chore: import upstream snapshot with attribution

examples/flows/standard/basic-maf/.env.example

@@ -0,0 +1,3 @@
+AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
+AZURE_OPENAI_API_KEY=your-api-key
+AZURE_OPENAI_DEPLOYMENT=gpt-35-turbo-instruct

examples/flows/standard/basic-maf/requirements.txt

@@ -0,0 +1,3 @@
+agent-framework>=1.0.1
+agent-framework-openai>=1.0.1
+python-dotenv

examples/flows/standard/basic-maf/test_basic.py

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+import asyncio
+
+from workflow import create_workflow
+
+
+async def main():
+    workflow = create_workflow()
+    result = await workflow.run("Hello World!")
+    output = result.get_outputs()[0]
+    print(f"Output: {output}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/flows/standard/basic-maf/workflow.py

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+import asyncio
+import os
+
+from dotenv import load_dotenv
+from typing_extensions import Never
+
+from agent_framework import Agent, Executor, WorkflowBuilder, WorkflowContext, handler
+from agent_framework.openai import OpenAIChatClient
+
+load_dotenv()
+
+_PROMPT_TEMPLATE = "Write a simple {text} program that displays the greeting message."
+
+
+class PromptExecutor(Executor):
+    @handler
+    async def receive(self, text: str, ctx: WorkflowContext[str]) -> None:
+        prompt = _PROMPT_TEMPLATE.format(text=text)
+        await ctx.send_message(prompt)
+
+
+class LLMExecutor(Executor):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        client = OpenAIChatClient(
+            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+            model=os.environ["AZURE_OPENAI_DEPLOYMENT"],
+            api_key=os.environ["AZURE_OPENAI_API_KEY"],
+        )
+        self._agent = Agent(
+            client=client,
+            name="BasicAgent",
+            instructions="You are a helpful assistant.",
+        )
+
+    @handler
+    async def call_llm(self, prompt: str, ctx: WorkflowContext[Never, str]) -> None:
+        response = await self._agent.run(prompt)
+        await ctx.yield_output(response.text)
+
+
+def create_workflow():
+    """Create a fresh workflow instance.
+
+    MAF workflows do not support concurrent execution, so each
+    concurrent caller needs its own workflow instance.
+    """
+    _prompt = PromptExecutor(id="hello_prompt")
+    _llm = LLMExecutor(id="llm")
+    return (
+        WorkflowBuilder(name="BasicWorkflow", start_executor=_prompt)
+        .add_edge(_prompt, _llm)
+        .build()
+    )
+
+
+async def main():
+    workflow = create_workflow()
+    result = await workflow.run("Hello World!")
+    print(result.get_outputs()[0])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())