openai--openai-agents-python/tests/models/test_openai_chatcompletions_stream.py

import logging
from collections.abc import AsyncIterator
from typing import Any, cast

import pytest
from openai.types.chat.chat_completion import ChatCompletion, Choice as ChatCompletionChoice
from openai.types.chat.chat_completion_chunk import (
    ChatCompletionChunk,
    Choice,
    ChoiceDelta,
    ChoiceDeltaToolCall,
    ChoiceDeltaToolCallFunction,
    ChoiceLogprobs,
)
from openai.types.chat.chat_completion_message import ChatCompletionMessage
from openai.types.chat.chat_completion_token_logprob import (
    ChatCompletionTokenLogprob,
    TopLogprob,
)
from openai.types.completion_usage import (
    CompletionTokensDetails,
    CompletionUsage,
    PromptTokensDetails,
)
from openai.types.responses import (
    Response,
    ResponseCompletedEvent,
    ResponseFunctionToolCall,
    ResponseOutputMessage,
    ResponseOutputRefusal,
    ResponseOutputText,
    ResponseReasoningItem,
)

from agents import Agent, Runner, function_tool
from agents.exceptions import ModelBehaviorError, UserError
from agents.model_settings import ModelSettings
from agents.models.chatcmpl_stream_handler import (
    ChatCmplStreamHandler,
    Part,
    SequenceNumber,
    StreamingState,
    _BufferedToolCall,
    _merge_buffered_metadata,
    _StreamOutputLayout,
)
from agents.models.interface import ModelTracing
from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel
from agents.models.openai_provider import OpenAIProvider
from tests.utils.simple_session import SimpleListSession


async def _empty_chat_completion_stream() -> AsyncIterator[ChatCompletionChunk]:
    chunks: list[ChatCompletionChunk] = []
    for chunk in chunks:
        yield chunk


def _empty_response() -> Response:
    return Response(
        id="resp-id",
        created_at=0,
        model="fake-model",
        object="response",
        output=[],
        tool_choice="none",
        tools=[],
        parallel_tool_calls=False,
    )


async def _completion_stream(
    *chunks: ChatCompletionChunk,
) -> AsyncIterator[ChatCompletionChunk]:
    for chunk in chunks:
        yield chunk


async def _collect_handler_events(
    *chunks: ChatCompletionChunk,
    model: str | None = None,
) -> list[Any]:
    return [
        event
        async for event in ChatCmplStreamHandler.handle_stream(
            _empty_response(), cast(Any, _completion_stream(*chunks)), model=model
        )
    ]


async def _collect_buffered_tool_call_chunks(
    *chunks: ChatCompletionChunk,
) -> list[ChatCompletionChunk]:
    return [
        chunk
        async for chunk in ChatCmplStreamHandler.buffer_tool_call_stream(
            _completion_stream(*chunks)
        )
    ]


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_yields_events_for_text_content(monkeypatch) -> None:
    """
    Validate that `stream_response` emits the correct sequence of events when
    streaming a simple assistant message consisting of plain text content.
    We simulate two chunks of text returned from the chat completion stream.
    """
    # Create two chunks that will be emitted by the fake stream.
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(content="He"))],
    )
    # Mark last chunk with usage so stream_response knows this is final.
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(content="llo"))],
        usage=CompletionUsage(
            completion_tokens=5,
            prompt_tokens=7,
            total_tokens=12,
            prompt_tokens_details=PromptTokensDetails.model_validate(
                {"cached_tokens": 2, "cache_write_tokens": 4}
            ),
            completion_tokens_details=CompletionTokensDetails(reasoning_tokens=3),
        ),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for c in (chunk1, chunk2):
            yield c

    # Patch _fetch_response to inject our fake stream
    async def patched_fetch_response(self, *args, **kwargs):
        # `_fetch_response` is expected to return a Response skeleton and the async stream
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)
    # We expect a response.created, then a response.output_item.added, content part added,
    # two content delta events (for "He" and "llo"), a content part done, the assistant message
    # output_item.done, and finally response.completed.
    # There should be 8 events in total.
    assert len(output_events) == 8
    # First event indicates creation.
    assert output_events[0].type == "response.created"
    # The output item added and content part added events should mark the assistant message.
    assert output_events[1].type == "response.output_item.added"
    assert output_events[2].type == "response.content_part.added"
    # Two text delta events.
    assert output_events[3].type == "response.output_text.delta"
    assert output_events[3].delta == "He"
    assert output_events[4].type == "response.output_text.delta"
    assert output_events[4].delta == "llo"
    # After streaming, the content part and item should be marked done.
    assert output_events[5].type == "response.content_part.done"
    assert output_events[6].type == "response.output_item.done"
    # Last event indicates completion of the stream.
    assert output_events[7].type == "response.completed"
    # The completed response should have one output message with full text.
    completed_resp = output_events[7].response
    assert isinstance(completed_resp.output[0], ResponseOutputMessage)
    assert isinstance(completed_resp.output[0].content[0], ResponseOutputText)
    assert completed_resp.output[0].content[0].text == "Hello"

    assert completed_resp.usage, "usage should not be None"
    assert completed_resp.usage.input_tokens == 7
    assert completed_resp.usage.output_tokens == 5
    assert completed_resp.usage.total_tokens == 12
    assert completed_resp.usage.input_tokens_details.cached_tokens == 2
    assert getattr(completed_resp.usage.input_tokens_details, "cache_write_tokens", None) == 4
    assert completed_resp.usage.output_tokens_details.reasoning_tokens == 3


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_close_closes_provider_stream_with_async_close(
    monkeypatch,
) -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(content="Hi"))],
    )

    class ClosableChatStream:
        def __init__(self) -> None:
            self._yielded = False
            self.close_calls = 0

        def __aiter__(self) -> "ClosableChatStream":
            return self

        async def __anext__(self) -> ChatCompletionChunk:
            if self._yielded:
                raise StopAsyncIteration
            self._yielded = True
            return chunk

        async def close(self) -> None:
            self.close_calls += 1

    provider_stream = ClosableChatStream()

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), provider_stream

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")

    stream = model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    )
    stream_agen = cast(Any, stream)

    event = await stream_agen.__anext__()
    assert event.type == "response.created"

    await stream_agen.aclose()

    assert provider_stream.close_calls == 1


@pytest.mark.asyncio
async def test_stream_handler_filters_multiple_choices_by_default(
    caplog: pytest.LogCaptureFixture,
) -> None:
    caplog.set_level(logging.WARNING, logger="openai.agents")
    chunks = [
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=1, delta=ChoiceDelta(content="ignored-first"))],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[
                Choice(index=0, delta=ChoiceDelta(content="kept")),
                Choice(index=1, delta=ChoiceDelta(content="ignored-second")),
            ],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=2, delta=ChoiceDelta(content="ignored-third"))],
            usage=CompletionUsage(completion_tokens=1, prompt_tokens=2, total_tokens=3),
        ),
    ]

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for chunk in chunks:
            yield chunk

    events = [
        event
        async for event in ChatCmplStreamHandler.handle_stream(
            _empty_response(), cast(Any, fake_stream())
        )
    ]

    text_delta_events = [event for event in events if event.type == "response.output_text.delta"]
    assert [event.delta for event in text_delta_events] == ["kept"]
    completed_event = next(event for event in events if event.type == "response.completed")
    assert isinstance(completed_event, ResponseCompletedEvent)
    assert isinstance(completed_event.response.output[0], ResponseOutputMessage)
    text_part = completed_event.response.output[0].content[0]
    assert isinstance(text_part, ResponseOutputText)
    assert text_part.text == "kept"
    assert completed_event.response.usage
    assert completed_event.response.usage.total_tokens == 3

    choice_warnings = [
        record
        for record in caplog.records
        if "multiple choices or nonzero choice indexes" in record.getMessage()
    ]
    assert len(choice_warnings) == 1


@pytest.mark.asyncio
async def test_stream_handler_keeps_empty_choice_usage_chunks() -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=2, total_tokens=3),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    events = [
        event
        async for event in ChatCmplStreamHandler.handle_stream(
            _empty_response(), cast(Any, fake_stream())
        )
    ]

    assert [event.type for event in events] == ["response.created", "response.completed"]
    completed_event = events[-1]
    assert isinstance(completed_event, ResponseCompletedEvent)
    assert completed_event.response.output == []
    assert completed_event.response.usage
    assert completed_event.response.usage.total_tokens == 3


@pytest.mark.asyncio
async def test_stream_handler_rejects_multiple_choices_in_strict_mode() -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[
            Choice(index=0, delta=ChoiceDelta(content="first")),
            Choice(index=1, delta=ChoiceDelta(content="second")),
        ],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    with pytest.raises(UserError, match="multiple choices or nonzero"):
        async for _ in ChatCmplStreamHandler.handle_stream(
            _empty_response(), cast(Any, fake_stream()), strict_feature_validation=True
        ):
            pass


@pytest.mark.asyncio
async def test_stream_handler_rejects_nonzero_choice_index_in_strict_mode() -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=1, delta=ChoiceDelta(content="second"))],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    with pytest.raises(UserError, match="multiple choices or nonzero"):
        async for _ in ChatCmplStreamHandler.handle_stream(
            _empty_response(), cast(Any, fake_stream()), strict_feature_validation=True
        ):
            pass


@pytest.mark.asyncio
async def test_buffer_tool_call_stream_merges_provider_metadata() -> None:
    tool_call_delta1 = ChoiceDeltaToolCall(
        index=0,
        id="tool-id",
        function=ChoiceDeltaToolCallFunction(name="my_func", arguments='{"a":'),
        type="function",
    )
    tool_call_delta1_any = cast(Any, tool_call_delta1)
    tool_call_delta1_any.provider_specific_fields = {
        "nested": {"keep": "provider", "stable": {"value": 1}},
        "replace": "old",
    }
    tool_call_delta1_any.extra_content = {
        "google": {"thought_signature": "sig-1", "stable": {"value": "kept"}}
    }
    tool_call_delta2 = ChoiceDeltaToolCall(
        index=0,
        id=None,
        function=ChoiceDeltaToolCallFunction(name=None, arguments="1}"),
        type="function",
    )
    tool_call_delta2_any = cast(Any, tool_call_delta2)
    tool_call_delta2_any.provider_specific_fields = {
        "nested": {"stable": {}, "new": "provider"},
        "replace": "new",
    }
    tool_call_delta2_any.extra_content = {"google": {"stable": {}, "new": "extra"}}
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
    )

    buffered_chunks = await _collect_buffered_tool_call_chunks(chunk1, chunk2)

    assert len(buffered_chunks) == 1
    buffered_delta = buffered_chunks[0].choices[0].delta
    assert buffered_delta.tool_calls
    buffered_tool_call = buffered_delta.tool_calls[0]
    assert buffered_tool_call.function
    assert buffered_tool_call.function.arguments == '{"a":1}'
    assert cast(Any, buffered_tool_call).provider_specific_fields == {
        "nested": {"keep": "provider", "stable": {"value": 1}, "new": "provider"},
        "replace": "new",
    }
    assert cast(Any, buffered_tool_call).extra_content == {
        "google": {"thought_signature": "sig-1", "stable": {"value": "kept"}, "new": "extra"}
    }


def test_stream_handler_internal_part_stores_text_and_type() -> None:
    part = Part(text="hello", type="output_text")

    assert part.text == "hello"
    assert part.type == "output_text"


def test_merge_buffered_metadata_keeps_existing_scalar_when_empty_dict_arrives() -> None:
    merged = _merge_buffered_metadata(
        {"stable": "keep-me"},
        {"stable": {}, "new": {}},
    )

    assert merged == {"stable": "keep-me", "new": {}}


def test_stream_output_layout_rejects_unknown_function_call_index() -> None:
    layout = _StreamOutputLayout()

    with pytest.raises(KeyError, match="Function call index 9 has not been tracked"):
        layout.function_call_output_index(StreamingState(), 9)


@pytest.mark.parametrize(
    ("buffered_call", "message"),
    [
        (
            _BufferedToolCall(index=0, name="my_func"),
            "without a tool call id",
        ),
        (
            _BufferedToolCall(index=0, call_id="tool-id"),
            "without a function name",
        ),
    ],
)
def test_buffered_tool_call_delta_requires_id_and_name(
    buffered_call: _BufferedToolCall,
    message: str,
) -> None:
    with pytest.raises(ModelBehaviorError, match=message):
        ChatCmplStreamHandler._buffered_tool_call_delta(buffered_call)


def test_function_call_item_omits_provider_data_when_absent() -> None:
    function_call = ResponseFunctionToolCall(
        id="fake-id",
        call_id="call-id",
        arguments="",
        name="my_func",
        type="function_call",
    )

    item = ChatCmplStreamHandler._function_call_item(
        StreamingState(),
        function_call,
        arguments="{}",
    )

    assert item.arguments == "{}"
    assert "provider_data" not in item.model_dump()


def test_finish_reasoning_summary_part_clears_invalid_active_index() -> None:
    reasoning_item = ResponseReasoningItem(id="fake-id", summary=[], type="reasoning")
    state = StreamingState(
        reasoning_content_index_and_output=(0, reasoning_item),
        active_reasoning_summary_index=0,
    )

    events = list(ChatCmplStreamHandler._finish_reasoning_summary_part(state, SequenceNumber()))

    assert events == []
    assert state.active_reasoning_summary_index is None


@pytest.mark.asyncio
async def test_buffer_tool_call_stream_preserves_empty_choice_chunks() -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[],
    )

    buffered_chunks = await _collect_buffered_tool_call_chunks(chunk)

    assert buffered_chunks == [chunk]


@pytest.mark.asyncio
async def test_buffer_tool_call_stream_keeps_passthrough_index_passthrough() -> None:
    custom_tool_call_delta = ChoiceDeltaToolCall.model_construct(
        index=0,
        id="custom-id",
        type="custom",
    )
    function_tool_call_delta = ChoiceDeltaToolCall(
        index=0,
        id="function-id",
        function=ChoiceDeltaToolCallFunction(name="my_func", arguments="{}"),
        type="function",
    )
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[custom_tool_call_delta]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[function_tool_call_delta]))],
    )

    buffered_chunks = await _collect_buffered_tool_call_chunks(chunk1, chunk2)

    assert len(buffered_chunks) == 2
    assert buffered_chunks[0].choices[0].delta.tool_calls == [custom_tool_call_delta]
    assert buffered_chunks[1].choices[0].delta.tool_calls == [function_tool_call_delta]


@pytest.mark.parametrize(
    ("delta", "expected"),
    [
        (None, False),
        (ChoiceDelta(), False),
        (ChoiceDelta(content="text"), True),
        (ChoiceDelta.model_construct(refusal="blocked"), True),
        (ChoiceDelta.model_construct(reasoning_content="summary"), True),
        (ChoiceDelta.model_construct(reasoning="scratchpad"), True),
        (ChoiceDelta.model_construct(thinking_blocks=[{"thinking": "hidden"}]), True),
    ],
)
def test_stream_handler_detects_passthrough_delta_shapes(
    delta: ChoiceDelta | None,
    expected: bool,
) -> None:
    assert ChatCmplStreamHandler._delta_has_passthrough_output(delta) is expected


@pytest.mark.asyncio
async def test_stream_handler_ignores_choice_without_delta() -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice.model_construct(index=0, delta=None)],
    )

    events = await _collect_handler_events(chunk)

    assert [event.type for event in events] == ["response.created", "response.completed"]
    completed_event = events[-1]
    assert isinstance(completed_event, ResponseCompletedEvent)
    assert completed_event.response.output == []


@pytest.mark.asyncio
async def test_stream_handler_converts_third_party_reasoning_text() -> None:
    reasoning_delta1 = ChoiceDelta.model_construct(reasoning="think ")
    reasoning_delta2 = ChoiceDelta.model_construct(reasoning="hard")
    chunks = [
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=reasoning_delta1)],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=reasoning_delta2)],
        ),
    ]

    events = await _collect_handler_events(*chunks, model="third-party")

    reasoning_delta_events = [
        event for event in events if event.type == "response.reasoning_text.delta"
    ]
    assert [event.delta for event in reasoning_delta_events] == ["think ", "hard"]

    reasoning_done_event = next(
        event
        for event in events
        if event.type == "response.output_item.done"
        and isinstance(event.item, ResponseReasoningItem)
    )
    reasoning_done_item = cast(ResponseReasoningItem, reasoning_done_event.item)
    assert reasoning_done_item.content
    assert cast(Any, reasoning_done_item.content[0]).text == "think hard"

    completed_event = next(event for event in events if event.type == "response.completed")
    assert isinstance(completed_event, ResponseCompletedEvent)
    completed_reasoning_item = completed_event.response.output[0]
    assert isinstance(completed_reasoning_item, ResponseReasoningItem)
    assert completed_reasoning_item.content
    assert cast(Any, completed_reasoning_item.content[0]).text == "think hard"
    assert completed_reasoning_item.model_dump().get("provider_data") == {
        "model": "third-party",
        "response_id": "chunk-id",
    }


@pytest.mark.asyncio
async def test_stream_handler_preserves_thinking_blocks_with_reasoning_summary() -> None:
    delta = ChoiceDelta.model_construct(
        reasoning_content="summary",
        thinking_blocks=[
            {"thinking": "hidden one ", "signature": "sig-1"},
            {"thinking": "hidden two", "signature": "sig-2"},
        ],
    )
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=delta)],
    )

    events = await _collect_handler_events(chunk)

    completed_event = next(event for event in events if event.type == "response.completed")
    reasoning_item = completed_event.response.output[0]
    assert isinstance(reasoning_item, ResponseReasoningItem)
    assert reasoning_item.summary[0].text == "summary"
    assert reasoning_item.content
    assert cast(Any, reasoning_item.content[0]).text == "hidden one hidden two"
    assert reasoning_item.encrypted_content == "sig-2"


@pytest.mark.asyncio
async def test_stream_handler_adds_third_party_reasoning_text_to_summary_item() -> None:
    chunks = [
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[
                Choice(index=0, delta=ChoiceDelta.model_construct(reasoning_content="summary"))
            ],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta.model_construct(reasoning="details"))],
        ),
    ]

    events = await _collect_handler_events(*chunks)

    completed_event = next(event for event in events if event.type == "response.completed")
    reasoning_item = completed_event.response.output[0]
    assert isinstance(reasoning_item, ResponseReasoningItem)
    assert reasoning_item.summary[0].text == "summary"
    assert reasoning_item.content
    assert cast(Any, reasoning_item.content[0]).text == "details"


@pytest.mark.asyncio
async def test_stream_handler_orders_refusal_after_reasoning_and_text() -> None:
    chunks = [
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[
                Choice(index=0, delta=ChoiceDelta.model_construct(reasoning_content="summary"))
            ],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta(content="partial"))],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta.model_construct(refusal="blocked"))],
        ),
    ]

    events = await _collect_handler_events(*chunks)

    completed_event = next(event for event in events if event.type == "response.completed")
    assistant_item = completed_event.response.output[1]
    assert isinstance(assistant_item, ResponseOutputMessage)
    assert isinstance(assistant_item.content[0], ResponseOutputText)
    assert isinstance(assistant_item.content[1], ResponseOutputRefusal)
    assert assistant_item.content[0].text == "partial"
    assert assistant_item.content[1].refusal == "blocked"


@pytest.mark.asyncio
async def test_stream_handler_places_text_after_existing_refusal_part() -> None:
    chunks = [
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta.model_construct(refusal="blocked"))],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta(content="partial"))],
        ),
    ]

    events = await _collect_handler_events(*chunks)

    text_part_added = next(
        event
        for event in events
        if event.type == "response.content_part.added"
        and isinstance(event.part, ResponseOutputText)
    )
    assert text_part_added.content_index == 1

    completed_event = next(event for event in events if event.type == "response.completed")
    assistant_item = completed_event.response.output[0]
    assert isinstance(assistant_item, ResponseOutputMessage)
    assert isinstance(assistant_item.content[0], ResponseOutputText)
    assert isinstance(assistant_item.content[1], ResponseOutputRefusal)
    assert assistant_item.content[0].text == "partial"
    assert assistant_item.content[1].refusal == "blocked"


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_passes_strict_validation_to_stream_handler(monkeypatch) -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=1, delta=ChoiceDelta(content="ignored"))],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        strict_feature_validation=True,
    ).get_model("gpt-4")

    with pytest.raises(UserError, match="multiple choices or nonzero"):
        async for _event in model.stream_response(
            system_instructions=None,
            input="",
            model_settings=ModelSettings(),
            tools=[],
            output_schema=None,
            handoffs=[],
            tracing=ModelTracing.DISABLED,
            previous_response_id=None,
            conversation_id=None,
            prompt=None,
        ):
            pass


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
@pytest.mark.parametrize(
    ("previous_response_id", "conversation_id", "expected_param"),
    [
        ("resp_123", None, "previous_response_id"),
        (None, "conv_123", "conversation_id"),
    ],
)
async def test_stream_response_warns_and_ignores_server_managed_conversation_state_by_default(
    monkeypatch: pytest.MonkeyPatch,
    caplog: pytest.LogCaptureFixture,
    previous_response_id: str | None,
    conversation_id: str | None,
    expected_param: str,
) -> None:
    called = False

    async def patched_fetch_response(self, *args, **kwargs):
        nonlocal called
        called = True
        return _empty_response(), _empty_chat_completion_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    caplog.set_level(logging.WARNING, logger="openai.agents")

    async for _event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=previous_response_id,
        conversation_id=conversation_id,
        prompt=None,
    ):
        pass

    assert expected_param in caplog.text
    assert "Ignoring unsupported server-managed conversation state" in caplog.text
    assert called is True


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_warns_and_ignores_prompt_by_default(
    monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture
) -> None:
    captured_prompt: Any = None

    async def patched_fetch_response(self, *args, **kwargs):
        nonlocal captured_prompt
        captured_prompt = kwargs.get("prompt")
        return _empty_response(), _empty_chat_completion_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    caplog.set_level(logging.WARNING, logger="openai.agents")

    async for _ in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=cast(Any, {"id": "pmpt_123"}),
    ):
        pass

    assert "Reusable prompts are only supported by the Responses API" in caplog.text
    assert "Ignoring `prompt`" in caplog.text
    assert captured_prompt is None


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
@pytest.mark.parametrize(
    ("previous_response_id", "conversation_id", "expected_param"),
    [
        ("resp_123", None, "previous_response_id"),
        (None, "conv_123", "conversation_id"),
    ],
)
async def test_stream_response_rejects_server_managed_conversation_state_in_strict_mode(
    monkeypatch: pytest.MonkeyPatch,
    previous_response_id: str | None,
    conversation_id: str | None,
    expected_param: str,
) -> None:
    called = False

    async def patched_fetch_response(self, *args, **kwargs):
        nonlocal called
        called = True
        raise AssertionError("_fetch_response should not be called")

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        strict_feature_validation=True,
    ).get_model("gpt-4")

    with pytest.raises(UserError, match="server-managed conversation state") as exc_info:
        async for _event in model.stream_response(
            system_instructions=None,
            input="",
            model_settings=ModelSettings(),
            tools=[],
            output_schema=None,
            handoffs=[],
            tracing=ModelTracing.DISABLED,
            previous_response_id=previous_response_id,
            conversation_id=conversation_id,
            prompt=None,
        ):
            pass

    assert expected_param in str(exc_info.value)
    assert called is False


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_rejects_prompt_in_strict_mode(monkeypatch) -> None:
    async def patched_fetch_response(self, *args, **kwargs):
        raise AssertionError("_fetch_response should not run when prompt is unsupported")

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        strict_feature_validation=True,
    ).get_model("gpt-4")

    with pytest.raises(UserError, match="Reusable prompts"):
        async for _ in model.stream_response(
            system_instructions=None,
            input="",
            model_settings=ModelSettings(),
            tools=[],
            output_schema=None,
            handoffs=[],
            tracing=ModelTracing.DISABLED,
            previous_response_id=None,
            conversation_id=None,
            prompt=cast(Any, {"id": "pmpt_123"}),
        ):
            pass


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_includes_logprobs(monkeypatch) -> None:
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[
            Choice(
                index=0,
                delta=ChoiceDelta(content="Hi"),
                logprobs=ChoiceLogprobs(
                    content=[
                        ChatCompletionTokenLogprob(
                            token="Hi",
                            logprob=-0.5,
                            bytes=[1],
                            top_logprobs=[TopLogprob(token="Hi", logprob=-0.5, bytes=[1])],
                        )
                    ]
                ),
            )
        ],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[
            Choice(
                index=0,
                delta=ChoiceDelta(content=" there"),
                logprobs=ChoiceLogprobs(
                    content=[
                        ChatCompletionTokenLogprob(
                            token=" there",
                            logprob=-0.25,
                            bytes=[2],
                            top_logprobs=[TopLogprob(token=" there", logprob=-0.25, bytes=[2])],
                        )
                    ]
                ),
            )
        ],
        usage=CompletionUsage(
            completion_tokens=5,
            prompt_tokens=7,
            total_tokens=12,
            prompt_tokens_details=PromptTokensDetails(cached_tokens=2),
            completion_tokens_details=CompletionTokensDetails(reasoning_tokens=3),
        ),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for c in (chunk1, chunk2):
            yield c

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    text_delta_events = [
        event for event in output_events if event.type == "response.output_text.delta"
    ]
    assert len(text_delta_events) == 2
    assert [lp.token for lp in text_delta_events[0].logprobs] == ["Hi"]
    assert [lp.token for lp in text_delta_events[1].logprobs] == [" there"]

    completed_event = next(event for event in output_events if event.type == "response.completed")
    assert isinstance(completed_event, ResponseCompletedEvent)
    completed_resp = completed_event.response
    assert isinstance(completed_resp.output[0], ResponseOutputMessage)
    text_part = completed_resp.output[0].content[0]
    assert isinstance(text_part, ResponseOutputText)
    assert text_part.text == "Hi there"
    assert text_part.logprobs is not None
    assert [lp.token for lp in text_part.logprobs] == ["Hi", " there"]


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_accumulates_logprobs_across_many_deltas(monkeypatch) -> None:
    # Each content delta carries its own logprobs, and the streamed output text part must
    # accumulate all of them in order across the whole stream.
    tokens = ["a", "b", "c", "d", "e"]

    def make_chunk(token: str) -> ChatCompletionChunk:
        return ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[
                Choice(
                    index=0,
                    delta=ChoiceDelta(content=token),
                    logprobs=ChoiceLogprobs(
                        content=[
                            ChatCompletionTokenLogprob(
                                token=token,
                                logprob=-0.5,
                                bytes=[1],
                                top_logprobs=[TopLogprob(token=token, logprob=-0.5, bytes=[1])],
                            )
                        ]
                    ),
                )
            ],
        )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for token in tokens:
            yield make_chunk(token)

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    completed_event = next(event for event in output_events if event.type == "response.completed")
    assert isinstance(completed_event, ResponseCompletedEvent)
    completed_resp = completed_event.response
    assert isinstance(completed_resp.output[0], ResponseOutputMessage)
    text_part = completed_resp.output[0].content[0]
    assert isinstance(text_part, ResponseOutputText)
    assert text_part.text == "".join(tokens)
    assert text_part.logprobs is not None
    assert [lp.token for lp in text_part.logprobs] == tokens


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_yields_events_for_refusal_content(monkeypatch) -> None:
    """
    Validate that when the model streams a refusal string instead of normal content,
    `stream_response` emits the appropriate sequence of events including
    `response.refusal.delta` events for each chunk of the refusal message and
    constructs a completed assistant message with a `ResponseOutputRefusal` part.
    """
    # Simulate refusal text coming in two pieces, like content but using the `refusal`
    # field on the delta rather than `content`.
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(refusal="No"))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(refusal="Thanks"))],
        usage=CompletionUsage(completion_tokens=2, prompt_tokens=2, total_tokens=4),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for c in (chunk1, chunk2):
            yield c

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)
    # Expect sequence similar to text: created, output_item.added, content part added,
    # two refusal delta events, content part done, output_item.done, completed.
    assert len(output_events) == 8
    assert output_events[0].type == "response.created"
    assert output_events[1].type == "response.output_item.added"
    assert output_events[2].type == "response.content_part.added"
    assert output_events[3].type == "response.refusal.delta"
    assert output_events[3].delta == "No"
    assert output_events[4].type == "response.refusal.delta"
    assert output_events[4].delta == "Thanks"
    assert output_events[5].type == "response.content_part.done"
    assert output_events[6].type == "response.output_item.done"
    assert output_events[7].type == "response.completed"
    completed_resp = output_events[7].response
    assert isinstance(completed_resp.output[0], ResponseOutputMessage)
    refusal_part = completed_resp.output[0].content[0]
    assert isinstance(refusal_part, ResponseOutputRefusal)
    assert refusal_part.refusal == "NoThanks"


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_yields_events_for_tool_call(monkeypatch) -> None:
    """
    Validate that `stream_response` emits the correct sequence of events when
    the model is streaming a function/tool call instead of plain text.
    The function call will be split across two chunks.
    """
    # Simulate a single tool call with complete function name in first chunk
    # and arguments split across chunks (reflecting real OpenAI API behavior)
    tool_call_delta1 = ChoiceDeltaToolCall(
        index=0,
        id="tool-id",
        function=ChoiceDeltaToolCallFunction(name="my_func", arguments="arg1"),
        type="function",
    )
    tool_call_delta2 = ChoiceDeltaToolCall(
        index=0,
        id="tool-id",
        function=ChoiceDeltaToolCallFunction(name=None, arguments="arg2"),
        type="function",
    )
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for c in (chunk1, chunk2):
            yield c

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)
    # Sequence should be: response.created, then after loop we expect function call-related events:
    # one response.output_item.added for function call, a response.function_call_arguments.delta,
    # a response.output_item.done, and finally response.completed.
    assert output_events[0].type == "response.created"
    # The next three events are about the tool call.
    assert output_events[1].type == "response.output_item.added"
    # The added item should be a ResponseFunctionToolCall.
    added_fn = output_events[1].item
    assert isinstance(added_fn, ResponseFunctionToolCall)
    assert added_fn.name == "my_func"  # Name should be complete from first chunk
    assert added_fn.arguments == ""  # Arguments start empty
    assert output_events[2].type == "response.function_call_arguments.delta"
    assert output_events[2].delta == "arg1"  # First argument chunk
    assert output_events[3].type == "response.function_call_arguments.delta"
    assert output_events[3].delta == "arg2"  # Second argument chunk
    assert output_events[4].type == "response.output_item.done"
    assert output_events[5].type == "response.completed"
    # Final function call should have complete arguments
    final_fn = output_events[4].item
    assert isinstance(final_fn, ResponseFunctionToolCall)
    assert final_fn.name == "my_func"
    assert final_fn.arguments == "arg1arg2"


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_buffers_tool_call_deltas_when_enabled(monkeypatch) -> None:
    tool_call_delta1 = ChoiceDeltaToolCall(
        index=0,
        id="tool-id",
        function=ChoiceDeltaToolCallFunction(name="my_func", arguments="arg1"),
        type="function",
    )
    tool_call_delta2 = ChoiceDeltaToolCall(
        index=0,
        id=None,
        function=ChoiceDeltaToolCallFunction(name=None, arguments="arg2"),
        type="function",
    )
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for chunk in (chunk1, chunk2):
            yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        buffer_streamed_tool_calls=True,
    ).get_model("gpt-4")

    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    argument_delta_events = [
        event for event in output_events if event.type == "response.function_call_arguments.delta"
    ]
    assert len(argument_delta_events) == 1
    assert argument_delta_events[0].delta == "arg1arg2"

    done_event = next(event for event in output_events if event.type == "response.output_item.done")
    final_fn = done_event.item
    assert isinstance(final_fn, ResponseFunctionToolCall)
    assert final_fn.call_id == "tool-id"
    assert final_fn.name == "my_func"
    assert final_fn.arguments == "arg1arg2"

    completed_event = next(event for event in output_events if event.type == "response.completed")
    assert isinstance(completed_event, ResponseCompletedEvent)
    assert completed_event.response.usage
    assert completed_event.response.usage.total_tokens == 2


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_buffered_tool_call_before_text_replays_as_single_assistant_session_message() -> None:
    tool_call_delta = ChoiceDeltaToolCall(
        index=0,
        id="call_lookup_status",
        function=ChoiceDeltaToolCallFunction(name="lookup_status", arguments="{}"),
        type="function",
    )
    tool_first_chunk = ChatCompletionChunk(
        id="chunk-tool",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta]))],
    )
    later_text_chunk = ChatCompletionChunk(
        id="chunk-text",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[
            Choice(
                index=0,
                delta=ChoiceDelta(content="I'll look that up first."),
            )
        ],
        usage=CompletionUsage(completion_tokens=5, prompt_tokens=5, total_tokens=10),
    )
    final_text_chunk = ChatCompletionChunk(
        id="chunk-final",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(content="first run done"))],
        usage=CompletionUsage(completion_tokens=3, prompt_tokens=7, total_tokens=10),
    )

    async def first_turn_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield tool_first_chunk
        yield later_text_chunk

    async def final_turn_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield final_text_chunk

    class DummyCompletions:
        def __init__(self) -> None:
            self.calls: list[dict[str, Any]] = []

        async def create(self, **kwargs: Any) -> Any:
            self.calls.append(kwargs)
            call_number = len(self.calls)

            if kwargs["stream"] is True:
                if call_number == 1:
                    return first_turn_stream()
                if call_number == 2:
                    return final_turn_stream()
                raise AssertionError(f"Unexpected streamed call {call_number}")

            return ChatCompletion(
                id="resp-id",
                created=0,
                model="fake",
                object="chat.completion",
                choices=[
                    ChatCompletionChoice(
                        index=0,
                        finish_reason="stop",
                        message=ChatCompletionMessage(
                            role="assistant",
                            content="second run done",
                        ),
                    )
                ],
                usage=None,
            )

    class DummyClient:
        def __init__(self, completions: DummyCompletions) -> None:
            self.chat = type("_Chat", (), {"completions": completions})()
            self.base_url = "http://fake"

    def lookup_status() -> str:
        return "lookup result"

    completions = DummyCompletions()
    model = OpenAIChatCompletionsModel(
        model="gpt-4",
        openai_client=DummyClient(completions),  # type: ignore[arg-type]
        buffer_streamed_tool_calls=True,
    )
    agent = Agent(
        name="test",
        model=model,
        tools=[function_tool(lookup_status, name_override="lookup_status")],
    )
    session = SimpleListSession()

    first_result = Runner.run_streamed(agent, input="first question", session=session)
    async for _ in first_result.stream_events():
        pass

    assert first_result.final_output == "first run done"
    await Runner.run(agent, input="second question", session=session)

    assert len(completions.calls) == 3
    replayed_messages = completions.calls[2]["messages"]
    assert [message["role"] for message in replayed_messages] == [
        "user",
        "assistant",
        "tool",
        "assistant",
        "user",
    ]

    assistant_with_tool = cast(dict[str, Any], replayed_messages[1])
    assert assistant_with_tool["content"] == "I'll look that up first."
    assert len(assistant_with_tool["tool_calls"]) == 1
    tool_call = assistant_with_tool["tool_calls"][0]
    assert tool_call["id"] == "call_lookup_status"
    assert tool_call["function"] == {"name": "lookup_status", "arguments": "{}"}

    tool_message = cast(dict[str, Any], replayed_messages[2])
    assert tool_message["tool_call_id"] == "call_lookup_status"
    assert tool_message["content"] == "lookup result"
    assert replayed_messages[3]["content"] == "first run done"
    assert replayed_messages[4]["content"] == "second question"


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_buffers_tool_call_usage_chunk_without_replay(
    monkeypatch,
) -> None:
    tool_call_delta = ChoiceDeltaToolCall(
        index=0,
        id="tool-id",
        function=ChoiceDeltaToolCallFunction(name="my_func", arguments="arg1"),
        type="function",
    )
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta]))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        buffer_streamed_tool_calls=True,
    ).get_model("gpt-4")

    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    argument_delta_events = [
        event for event in output_events if event.type == "response.function_call_arguments.delta"
    ]
    assert len(argument_delta_events) == 1
    assert argument_delta_events[0].delta == "arg1"

    function_done_events = [
        event
        for event in output_events
        if event.type == "response.output_item.done"
        and isinstance(event.item, ResponseFunctionToolCall)
    ]
    assert len(function_done_events) == 1

    completed_event = next(event for event in output_events if event.type == "response.completed")
    assert isinstance(completed_event, ResponseCompletedEvent)
    assert completed_event.response.usage
    assert completed_event.response.usage.total_tokens == 2


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_buffers_tool_call_provider_fields(monkeypatch) -> None:
    tool_call_delta1 = ChoiceDeltaToolCall(
        index=0,
        id="tool-id",
        function=ChoiceDeltaToolCallFunction(name="my_func", arguments=None),
        type="function",
    )
    cast(Any, tool_call_delta1).provider_specific_fields = {"thought_signature": "thought-sig"}
    tool_call_delta2 = ChoiceDeltaToolCall(
        index=0,
        id=None,
        function=ChoiceDeltaToolCallFunction(name=None, arguments="arg1"),
        type="function",
    )
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="gemini/gemini-3-pro",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="gemini/gemini-3-pro",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for chunk in (chunk1, chunk2):
            yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        buffer_streamed_tool_calls=True,
    ).get_model("gemini/gemini-3-pro")

    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    function_done_events = [
        event
        for event in output_events
        if event.type == "response.output_item.done"
        and isinstance(event.item, ResponseFunctionToolCall)
    ]
    assert len(function_done_events) == 1
    provider_data = function_done_events[0].item.model_dump().get("provider_data", {})
    assert provider_data["thought_signature"] == "thought-sig"


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_buffered_tool_calls_raise_for_missing_tool_call_delta(
    monkeypatch,
) -> None:
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(), finish_reason="tool_calls")],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        buffer_streamed_tool_calls=True,
    ).get_model("gpt-4")

    with pytest.raises(ModelBehaviorError, match="finish_reason='tool_calls'"):
        async for _event in model.stream_response(
            system_instructions=None,
            input="",
            model_settings=ModelSettings(),
            tools=[],
            output_schema=None,
            handoffs=[],
            tracing=ModelTracing.DISABLED,
            previous_response_id=None,
            conversation_id=None,
            prompt=None,
        ):
            pass


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_buffered_tool_calls_preserve_nonzero_choice_validation(monkeypatch) -> None:
    tool_call_delta = ChoiceDeltaToolCall(
        index=0,
        id="tool-id",
        function=ChoiceDeltaToolCallFunction(name="my_func", arguments="arg"),
        type="function",
    )
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=1, delta=ChoiceDelta(tool_calls=[tool_call_delta]))],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        strict_feature_validation=True,
        buffer_streamed_tool_calls=True,
    ).get_model("gpt-4")

    with pytest.raises(UserError, match="multiple choices or nonzero choice indexes"):
        async for _event in model.stream_response(
            system_instructions=None,
            input="",
            model_settings=ModelSettings(),
            tools=[],
            output_schema=None,
            handoffs=[],
            tracing=ModelTracing.DISABLED,
            previous_response_id=None,
            conversation_id=None,
            prompt=None,
        ):
            pass


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_buffered_tool_calls_do_not_merge_nonzero_choice_tool_call_indexes(
    monkeypatch,
) -> None:
    choice_zero_tool_call = ChoiceDeltaToolCall(
        index=0,
        id="choice-zero-tool-id",
        function=ChoiceDeltaToolCallFunction(name="choice_zero_func", arguments="choice-zero"),
        type="function",
    )
    choice_one_tool_call = ChoiceDeltaToolCall(
        index=0,
        id="choice-one-tool-id",
        function=ChoiceDeltaToolCallFunction(name="choice_one_func", arguments="choice-one"),
        type="function",
    )
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[
            Choice(index=0, delta=ChoiceDelta(tool_calls=[choice_zero_tool_call])),
            Choice(index=1, delta=ChoiceDelta(tool_calls=[choice_one_tool_call])),
        ],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        buffer_streamed_tool_calls=True,
    ).get_model("gpt-4")

    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    function_done_events = [
        event
        for event in output_events
        if event.type == "response.output_item.done"
        and isinstance(event.item, ResponseFunctionToolCall)
    ]
    assert len(function_done_events) == 1
    final_fn = function_done_events[0].item
    assert isinstance(final_fn, ResponseFunctionToolCall)
    assert final_fn.call_id == "choice-zero-tool-id"
    assert final_fn.name == "choice_zero_func"
    assert final_fn.arguments == "choice-zero"


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_buffered_tool_calls_preserve_custom_tool_call_strict_error(
    monkeypatch,
) -> None:
    custom_tool_call_delta = ChoiceDeltaToolCall.model_construct(
        index=0,
        id="tool-call-123",
        type="custom",
    )
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[
            Choice(
                index=0,
                delta=ChoiceDelta(tool_calls=[custom_tool_call_delta]),
                finish_reason="tool_calls",
            )
        ],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        strict_feature_validation=True,
        buffer_streamed_tool_calls=True,
    ).get_model("gpt-4")

    with pytest.raises(UserError, match="Custom tool calls are not supported"):
        async for _event in model.stream_response(
            system_instructions=None,
            input="",
            model_settings=ModelSettings(),
            tools=[],
            output_schema=None,
            handoffs=[],
            tracing=ModelTracing.DISABLED,
            previous_response_id=None,
            conversation_id=None,
            prompt=None,
        ):
            pass


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_buffered_tool_calls_ignore_custom_tool_call_by_default(monkeypatch) -> None:
    custom_tool_call_delta = ChoiceDeltaToolCall.model_construct(
        index=0,
        id="tool-call-123",
        type="custom",
    )
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[
            Choice(
                index=0,
                delta=ChoiceDelta(tool_calls=[custom_tool_call_delta]),
                finish_reason="tool_calls",
            )
        ],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(
        use_responses=False,
        buffer_streamed_tool_calls=True,
    ).get_model("gpt-4")

    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    completed_event = next(event for event in output_events if event.type == "response.completed")
    assert isinstance(completed_event, ResponseCompletedEvent)
    assert completed_event.response.output == []


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_with_custom_tool_call_raises_in_strict_mode(monkeypatch) -> None:
    custom_tool_call_delta = ChoiceDeltaToolCall.model_construct(
        index=0,
        id="tool-call-123",
        type="custom",
    )
    chunk = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[custom_tool_call_delta]))],
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False, strict_feature_validation=True).get_model("gpt-4")

    with pytest.raises(UserError, match="Custom tool calls are not supported"):
        async for _event in model.stream_response(
            system_instructions=None,
            input="",
            model_settings=ModelSettings(),
            tools=[],
            output_schema=None,
            handoffs=[],
            tracing=ModelTracing.DISABLED,
            previous_response_id=None,
            conversation_id=None,
            prompt=None,
        ):
            pass


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_ignores_custom_tool_call_chunks_by_default(monkeypatch) -> None:
    custom_tool_call_delta = ChoiceDeltaToolCall.model_construct(
        index=0,
        id="tool-call-123",
        type="custom",
    )
    omitted_type_tool_call_delta = ChoiceDeltaToolCall.model_construct(
        index=0,
        function=ChoiceDeltaToolCallFunction(name="custom_tool", arguments="payload"),
    )
    chunks = [
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[custom_tool_call_delta]))],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[omitted_type_tool_call_delta]))],
        ),
        ChatCompletionChunk(
            id="chunk-id",
            created=1,
            model="fake",
            object="chat.completion.chunk",
            choices=[Choice(index=0, delta=ChoiceDelta(content="done"))],
        ),
    ]

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for chunk in chunks:
            yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        return _empty_response(), fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")

    events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        events.append(event)

    function_call_events = []
    for event in events:
        item = getattr(event, "item", None)
        if isinstance(item, ResponseFunctionToolCall):
            function_call_events.append(event)
    assert function_call_events == []
    completed_event = events[-1]
    assert isinstance(completed_event, ResponseCompletedEvent)
    assert all(
        not isinstance(item, ResponseFunctionToolCall) for item in completed_event.response.output
    )
    assert len(completed_event.response.output) == 1
    message = completed_event.response.output[0]
    assert isinstance(message, ResponseOutputMessage)
    assert len(message.content) == 1
    assert isinstance(message.content[0], ResponseOutputText)
    assert message.content[0].text == "done"


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_stream_response_yields_real_time_function_call_arguments(monkeypatch) -> None:
    """
    Validate that `stream_response` emits function call arguments in real-time as they
    are received, not just at the end. This test simulates the real OpenAI API behavior
    where function name comes first, then arguments are streamed incrementally.
    """
    # Simulate realistic OpenAI API chunks: name first, then arguments incrementally
    tool_call_delta1 = ChoiceDeltaToolCall(
        index=0,
        id="tool-call-123",
        function=ChoiceDeltaToolCallFunction(name="write_file", arguments=""),
        type="function",
    )
    tool_call_delta2 = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(arguments='{"filename": "'),
        type="function",
    )
    tool_call_delta3 = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(arguments='test.py", "content": "'),
        type="function",
    )
    tool_call_delta4 = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(arguments='print(hello)"}'),
        type="function",
    )

    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
    )
    chunk3 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta3]))],
    )
    chunk4 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta4]))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for c in (chunk1, chunk2, chunk3, chunk4):
            yield c

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    # Extract events by type
    created_events = [e for e in output_events if e.type == "response.created"]
    output_item_added_events = [e for e in output_events if e.type == "response.output_item.added"]
    function_args_delta_events = [
        e for e in output_events if e.type == "response.function_call_arguments.delta"
    ]
    output_item_done_events = [e for e in output_events if e.type == "response.output_item.done"]
    completed_events = [e for e in output_events if e.type == "response.completed"]

    # Verify event structure
    assert len(created_events) == 1
    assert len(output_item_added_events) == 1
    assert len(function_args_delta_events) == 3  # Three incremental argument chunks
    assert len(output_item_done_events) == 1
    assert len(completed_events) == 1

    # Verify the function call started as soon as we had name and ID
    added_event = output_item_added_events[0]
    assert isinstance(added_event.item, ResponseFunctionToolCall)
    assert added_event.item.name == "write_file"
    assert added_event.item.call_id == "tool-call-123"
    assert added_event.item.arguments == ""  # Should be empty at start

    # Verify real-time argument streaming
    expected_deltas = ['{"filename": "', 'test.py", "content": "', 'print(hello)"}']
    for i, delta_event in enumerate(function_args_delta_events):
        assert delta_event.delta == expected_deltas[i]
        assert delta_event.item_id == "__fake_id__"  # FAKE_RESPONSES_ID
        assert delta_event.output_index == 0

    # Verify completion event has full arguments
    done_event = output_item_done_events[0]
    assert isinstance(done_event.item, ResponseFunctionToolCall)
    assert done_event.item.name == "write_file"
    assert done_event.item.arguments == '{"filename": "test.py", "content": "print(hello)"}'

    # Verify final response
    completed_event = completed_events[0]
    function_call_output = completed_event.response.output[0]
    assert isinstance(function_call_output, ResponseFunctionToolCall)
    assert function_call_output.name == "write_file"
    assert function_call_output.arguments == '{"filename": "test.py", "content": "print(hello)"}'


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_fallback_function_calls_have_unique_output_indexes(monkeypatch) -> None:
    tool_call_delta1 = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(
            name="first_tool",
            arguments='{"a": 1}',
        ),
        type="function",
    )
    tool_call_delta2 = ChoiceDeltaToolCall(
        index=1,
        function=ChoiceDeltaToolCallFunction(
            name="second_tool",
            arguments='{"b": 2}',
        ),
        type="function",
    )

    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta1]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[tool_call_delta2]))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for c in (chunk1, chunk2):
            yield c

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")

    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    added_indexes = [
        event.output_index for event in output_events if event.type == "response.output_item.added"
    ]
    delta_indexes = [
        event.output_index
        for event in output_events
        if event.type == "response.function_call_arguments.delta"
    ]
    done_indexes = [
        event.output_index for event in output_events if event.type == "response.output_item.done"
    ]

    assert added_indexes == [0, 1]
    assert delta_indexes == [0, 1]
    assert done_indexes == [0, 1]


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_fallback_function_call_keeps_index_before_streamed_call(monkeypatch) -> None:
    fallback_first = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(
            name="fallback_first",
            arguments='{"a": 1}',
        ),
        type="function",
    )
    streamed_second_start = ChoiceDeltaToolCall(
        index=1,
        id="tool-call-2",
        function=ChoiceDeltaToolCallFunction(
            name="streamed_second",
            arguments="",
        ),
        type="function",
    )
    streamed_second_args = ChoiceDeltaToolCall(
        index=1,
        function=ChoiceDeltaToolCallFunction(arguments='{"b": 2}'),
        type="function",
    )

    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[fallback_first]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[streamed_second_start]))],
    )
    chunk3 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[streamed_second_args]))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for c in (chunk1, chunk2, chunk3):
            yield c

    async def patched_fetch_response(self, *args, **kwargs):
        resp = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return resp, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")

    output_events = []
    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    completed = next(
        event.response for event in output_events if event.type == "response.completed"
    )
    assert [
        item.name for item in completed.output if isinstance(item, ResponseFunctionToolCall)
    ] == [
        "fallback_first",
        "streamed_second",
    ]

    added_by_name = {
        event.item.name: event.output_index
        for event in output_events
        if event.type == "response.output_item.added"
        and isinstance(event.item, ResponseFunctionToolCall)
    }
    delta_indexes = [
        event.output_index
        for event in output_events
        if event.type == "response.function_call_arguments.delta"
    ]
    done_by_name = {
        event.item.name: event.output_index
        for event in output_events
        if event.type == "response.output_item.done"
        and isinstance(event.item, ResponseFunctionToolCall)
    }

    assert added_by_name == {"fallback_first": 0, "streamed_second": 1}
    assert delta_indexes == [1, 0]
    assert done_by_name == {"streamed_second": 1, "fallback_first": 0}


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_fallback_function_call_before_text_uses_final_output_index(
    monkeypatch,
) -> None:
    fallback_call = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(name="first_tool", arguments='{"a": 1}'),
        type="function",
    )
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[fallback_call]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(content="answer"))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for chunk in (chunk1, chunk2):
            yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        response = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return response, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []

    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    added_events = [event for event in output_events if event.type == "response.output_item.added"]
    delta_events = [
        event for event in output_events if event.type == "response.function_call_arguments.delta"
    ]
    done_events = [event for event in output_events if event.type == "response.output_item.done"]
    completed_event = next(event for event in output_events if event.type == "response.completed")

    added_message_event = next(
        event for event in added_events if isinstance(event.item, ResponseOutputMessage)
    )
    added_tool_event = next(
        event for event in added_events if isinstance(event.item, ResponseFunctionToolCall)
    )
    done_message_event = next(
        event for event in done_events if isinstance(event.item, ResponseOutputMessage)
    )
    done_tool_event = next(
        event for event in done_events if isinstance(event.item, ResponseFunctionToolCall)
    )

    assert added_message_event.output_index == 0
    assert added_tool_event.output_index == 1
    assert [event.output_index for event in delta_events] == [1]
    assert done_message_event.output_index == 0
    assert done_tool_event.output_index == 1
    assert isinstance(completed_event.response.output[0], ResponseOutputMessage)
    assert isinstance(completed_event.response.output[1], ResponseFunctionToolCall)


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_streamed_function_call_before_text_keeps_realtime_order(
    monkeypatch,
) -> None:
    streamed_call_start = ChoiceDeltaToolCall(
        index=0,
        id="tool-call-1",
        function=ChoiceDeltaToolCallFunction(name="first_tool", arguments=""),
        type="function",
    )
    streamed_call_args = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(arguments='{"a": 1}'),
        type="function",
    )
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[streamed_call_start]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[streamed_call_args]))],
    )
    chunk3 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(content="answer"))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for chunk in (chunk1, chunk2, chunk3):
            yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        response = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return response, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []

    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    added_events = [event for event in output_events if event.type == "response.output_item.added"]
    delta_events = [
        event for event in output_events if event.type == "response.function_call_arguments.delta"
    ]
    done_events = [event for event in output_events if event.type == "response.output_item.done"]
    completed_event = next(event for event in output_events if event.type == "response.completed")

    added_message_event = next(
        event for event in added_events if isinstance(event.item, ResponseOutputMessage)
    )
    added_tool_event = next(
        event for event in added_events if isinstance(event.item, ResponseFunctionToolCall)
    )
    done_message_event = next(
        event for event in done_events if isinstance(event.item, ResponseOutputMessage)
    )
    done_tool_event = next(
        event for event in done_events if isinstance(event.item, ResponseFunctionToolCall)
    )

    assert added_tool_event.output_index == 0
    assert added_message_event.output_index == 1
    assert [event.output_index for event in delta_events] == [0]
    assert done_tool_event.output_index == 0
    assert done_message_event.output_index == 1
    assert isinstance(completed_event.response.output[0], ResponseFunctionToolCall)
    assert isinstance(completed_event.response.output[1], ResponseOutputMessage)


@pytest.mark.allow_call_model_methods
@pytest.mark.asyncio
async def test_mixed_function_calls_before_text_keep_tracked_order(
    monkeypatch,
) -> None:
    fallback_first = ChoiceDeltaToolCall(
        index=0,
        function=ChoiceDeltaToolCallFunction(name="fallback_first", arguments='{"a": 1}'),
        type="function",
    )
    streamed_second_start = ChoiceDeltaToolCall(
        index=1,
        id="tool-call-2",
        function=ChoiceDeltaToolCallFunction(name="streamed_second", arguments=""),
        type="function",
    )
    streamed_second_args = ChoiceDeltaToolCall(
        index=1,
        function=ChoiceDeltaToolCallFunction(arguments='{"b": 2}'),
        type="function",
    )
    chunk1 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[fallback_first]))],
    )
    chunk2 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[streamed_second_start]))],
    )
    chunk3 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(tool_calls=[streamed_second_args]))],
    )
    chunk4 = ChatCompletionChunk(
        id="chunk-id",
        created=1,
        model="fake",
        object="chat.completion.chunk",
        choices=[Choice(index=0, delta=ChoiceDelta(content="answer"))],
        usage=CompletionUsage(completion_tokens=1, prompt_tokens=1, total_tokens=2),
    )

    async def fake_stream() -> AsyncIterator[ChatCompletionChunk]:
        for chunk in (chunk1, chunk2, chunk3, chunk4):
            yield chunk

    async def patched_fetch_response(self, *args, **kwargs):
        response = Response(
            id="resp-id",
            created_at=0,
            model="fake-model",
            object="response",
            output=[],
            tool_choice="none",
            tools=[],
            parallel_tool_calls=False,
        )
        return response, fake_stream()

    monkeypatch.setattr(OpenAIChatCompletionsModel, "_fetch_response", patched_fetch_response)
    model = OpenAIProvider(use_responses=False).get_model("gpt-4")
    output_events = []

    async for event in model.stream_response(
        system_instructions=None,
        input="",
        model_settings=ModelSettings(),
        tools=[],
        output_schema=None,
        handoffs=[],
        tracing=ModelTracing.DISABLED,
        previous_response_id=None,
        conversation_id=None,
        prompt=None,
    ):
        output_events.append(event)

    added_events = [event for event in output_events if event.type == "response.output_item.added"]
    delta_events = [
        event for event in output_events if event.type == "response.function_call_arguments.delta"
    ]
    completed_event = next(event for event in output_events if event.type == "response.completed")

    added_message_event = next(
        event for event in added_events if isinstance(event.item, ResponseOutputMessage)
    )
    added_tool_indexes = {
        event.item.name: event.output_index
        for event in added_events
        if isinstance(event.item, ResponseFunctionToolCall)
    }

    assert added_tool_indexes == {"streamed_second": 1, "fallback_first": 0}
    assert added_message_event.output_index == 2
    assert {event.delta: event.output_index for event in delta_events} == {
        '{"b": 2}': 1,
        '{"a": 1}': 0,
    }
    assert isinstance(completed_event.response.output[0], ResponseFunctionToolCall)
    assert isinstance(completed_event.response.output[1], ResponseFunctionToolCall)
    assert isinstance(completed_event.response.output[2], ResponseOutputMessage)