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https://github.com/BerriAI/litellm.git
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fix(cursor): convert tools and tool_choice through one envelope rule
Chat Completions nests a named tool_choice under its tool type while the
Responses API keeps it flat; ChatCompletionNamedToolChoiceParam and
ChatCompletionNamedToolChoiceCustomParam both mark the nested key required. The
messages arm normalized tool definitions but forwarded tool_choice at whatever
level Cursor sent it, so a flat {"type": "custom", "name": "ApplyPatch"} reached
OpenAI unchanged and was rejected while the tool defs beside it nested correctly
A tool definition and a named tool_choice carry the same envelope, so both now
convert through a single _convert_tool_envelope, and _normalize_tool_dialect
moves tools and tool_choice together on each arm. That covers all four cells of
{tool def, tool_choice} x {to chat, to responses} and removes the shape where
one field can be converted while the other is missed, replacing three helpers
with two and cutting 24 lines
Also restores the end-to-end assertion that a flat tool_choice reaches
chat_completion nested, which had been flipped to pin the passthrough behavior
This commit is contained in:
Tin Chi Lo
parent
56cc475c80
commit
c7c656e8a9
2 changed files with 140 additions and 164 deletions
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@ -24,57 +24,47 @@ router = APIRouter()
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_user_api_key_auth_dep = Depends(user_api_key_auth)
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_FLAT_CUSTOM_TOOL_KEYS = ("name", "description", "format")
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_FLAT_FUNCTION_TOOL_KEYS = ("name", "description", "parameters", "strict")
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_TOOL_PAYLOAD_KEYS = {
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"custom": ("name", "description", "format"),
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"function": ("name", "description", "parameters", "strict"),
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}
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def _nest_flat_chat_tool(tool: object) -> object:
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def _convert_tool_envelope(obj: object, *, to_chat: bool) -> object:
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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convert_custom_tool_format_to_chat_shape,
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)
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if not isinstance(tool, dict):
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return tool
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if tool.get("type") == "custom":
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if isinstance(tool.get("custom"), dict):
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envelope = tool
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payload = tool["custom"]
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elif "name" in tool:
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envelope = {"type": "custom"}
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payload = {k: tool[k] for k in _FLAT_CUSTOM_TOOL_KEYS if k in tool}
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else:
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return tool
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if isinstance(payload.get("format"), dict):
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payload = {**payload, "format": convert_custom_tool_format_to_chat_shape(payload["format"])}
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return {**envelope, "custom": payload}
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if tool.get("type") == "function" and "function" not in tool and "name" in tool:
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return {"type": "function", "function": {k: tool[k] for k in _FLAT_FUNCTION_TOOL_KEYS if k in tool}}
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return tool
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def _flatten_chat_tool_for_responses(tool: object) -> object:
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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convert_custom_tool_format_to_responses_shape,
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)
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if not isinstance(tool, dict):
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return tool
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if tool.get("type") == "custom":
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if isinstance(tool.get("custom"), dict):
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payload = {k: tool["custom"][k] for k in _FLAT_CUSTOM_TOOL_KEYS if k in tool["custom"]}
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elif "name" in tool:
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payload = {k: tool[k] for k in _FLAT_CUSTOM_TOOL_KEYS if k in tool}
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else:
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return tool
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if isinstance(payload.get("format"), dict):
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payload = {**payload, "format": convert_custom_tool_format_to_responses_shape(payload["format"])}
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return {"type": "custom", **payload}
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if tool.get("type") == "function" and isinstance(tool.get("function"), dict):
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return {
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"type": "function",
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**{k: tool["function"][k] for k in _FLAT_FUNCTION_TOOL_KEYS if k in tool["function"]},
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}
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return tool
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if not isinstance(obj, dict):
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return obj
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tool_type = obj.get("type")
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payload_keys = _TOOL_PAYLOAD_KEYS.get(tool_type)
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if payload_keys is None:
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return obj
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nested = obj.get(tool_type)
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source = nested if isinstance(nested, dict) else obj
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if source is obj and "name" not in obj:
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return obj
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payload = {key: source[key] for key in payload_keys if key in source}
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if isinstance(payload.get("format"), dict):
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convert = convert_custom_tool_format_to_chat_shape if to_chat else convert_custom_tool_format_to_responses_shape
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payload = {**payload, "format": convert(payload["format"])}
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return {"type": tool_type, tool_type: payload} if to_chat else {"type": tool_type, **payload}
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def _normalize_tool_dialect(data: dict, *, to_chat: bool) -> dict:
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converted: dict = {}
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tools = data.get("tools")
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if isinstance(tools, list):
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normalized_tools = [_convert_tool_envelope(tool, to_chat=to_chat) for tool in tools]
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if normalized_tools != tools:
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converted["tools"] = normalized_tools
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tool_choice = data.get("tool_choice")
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normalized_choice = _convert_tool_envelope(tool_choice, to_chat=to_chat)
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if normalized_choice != tool_choice:
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converted["tool_choice"] = normalized_choice
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return {**data, **converted} if converted else data
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def _is_chat_completions_body(data: dict) -> bool:
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@ -84,18 +74,6 @@ def _is_chat_completions_body(data: dict) -> bool:
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return "messages" in data and "input" not in data
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def _flatten_chat_tool_choice_for_responses(tool_choice: object) -> object:
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if not isinstance(tool_choice, dict):
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return tool_choice
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choice_type = tool_choice.get("type")
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if choice_type not in ("custom", "function"):
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return tool_choice
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nested = tool_choice.get(choice_type)
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if isinstance(nested, dict) and isinstance(nested.get("name"), str):
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return {"type": choice_type, "name": nested["name"]}
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return tool_choice
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@router.post(
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"/v1/responses",
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dependencies=[Depends(user_api_key_auth)],
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@ -450,14 +428,9 @@ async def cursor_chat_completions(
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# already fixed); delegate so behavior matches /chat/completions exactly.
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# Keyed on messages CONTENT, not key presence: Cursor can send a null or
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# empty messages stub alongside a real agent-mode input array
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tools = data.get("tools")
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normalized: dict = {}
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if isinstance(tools, list):
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nested_tools = [_nest_flat_chat_tool(tool) for tool in tools]
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if nested_tools != tools:
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normalized["tools"] = nested_tools
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if normalized:
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_safe_set_request_parsed_body(request=request, parsed_body={**data, **normalized})
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normalized = _normalize_tool_dialect(data, to_chat=True)
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if normalized is not data:
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_safe_set_request_parsed_body(request=request, parsed_body=normalized)
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return await chat_completion(
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request=request,
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fastapi_response=fastapi_response,
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@ -472,13 +445,7 @@ async def cursor_chat_completions(
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# cache's key snapshot so later readers get an empty body
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data = {key: value for key, value in data.items() if key != "stream_options"}
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tools = data.get("tools")
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if isinstance(tools, list):
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data = {**data, "tools": [_flatten_chat_tool_for_responses(tool) for tool in tools]}
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tool_choice = data.get("tool_choice")
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flattened_tool_choice = _flatten_chat_tool_choice_for_responses(tool_choice)
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if flattened_tool_choice != tool_choice:
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data = {**data, "tool_choice": flattened_tool_choice}
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data = _normalize_tool_dialect(data, to_chat=False)
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processor = ProxyBaseLLMRequestProcessing(data=data)
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@ -911,10 +911,11 @@ def test_cursor_models_route_delegates_to_model_list():
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class TestNestFlatChatTools:
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def test_flat_custom_tool_is_nested(self):
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from litellm.proxy.response_api_endpoints.endpoints import _nest_flat_chat_tool
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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result = _nest_flat_chat_tool(
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{"type": "custom", "name": "ApplyPatch", "description": "V4A patch", "format": {"type": "text"}}
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result = _convert_tool_envelope(
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{"type": "custom", "name": "ApplyPatch", "description": "V4A patch", "format": {"type": "text"}},
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to_chat=True,
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)
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assert result == {
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"type": "custom",
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@ -922,10 +923,11 @@ class TestNestFlatChatTools:
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}
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def test_flat_function_tool_is_nested(self):
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from litellm.proxy.response_api_endpoints.endpoints import _nest_flat_chat_tool
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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result = _nest_flat_chat_tool(
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{"type": "function", "name": "read_file", "description": "d", "parameters": {"type": "object"}}
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result = _convert_tool_envelope(
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{"type": "function", "name": "read_file", "description": "d", "parameters": {"type": "object"}},
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to_chat=True,
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)
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assert result == {
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"type": "function",
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@ -933,7 +935,7 @@ class TestNestFlatChatTools:
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}
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def test_already_nested_and_unrecognized_tools_pass_through_unchanged(self):
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from litellm.proxy.response_api_endpoints.endpoints import _nest_flat_chat_tool
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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tools = [
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{"type": "custom", "custom": {"name": "already_nested"}},
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@ -946,7 +948,7 @@ class TestNestFlatChatTools:
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None,
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42,
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]
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assert [_nest_flat_chat_tool(tool) for tool in tools] == tools
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assert [_convert_tool_envelope(tool, to_chat=True) for tool in tools] == tools
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class TestCursorMessagesArmToolNormalization:
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@ -1010,7 +1012,7 @@ class TestCursorMessagesArmToolNormalization:
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},
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},
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]
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assert seen["body"]["tool_choice"] == {"type": "custom", "name": "ApplyPatch"}
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assert seen["body"]["tool_choice"] == {"type": "custom", "custom": {"name": "ApplyPatch"}}
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assert seen["body"]["messages"] == [{"role": "user", "content": "use ApplyPatch"}]
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@pytest.mark.asyncio
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@ -1048,21 +1050,23 @@ class TestCursorMessagesArmToolNormalization:
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assert seen["body"]["messages"] == body["messages"]
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class TestNestFlatChatToolShapeMatrix:
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class TestToolEnvelopeConversionMatrix:
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"""
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Cursor mixes Responses API shapes into chat bodies PER LEVEL, independently
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(live-captured: a pre-nested custom envelope carrying a flat grammar format).
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Every cell of envelope x format must land on the canonical chat shape.
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Tool definitions and tool_choice share one envelope rule, so every cell of
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direction x envelope x format must land on that direction's canonical shape.
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"""
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FLAT_GRAMMAR = {"type": "grammar", "definition": "start: patch", "syntax": "lark"}
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NESTED_GRAMMAR = {"type": "grammar", "grammar": {"definition": "start: patch", "syntax": "lark"}}
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TEXT = {"type": "text"}
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@pytest.mark.parametrize("to_chat", [True, False])
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@pytest.mark.parametrize("envelope", ["flat", "nested"])
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@pytest.mark.parametrize("format_shape", ["absent", "text", "flat_grammar", "nested_grammar"])
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def test_every_envelope_and_format_combination_lands_canonical(self, envelope, format_shape):
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from litellm.proxy.response_api_endpoints.endpoints import _nest_flat_chat_tool
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def test_every_direction_envelope_and_format_lands_canonical(self, to_chat, envelope, format_shape):
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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format_value = {
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"absent": None,
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@ -1077,109 +1081,114 @@ class TestNestFlatChatToolShapeMatrix:
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canonical_payload = {"name": "ApplyPatch", "description": "V4A patch"}
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if format_shape in ("flat_grammar", "nested_grammar"):
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canonical_payload["format"] = self.NESTED_GRAMMAR
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canonical_payload["format"] = self.NESTED_GRAMMAR if to_chat else self.FLAT_GRAMMAR
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elif format_shape == "text":
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canonical_payload["format"] = self.TEXT
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expected = (
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{"type": "custom", "custom": canonical_payload} if to_chat else {"type": "custom", **canonical_payload}
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)
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assert _nest_flat_chat_tool(tool) == {"type": "custom", "custom": canonical_payload}
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assert _convert_tool_envelope(tool, to_chat=to_chat) == expected
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def test_nested_envelope_with_flat_grammar_matches_live_cursor_capture(self):
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from litellm.proxy.response_api_endpoints.endpoints import _nest_flat_chat_tool
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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cursor_tool = {
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cursor_tool = {"type": "custom", "custom": {"name": "ApplyPatch", "format": self.FLAT_GRAMMAR}}
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assert _convert_tool_envelope(cursor_tool, to_chat=True) == {
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"type": "custom",
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"custom": {
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"name": "ApplyPatch",
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"format": {"type": "grammar", "definition": "start: patch", "syntax": "lark"},
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},
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}
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assert _nest_flat_chat_tool(cursor_tool) == {
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"type": "custom",
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"custom": {
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"name": "ApplyPatch",
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"format": {
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"type": "grammar",
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"grammar": {"definition": "start: patch", "syntax": "lark"},
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},
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},
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"custom": {"name": "ApplyPatch", "format": self.NESTED_GRAMMAR},
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}
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def test_canonical_nested_tool_is_returned_equal(self):
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from litellm.proxy.response_api_endpoints.endpoints import _nest_flat_chat_tool
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@pytest.mark.parametrize("to_chat", [True, False])
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def test_conversion_is_idempotent(self, to_chat):
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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canonical = {
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"type": "custom",
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"custom": {"name": "A", "format": {"type": "grammar", "grammar": {"definition": "d", "syntax": "lark"}}},
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}
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assert _nest_flat_chat_tool(canonical) == canonical
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once = _convert_tool_envelope({"type": "custom", "name": "A", "format": self.FLAT_GRAMMAR}, to_chat=to_chat)
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assert _convert_tool_envelope(once, to_chat=to_chat) == once
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class TestFlattenChatToolsForResponsesInputArm:
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"""
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Mirror of TestNestFlatChatToolShapeMatrix for the input arm: chat-nested shapes in a
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Responses-shaped body must flatten to the Responses dialect, per level, idempotently.
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"""
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FLAT_GRAMMAR = {"type": "grammar", "definition": "start: patch", "syntax": "lark"}
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NESTED_GRAMMAR = {"type": "grammar", "grammar": {"definition": "start: patch", "syntax": "lark"}}
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@pytest.mark.parametrize("envelope", ["flat", "nested"])
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@pytest.mark.parametrize("format_shape", ["absent", "text", "flat_grammar", "nested_grammar"])
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def test_every_envelope_and_format_combination_lands_flat(self, envelope, format_shape):
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from litellm.proxy.response_api_endpoints.endpoints import _flatten_chat_tool_for_responses
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format_value = {
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"absent": None,
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"text": {"type": "text"},
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"flat_grammar": self.FLAT_GRAMMAR,
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"nested_grammar": self.NESTED_GRAMMAR,
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}[format_shape]
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payload = {"name": "ApplyPatch", "description": "V4A patch"}
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if format_value is not None:
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payload["format"] = format_value
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tool = {"type": "custom", "custom": payload} if envelope == "nested" else {"type": "custom", **payload}
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canonical = {"type": "custom", "name": "ApplyPatch", "description": "V4A patch"}
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if format_shape in ("flat_grammar", "nested_grammar"):
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canonical["format"] = self.FLAT_GRAMMAR
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elif format_shape == "text":
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canonical["format"] = {"type": "text"}
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assert _flatten_chat_tool_for_responses(tool) == canonical
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def test_nested_function_tool_is_flattened_and_flat_passes_through(self):
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from litellm.proxy.response_api_endpoints.endpoints import _flatten_chat_tool_for_responses
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def test_nested_function_tool_flattens_and_flat_passes_through(self):
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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nested = {"type": "function", "function": {"name": "read_file", "parameters": {"type": "object"}}}
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flat = {"type": "function", "name": "read_file", "parameters": {"type": "object"}}
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assert _flatten_chat_tool_for_responses(nested) == flat
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assert _flatten_chat_tool_for_responses(flat) == flat
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assert _convert_tool_envelope(nested, to_chat=False) == flat
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assert _convert_tool_envelope(flat, to_chat=False) == flat
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def test_unrecognized_entries_pass_through(self):
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from litellm.proxy.response_api_endpoints.endpoints import _flatten_chat_tool_for_responses
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@pytest.mark.parametrize("to_chat", [True, False])
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def test_unrecognized_entries_pass_through(self, to_chat):
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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entries = [{"type": "web_search"}, {"type": "custom"}, "junk", None, {}]
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assert [_flatten_chat_tool_for_responses(entry) for entry in entries] == entries
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entries = [{"type": "web_search"}, {"type": "custom"}, "junk", None, {}, 42, {"type": "auto"}]
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assert [_convert_tool_envelope(entry, to_chat=to_chat) for entry in entries] == entries
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class TestFlattenChatToolChoiceForResponsesInputArm:
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def test_nested_custom_and_function_tool_choice_flatten(self):
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from litellm.proxy.response_api_endpoints.endpoints import _flatten_chat_tool_choice_for_responses
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class TestToolChoiceSharesTheToolEnvelopeRule:
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"""
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tool_choice carries the same {"type": T, T: {...}} chat envelope as a tool
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definition, so it converts through the same function in both directions.
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OpenAI requires the nested key on chat (SDK ChatCompletionNamedToolChoiceParam
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and ChatCompletionNamedToolChoiceCustomParam both mark it Required).
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"""
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assert _flatten_chat_tool_choice_for_responses({"type": "custom", "custom": {"name": "ApplyPatch"}}) == {
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"type": "custom",
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@pytest.mark.parametrize("choice_type", ["custom", "function"])
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def test_flat_tool_choice_is_nested_for_chat(self, choice_type):
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from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
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assert _convert_tool_envelope({"type": choice_type, "name": "ApplyPatch"}, to_chat=True) == {
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"type": choice_type,
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choice_type: {"name": "ApplyPatch"},
|
||||
}
|
||||
|
||||
@pytest.mark.parametrize("choice_type", ["custom", "function"])
|
||||
def test_nested_tool_choice_is_flattened_for_responses(self, choice_type):
|
||||
from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
|
||||
|
||||
assert _convert_tool_envelope({"type": choice_type, choice_type: {"name": "ApplyPatch"}}, to_chat=False) == {
|
||||
"type": choice_type,
|
||||
"name": "ApplyPatch",
|
||||
}
|
||||
assert _flatten_chat_tool_choice_for_responses({"type": "function", "function": {"name": "f"}}) == {
|
||||
"type": "function",
|
||||
"name": "f",
|
||||
}
|
||||
|
||||
def test_flat_and_string_tool_choice_pass_through(self):
|
||||
from litellm.proxy.response_api_endpoints.endpoints import _flatten_chat_tool_choice_for_responses
|
||||
@pytest.mark.parametrize("to_chat", [True, False])
|
||||
def test_sentinel_and_malformed_tool_choice_pass_through(self, to_chat):
|
||||
from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope
|
||||
|
||||
for unchanged in ("auto", "required", None, {"type": "custom", "name": "x"}, {"type": "auto"}, 42):
|
||||
assert _flatten_chat_tool_choice_for_responses(unchanged) == unchanged
|
||||
for unchanged in ("auto", "required", "none", None, {"type": "auto"}, 42):
|
||||
assert _convert_tool_envelope(unchanged, to_chat=to_chat) == unchanged
|
||||
|
||||
|
||||
class TestNormalizeToolDialectCoversBothFields:
|
||||
"""
|
||||
The regression that motivated one normalizer: tools were converted while
|
||||
tool_choice was left flat, so OpenAI rejected the request. Both fields move
|
||||
together in a single call, on both arms.
|
||||
"""
|
||||
|
||||
@pytest.mark.parametrize("to_chat", [True, False])
|
||||
def test_tools_and_tool_choice_convert_together(self, to_chat):
|
||||
from litellm.proxy.response_api_endpoints.endpoints import _normalize_tool_dialect
|
||||
|
||||
flat = {"type": "custom", "name": "ApplyPatch"}
|
||||
nested = {"type": "custom", "custom": {"name": "ApplyPatch"}}
|
||||
source = flat if to_chat else nested
|
||||
expected = nested if to_chat else flat
|
||||
|
||||
out = _normalize_tool_dialect({"messages": [], "tools": [source], "tool_choice": source}, to_chat=to_chat)
|
||||
assert out["tools"] == [expected]
|
||||
assert out["tool_choice"] == expected
|
||||
|
||||
def test_body_needing_no_conversion_is_returned_by_identity(self):
|
||||
from litellm.proxy.response_api_endpoints.endpoints import _normalize_tool_dialect
|
||||
|
||||
data = {"messages": [], "tools": [{"type": "function", "function": {"name": "f"}}], "tool_choice": "auto"}
|
||||
assert _normalize_tool_dialect(data, to_chat=True) is data
|
||||
|
||||
def test_absent_tool_fields_are_not_invented(self):
|
||||
from litellm.proxy.response_api_endpoints.endpoints import _normalize_tool_dialect
|
||||
|
||||
data = {"messages": [{"role": "user", "content": "hi"}]}
|
||||
result = _normalize_tool_dialect(data, to_chat=True)
|
||||
assert result == data
|
||||
assert "tools" not in result and "tool_choice" not in result
|
||||
|
||||
|
||||
class TestCursorInputArmFlattening:
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue