chore: import upstream snapshot with attribution

python/samples/03-workflows/functional/parallel_pipeline.py

@@ -0,0 +1,66 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Parallel pipeline using asyncio.gather with functional workflows.
+
+Fan-out/fan-in uses native Python concurrency via asyncio.gather.
+No @step needed — still just plain async functions.
+"""
+
+import asyncio
+
+from agent_framework import workflow
+
+
+# Plain async functions — asyncio.gather handles the concurrency,
+# no framework primitives needed for parallelism.
+async def research_web(topic: str) -> str:
+    """Simulate web research."""
+    await asyncio.sleep(0.05)
+    return f"Web results for '{topic}': 10 articles found"
+
+
+async def research_papers(topic: str) -> str:
+    """Simulate academic paper search."""
+    await asyncio.sleep(0.05)
+    return f"Papers on '{topic}': 3 relevant papers"
+
+
+async def research_news(topic: str) -> str:
+    """Simulate news search."""
+    await asyncio.sleep(0.05)
+    return f"News about '{topic}': 5 recent articles"
+
+
+async def synthesize(sources: list[str]) -> str:
+    """Combine research results into a summary."""
+    return "Research Summary:\n" + "\n".join(f"  - {s}" for s in sources)
+
+
+# @workflow wraps the orchestration logic so you get .run(), streaming,
+# and events. The functions it calls are plain Python — no decorators
+# needed just because they're inside a workflow.
+@workflow
+async def research_pipeline(topic: str) -> str:
+    """Fan-out to three research tasks, then synthesize results."""
+    # asyncio.gather runs all three concurrently — this is standard Python,
+    # not a framework concept. Use it the same way you would anywhere else.
+    #
+    # Tip: if any of these were wrapped with @step (e.g. an expensive agent call),
+    # the pattern is identical — @step composes with asyncio.gather, so each
+    # branch is independently cached on HITL resume or checkpoint restore.
+    web, papers, news = await asyncio.gather(
+        research_web(topic),
+        research_papers(topic),
+        research_news(topic),
+    )
+
+    return await synthesize([web, papers, news])
+
+
+async def main():
+    result = await research_pipeline.run("AI agents")
+    print(result.get_outputs()[0])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())