Merge 7c2c185444 into 77b4644523

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -1946,6 +1946,8 @@ def convert_to_anthropic_tool_result(
             tool_use_id=sanitized_tool_use_id,
             content=anthropic_content,
         )
+        if "is_error" in tool_message:
+            anthropic_tool_result["is_error"] = tool_message["is_error"]
 
     if message["role"] == "function":
         function_message: Final[ChatCompletionFunctionMessage] = message

litellm/llms/anthropic/chat/guardrail_translation/handler.py

@@ -857,7 +857,7 @@ class AnthropicMessagesHandler(BaseTranslation):
         )
         translation_source: Final = {**{key: value for key, value in data.items() if key != "system"}, "model": model}
         chat_completion_compatible_request: Final = self._translate_to_openai(
-            translation_source, keep_cache_control=keep_cache_control
+            translation_source, keep_cache_control=keep_cache_control, keep_tool_errors=True
         )
 
         full_structured_messages: Final = cast(
@@ -1047,7 +1047,10 @@ class AnthropicMessagesHandler(BaseTranslation):
         """Convert an OpenAI system message to the client's Anthropic-shaped entry."""
         content: Final = message.get("content")
         if isinstance(content, str):
-            return {"role": "system", "content": content} if content else None
+            result: dict[str, object] = {"role": "system", "content": content}
+            if "output_config" in message:
+                result["output_config"] = deepcopy(message["output_config"])
+            return result if content else None
         if not isinstance(content, list):
             return None
         blocks: Final[list[dict[str, object]]] = []  # mutable-ok: API message payload
@@ -1065,7 +1068,12 @@ class AnthropicMessagesHandler(BaseTranslation):
             if cache_control:
                 anthropic_block["cache_control"] = deepcopy(cache_control)
             blocks.append(anthropic_block)
-        return {"role": "system", "content": blocks} if blocks else None
+        if not blocks:
+            return None
+        result: dict[str, object] = {"role": "system", "content": blocks}
+        if "output_config" in message:
+            result["output_config"] = deepcopy(message["output_config"])
+        return result
 
     @staticmethod
     def _fold_leading_systems_into_top_level(

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

@@ -1025,7 +1025,10 @@ class LiteLLMAnthropicMessagesAdapter:
         """Translate an in-sequence system entry without changing its role or position."""
         content: Final = message.get("content")
         if isinstance(content, str):
-            return ChatCompletionSystemMessage(role="system", content=content) if content else None
+            result = ChatCompletionSystemMessage(role="system", content=content) if content else None
+            if result and "output_config" in message:
+                result["output_config"] = message["output_config"]
+            return result
         if not isinstance(content, list):
             return None
         text_parts: Final[list[ChatCompletionTextObject]] = []  # mutable-ok: API message payload
@@ -1038,7 +1041,12 @@ class LiteLLMAnthropicMessagesAdapter:
             text_obj = ChatCompletionTextObject(type="text", text=text)
             self._add_cache_control_if_applicable(block, text_obj, model)
             text_parts.append(self._add_prompt_cache_breakpoint_if_present(block, text_obj))
-        return ChatCompletionSystemMessage(role="system", content=text_parts) if text_parts else None
+        if not text_parts:
+            return None
+        result = ChatCompletionSystemMessage(role="system", content=text_parts)
+        if "output_config" in message:
+            result["output_config"] = message["output_config"]
+        return result
 
     def _add_system_message_to_messages(
         self,

tests/unit/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_field_preservation.py

@@ -0,0 +1,146 @@
+"""
+Test that Anthropic guardrail write-back preserves tool_result.is_error and system output_config.
+
+Regression tests for https://github.com/BerriAI/litellm/issues/50269
+"""
+
+from litellm.litellm_core_utils.prompt_templates.factory import convert_to_anthropic_tool_result
+from litellm.llms.anthropic.chat.guardrail_translation.handler import AnthropicMessagesHandler
+from litellm.types.llms.openai import ChatCompletionToolMessage
+
+
+def test_convert_to_anthropic_tool_result_preserves_is_error_true():
+    """Tool message with is_error=true must survive OpenAI->Anthropic conversion."""
+    tool_message: ChatCompletionToolMessage = {
+        "role": "tool",
+        "tool_call_id": "toolu_01abc",
+        "content": "cat: file.txt: No such file or directory",
+        "is_error": True,
+    }
+
+    result = convert_to_anthropic_tool_result(tool_message)
+
+    assert result["type"] == "tool_result"
+    assert result["tool_use_id"] == "toolu_01abc"
+    assert result["content"] == "cat: file.txt: No such file or directory"
+    assert result.get("is_error") is True
+
+
+def test_convert_to_anthropic_tool_result_preserves_is_error_false():
+    """Tool message with is_error=false must survive OpenAI->Anthropic conversion."""
+    tool_message: ChatCompletionToolMessage = {
+        "role": "tool",
+        "tool_call_id": "toolu_02xyz",
+        "content": "success output",
+        "is_error": False,
+    }
+
+    result = convert_to_anthropic_tool_result(tool_message)
+
+    assert result["type"] == "tool_result"
+    assert result["tool_use_id"] == "toolu_02xyz"
+    assert result["content"] == "success output"
+    assert result.get("is_error") is False
+
+
+def test_convert_to_anthropic_tool_result_no_is_error():
+    """Tool message without is_error field should not add one."""
+    tool_message: ChatCompletionToolMessage = {
+        "role": "tool",
+        "tool_call_id": "toolu_03def",
+        "content": "normal output",
+    }
+
+    result = convert_to_anthropic_tool_result(tool_message)
+
+    assert result["type"] == "tool_result"
+    assert result["tool_use_id"] == "toolu_03def"
+    assert result["content"] == "normal output"
+    assert "is_error" not in result
+
+
+def test_openai_system_message_to_anthropic_preserves_output_config():
+    """System message with output_config must survive OpenAI->Anthropic conversion."""
+    openai_system_message = {
+        "role": "system",
+        "content": [
+            {
+                "type": "text",
+                "text": "Environment update",
+            }
+        ],
+        "output_config": {"effort": "medium"},
+    }
+
+    result = AnthropicMessagesHandler._openai_system_message_to_anthropic(openai_system_message)
+
+    assert result is not None
+    assert result["role"] == "system"
+    assert isinstance(result["content"], list)
+    assert len(result["content"]) == 1
+    assert result["content"][0]["type"] == "text"
+    assert result["content"][0]["text"] == "Environment update"
+    assert result.get("output_config") == {"effort": "medium"}
+
+
+def test_openai_system_message_to_anthropic_preserves_cache_control():
+    """System message with cache_control must still be preserved."""
+    openai_system_message = {
+        "role": "system",
+        "content": [
+            {
+                "type": "text",
+                "text": "Cached content",
+                "cache_control": {"type": "ephemeral"},
+            }
+        ],
+    }
+
+    result = AnthropicMessagesHandler._openai_system_message_to_anthropic(openai_system_message)
+
+    assert result is not None
+    assert result["role"] == "system"
+    assert isinstance(result["content"], list)
+    assert len(result["content"]) == 1
+    assert result["content"][0]["type"] == "text"
+    assert result["content"][0]["text"] == "Cached content"
+    assert result["content"][0]["cache_control"] == {"type": "ephemeral"}
+
+
+def test_openai_system_message_to_anthropic_preserves_both_fields():
+    """System message with both output_config and cache_control must preserve both."""
+    openai_system_message = {
+        "role": "system",
+        "content": [
+            {
+                "type": "text",
+                "text": "Complex system message",
+                "cache_control": {"type": "ephemeral"},
+            }
+        ],
+        "output_config": {"effort": "high"},
+    }
+
+    result = AnthropicMessagesHandler._openai_system_message_to_anthropic(openai_system_message)
+
+    assert result is not None
+    assert result["role"] == "system"
+    assert isinstance(result["content"], list)
+    assert result["content"][0]["cache_control"] == {"type": "ephemeral"}
+    assert result.get("output_config") == {"effort": "high"}
+
+
+def test_openai_system_message_to_anthropic_string_content_with_output_config():
+    """System message with string content and output_config must preserve output_config."""
+    openai_system_message = {
+        "role": "system",
+        "content": "Simple system prompt",
+        "output_config": {"effort": "low"},
+    }
+
+    result = AnthropicMessagesHandler._openai_system_message_to_anthropic(openai_system_message)
+
+    assert result is not None
+    assert result["role"] == "system"
+    assert result["content"] == "Simple system prompt"
+    assert result.get("output_config") == {"effort": "low"}