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[Bug]: gpt-oss response api: streaming + code interpreter has bugs #30222

@jordane95

Description

@jordane95

Your current environment

The output of python collect_env.py
Your output of `python collect_env.py` here

🐛 Describe the bug

Gpt-oss in streaming mode cannot see internal code interpreter output

the problem is with

def append_tool_output(self, output: list[Message]) -> None:
# Handle the case of tool output in direct message format
assert len(output) == 1, "Tool output should be a single message"
msg = output[0]
# Sometimes the recipient is not set for tool messages,
# so we set it to "assistant"
if msg.author.role == Role.TOOL and msg.recipient is None:
msg.recipient = "assistant"
toks = self.encoding.render(msg)
for tok in toks:
self.parser.process(tok)
self.last_tok = toks[-1]
# TODO: add tool_output messages to self._messages

I can see that tool call result is not appended to message.

My basic testing code looks like this

stream = client.responses.create(
    model="vllm-model",
    input=[{"role": "user", "content": "what is 123^456 mod 1000000007? use python tool to solve this problem"}],
    tools=[{"type": "code_interpreter", "container": {"type": "auto"}}],
    max_output_tokens=32768,
    temperature=1.0,
    reasoning={"effort": "high"},
    stream=True,
    instructions=system_prompt,
    extra_body={
        "min_p": 0.02,
        "stop_token_ids": stop_token_ids,
        "chat_template_kwargs": {"enable_thinking": True},
    }
)

current_tool_code = ""

for event in stream:
    generation_idx += 1

    # Reasoning text
    if event.type == "response.reasoning_text.delta":
        delta = event.delta
        reasoning_response += delta
        text_response += delta
        print(delta, end="", flush=True)  # Real-time output

    # Message text
    elif event.type == "response.output_text.delta":
        delta = event.delta
        text_response += delta
        print(delta, end="", flush=True)

    # Tool call events
    elif event.type == "response.code_interpreter_call_code.delta":
        current_tool_code += event.delta

    elif event.type == "response.code_interpreter_call_code.done":
        tool_calls_log.append({
            "code": event.code,
            "type": "code_interpreter"
        })
        current_tool_code = ""
        print(event.code)

    elif event.type == "response.completed":
        # Final event - could extract full response here if needed
        pass

model response (ignore the pretty looking, it is just another version for visualization)

============================================================


💭 REASONING:
We need to compute 123^456 mod 1000000007. It's a big power but within modular exponent. We can compute quickly with pow in Python: pow(123, 456, 1000000007). But the prompt says please use python tool to solve this problem. We'll use python.
📝 CODE EXECUTED:
pow(123, 456, 1000000007)

------------------------------------------------------------


💭 REASONING:
Let's see result.

💭 REASONING:
It printed something? Wait, no output visible yet. We may need to capture the output. Let's assign.
📝 CODE EXECUTED:
result = pow(123, 456, 1000000007)
result

------------------------------------------------------------


💭 REASONING:
It returned something? Let's see.

💭 REASONING:
It didn't print, but the value is stored. We should print the result.
📝 CODE EXECUTED:
print(result)

------------------------------------------------------------


💭 REASONING:
565291922
So answer is 565291922. Provide box.

📄 FINAL ANSWER:
The value of \(123^{456} \bmod 1000000007\) is  
\[
\boxed{565291922}
\]
============================================================
✅ RESPONSE COMPLETED
Tool output tokens: 82

============================================================

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