fix(mistral): a named tool_choice is dropped, the model picks any tool

Mistral's chat API takes tool_choice as an enum or as an object naming
one function, {"type": "function", "function": {"name": ...}}. The
mapper only handled strings, so the object was skipped and the request
reached Mistral with no tool_choice at all.

The pin is now rebuilt from the function name alone, since Mistral
rejects fields it does not define. Enum mapping is unchanged, and an
object without a usable name is still left out.

Fixes #39736

litellm/llms/mistral/chat/transformation.py

@@ -45,7 +45,13 @@ from litellm.types.llms.mistral import (
     MistralThinkingBlock,
     MistralToolCallMessage,
 )
-from litellm.types.llms.openai import AllMessageValues, ChatCompletionAssistantMessage, ChatCompletionTextObject
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionAssistantMessage,
+    ChatCompletionTextObject,
+    ChatCompletionToolChoiceFunctionParam,
+    ChatCompletionToolChoiceObjectParam,
+)
 from litellm.types.utils import ModelResponse, ModelResponseStream
 from litellm.utils import convert_to_model_response_object, supports_reasoning
 
@@ -118,7 +124,7 @@ class MistralConfig(OpenAIGPTConfig):
 
     - `tools` (list or null): A list of available tools for the model. Use this to specify functions for which the model can generate JSON inputs.
 
-    - `tool_choice` (string - 'auto'/'any'/'none' or null): Specifies if/how functions are called. If set to none the model won't call a function and will generate a message instead. If set to auto the model can choose to either generate a message or call a function. If set to any the model is forced to call a function. Default - 'auto'.
+    - `tool_choice` (string - 'auto'/'any'/'none', an object naming one function such as {"type": "function", "function": {"name": "my_function"}}, or null): Specifies if/how functions are called. If set to none the model won't call a function and will generate a message instead. If set to auto the model can choose to either generate a message or call a function. If set to any the model is forced to call a function. Default - 'auto'.
 
     - `stop` (string or array of strings): Stop generation if this token is detected. Or if one of these tokens is detected when providing an array
 
@@ -135,7 +141,7 @@ class MistralConfig(OpenAIGPTConfig):
     top_p: int | None = None
     max_tokens: int | None = None
     tools: list | None = None
-    tool_choice: Literal["auto", "any", "none"] | None = None
+    tool_choice: Literal["auto", "any", "none"] | ChatCompletionToolChoiceObjectParam | None = None
     random_seed: int | None = None
     safe_prompt: bool | None = None
     response_format: dict | None = None
@@ -147,7 +153,7 @@ class MistralConfig(OpenAIGPTConfig):
         top_p: int | None = None,
         max_tokens: int | None = None,
         tools: list | None = None,
-        tool_choice: Literal["auto", "any", "none"] | None = None,
+        tool_choice: Literal["auto", "any", "none"] | ChatCompletionToolChoiceObjectParam | None = None,
         random_seed: int | None = None,
         safe_prompt: bool | None = None,
         response_format: dict | None = None,
@@ -192,13 +198,20 @@ class MistralConfig(OpenAIGPTConfig):
             *(("prompt_cache_key",) if accepts_prompt_cache_key else ()),
         ]
 
-    def _map_tool_choice(self, tool_choice: str) -> str:
-        if tool_choice == "auto" or tool_choice == "none":
-            return tool_choice
-        elif tool_choice == "required":
-            return "any"
-        else:  # openai 'tool_choice' object param not supported by Mistral API
-            return "any"
+    @staticmethod
+    def _map_tool_choice(tool_choice: object) -> str | ChatCompletionToolChoiceObjectParam | None:
+        match tool_choice:
+            case str(choice) if choice in ("auto", "none"):
+                return choice
+            case {"type": "function", "function": {"name": str(name)}} if name:
+                return ChatCompletionToolChoiceObjectParam(
+                    type="function", function=ChatCompletionToolChoiceFunctionParam(name=name)
+                )
+            case str():
+                return "any"
+            case _:
+                pass
+        return None
 
     @staticmethod
     def _get_mistral_reasoning_system_prompt() -> str:
@@ -250,8 +263,16 @@ class MistralConfig(OpenAIGPTConfig):
                 optional_params["top_p"] = value
             if param == "stop":
                 optional_params["stop"] = value
-            if param == "tool_choice" and isinstance(value, str):
-                optional_params["tool_choice"] = self._map_tool_choice(tool_choice=value)
+            if (
+                param == "tool_choice"
+                and (
+                    mapped_tool_choice := self._map_tool_choice(
+                        tool_choice=cast(object, value)  # cast-ok: untyped request params, the match narrows it
+                    )
+                )
+                is not None
+            ):
+                optional_params["tool_choice"] = mapped_tool_choice
             if param == "seed":
                 optional_params["extra_body"] = {"random_seed": value}
             if param == "response_format":

tests/unit/llms/mistral/test_mistral_chat_transformation.py

@@ -1,7 +1,7 @@
 import copy
 import json
 import logging
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from typing import Final, List, cast
 from unittest.mock import MagicMock, patch
 from urllib.parse import urlparse
@@ -18,7 +18,7 @@ from litellm.llms.mistral.chat.transformation import (
     MistralConfig,
 )
 from litellm.llms.vertex_ai.vertex_ai_partner_models.mistral.transformation import VertexAIMistralConfig
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
 from litellm.types.utils import ModelResponse
 from litellm.utils import supports_reasoning
 
@@ -1690,7 +1690,9 @@ def test_mistral_request_drops_a_user_name_without_touching_the_callers_message(
 
 def _warnings_naming(caplog: pytest.LogCaptureFixture, param: str) -> tuple[str, ...]:
     return tuple(
-        r.getMessage() for r in caplog.records if r.levelno == logging.WARNING and f"Dropping {param}=" in r.getMessage()
+        r.getMessage()
+        for r in caplog.records
+        if r.levelno == logging.WARNING and f"Dropping {param}=" in r.getMessage()
     )
 
 
@@ -2060,3 +2062,69 @@ def test_prompt_cache_key_stays_unsupported_for_vertex_hosted_mistral(local_mode
         model="mistral-medium-3", custom_llm_provider="vertex_ai", prompt_cache_key="s", drop_params=True
     )
     assert "prompt_cache_key" not in dropped
+
+
+class TestMistralToolChoice:
+    """Mistral takes tool_choice as an enum or as {"type": "function", "function": {"name": ...}}, per
+    https://docs.mistral.ai/api/endpoint/chat (checked 2026-11-03)"""
+
+    TOOLS: Final[Sequence[ChatCompletionToolParam]] = [
+        {
+            "type": "function",
+            "function": {"name": "get_weather", "parameters": {"type": "object", "properties": {}}},
+        }
+    ]
+
+    def _request_body(self, tool_choice: str | Mapping[str, object]) -> Mapping[str, object]:
+        import litellm
+
+        optional_params: Final = litellm.get_optional_params(
+            model="mistral-large-latest",
+            custom_llm_provider="mistral",
+            tools=list(self.TOOLS),
+            tool_choice=tool_choice,
+        )
+        return MistralConfig().transform_request(
+            model="mistral-large-latest",
+            messages=[{"role": "user", "content": "what is the weather in Madrid"}],
+            optional_params=optional_params,
+            litellm_params={},
+            headers={},
+        )
+
+    def test_named_function_is_forwarded_to_mistral(self, local_model_cost_map: None) -> None:
+        body: Final = self._request_body({"type": "function", "function": {"name": "get_weather"}})
+
+        assert body["tool_choice"] == {"type": "function", "function": {"name": "get_weather"}}
+
+    def test_keys_mistral_does_not_define_are_not_replayed(self, local_model_cost_map: None) -> None:
+        body: Final = self._request_body(
+            {
+                "type": "function",
+                "function": {"name": "get_weather", "strict": True},
+                "cache_control": {"type": "ephemeral"},
+            }
+        )
+
+        assert body["tool_choice"] == {"type": "function", "function": {"name": "get_weather"}}
+
+    @pytest.mark.parametrize(
+        "tool_choice, expected",
+        [("auto", "auto"), ("none", "none"), ("required", "any"), ("any", "any")],
+    )
+    def test_enum_tool_choice_is_mapped(self, local_model_cost_map: None, tool_choice: str, expected: str) -> None:
+        assert self._request_body(tool_choice)["tool_choice"] == expected
+
+    @pytest.mark.parametrize(
+        "tool_choice",
+        [
+            {"type": "function", "function": {}},
+            {"type": "function", "function": {"name": ""}},
+            {"type": "function", "function": {"name": 123}},
+            {"type": "function"},
+        ],
+    )
+    def test_function_objects_without_a_usable_name_are_left_out(
+        self, local_model_cost_map: None, tool_choice: Mapping[str, object]
+    ) -> None:
+        assert "tool_choice" not in self._request_body(tool_choice)