chore: import upstream snapshot with attribution

python/samples/02-agents/providers/openai/client_reasoning.py

@@ -0,0 +1,86 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import Agent
+from agent_framework.openai import OpenAIChatClient, OpenAIChatOptions
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+OpenAI Chat Client Reasoning Example
+
+This sample demonstrates advanced reasoning capabilities using OpenAI's gpt-5 models,
+showing step-by-step reasoning process visualization and complex problem-solving.
+
+This uses the default_options parameter to enable reasoning with high effort and detailed summaries.
+You can also set these options at the run level using the options parameter.
+Since these are api and/or provider specific, you will need to lookup
+the correct values for your provider, as they are passed through as-is.
+
+In this case they are here: https://platform.openai.com/docs/api-reference/responses/create#responses-create-reasoning
+"""
+
+
+agent = Agent(
+    client=OpenAIChatClient[OpenAIChatOptions](model="gpt-5"),
+    name="MathHelper",
+    instructions="You are a personal math tutor. When asked a math question, "
+    "reason over how best to approach the problem and share your thought process.",
+    default_options={"reasoning": {"effort": "high", "summary": "detailed"}},
+)
+
+
+async def reasoning_example() -> None:
+    """Example of reasoning response (get results as they are generated)."""
+    print("\033[92m=== Reasoning Example ===\033[0m")
+
+    query = "I need to solve the equation 3x + 11 = 14 and I need to prove the pythagorean theorem. Can you help me?"
+    print(f"User: {query}")
+    print(f"{agent.name}: ", end="", flush=True)
+    response = await agent.run(query)
+    for msg in response.messages:
+        if msg.contents:
+            for content in msg.contents:
+                if content.type == "text_reasoning":
+                    print(f"\033[94m{content.text}\033[0m", end="", flush=True)
+                elif content.type == "text":
+                    print(content.text, end="", flush=True)
+    print("\n")
+    if response.usage_details:
+        print(f"Usage: {response.usage_details}")
+
+
+async def streaming_reasoning_example() -> None:
+    """Example of reasoning response (get results as they are generated)."""
+    print("\033[92m=== Streaming Reasoning Example ===\033[0m")
+
+    query = "I need to solve the equation 3x + 11 = 14 and I need to prove the pythagorean theorem. Can you help me?"
+    print(f"User: {query}")
+    print(f"{agent.name}: ", end="", flush=True)
+    usage = None
+    async for chunk in agent.run(query, stream=True):
+        if chunk.contents:
+            for content in chunk.contents:
+                if content.type == "text_reasoning":
+                    print(f"\033[94m{content.text}\033[0m", end="", flush=True)
+                elif content.type == "text":
+                    print(content.text, end="", flush=True)
+                elif content.type == "usage":
+                    usage = content
+    print("\n")
+    if usage:
+        print(f"Usage: {usage.usage_details}")
+
+
+async def main() -> None:
+    print("\033[92m=== Basic OpenAI Chat Reasoning Agent Example ===\033[0m")
+
+    await reasoning_example()
+    await streaming_reasoning_example()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())