chore: import upstream snapshot with attribution

python/samples/02-agents/providers/ollama/ollama_agent_reasoning.py

@@ -0,0 +1,44 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import Agent
+from agent_framework.ollama import OllamaChatClient
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+Ollama Agent Reasoning Example
+
+This sample demonstrates implementing a Ollama agent with reasoning.
+
+Ensure to install Ollama and have a model running locally before running the sample
+Not all Models support reasoning, to test reasoning try qwen3:8b
+Set the model to use via the OLLAMA_MODEL environment variable or modify the code below.
+https://ollama.com/
+
+"""
+
+
+async def main() -> None:
+    print("=== Response Reasoning Example ===")
+
+    agent = Agent(
+        client=OllamaChatClient(),
+        name="TimeAgent",
+        instructions="You are a helpful agent answer in one sentence.",
+        default_options={"think": True},  # Enable Reasoning on agent level
+    )
+    query = "Hey what is 3+4? Can you explain how you got to that answer?"
+    print(f"User: {query}")
+    # Enable Reasoning on per request level
+    result = await agent.run(query)
+    reasoning = "".join((c.text or "") for c in result.messages[-1].contents if c.type == "text_reasoning")
+    print(f"Reasoning: {reasoning}")
+    print(f"Answer: {result}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())