diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 0f613ceb504..fe57046f808 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -46,6 +46,7 @@ from litellm.types.llms.openai import ( ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, ChatCompletionToolParam, + OpenAIChatCompletionFinishReason, OpenAIMcpServerTool, OpenAIWebSearchOptions, ) @@ -54,10 +55,7 @@ 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, @@ -251,10 +249,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): # All numeric/string/array constraints not supported by Anthropic unsupported_fields = { - "maxItems", "minItems", # array constraints - "minimum", "maximum", # numeric constraints - "exclusiveMinimum", "exclusiveMaximum", # numeric constraints - "minLength", "maxLength", # string constraints + "maxItems", + "minItems", # array constraints + "minimum", + "maximum", # numeric constraints + "exclusiveMinimum", + "exclusiveMaximum", # numeric constraints + "minLength", + "maxLength", # string constraints } # Build description additions from removed constraints @@ -844,7 +846,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): @staticmethod def map_openai_context_management_to_anthropic( - context_management: Union[List[Dict[str, Any]], Dict[str, Any]] + context_management: Union[List[Dict[str, Any]], Dict[str, Any]], ) -> Optional[Dict[str, Any]]: """ OpenAI format: [{"type": "compaction", "compact_threshold": 200000}] @@ -876,19 +878,22 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): entry_type = entry.get("type") if entry_type == "compaction": - anthropic_edit: Dict[str, Any] = { - "type": "compact_20260112" - } + anthropic_edit: Dict[str, Any] = {"type": "compact_20260112"} compact_threshold = entry.get("compact_threshold") # Rewrite to 'trigger' with correct nesting if threshold exists - if compact_threshold is not None and isinstance(compact_threshold, (int, float)): + if compact_threshold is not None and isinstance( + compact_threshold, (int, float) + ): anthropic_edit["trigger"] = { "type": "input_tokens", - "value": int(compact_threshold) + "value": int(compact_threshold), } # Map any other keys by passthrough except handled ones for k in entry: - if k not in {"type", "compact_threshold"}: # only passthrough other keys + if k not in { + "type", + "compact_threshold", + }: # only passthrough other keys anthropic_edit[k] = entry[k] anthropic_edits.append(anthropic_edit) @@ -911,10 +916,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): for param, value in non_default_params.items(): if param == "max_tokens": - optional_params["max_tokens"] = value - if param == "max_completion_tokens": - optional_params["max_tokens"] = value - if param == "tools": + optional_params["max_tokens"] = ( + value if isinstance(value, int) else max(1, int(round(value))) + ) + elif param == "max_completion_tokens": + optional_params["max_tokens"] = ( + value if isinstance(value, int) else max(1, int(round(value))) + ) + elif param == "tools": # check if optional params already has tools anthropic_tools, mcp_servers = self._map_tools(value) optional_params = self._add_tools_to_optional_params( @@ -922,7 +931,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) if mcp_servers: optional_params["mcp_servers"] = mcp_servers - if param == "tool_choice" or param == "parallel_tool_calls": + elif param == "tool_choice" or param == "parallel_tool_calls": _tool_choice: Optional[AnthropicMessagesToolChoice] = ( self._map_tool_choice( tool_choice=non_default_params.get("tool_choice"), @@ -932,17 +941,19 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if _tool_choice is not None: optional_params["tool_choice"] = _tool_choice - if param == "stream" and value is True: + elif param == "stream" and value is True: optional_params["stream"] = value - if param == "stop" and (isinstance(value, str) or isinstance(value, list)): + elif param == "stop" and ( + isinstance(value, str) or isinstance(value, list) + ): _value = self._map_stop_sequences(value) if _value is not None: optional_params["stop_sequences"] = _value - if param == "temperature": + elif param == "temperature": optional_params["temperature"] = value - if param == "top_p": + elif param == "top_p": optional_params["top_p"] = value - if param == "response_format" and isinstance(value, dict): + elif param == "response_format" and isinstance(value, dict): if any( substring in model for substring in { @@ -982,14 +993,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): optional_params=optional_params, tools=[_tool] ) optional_params["json_mode"] = True - if ( + elif ( param == "user" and value is not None and isinstance(value, str) and _valid_user_id(value) # anthropic fails on emails ): optional_params["metadata"] = {"user_id": value} - if param == "thinking": + elif param == "thinking": optional_params["thinking"] = value elif param == "reasoning_effort" and isinstance(value, str): optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( @@ -1007,9 +1018,13 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): elif param == "context_management": # Supports both OpenAI list format and Anthropic dict format if isinstance(value, (list, dict)): - anthropic_context_management = self.map_openai_context_management_to_anthropic(value) + anthropic_context_management = ( + self.map_openai_context_management_to_anthropic(value) + ) if anthropic_context_management is not None: - optional_params["context_management"] = anthropic_context_management + optional_params["context_management"] = ( + anthropic_context_management + ) elif param == "speed" and isinstance(value, str): # Pass through Anthropic-specific speed parameter for fast mode optional_params["speed"] = value @@ -1071,7 +1086,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if not system_message_block["content"]: continue # Skip system messages containing x-anthropic-billing-header metadata - if system_message_block["content"].startswith("x-anthropic-billing-header:"): + if system_message_block["content"].startswith( + "x-anthropic-billing-header:" + ): continue anthropic_system_message_content = AnthropicSystemMessageContent( type="text", @@ -1091,7 +1108,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if _content.get("type") == "text" and not text_value: continue # Skip system messages containing x-anthropic-billing-header metadata - if _content.get("type") == "text" and text_value and text_value.startswith("x-anthropic-billing-header:"): + if ( + _content.get("type") == "text" + and text_value + and text_value.startswith("x-anthropic-billing-header:") + ): continue anthropic_system_message_content = ( AnthropicSystemMessageContent( @@ -1201,7 +1222,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): # Add context management header if any other edits/entries exist if has_other: self._ensure_beta_header( - headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value + headers, + ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value, ) def update_headers_with_optional_anthropic_beta( @@ -1227,7 +1249,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ANTHROPIC_HOSTED_TOOLS.MEMORY.value ): self._ensure_beta_header( - headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value + headers, + ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value, ) if optional_params.get("context_management") is not None: self._ensure_context_management_beta_header( @@ -1491,7 +1514,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if thinking_content is not None: reasoning_content += thinking_content - return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results, compaction_blocks + return ( + text_content, + citations, + thinking_blocks, + reasoning_content, + tool_calls, + web_search_results, + tool_results, + compaction_blocks, + ) def calculate_usage( self, @@ -1576,7 +1608,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) completion_token_details = CompletionTokensDetailsWrapper( reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else 0, - text_tokens=completion_tokens - reasoning_tokens if reasoning_tokens > 0 else completion_tokens, + text_tokens=( + completion_tokens - reasoning_tokens + if reasoning_tokens > 0 + else completion_tokens + ), ) total_tokens = prompt_tokens + completion_tokens @@ -1696,8 +1732,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): "content" ] # allow user to access raw anthropic tool calling response - model_response.choices[0].finish_reason = map_finish_reason( - completion_response["stop_reason"] + model_response.choices[0].finish_reason = cast( + OpenAIChatCompletionFinishReason, + map_finish_reason(completion_response["stop_reason"]), ) ## CALCULATING USAGE diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py index 5d7a02f90d8..40ecfdd3050 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -2924,3 +2924,23 @@ def test_fast_mode_parameter_mapping(): assert "speed" in result assert result["speed"] == "fast" + + +def test_map_openai_params_max_tokens_normalized_to_int(): + """ + Test that map_openai_params normalizes max_tokens to an integer (e.g. 0.7 -> 1). + """ + config = AnthropicConfig() + + non_default_params = {"max_tokens": 0.7} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model="claude-3-5-sonnet-20241022", + drop_params=False, + ) + + assert "max_tokens" in result + assert result["max_tokens"] == 1