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Add reasoning content - fix on openai#494 (openai#871)
my personal update on openai#494 Key changes: - Updated `ResponseReasoningItem` to use the correct structure with `id`, `summary`, and `type="reasoning"` fields - Fixed `ResponseReasoningSummaryPartAddedEvent` to include `summary_index` parameter and use a dictionary for the `part` parameter - Fixed `ResponseReasoningSummaryTextDeltaEvent` to include `summary_index` parameter - Updated `ResponseReasoningSummaryPartDoneEvent` to include `summary_index` and use a dictionary for `part` - Changed how the `Summary` object is accessed in tests (using `.text` property instead of subscript notation) - Updated `chatcmpl_converter.py` to use the `Summary` class instead of a dictionary for better handlign Fixes openai#494
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‎examples/reasoning_content/__init__.py

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"""
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Examples demonstrating how to use models that provide reasoning content.
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"""

‎examples/reasoning_content/main.py

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"""
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Example demonstrating how to use the reasoning content feature with models that support it.
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Some models, like deepseek-reasoner, provide a reasoning_content field in addition to the regular content.
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This example shows how to access and use this reasoning content from both streaming and non-streaming responses.
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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 content (e.g., deepseek-reasoner)
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"""
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import asyncio
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import os
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from typing import Any, cast
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from agents import ModelSettings
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from agents.models.interface import ModelTracing
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from agents.models.openai_provider import OpenAIProvider
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from agents.types import ResponseOutputRefusal, ResponseOutputText # type: ignore
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MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner"
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async def stream_with_reasoning_content():
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"""
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Example of streaming a response from a model that provides reasoning content.
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The reasoning content will be emitted as separate events.
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"""
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provider = OpenAIProvider()
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model = provider.get_model(MODEL_NAME)
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print("\n=== Streaming Example ===")
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print("Prompt: Write a haiku about recursion in programming")
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reasoning_content = ""
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regular_content = ""
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async for event in model.stream_response(
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system_instructions="You are a helpful assistant that writes creative content.",
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input="Write a haiku about recursion in programming",
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model_settings=ModelSettings(),
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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prompt=None
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):
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if event.type == "response.reasoning_summary_text.delta":
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print(
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f"\033[33m{event.delta}\033[0m", end="", flush=True
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) # Yellow for reasoning content
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reasoning_content += event.delta
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elif event.type == "response.output_text.delta":
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print(f"\033[32m{event.delta}\033[0m", end="", flush=True) # Green for regular content
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regular_content += event.delta
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print("\n\nReasoning Content:")
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print(reasoning_content)
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print("\nRegular Content:")
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print(regular_content)
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print("\n")
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async def get_response_with_reasoning_content():
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"""
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Example of getting a complete response from a model that provides reasoning content.
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The reasoning content will be available as a separate item in the response.
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"""
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provider = OpenAIProvider()
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model = provider.get_model(MODEL_NAME)
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print("\n=== Non-streaming Example ===")
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print("Prompt: Explain the concept of recursion in programming")
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response = await model.get_response(
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system_instructions="You are a helpful assistant that explains technical concepts clearly.",
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input="Explain the concept of recursion in programming",
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model_settings=ModelSettings(),
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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prompt=None
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)
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# Extract reasoning content and regular content from the response
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reasoning_content = None
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regular_content = None
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for item in response.output:
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if hasattr(item, "type") and item.type == "reasoning":
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reasoning_content = item.summary[0].text
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elif hasattr(item, "type") and item.type == "message":
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if item.content and len(item.content) > 0:
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content_item = item.content[0]
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if isinstance(content_item, ResponseOutputText):
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regular_content = content_item.text
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elif isinstance(content_item, ResponseOutputRefusal):
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refusal_item = cast(Any, content_item)
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regular_content = refusal_item.refusal
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print("\nReasoning Content:")
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print(reasoning_content or "No reasoning content provided")
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print("\nRegular Content:")
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print(regular_content or "No regular content provided")
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print("\n")
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async def main():
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try:
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await stream_with_reasoning_content()
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await get_response_with_reasoning_content()
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except Exception as e:
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print(f"Error: {e}")
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print("\nNote: This example requires a model that supports reasoning content.")
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print("You may need to use a specific model like deepseek-reasoner or similar.")
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if __name__ == "__main__":
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asyncio.run(main())

‎examples/reasoning_content/runner_example.py

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"""
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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 content (e.g., deepseek-reasoner)
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"""
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import asyncio
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import os
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from typing import Any
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from agents import Agent, Runner, trace
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from agents.items import ReasoningItem
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MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner"
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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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)
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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_content = None
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# RunResult has 'response' attribute which has 'output' attribute
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for item in result.response.output: # type: ignore
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if isinstance(item, ReasoningItem):
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reasoning_content = item.summary[0].text # type: ignore
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break
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print("\nReasoning Content:")
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print(reasoning_content or "No reasoning content provided")
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print("\nFinal 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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print("\nStreaming response:")
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# Buffers to collect reasoning and regular content
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reasoning_buffer = ""
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content_buffer = ""
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# RunResultStreaming is async iterable
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stream = Runner.run_streamed(agent, "What is 15 x 27? Please explain your reasoning.")
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async for event in stream: # type: ignore
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if isinstance(event, ReasoningItem):
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# This is reasoning content
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reasoning_item: Any = event
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reasoning_buffer += reasoning_item.summary[0].text
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print(
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f"\033[33m{reasoning_item.summary[0].text}\033[0m", end="", flush=True
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) # Yellow for reasoning
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elif hasattr(event, "text"):
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# This is regular content
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content_buffer += event.text
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print(
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f"\033[32m{event.text}\033[0m", end="", flush=True
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) # Green for regular content
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print("\n\nCollected Reasoning Content:")
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print(reasoning_buffer)
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print("\nCollected Final Answer:")
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print(content_buffer)
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if __name__ == "__main__":
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asyncio.run(main())

‎src/agents/models/chatcmpl_converter.py

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ResponseOutputMessageParam,
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ResponseOutputRefusal,
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ResponseOutputText,
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ResponseReasoningItem,
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)
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from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message
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from openai.types.responses.response_reasoning_item import Summary
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from ..agent_output import AgentOutputSchemaBase
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from ..exceptions import AgentsException, UserError
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def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]:
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items: list[TResponseOutputItem] = []
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# Handle reasoning content if available
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if hasattr(message, "reasoning_content") and message.reasoning_content:
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items.append(
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ResponseReasoningItem(
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id=FAKE_RESPONSES_ID,
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summary=[Summary(text=message.reasoning_content, type="summary_text")],
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type="reasoning",
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)
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)
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message_item = ResponseOutputMessage(
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id=FAKE_RESPONSES_ID,
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content=[],

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