Merge cd08abb92d into d5ca88900c

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -1750,11 +1750,9 @@ def convert_to_gemini_tool_call_result(
         # Not valid JSON, wrap in content field
         response_data = {"content": content_str}
 
-    # We can't determine from openai message format whether it's a successful or
-    # error call result so default to the successful result template
     _function_response: Final = VertexFunctionResponse(
         name=name,
-        response=response_data,
+        response={"error": content_str} if message.get("is_error") is True else response_data,
     )
     if gemini_call_id:
         _function_response["id"] = gemini_call_id

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

@@ -562,6 +562,11 @@ class LiteLLMAnthropicMessagesAdapter:
                                 content=self._tool_result_content(content.get("content")),
                             )
                             self._add_cache_control_if_applicable(content, tool_result, model)
+                            tool_result_block = cast(Mapping[str, object], content)  # cast-ok: untrusted client payload
+                            if tool_result_block.get("is_error") is True and self.target_consumes_tool_error(
+                                model, custom_llm_provider
+                            ):
+                                tool_result["is_error"] = True
                             tool_message_list.append(tool_result)
 
             if len(tool_message_list) > 0:
@@ -737,6 +742,13 @@ class LiteLLMAnthropicMessagesAdapter:
     def target_consumes_cache_control(cls, model: str) -> bool:
         return cls.is_anthropic_claude_model(model) or cls.is_bedrock_arn_model(model) or "gemini" in model.lower()
 
+    @staticmethod
+    def target_consumes_tool_error(model: str | None, custom_llm_provider: str | None) -> bool:
+        # Decide on the provider alone: aliased model names ("prod-model" routed to
+        # gemini) carry no "gemini" substring.
+        provider: Final = custom_llm_provider or (model.split("/", 1)[0] if model and "/" in model else None)
+        return provider in ("gemini", "vertex_ai", "vertex_ai_beta")
+
     @staticmethod
     def translate_thinking_for_model(
         thinking: AnthropicThinkingParam,

litellm/proxy/_lazy_openapi_snapshot.json

@@ -49412,6 +49412,10 @@
               ],
               "title": "Content"
             },
+            "is_error": {
+              "title": "Is Error",
+              "type": "boolean"
+            },
             "role": {
               "const": "tool",
               "title": "Role",

litellm/types/llms/openai.py

@@ -957,6 +957,7 @@ class ChatCompletionToolMessage(TypedDict):
     role: Literal["tool"]
     content: str | Iterable[ToolMessageContentPart]  # writable-ok: Pydantic warns on ReadOnly TypedDict fields
     tool_call_id: str
+    is_error: NotRequired[bool]  # writable-ok: Pydantic warns on ReadOnly TypedDict fields
 
 
 class ChatCompletionFunctionMessage(TypedDict):

tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py

@@ -2279,6 +2279,36 @@ def test_convert_gemini_tool_call_result_with_data_url_extra_params():
     )
 
 
+def test_convert_gemini_tool_call_result_error_goes_to_response_error():
+    """https://github.com/BerriAI/litellm/issues/44979"""
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_1",
+                "type": "function",
+                "index": 0,
+                "function": {"name": "deploy", "arguments": "{}"},
+            }
+        ],
+    }
+
+    failed = convert_to_gemini_tool_call_result(
+        message=ChatCompletionToolMessage(
+            role="tool", tool_call_id="call_1", content="exit code 1: migration failed", is_error=True
+        ),
+        last_message_with_tool_calls=last_message_with_tool_calls,
+    )
+    succeeded = convert_to_gemini_tool_call_result(
+        message=ChatCompletionToolMessage(role="tool", tool_call_id="call_1", content="deployed"),
+        last_message_with_tool_calls=last_message_with_tool_calls,
+    )
+
+    assert failed["function_response"]["response"] == {"error": "exit code 1: migration failed"}
+    assert succeeded["function_response"]["response"] == {"content": "deployed"}
+
+
 def _gemini_tool_result_fixture(
     content: str,
 ) -> tuple[ChatCompletionToolMessage, dict]:

tests/unit/llms/anthropic/messages/adapters/test_anthropic_messages_adapters_transformation.py

@@ -2736,6 +2736,41 @@ def test_cache_control_not_preserved_in_tool_result_for_non_claude():
     assert "cache_control" not in tool_message
 
 
+@pytest.mark.parametrize(
+    "model,custom_llm_provider,keeps_is_error",
+    [
+        ("gemini/gemini-3.5-flash-lite", None, True),
+        ("gemini-3.5-flash-lite", "vertex_ai", True),
+        ("gpt-4", "openai", False),
+        ("openrouter/google/gemini-3.5-flash-lite", None, False),
+        ("prod-model", "gemini", True),
+    ],
+)
+def test_tool_result_is_error_kept_only_for_gemini_targets(model, custom_llm_provider, keeps_is_error):
+    """https://github.com/BerriAI/litellm/issues/44979"""
+    anthropic_messages = [
+        AnthropicMessagesUserMessageParam(
+            role="user",
+            content=[
+                {
+                    "type": "tool_result",
+                    "tool_use_id": "toolu_1",
+                    "content": "exit code 1: migration failed",
+                    "is_error": True,
+                }
+            ],
+        )
+    ]
+
+    result = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai(
+        messages=anthropic_messages, model=model, custom_llm_provider=custom_llm_provider
+    )
+
+    tool_message = next(msg for msg in result if msg.get("role") == "tool")
+    assert tool_message["content"] == "exit code 1: migration failed"
+    assert tool_message.get("is_error") is (True if keeps_is_error else None)
+
+
 def test_cache_control_preserved_in_assistant_text_for_claude():
     """Cache control should be preserved in assistant text blocks for Claude models."""
     anthropic_messages = [

ui/litellm-dashboard/src/lib/http/schema.d.ts

@@ -29462,6 +29462,8 @@ export interface components {
         ChatCompletionToolMessage: {
             /** Content */
             content: string | (components["schemas"]["ChatCompletionTextObject"] | components["schemas"]["ChatCompletionImageObject"] | components["schemas"]["ChatCompletionAudioObject"] | components["schemas"]["ChatCompletionFileObject"] | components["schemas"]["ChatCompletionToolReferenceObject"])[];
+            /** Is Error */
+            is_error?: boolean;
             /**
              * Role
              * @constant