Kazuhiro Sera
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
"""
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Example demonstrating how to use the reasoning content feature with the Runner API.
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This example shows how to extract and use reasoning content from responses when using
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the Runner API, which is the most common way users interact with the Agents library.
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To run this example, you need to:
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1. Set your OPENAI_API_KEY environment variable
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2. Use a model that supports reasoning summaries (e.g., gpt-5.6)
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"""
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import asyncio
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import os
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from openai.types.shared.reasoning import Reasoning
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from agents import Agent, ModelSettings, Runner, trace
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from agents.items import ReasoningItem
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MODEL_NAME = os.getenv("REASONING_MODEL_NAME") or "gpt-5.6"
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async def main():
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print(f"Using model: {MODEL_NAME}")
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# Create an agent with a model that supports reasoning content
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agent = Agent(
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name="Reasoning Agent",
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instructions="You are a helpful assistant that explains your reasoning step by step.",
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model=MODEL_NAME,
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model_settings=ModelSettings(reasoning=Reasoning(effort="high", summary="auto")),
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)
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# Example 1: Non-streaming response
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with trace("Reasoning Content - Non-streaming"):
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print("\n=== Example 1: Non-streaming response ===")
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result = await Runner.run(
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agent, "What is the square root of 841? Please explain your reasoning."
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)
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# Extract reasoning content from the result items
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reasoning_parts: list[str] = []
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for item in result.new_items:
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if isinstance(item, ReasoningItem):
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reasoning_parts.extend(summary.text for summary in item.raw_item.summary)
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reasoning_content = "\n".join(reasoning_parts)
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if not reasoning_content:
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raise RuntimeError(f"Model {MODEL_NAME} returned no reasoning summary.")
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print("\n### Reasoning Content:")
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print(reasoning_content)
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print("\n### Final Output:")
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print(result.final_output)
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# Example 2: Streaming response
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with trace("Reasoning Content - Streaming"):
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print("\n=== Example 2: Streaming response ===")
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stream = Runner.run_streamed(
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agent,
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"A recursive function uses T(n) = 2 * T(n - 1) + 1 with T(0) = 1. "
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"Compute T(20) and derive a closed form.",
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)
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output_text_already_started = False
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saw_reasoning_summary_delta = False
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saw_output_text_delta = False
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async for event in stream.stream_events():
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if event.type == "raw_response_event":
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if event.data.type == "response.reasoning_summary_text.delta":
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saw_reasoning_summary_delta = True
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print(f"\033[33m{event.data.delta}\033[0m", end="", flush=True)
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elif event.data.type == "response.output_text.delta":
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saw_output_text_delta = True
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if not output_text_already_started:
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print("\n")
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output_text_already_started = True
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print(f"\033[32m{event.data.delta}\033[0m", end="", flush=True)
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if not saw_reasoning_summary_delta:
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raise RuntimeError(f"Model {MODEL_NAME} returned no streaming reasoning summary.")
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if not saw_output_text_delta:
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raise RuntimeError(f"Model {MODEL_NAME} returned no streaming output text.")
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print("\n")
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if __name__ == "__main__":
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asyncio.run(main())
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