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Merge pull request #40462 from BerriAI/litellm_fix_responses_stream_named_tool_choice

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fix(responses): echo a named tool_choice in the Responses API shape on the chat-completions bridge
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Mateo Wang 2026-09-09 18:48:32 -07:00 • committed by GitHub
commit db7ca65b69
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4 changed files with 193 additions and 9 deletions
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11
litellm/responses/litellm_completion_transformation/streaming_iterator.py
View file

@ -437,14 +437,11 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator):
response_created_event_data["temperature"] = self.responses_api_request["temperature"]
if "text" in self.responses_api_request:
response_created_event_data["text"] = self.responses_api_request["text"]
if "tool_choice" in self.responses_api_request:
# Transform tool_choice from dict format (e.g., {"type": "auto"}) to string format
response_created_event_data["tool_choice"] = (
LiteLLMCompletionResponsesConfig._transform_tool_choice(self.responses_api_request["tool_choice"])
or "auto"
response_created_event_data["tool_choice"] = (
LiteLLMCompletionResponsesConfig._transform_tool_choice_for_responses_api_response(
self.responses_api_request.get("tool_choice")
)
else:
response_created_event_data["tool_choice"] = "auto"
)
if "tools" in self.responses_api_request:
response_created_event_data["tools"] = self.responses_api_request["tools"]
else:

31
litellm/responses/litellm_completion_transformation/transformation.py
View file

@ -27,8 +27,10 @@ from openai.types.chat.chat_completion_named_tool_choice_param import (
)
from openai.types.responses import ResponseFunctionToolCall
from openai.types.responses.response_create_params import ResponseInputParam
from openai.types.responses.tool_choice_custom_param import ToolChoiceCustomParam
from openai.types.responses.tool_choice_function_param import ToolChoiceFunctionParam
from openai.types.responses.tool_param import FunctionToolParam
from pydantic import TypeAdapter
from pydantic import TypeAdapter, ValidationError
from typing_extensions import ReadOnly, TypedDict
from litellm._logging import verbose_logger
@ -68,6 +70,7 @@ from litellm.types.llms.openai import (
ResponsesAPIOptionalRequestParams,
ResponsesAPIResponse,
ResponsesAPIStatus,
ToolChoice,
ValidChatCompletionMessageContentTypes,
ValidChatCompletionMessageContentTypesLiteral,
)
@ -126,6 +129,7 @@ _STR_KEY_DICT_ADAPTER: Final = TypeAdapter(dict[str, object])
_OBJECT_LIST_ADAPTER: Final = TypeAdapter(list[object])
_DICT_ITEMS_LIST_ADAPTER: Final = TypeAdapter(list[dict[object, object]])
_TEXT_ADAPTER: Final = TypeAdapter(str)
_RESPONSES_API_TOOL_CHOICE_ADAPTER: Final = TypeAdapter(ToolChoice)
@runtime_checkable
@ -267,6 +271,27 @@ class LiteLLMCompletionResponsesConfig:
# Return as-is for unknown formats
return tool_choice
@staticmethod
def _transform_tool_choice_for_responses_api_response(tool_choice: object) -> ToolChoice:
if tool_choice is None:
return "auto"
try:
return _RESPONSES_API_TOOL_CHOICE_ADAPTER.validate_python(tool_choice)
except ValidationError:
return LiteLLMCompletionResponsesConfig._chat_tool_choice_as_responses_api_tool_choice(tool_choice)
@staticmethod
def _chat_tool_choice_as_responses_api_tool_choice(tool_choice: object) -> ToolChoice:
match tool_choice, LiteLLMCompletionResponsesConfig._transform_tool_choice(tool_choice):
case {"type": "custom"}, {"function": {"name": str(custom_name)}}:
return ToolChoiceCustomParam(type="custom", name=custom_name)
case _, {"type": "function", "function": {"name": str(function_name)}}:
return ToolChoiceFunctionParam(type="function", name=function_name)
case _, "none" | "auto" | "required" as normalized:
return normalized
case _, _:
return "auto"
@staticmethod
def _should_drop_derived_web_search_options(model: str, custom_llm_provider: str | None) -> bool:
"""
@ -2263,7 +2288,9 @@ class LiteLLMCompletionResponsesConfig:
),
parallel_tool_calls=getattr(chat_completion_response, "parallel_tool_calls", False),
temperature=getattr(chat_completion_response, "temperature", 0),
tool_choice=getattr(chat_completion_response, "tool_choice", "auto"),
tool_choice=LiteLLMCompletionResponsesConfig._transform_tool_choice_for_responses_api_response(
responses_api_request.get("tool_choice")
),
tools=getattr(chat_completion_response, "tools", []),
top_p=getattr(chat_completion_response, "top_p", None),
max_output_tokens=getattr(chat_completion_response, "max_output_tokens", None),

83
tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py
View file

@ -1,4 +1,5 @@
import json
from typing import Final
import pytest
@ -1421,6 +1422,88 @@ class TestToolChoiceTransformation:
)
assert result == "required"
@pytest.mark.parametrize(
"request_tool_choice,expected",
[
({"type": "function", "name": "run_command"}, {"type": "function", "name": "run_command"}),
({"type": "function", "function": {"name": "run_command"}}, {"type": "function", "name": "run_command"}),
({"type": "custom", "name": "ApplyPatch"}, {"type": "custom", "name": "ApplyPatch"}),
({"type": "custom", "custom": {"name": "ApplyPatch"}}, {"type": "custom", "name": "ApplyPatch"}),
({"type": "function"}, "required"),
({"type": "tool"}, "required"),
({"type": "auto"}, "auto"),
("required", "required"),
("none", "none"),
(None, "auto"),
("any", "auto"),
("run_command", "auto"),
({"name": "run_command"}, "auto"),
],
)
def test_transform_tool_choice_for_responses_api_response(
self, request_tool_choice: object, expected: str | dict[str, str]
) -> None:
result: Final = LiteLLMCompletionResponsesConfig._transform_tool_choice_for_responses_api_response(
request_tool_choice
)
assert result == expected
def test_non_streamed_response_echoes_named_tool_choice_in_responses_api_shape(self) -> None:
chat_completion_response: Final = ModelResponse(
id="chatcmpl-named-tool-choice",
created=1748575031,
model="claude-haiku-4-5",
object="chat.completion",
choices=[
Choices(
index=0,
finish_reason="tool_calls",
message=Message(
role="assistant",
content=None,
tool_calls=[
ChatCompletionMessageToolCall(
id="call_pwd",
type="function",
function=Function(name="run_command", arguments='{"command":"pwd"}'),
)
],
),
)
],
)
responses_api_response: Final = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response(
request_input="Run the command pwd.",
responses_api_request={"tool_choice": {"type": "function", "name": "run_command"}},
chat_completion_response=chat_completion_response,
)
assert responses_api_response.tool_choice == {"type": "function", "name": "run_command"}
def test_non_streamed_response_with_unrecognized_tool_choice_echoes_auto(self) -> None:
chat_completion_response: Final = ModelResponse(
id="chatcmpl-unrecognized-tool-choice",
created=1748575031,
model="claude-haiku-4-5",
object="chat.completion",
choices=[
Choices(
index=0,
finish_reason="stop",
message=Message(role="assistant", content="/Users/dev"),
)
],
)
responses_api_response: Final = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response(
request_input="Run the command pwd.",
responses_api_request={"tool_choice": "any"},
chat_completion_response=chat_completion_response,
)
assert responses_api_response.tool_choice == "auto"
class TestContentTypeTransformation:
"""Test content type transformation from Responses API to Chat Completion format"""

77
tests/test_litellm/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py
View file

@ -11,6 +11,7 @@ spend tracking stores, so a follow-up previous_response_id still finds the conve
"""
import json
from typing import Final
from unittest.mock import AsyncMock, MagicMock
import pytest
@ -628,3 +629,79 @@ def test_streamed_anthropic_tool_call_events_correlate_on_normalized_item_id():
assert item_dones[0].item.call_id == "toolu_01AbCdEf"
for evt in deltas + dones:
assert evt.item_id == added[0].item.id
def _tool_call_chunk(finish_reason: str | None = None) -> ModelResponseStream:
return ModelResponseStream(
id=CHAT_COMPLETION_ID,
created=1748575031,
model="claude-haiku-4-5",
object="chat.completion.chunk",
choices=[
StreamingChoices(
index=0,
delta=Delta(
role="assistant",
content=None,
tool_calls=[
{
"id": "call_pwd",
"type": "function",
"function": {"name": "run_command", "arguments": '{"command":"pwd"}'},
"index": 0,
}
],
),
finish_reason=finish_reason,
)
],
)
def test_streamed_named_tool_choice_is_echoed_in_responses_api_shape() -> None:
iterator: Final = LiteLLMCompletionStreamingIterator(
model="claude-haiku-4-5",
litellm_custom_stream_wrapper=_FakeStreamWrapper([_tool_call_chunk(finish_reason="tool_calls")]),
request_input="Run the command pwd.",
responses_api_request={
"tools": [{"type": "function", "name": "run_command", "parameters": {"type": "object"}}],
"tool_choice": {"type": "function", "name": "run_command"},
},
custom_llm_provider="anthropic",
litellm_metadata={},
)
events: Final = list(iterator)
response_events: Final = [event for event in events if getattr(event, "type", None) in RESPONSE_ID_EVENT_TYPES]
assert [event.type for event in response_events] == [
"response.created",
"response.in_progress",
"response.completed",
]
assert [event.response.tool_choice for event in response_events] == [
{"type": "function", "name": "run_command"},
{"type": "function", "name": "run_command"},
{"type": "function", "name": "run_command"},
]
assert any(getattr(event, "type", None) == "response.output_item.done" for event in events)
def test_streamed_unrecognized_tool_choice_is_echoed_as_auto() -> None:
iterator: Final = LiteLLMCompletionStreamingIterator(
model="claude-haiku-4-5",
litellm_custom_stream_wrapper=_FakeStreamWrapper([_tool_call_chunk(finish_reason="tool_calls")]),
request_input="Run the command pwd.",
responses_api_request={
"tools": [{"type": "function", "name": "run_command", "parameters": {"type": "object"}}],
"tool_choice": "any",
},
custom_llm_provider="anthropic",
litellm_metadata={},
)
response_events: Final = [
event for event in iterator if getattr(event, "type", None) in RESPONSE_ID_EVENT_TYPES
]
assert [event.response.tool_choice for event in response_events] == ["auto", "auto", "auto"]