Bug: server-side compaction is not emitted on Responses tool-call-only turns

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Seit 02.5.2026.

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Bewertung

Schwierigkeit
4/5
Geschätzter Aufwand
3-5 Tage
Anfängerfreundlichkeit
38/100
Issue-Typ
Bug
Klarheit
Muss geklärt werden
Aktivitätsstatus
Ruhig
Tech-Stack
python
Bereich
api

Rechercherichtung

Beginne damit, die gemeldete Abfolge mit responses.create und responses.parse unter Verwendung der bereitgestellten Tool-Call-Schleife und der context_management-Einstellungen zu reproduzieren. Vergleiche die vom SDK zurückgegebenen Ausgabe-Elemente mit dem zugrunde liegenden Verhalten der Responses API. Als abgeschlossen gilt die Aufgabe, wenn festgestellt ist, ob die Python-Bibliothek compaction items bei ausschließlich aus Tool-Calls bestehenden Turns verwirft und, falls ja, ein Regressionstest für das korrigierte Verhalten definiert ist.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Beschreibung

bug
Confirm this is an issue with the Python library and not an underlying OpenAI API
  • This is an issue with the Python library
Describe the bug

Describe the bug

I am using the Responses API through openai-python with:

  • context_management=[{"type": "compaction", "compact_threshold": 1000}]
  • store=False
  • gpt-5.4

I see different behavior depending on the output type of the turn:

  • For a long plain request, response.output contains:

    • message
    • compaction

  • For a long request that returns only function_call, response.output contains only:

    • function_call

  • If I continue the tool loop and the next turn is again only function_call, there is still no compaction.

  • Only when the model finally returns an assistant message does response.output include:

    • message
    • compaction

This means that in tool-heavy agent loops with several consecutive tool-call turns, context can continue growing without any emitted compaction item, and the loop can eventually hit
context_length_exceeded before compaction appears.

I reproduced this through openai-python using both client.responses.create(...) and client.responses.parse(...).

If this is expected backend/API behavior rather than a Python SDK issue, please let me know and I can move the report.

To Reproduce

  1. Create a long input that is clearly above the compaction threshold.
  2. Enable server-side compaction with a very low threshold, for example:
    context_management=[{"type": "compaction", "compact_threshold": 1000}]
  3. Force the first turn to produce a function_call.
  4. Send the corresponding function_call_output.
  5. If the model produces another function_call, observe that there is still no compaction item in response.output.
  6. Observe that compaction only appears once the model finally emits an assistant message.

Observed output from my repro:

R1 input_tokens 5084
R1 output_types ['function_call']

R2 input_tokens 5119
R2 output_types ['function_call']

R3 input_tokens 5154
R3 output_types ['message', 'compaction']

For comparison, a plain long request with the same threshold produces compaction immediately:

CREATE input_tokens 5007
CREATE output_types ['message', 'compaction']
To Reproduce
import asyncio
from openai import AsyncOpenAI
from azure.identity.aio import DefaultAzureCredential, get_bearer_token_provider

AZURE_ENDPOINT = "https://<your-resource>.openai.azure.com/openai/v1/"
MODEL = "gpt-5.4"

async def main():
    cred = DefaultAzureCredential()
    token_provider = get_bearer_token_provider(
        cred,
        "https://cognitiveservices.azure.com/.default"
    )

    client = AsyncOpenAI(
        base_url=AZURE_ENDPOINT,
        api_key=token_provider,
    )

    long_text = "context " * 5000

    tools = [{
        "type": "function",
        "name": "echo_tool",
        "description": "Echo a short string",
        "parameters": {
            "type": "object",
            "properties": {
                "text": {"type": "string"}
            },
            "required": ["text"],
            "additionalProperties": False
        }
    }]

    cm = [{"type": "compaction", "compact_threshold": 1000}]

    conversation = [{
        "role": "user",
        "content": (
            long_text +
            "\n\nCall echo_tool twice in sequence. "
            "First with text=first. After I return the tool result, "
            "call echo_tool again with text=second. "
            "Only after the second tool result, answer DONE."
        )
    }]

    for step in range(1, 5):
        response = await client.responses.create(
            model=MODEL,
            input=conversation,
            tools=tools,
            store=False,
            context_management=cm,
        )

        print(f"R{step} input_tokens:", response.usage.input_tokens)
        print(f"R{step} output_types:", [getattr(i, 'type', None) for i in response.output])

        conversation.extend(response.output)

        function_calls = [i for i in response.output if getattr(i, "type", None) == "function_call"]
        if function_calls:
            for idx, fc in enumerate(function_calls, start=1):
                conversation.append({
                    "type": "function_call_output",
                    "call_id": fc.call_id,
                    "output": f"tool-result-{step}-{idx}",
                })
        else:
            break

    await client.close()
    await cred.close()

asyncio.run(main())
Code snippets
OS

Windows

Python version

3.11.5

Library version

openai 2.21.0

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