    **Target Messaging:**
+
+    *For Young Professionals:*
+    ...
+    ------------------------------------------------------------
+
+    04 [legal]:
+    **Constraints, Disclaimers, & Policy Concerns for Launching a Budget-Friendly Electric Bike for Urban Commuters:**
+
+    **1. Regulatory Compliance**
+    - Verify that the electric bike meets all applicable federal, state, and local regulations
+        regarding e-bike classification, speed limits, power output, and safety features.
+    - Ensure necessary certifications (e.g., UL certification for batteries, CE markings if sold internationally) are obtained.
+
+    **2. Product Safety**
+    - Include consumer safety warnings regarding use, battery handling, charging protocols, and age restrictions.
+    ...
+    """  # noqa: E501
+
+
+if __name__ == "__main__":
+    asyncio.run(main())