From 45ba9e1f7ef08e747b3e16c124ed76b9f709c1f0 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 13 Mar 2026 11:34:18 +0530 Subject: [PATCH] fix(anthropic): preserve native tool format when guardrails convert tools for Anthropic Messages API - Keep Anthropic-native tools (tool_search_tool_regex, web_search, bash, etc.) in original format when translating to OpenAI format for guardrails - Convert guardrail-returned tools back from OpenAI to Anthropic format (type=custom for user tools) - Add TOOL_SEARCH_TOOL to ANTHROPIC_HOSTED_TOOLS enum; use prefix matching for native tool detection - Set type=custom explicitly when mapping OpenAI function tools to AnthropicMessagesTool - Add test for Anthropic native tools with guardrails Made-with: Cursor --- .../chat/guardrail_translation/handler.py | 10 ++- litellm/llms/anthropic/chat/transformation.py | 6 +- .../adapters/transformation.py | 9 +++ litellm/types/llms/anthropic.py | 1 + .../test_anthropic_guardrail_handler.py | 63 +++++++++++++++++++ 5 files changed, 87 insertions(+), 2 deletions(-) diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 0bc0777e37a..5372757cbb6 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -127,7 +127,15 @@ class AnthropicMessagesHandler(BaseTranslation): guardrailed_texts = guardrailed_inputs.get("texts", []) guardrailed_tools = guardrailed_inputs.get("tools") if guardrailed_tools is not None: - data["tools"] = guardrailed_tools + # Convert tools back from OpenAI format to Anthropic format + anthropic_config = AnthropicConfig() + anthropic_tools: List[AllAnthropicToolsValues] = [] + for tool in guardrailed_tools: + converted_tool, mcp_server = anthropic_config._map_tool_helper(tool) + if converted_tool is not None: + anthropic_tools.append(converted_tool) + # Note: MCP servers are handled separately in the main transformation + data["tools"] = anthropic_tools # Step 3: Map guardrail responses back to original message structure await self._apply_guardrail_responses_to_input( diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 1b912bfc2a0..47cdd8287e0 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -55,7 +55,10 @@ from litellm.types.utils import ( CompletionTokensDetailsWrapper, ) from litellm.types.utils import Message as LitellmMessage -from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse +from litellm.types.utils import ( + PromptTokensDetailsWrapper, + ServerToolUse, +) from litellm.utils import ( ModelResponse, Usage, @@ -420,6 +423,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _tool = AnthropicMessagesTool( name=tool["function"]["name"], input_schema=input_anthropic_schema, + type="custom", ) _description = tool["function"].get("description") diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 43a6fa8045d..c1a6bd67501 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -68,6 +68,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( parse_tool_call_arguments, ) from litellm.types.llms.anthropic import ( + ANTHROPIC_HOSTED_TOOLS, AllAnthropicToolsValues, AnthopicMessagesAssistantMessageParam, AnthropicFinishReason, @@ -771,7 +772,15 @@ class LiteLLMAnthropicMessagesAdapter: new_tools: List[ChatCompletionToolParam] = [] tool_name_mapping: Dict[str, str] = {} mapped_tool_params = ["name", "input_schema", "description", "cache_control"] + for tool in tools: + # Check if this is an Anthropic-native tool that should be kept as-is + tool_type = tool.get("type", "") + if any(tool_type.startswith(t.value) for t in ANTHROPIC_HOSTED_TOOLS): + # Keep Anthropic-native tools in their original format + new_tools.append(tool) # type: ignore[arg-type] + continue + original_name = tool["name"] truncated_name = truncate_tool_name(original_name) diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py index 478fcbdbd1d..37044c2b4f5 100644 --- a/litellm/types/llms/anthropic.py +++ b/litellm/types/llms/anthropic.py @@ -639,6 +639,7 @@ class ANTHROPIC_HOSTED_TOOLS(str, Enum): CODE_EXECUTION = "code_execution" WEB_FETCH = "web_fetch" MEMORY = "memory" + TOOL_SEARCH_TOOL = "tool_search_tool" class ANTHROPIC_BETA_HEADER_VALUES(str, Enum): diff --git a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py index 82517b7af9e..9f70c7371d3 100644 --- a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py @@ -231,6 +231,69 @@ class TestAnthropicMessagesHandlerInputProcessing: assert result == responses_so_far + @pytest.mark.asyncio + async def test_process_input_messages_with_anthropic_native_tools(self): + """Test that Anthropic native tools (tool_search_tool_regex) are preserved correctly + + This test verifies the fix for the bug where Anthropic native tools like + tool_search_tool_regex_20251119 were being converted to OpenAI format and then + not properly converted back, causing API errors. + + The guardrail converts tools to OpenAI format for processing, then they need to be + converted back to Anthropic format. Native Anthropic tools should be preserved as-is, + while regular tools should be converted to type="custom". + """ + handler = AnthropicMessagesHandler() + guardrail = MockPassThroughGuardrail(guardrail_name="test") + + data = { + "model": "claude-opus-4-6", + "messages": [{"role": "user", "content": "What is the weather in San Francisco?"}], + "tools": [ + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + { + "name": "get_weather", + "description": "Get the weather at a specific location", + "input_schema": { + "type": "object", + "properties": { + "location": {"type": "string"}, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + }, + "defer_loading": True + } + ] + } + + result = await handler.process_input_messages( + data=data, + guardrail_to_apply=guardrail, + litellm_logging_obj=MagicMock() + ) + + # Verify tools are in correct Anthropic format + tools = result["tools"] + assert len(tools) == 2 + + # First tool should be preserved as Anthropic native tool + assert tools[0]["type"] == "tool_search_tool_regex_20251119" + assert tools[0]["name"] == "tool_search_tool_regex" + + # Second tool should be converted to Anthropic custom tool format + assert tools[1]["type"] == "custom" + assert tools[1]["name"] == "get_weather" + assert tools[1]["description"] == "Get the weather at a specific location" + assert "input_schema" in tools[1] + + if __name__ == "__main__": # Run the tests pytest.main([__file__, "-v"])