Merge cd61746354 into 2dfeb80e04

litellm/llms/anthropic/pass_through/adapters/transformation.py

@@ -201,7 +201,7 @@ from litellm.types.llms.openai import (
     ChatCompletionUserMessage,
     ToolMessageContentPart,
 )
-from litellm.types.utils import Choices, ModelResponse, StreamingChoices, Usage
+from litellm.types.utils import Choices, FunctionCall, ModelResponse, StreamingChoices, Usage
 from litellm.utils import supports_mid_conversation_system
 
 from .streaming_iterator import AnthropicSSEStream, AnthropicStreamWrapper
@@ -1436,15 +1436,40 @@ class LiteLLMAnthropicMessagesAdapter:
                     if provider_specific_fields:
                         tool_use_block.provider_specific_fields = provider_specific_fields
                     new_content.append(tool_use_block.model_dump(exclude_none=True))
+            if choice.message.function_call is not None:
+                new_content.append(
+                    self._legacy_function_call_block(choice.message.function_call, tool_name_mapping).model_dump(
+                        exclude_none=True
+                    )
+                )
 
         return new_content
 
+    @staticmethod
+    def _legacy_function_call_block(
+        function_call: FunctionCall, tool_name_mapping: Mapping[str, str] | None
+    ) -> AnthropicResponseContentBlockToolUse:
+        from litellm._uuid import uuid
+
+        raw_name: Final = function_call.name or ""
+        name: Final = tool_name_mapping.get(raw_name, raw_name) if tool_name_mapping else raw_name
+        return AnthropicResponseContentBlockToolUse(
+            type="tool_use",
+            id=normalize_anthropic_tool_use_id(str(uuid.uuid4())),
+            name=name,
+            input=parse_tool_call_arguments(
+                function_call.arguments,
+                tool_name=name,
+                context="Anthropic pass-through adapter",
+            ),
+        )
+
     def _translate_openai_finish_reason_to_anthropic(self, openai_finish_reason: str) -> AnthropicFinishReason:
         if openai_finish_reason == "stop":
             return "end_turn"
         elif openai_finish_reason == "length":
             return "max_tokens"
-        elif openai_finish_reason == "tool_calls":
+        elif openai_finish_reason in ("tool_calls", "function_call"):
             return "tool_use"
         elif openai_finish_reason in ["content_filter", "refusal"]:
             return "refusal"

tests/unit/llms/anthropic/pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py

@@ -5235,3 +5235,32 @@ def test_eager_input_streaming_tool_reaches_bedrock_converse_as_beta():
 
     assert data["additionalModelRequestFields"]["anthropic_beta"] == ["fine-grained-tool-streaming-2025-05-14"]
     assert data["toolConfig"]["tools"][0]["toolSpec"]["inputSchema"]["json"] == EAGER_INPUT_SCHEMA
+
+
+def test_translate_openai_function_call_response_to_anthropic_tool_use():
+    """A legacy OpenAI function_call finish maps to an Anthropic tool_use turn."""
+    response: Final = ModelResponse(
+        id="chatcmpl-function-call",
+        model="legacy-function-model",
+        choices=[
+            Choices(
+                index=0,
+                finish_reason="function_call",
+                message=Message(
+                    role="assistant",
+                    content=None,
+                    function_call={"name": "get_weather", "arguments": '{"location": "Boston"}'},
+                ),
+            )
+        ],
+        usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15),
+    )
+
+    result: Final = LiteLLMAnthropicMessagesAdapter().translate_openai_response_to_anthropic(response)
+
+    assert result["stop_reason"] == "tool_use"
+    [block] = result["content"] or []
+    assert block["type"] == "tool_use"
+    assert block["name"] == "get_weather"
+    assert block["input"] == {"location": "Boston"}
+    assert block["id"] and all(c.isalnum() or c in "-_" for c in block["id"])