From a2ab173a5fbf1a05b300fb19f3aacd0c7ed7b491 Mon Sep 17 00:00:00 2001 From: Mateo Wang <277851410+mateo-berri@users.noreply.github.com> Date: Tue, 9 Jun 2026 20:20:15 -0700 Subject: [PATCH 1/9] Add Claude Fable 5 across Anthropic, Bedrock, Vertex AI, and Azure AI (#30064) * Add Claude Fable 5 across Anthropic, Bedrock, Vertex AI, and Azure AI Adds cost map entries for claude-fable-5 ($10/$50 per MTok, 1M context, 128K output, adaptive thinking only) on the Anthropic API, Bedrock converse (base, global, and us/eu geo inference profiles at the 10% regional premium), Vertex AI, and Azure AI (Microsoft Foundry, which serves Fable 5 with the full 1M context window unlike Opus 4.8). Registers anthropic.claude-fable-5 in BEDROCK_CONVERSE_MODELS, lists the model in the setup wizard, and extends the reasoning effort e2e grid. The Bedrock, Vertex, and Azure grid cells carry fail_reason markers until the CI accounts are provisioned: Bedrock needs the provider data sharing opt-in Fable 5 requires, and the Foundry resource needs a claude-fable-5 deployment. The first-party entry carries provider_specific_entry {us: 1.1} for the inference_geo premium and deliberately no fast multiplier since Fable 5 has no fast mode. https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm * Drop removed sampling params for Claude 4.7+ when drop_params is set Fable 5, Opus 4.7, and Opus 4.8 removed sampling params: the API rejects top_p, top_k, and any temperature other than 1 with a 400. LiteLLM was forwarding them even with drop_params enabled because the Anthropic and Bedrock converse transformations passed temperature/top_p through unconditionally. Mirror the GPT-5/o-series handling: temperature=1 still passes through, other values and any top_p are dropped when drop_params is set, and without drop_params a clean client-side UnsupportedParamsError tells the caller how to opt in, instead of surfacing the raw provider error. https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm * Drive sampling param gating from the cost map and cover top_k Greptile review follow-ups on the sampling param fix: the restriction for Fable 5 / Opus 4.7 / 4.8 is now declared as supports_sampling_params: false on every affected cost map entry (perplexity excluded; that route is OpenAI-compatible and maps sampling params upstream) and read back through a tri-state map lookup, keeping the name check only as a fallback for provider-routed ids whose hosted map entries predate the flag, the same layering supports_adaptive_thinking uses. top_k bypasses map_openai_params as a provider-specific kwarg, so it is gated at the shared AnthropicConfig.transform_request boundary (direct, Bedrock invoke, Vertex, Azure) and in the Bedrock converse _handle_top_k_value path, with drop_params threaded through the converse transform helpers. Also updates the reasoning effort grid cell count assertion for the four Fable 5 rows added on this branch (29 x 11 cells). https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm * Declare supports_sampling_params in the cost map schema The model map validation schema uses additionalProperties: false, so the new flag must be declared for the 28 entries that carry it; this was the one failing job (misc / Run tests) on the previous commit. https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm * fix(bedrock): gate top_k=0 on converse to match Anthropic boundary Truthiness check let top_k=0 silently disappear on models that removed sampling params, while AnthropicConfig.transform_request treats 0 as present and raises UnsupportedParamsError (or drops when drop_params is set). Switch to 'is not None' so converse, direct Anthropic, invoke, Vertex, and Azure all behave the same for top_k=0. --------- Co-authored-by: Cursor Agent --- litellm/constants.py | 1 + litellm/llms/anthropic/chat/transformation.py | 27 +- litellm/llms/anthropic/common_utils.py | 122 +++++++- .../bedrock/chat/converse_transformation.py | 39 ++- ...odel_prices_and_context_window_backup.json | 275 ++++++++++++++++++ litellm/setup_wizard.py | 3 +- model_prices_and_context_window.json | 275 ++++++++++++++++++ .../reasoning_effort_grid/grid_spec.py | 52 +++- .../test_reasoning_effort_grid.py | 5 +- .../test_anthropic_chat_transformation.py | 137 +++++++++ .../chat/test_converse_transformation.py | 119 ++++++++ .../test_claude_fable_5_config.py | 230 +++++++++++++++ tests/test_litellm/test_utils.py | 1 + 13 files changed, 1263 insertions(+), 23 deletions(-) create mode 100644 tests/test_litellm/test_claude_fable_5_config.py diff --git a/litellm/constants.py b/litellm/constants.py index ae98b37d6e6..c08cb3b60d6 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1147,6 +1147,7 @@ BEDROCK_CONVERSE_MODELS = [ "openai.gpt-oss-120b-1:0", "anthropic.claude-haiku-4-5-20251001-v1:0", "anthropic.claude-sonnet-4-5-20250929-v1:0", + "anthropic.claude-fable-5", "anthropic.claude-opus-4-8", "anthropic.claude-opus-4-7", "anthropic.claude-opus-4-6-v1:0", diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 57609cfcd26..43d31dccba1 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -1463,10 +1463,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _value = self._map_stop_sequences(value) if _value is not None: optional_params["stop_sequences"] = _value - elif param == "temperature": - optional_params["temperature"] = value - elif param == "top_p": - optional_params["top_p"] = value + elif param == "temperature" or param == "top_p": + AnthropicConfig._apply_sampling_param( + optional_params=optional_params, + model=model, + param=param, + value=value, + drop_params=drop_params, + output_key=param, + ) elif param == "response_format" and isinstance(value, dict): if any( substring in model @@ -1974,6 +1979,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): # Remove internal LiteLLM parameters that should not be sent to Anthropic API optional_params.pop("is_vertex_request", None) + # ``top_k`` is a provider-specific kwarg that bypasses + # ``map_openai_params``; gate it here, the single boundary shared by + # the direct Anthropic, Bedrock invoke, Vertex, and Azure paths. + top_k = optional_params.pop("top_k", None) + if top_k is not None: + AnthropicConfig._apply_sampling_param( + optional_params=optional_params, + model=model, + param="top_k", + value=top_k, + drop_params=litellm_params.get("drop_params") is True, + output_key="top_k", + ) + data = { "model": model, "messages": anthropic_messages, diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 31131d722ab..5741513903c 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -272,19 +272,133 @@ class AnthropicModelInfo(BaseLLMModelInfo): ) @staticmethod - def _is_adaptive_thinking_model(model: str) -> bool: - """Claude 4.6+ models use adaptive thinking with ``output_config.effort``.""" + def _supports_sampling_params(model: str) -> bool: + """Claude 4.7+ (Opus 4.7/4.8, Fable 5) removed sampling params: the API + rejects ``top_p``, ``top_k``, and any ``temperature`` other than 1 with + a 400 ("`temperature` is deprecated for this model"). + + Driven by the ``supports_sampling_params`` flag in the model map; the + name check remains only as a fallback for provider-routed ids whose + map entries predate the flag.""" + flag = AnthropicModelInfo._get_model_capability( + model, "supports_sampling_params" + ) + if flag is not None: + return flag + model_lower = model.lower() + return not any( + v in model_lower + for v in ( + "fable", + "opus-4-7", + "opus_4_7", + "opus-4.7", + "opus_4.7", + "opus-4-8", + "opus_4_8", + "opus-4.8", + "opus_4.8", + ) + ) + + @staticmethod + def _apply_sampling_param( + optional_params: dict, + model: str, + param: str, + value: Any, + drop_params: bool, + output_key: str, + ) -> None: + """Forward ``temperature``/``top_p``/``top_k`` to + ``optional_params[output_key]`` unless the model removed sampling + params, in which case drop the param (with drop_params) or raise a + clean client-side 400.""" + if AnthropicModelInfo._supports_sampling_params(model) or ( + param == "temperature" and value == 1 + ): + optional_params[output_key] = value + elif not (litellm.drop_params or drop_params): + supported_hint = ( + "Only temperature=1 is supported. " if param == "temperature" else "" + ) + raise litellm.utils.UnsupportedParamsError( + message=( + f"{model} does not support {param}={value}. {supported_hint}" + "To drop unsupported params, set `litellm.drop_params = True`." + ), + status_code=400, + ) + + @staticmethod + def _model_map_lookup_candidates(model: str) -> List[str]: + """Model-map keys to try for ``model``, stripping bedrock/vertex + prefixes so a provider-routed Claude still resolves to its entry.""" + candidates = [model] + for prefix in ( + "bedrock/converse/", + "bedrock/invoke/", + "bedrock/", + "vertex_ai/", + ): + if model.startswith(prefix): + candidates.append(model[len(prefix) :]) + try: + from litellm.llms.bedrock.common_utils import BedrockModelInfo + + base = BedrockModelInfo.get_base_model(model) + if base: + candidates.append(base) + candidates.append(f"bedrock/{base}") + except Exception: + pass + return candidates + + @staticmethod + def _get_model_capability(model: str, key: str) -> Optional[bool]: + """Read boolean capability ``key`` from the model map, or None when + no entry declares it.""" + try: + for cand in AnthropicModelInfo._model_map_lookup_candidates(model): + value = litellm.model_cost.get(cand, {}).get(key) + if isinstance(value, bool): + return value + except Exception: + pass + return None + + @staticmethod + def _supports_model_capability(model: str, key: str) -> bool: + """Check a boolean capability ``key`` in the model map. + + Strips bedrock/vertex prefixes so a provider-routed Claude still + resolves to the Anthropic model-map entry. + """ from litellm.utils import _supports_factory try: if _supports_factory( model=model, - custom_llm_provider=None, - key="supports_adaptive_thinking", + custom_llm_provider="anthropic", + key=key, ): return True except Exception: pass + return AnthropicModelInfo._get_model_capability(model, key) is True + + @staticmethod + def _is_adaptive_thinking_model(model: str) -> bool: + """Claude 4.6+ models use adaptive thinking with ``output_config.effort``. + + Driven by the ``supports_adaptive_thinking`` flag in the model map; the + 4.6/4.7 name checks remain only as a fallback for provider-routed ids + whose map entries predate the flag. + """ + if AnthropicModelInfo._supports_model_capability( + model, "supports_adaptive_thinking" + ): + return True return AnthropicModelInfo._is_claude_4_6_model( model ) or AnthropicModelInfo._is_claude_4_7_model(model) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 90dfa13e938..e56cd7c617b 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -920,10 +920,15 @@ class AmazonConverseConfig(BaseConfig): continue value = [value] optional_params["stopSequences"] = value - if param == "temperature": - optional_params["temperature"] = value - if param == "top_p": - optional_params["topP"] = value + if param == "temperature" or param == "top_p": + AnthropicConfig._apply_sampling_param( + optional_params=optional_params, + model=model, + param=param, + value=value, + drop_params=drop_params, + output_key="topP" if param == "top_p" else param, + ) if param == "tools" and isinstance(value, list): self._apply_tool_call_transformation( tools=cast(List[OpenAIChatCompletionToolParam], value), @@ -1221,7 +1226,9 @@ class AmazonConverseConfig(BaseConfig): inference_params["topK"] = inference_params.pop("top_k") return InferenceConfig(**inference_params) - def _handle_top_k_value(self, model: str, inference_params: dict) -> dict: + def _handle_top_k_value( + self, model: str, inference_params: dict, drop_params: bool = False + ) -> dict: base_model = BedrockModelInfo.get_base_model(model) val_top_k = None @@ -1230,16 +1237,25 @@ class AmazonConverseConfig(BaseConfig): elif "top_k" in inference_params: val_top_k = inference_params.pop("top_k") - if val_top_k: + if val_top_k is not None: if base_model.startswith("anthropic"): - return {"top_k": val_top_k} + top_k_params: dict = {} + AnthropicConfig._apply_sampling_param( + optional_params=top_k_params, + model=model, + param="top_k", + value=val_top_k, + drop_params=drop_params, + output_key="top_k", + ) + return top_k_params if base_model.startswith("amazon.nova"): return {"inferenceConfig": {"topK": val_top_k}} return {} def _prepare_request_params( - self, optional_params: dict, model: str + self, optional_params: dict, model: str, drop_params: bool = False ) -> Tuple[dict, dict, dict, Optional[OutputConfigBlock]]: """Prepare and separate request parameters.""" # Consume the internal ``_output_config_normalized`` marker set by @@ -1338,7 +1354,7 @@ class AmazonConverseConfig(BaseConfig): # Only set the topK value in for models that support it additional_request_params.update( - self._handle_top_k_value(model, inference_params) + self._handle_top_k_value(model, inference_params, drop_params) ) # Filter out internal/MCP-related parameters that shouldn't be sent to the API @@ -1572,6 +1588,7 @@ class AmazonConverseConfig(BaseConfig): optional_params: dict, messages: Optional[List[AllMessageValues]] = None, headers: Optional[dict] = None, + drop_params: bool = False, ) -> CommonRequestObject: ## VALIDATE REQUEST """ @@ -1618,7 +1635,7 @@ class AmazonConverseConfig(BaseConfig): additional_request_params, request_metadata, output_config, - ) = self._prepare_request_params(optional_params, model) + ) = self._prepare_request_params(optional_params, model, drop_params) original_tools = inference_params.pop("tools", []) @@ -1699,6 +1716,7 @@ class AmazonConverseConfig(BaseConfig): optional_params=optional_params, messages=messages, headers=headers, + drop_params=litellm_params.get("drop_params") is True, ) bedrock_messages = ( @@ -1756,6 +1774,7 @@ class AmazonConverseConfig(BaseConfig): optional_params=optional_params, messages=messages, headers=headers, + drop_params=litellm_params.get("drop_params") is True, ) ## TRANSFORMATION ## diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index ce6d4ac824c..d74bf429f03 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1151,6 +1151,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1197,6 +1198,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1228,6 +1230,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1258,6 +1261,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1288,6 +1292,139 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "global.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "us.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 1.1e-06, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "eu.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 1.1e-06, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1319,6 +1456,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1350,6 +1488,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1381,6 +1520,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1412,6 +1552,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1443,6 +1584,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -2161,6 +2303,37 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true + }, + "azure_ai/claude-fable-5": { + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -2189,6 +2362,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -10060,6 +10234,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -10094,6 +10269,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -10104,6 +10280,40 @@ }, "supports_output_config": true }, + "claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "provider_specific_entry": { + "us": 1.1 + }, + "supports_output_config": true + }, "claude-opus-4-8": { "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, @@ -10128,6 +10338,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33871,6 +34082,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33898,6 +34110,67 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true + }, + "vertex_ai/claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true + }, + "vertex_ai/claude-fable-5@default": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33926,6 +34199,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33954,6 +34228,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, diff --git a/litellm/setup_wizard.py b/litellm/setup_wizard.py index 862ca13e7ba..2f0cb1233ae 100644 --- a/litellm/setup_wizard.py +++ b/litellm/setup_wizard.py @@ -52,11 +52,12 @@ PROVIDERS: List[Dict] = [ { "id": "anthropic", "name": "Anthropic", - "description": "Claude Opus 4.8, Opus 4.7, Opus 4.6, Sonnet 4.6, Haiku 4.5", + "description": "Claude Fable 5, Opus 4.8, Opus 4.7, Opus 4.6, Sonnet 4.6, Haiku 4.5", "env_key": "ANTHROPIC_API_KEY", "key_hint": "sk-ant-...", "test_model": "claude-haiku-4-5-20251001", "models": [ + "claude-fable-5", "claude-opus-4-8", "claude-opus-4-7", "claude-opus-4-6", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 80c2f32dc70..a286072bb1e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -1151,6 +1151,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1197,6 +1198,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1228,6 +1230,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1258,6 +1261,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1288,6 +1292,139 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "global.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "us.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 1.1e-06, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh" + }, + "eu.anthropic.claude-fable-5": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 1.1e-06, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1319,6 +1456,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1350,6 +1488,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1381,6 +1520,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1412,6 +1552,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -1443,6 +1584,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -2161,6 +2303,37 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true + }, + "azure_ai/claude-fable-5": { + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -2189,6 +2362,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -10060,6 +10234,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -10094,6 +10269,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -10104,6 +10280,40 @@ }, "supports_output_config": true }, + "claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "provider_specific_entry": { + "us": 1.1 + }, + "supports_output_config": true + }, "claude-opus-4-8": { "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, @@ -10128,6 +10338,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33746,6 +33957,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33773,6 +33985,67 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true + }, + "vertex_ai/claude-fable-5": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true + }, + "vertex_ai/claude-fable-5@default": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33801,6 +34074,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, @@ -33829,6 +34103,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, diff --git a/tests/llm_translation/reasoning_effort_grid/grid_spec.py b/tests/llm_translation/reasoning_effort_grid/grid_spec.py index a08013cd439..83a2c286d64 100644 --- a/tests/llm_translation/reasoning_effort_grid/grid_spec.py +++ b/tests/llm_translation/reasoning_effort_grid/grid_spec.py @@ -1,7 +1,6 @@ from dataclasses import dataclass, field from typing import Dict, FrozenSet, List, Optional, Tuple - OMIT = object() @@ -136,6 +135,13 @@ _CAPS_NONE: FrozenSet[str] = frozenset() ANTHROPIC_DIRECT_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="claude-fable-5", + model="anthropic/claude-fable-5", + mode="adaptive", + required_env=_ANTHROPIC_REQ, + caps=_CAPS_XHIGH_MAX, + ), ModelEntry( alias="claude-opus-4-8", model="anthropic/claude-opus-4-8", @@ -168,6 +174,19 @@ ANTHROPIC_DIRECT_MODELS: Tuple[ModelEntry, ...] = ( AZURE_AI_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="azure-claude-fable-5", + model="azure_ai/claude-fable-5", + mode="adaptive", + required_env=_AZURE_FOUNDRY_REQ, + caps=_CAPS_XHIGH_MAX, + fail_reason=( + "claude-fable-5 has no deployment on the CI Microsoft Foundry " + "resource yet; Foundry returns DeploymentNotFound until someone " + "creates the fable-5 deployment, so this cell stays loud in CI. " + "Remove this fail_reason once the deployment exists." + ), + ), ModelEntry( alias="azure-claude-opus-4-8", model="azure_ai/claude-opus-4-8", @@ -213,6 +232,20 @@ AZURE_AI_MODELS: Tuple[ModelEntry, ...] = ( VERTEX_AI_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="vertex-claude-fable-5", + model="vertex_ai/claude-fable-5", + mode="adaptive", + extra_params=(("vertex_location", "global"),), + required_env=_VERTEX_REQ, + caps=_CAPS_XHIGH_MAX, + fail_reason=( + "claude-fable-5 availability on the CI Vertex project is not yet " + "confirmed for this brand-new release, so this cell stays loud in " + "CI until verified. Remove this fail_reason once the model is " + "confirmed available on the global Vertex endpoint." + ), + ), ModelEntry( alias="vertex-claude-opus-4-8", model="vertex_ai/claude-opus-4-8", @@ -263,6 +296,23 @@ VERTEX_AI_MODELS: Tuple[ModelEntry, ...] = ( BEDROCK_CONVERSE_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="bedrock-claude-fable-5", + model="bedrock/converse/us.anthropic.claude-fable-5", + mode="adaptive", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_XHIGH_MAX, + bedrock_effort_ceiling="xhigh", + unavailable_error="is not available for this account", + fail_reason=( + "claude-fable-5 on Bedrock requires the account to opt in to " + "provider data sharing (data retention mode " + "'provider_data_sharing' via the Data Retention API); the CI " + "account has not opted in yet, so this cell stays loud in CI. " + "Remove this fail_reason once the opt-in is done." + ), + ), ModelEntry( alias="bedrock-claude-opus-4-8", model="bedrock/converse/us.anthropic.claude-opus-4-8", diff --git a/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py b/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py index 551ab8459d1..a5f16f928e5 100644 --- a/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py +++ b/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py @@ -15,7 +15,6 @@ from .grid_spec import ( all_cells, ) - _PROMPT_MESSAGES: List[Dict[str, str]] = [ {"role": "user", "content": "Step by step, calculate 47 * 53. Show your work."} ] @@ -201,8 +200,8 @@ async def test_reasoning_effort_grid( def test_grid_cell_count() -> None: - assert len(_PARAMS) == 25 * 11, ( - f"expected 275 cells (25 provider x model combos x 11 efforts), " + assert len(_PARAMS) == 29 * 11, ( + f"expected 319 cells (29 provider x model combos x 11 efforts), " f"got {len(_PARAMS)}" ) 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 687c5a2e733..5d8da12d4cf 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 @@ -4889,3 +4889,140 @@ def test_sanitize_tool_names_in_request_no_tools_is_noop(): forward, reverse = AnthropicConfig._sanitize_tool_names_in_request({"tools": []}) assert forward == {} assert reverse == {} + + +@pytest.mark.parametrize( + "model", + ["claude-fable-5", "claude-opus-4-7", "claude-opus-4-8-20260120"], +) +def test_sampling_params_dropped_for_models_that_removed_them(model): + """Fable 5 / Opus 4.7 / 4.8 reject temperature != 1 and any top_p with a + 400; with drop_params set they must be dropped, not forwarded (#30064).""" + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"temperature": 0.5, "top_p": 0.9}, + optional_params={}, + model=model, + drop_params=True, + ) + + assert "temperature" not in result + assert "top_p" not in result + + +@pytest.mark.parametrize("params", [{"temperature": 0.5}, {"top_p": 0.9}, {"top_p": 1}]) +def test_sampling_params_raise_clean_error_without_drop_params(params, monkeypatch): + monkeypatch.setattr(litellm, "drop_params", False) + config = AnthropicConfig() + + with pytest.raises(litellm.utils.UnsupportedParamsError, match="drop_params"): + config.map_openai_params( + non_default_params=params, + optional_params={}, + model="claude-fable-5", + drop_params=False, + ) + + +def test_temperature_1_forwarded_on_models_that_removed_sampling_params(): + """temperature=1 (the API default) is still accepted and must pass through.""" + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"temperature": 1}, + optional_params={}, + model="claude-fable-5", + drop_params=False, + ) + + assert result["temperature"] == 1 + + +@pytest.mark.parametrize("model", ["claude-opus-4-6", "claude-sonnet-4-6"]) +def test_sampling_params_forwarded_on_models_that_accept_them(model): + config = AnthropicConfig() + + result = config.map_openai_params( + non_default_params={"temperature": 0.5, "top_p": 0.9}, + optional_params={}, + model=model, + drop_params=True, + ) + + assert result["temperature"] == 0.5 + assert result["top_p"] == 0.9 + + +def test_sampling_param_gating_driven_by_model_map_flag(monkeypatch): + """The drop/raise decision must come from ``supports_sampling_params`` in + the model map, not just name matching: a flagged entry gates a model whose + name says nothing, and an explicit ``true`` overrides the name fallback.""" + monkeypatch.setitem( + litellm.model_cost, "claude-zeta-9", {"supports_sampling_params": False} + ) + monkeypatch.setitem( + litellm.model_cost, "claude-fable-5-test", {"supports_sampling_params": True} + ) + config = AnthropicConfig() + + flagged_off = config.map_openai_params( + non_default_params={"top_p": 0.9}, + optional_params={}, + model="claude-zeta-9", + drop_params=True, + ) + assert "top_p" not in flagged_off + + flagged_on = config.map_openai_params( + non_default_params={"top_p": 0.9}, + optional_params={}, + model="claude-fable-5-test", + drop_params=True, + ) + assert flagged_on["top_p"] == 0.9 + + +def test_top_k_dropped_at_transform_for_models_that_removed_it(): + """``top_k`` is a provider-specific kwarg that bypasses + ``map_openai_params``, so it must be stripped at the transform_request + boundary shared by the direct, invoke, Vertex, and Azure paths (#30064).""" + config = AnthropicConfig() + + result = config.transform_request( + model="claude-fable-5", + messages=[{"role": "user", "content": "hello"}], + optional_params={"max_tokens": 10, "top_k": 40}, + litellm_params={"drop_params": True}, + headers={}, + ) + + assert "top_k" not in result + + +def test_top_k_raises_at_transform_without_drop_params(monkeypatch): + monkeypatch.setattr(litellm, "drop_params", False) + config = AnthropicConfig() + + with pytest.raises(litellm.utils.UnsupportedParamsError, match="drop_params"): + config.transform_request( + model="claude-fable-5", + messages=[{"role": "user", "content": "hello"}], + optional_params={"max_tokens": 10, "top_k": 40}, + litellm_params={}, + headers={}, + ) + + +def test_top_k_forwarded_at_transform_on_models_that_accept_it(): + config = AnthropicConfig() + + result = config.transform_request( + model="claude-sonnet-4-6", + messages=[{"role": "user", "content": "hello"}], + optional_params={"max_tokens": 10, "top_k": 40}, + litellm_params={"drop_params": True}, + headers={}, + ) + + assert result["top_k"] == 40 diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py index a6aa35ee6d1..fec215e5c44 100644 --- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py @@ -5268,3 +5268,122 @@ def test_transform_response_does_not_leak_body_on_parse_failure(): msg = str(exc_info.value) assert "secret content" not in msg assert "Error converting to valid response block" in msg + + +def test_converse_drops_sampling_params_for_models_that_removed_them(): + """Fable 5 / Opus 4.7 / 4.8 reject temperature != 1 and any top_p; with + drop_params set, converse must drop them instead of forwarding (#30064).""" + config = AmazonConverseConfig() + + result = config.map_openai_params( + non_default_params={"temperature": 0.5, "top_p": 0.9}, + optional_params={}, + model="us.anthropic.claude-fable-5", + drop_params=True, + ) + + assert "temperature" not in result + assert "topP" not in result + + +def test_converse_sampling_params_raise_without_drop_params(monkeypatch): + monkeypatch.setattr(litellm, "drop_params", False) + config = AmazonConverseConfig() + + with pytest.raises(litellm.utils.UnsupportedParamsError, match="drop_params"): + config.map_openai_params( + non_default_params={"temperature": 0.5}, + optional_params={}, + model="global.anthropic.claude-opus-4-8-v1:0", + drop_params=False, + ) + + +def test_converse_sampling_params_forwarded_on_models_that_accept_them(): + config = AmazonConverseConfig() + + result = config.map_openai_params( + non_default_params={"temperature": 0.5, "top_p": 0.9}, + optional_params={}, + model="us.anthropic.claude-sonnet-4-6", + drop_params=True, + ) + + assert result["temperature"] == 0.5 + assert result["topP"] == 0.9 + + +def test_converse_top_k_dropped_for_models_that_removed_it(): + """``top_k`` reaches converse as a provider-specific kwarg destined for + ``additionalModelRequestFields``, bypassing ``map_openai_params``; the + transform must strip it for models that removed sampling params (#30064).""" + config = AmazonConverseConfig() + + result = config.transform_request( + model="us.anthropic.claude-fable-5", + messages=[{"role": "user", "content": "hello"}], + optional_params={"top_k": 40}, + litellm_params={"drop_params": True}, + headers={}, + ) + + assert "top_k" not in result.get("additionalModelRequestFields", {}) + + +def test_converse_top_k_raises_without_drop_params(monkeypatch): + monkeypatch.setattr(litellm, "drop_params", False) + config = AmazonConverseConfig() + + with pytest.raises(litellm.utils.UnsupportedParamsError, match="drop_params"): + config.transform_request( + model="us.anthropic.claude-fable-5", + messages=[{"role": "user", "content": "hello"}], + optional_params={"top_k": 40}, + litellm_params={}, + headers={}, + ) + + +def test_converse_top_k_forwarded_on_models_that_accept_it(): + config = AmazonConverseConfig() + + result = config.transform_request( + model="us.anthropic.claude-sonnet-4-6", + messages=[{"role": "user", "content": "hello"}], + optional_params={"top_k": 40}, + litellm_params={"drop_params": True}, + headers={}, + ) + + assert result["additionalModelRequestFields"]["top_k"] == 40 + + +def test_converse_top_k_zero_raises_without_drop_params(monkeypatch): + """``top_k=0`` must hit the same gating as any other value; previously the + truthiness check let it silently disappear on models that removed sampling + params, diverging from the Anthropic boundary that treats ``0`` as present.""" + monkeypatch.setattr(litellm, "drop_params", False) + config = AmazonConverseConfig() + + with pytest.raises(litellm.utils.UnsupportedParamsError, match="drop_params"): + config.transform_request( + model="us.anthropic.claude-fable-5", + messages=[{"role": "user", "content": "hello"}], + optional_params={"top_k": 0}, + litellm_params={}, + headers={}, + ) + + +def test_converse_top_k_zero_forwarded_on_models_that_accept_it(): + config = AmazonConverseConfig() + + result = config.transform_request( + model="us.anthropic.claude-sonnet-4-6", + messages=[{"role": "user", "content": "hello"}], + optional_params={"top_k": 0}, + litellm_params={"drop_params": True}, + headers={}, + ) + + assert result["additionalModelRequestFields"]["top_k"] == 0 diff --git a/tests/test_litellm/test_claude_fable_5_config.py b/tests/test_litellm/test_claude_fable_5_config.py new file mode 100644 index 00000000000..d8d95fba0da --- /dev/null +++ b/tests/test_litellm/test_claude_fable_5_config.py @@ -0,0 +1,230 @@ +""" +Validate Claude Fable 5 model configuration entries. + +Fable 5 is a new tier above Opus ($10/$50 per MTok) with the same adaptive-only +API surface as Opus 4.7/4.8. The cost-map entries below are what make the model +resolvable across Anthropic, Bedrock, Vertex AI, and Azure AI (Microsoft +Foundry), and the ``supports_adaptive_thinking`` flag is what makes LiteLLM send +``thinking.type='adaptive'`` instead of the legacy ``enabled``/``budget_tokens`` +shape, which Fable 5 rejects with a 400. +""" + +import json +import os + +import pytest + +import litellm +from litellm.constants import BEDROCK_CONVERSE_MODELS +from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap + +REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..") + + +def _load_root_cost_map() -> dict: + json_path = os.path.join(REPO_ROOT, "model_prices_and_context_window.json") + with open(json_path) as f: + return json.load(f) + + +@pytest.fixture +def local_model_cost_map(monkeypatch): + """Force the bundled backup cost map so assertions don't depend on the + network-fetched ``main`` copy (which lags this branch until merge).""" + original_model_cost = litellm.model_cost + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + litellm.model_cost = litellm.get_model_cost_map(url="") + litellm.get_model_info.cache_clear() + try: + yield + finally: + litellm.model_cost = original_model_cost + litellm.get_model_info.cache_clear() + + +def test_fable_5_model_pricing_and_capabilities(): + model_data = _load_root_cost_map() + + expected_models = [ + ("claude-fable-5", "anthropic"), + ("anthropic.claude-fable-5", "bedrock_converse"), + ("vertex_ai/claude-fable-5", "vertex_ai-anthropic_models"), + # Unlike Opus 4.8 (200k on Foundry), Fable 5 has the full 1M context + # window on Microsoft Foundry. + ("azure_ai/claude-fable-5", "azure_ai"), + ] + + for model_name, provider in expected_models: + assert model_name in model_data, f"Missing model entry: {model_name}" + info = model_data[model_name] + + assert info["litellm_provider"] == provider + assert info["mode"] == "chat" + assert info["max_input_tokens"] == 1000000 + assert info["max_output_tokens"] == 128000 + assert info["max_tokens"] == 128000 + + # $10 / $50 per MTok (2x Opus 4.8), with the standard 1.25x 5m + # cache-write, 2x 1h cache-write, and 0.1x cache-read multipliers. + assert info["input_cost_per_token"] == 1e-05 + assert info["output_cost_per_token"] == 5e-05 + assert info["cache_creation_input_token_cost"] == 1.25e-05 + assert info["cache_creation_input_token_cost_above_1hr"] == 2e-05 + assert info["cache_read_input_token_cost"] == 1e-06 + + # Flat-rate across the full 1M context window. + assert "input_cost_per_token_above_200k_tokens" not in info + assert "output_cost_per_token_above_200k_tokens" not in info + + assert info["supports_assistant_prefill"] is False + assert info["supports_function_calling"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_reasoning"] is True + assert info["supports_tool_choice"] is True + assert info["supports_vision"] is True + assert info["supports_xhigh_reasoning_effort"] is True + assert info["supports_max_reasoning_effort"] is True + + +def test_fable_5_bedrock_regional_model_pricing(): + model_data = _load_root_cost_map() + + # Fable 5 launched with us/eu geo inference profiles plus a global profile + # (no au/apac/jp). Global uses base pricing; geo profiles carry the + # standard 10% regional premium. + expected_models = { + "global.anthropic.claude-fable-5": { + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "cache_creation_input_token_cost": 1.25e-05, + "cache_read_input_token_cost": 1e-06, + }, + "us.anthropic.claude-fable-5": { + "input_cost_per_token": 1.1e-05, + "output_cost_per_token": 5.5e-05, + "cache_creation_input_token_cost": 1.375e-05, + "cache_read_input_token_cost": 1.1e-06, + }, + "eu.anthropic.claude-fable-5": { + "input_cost_per_token": 1.1e-05, + "output_cost_per_token": 5.5e-05, + "cache_creation_input_token_cost": 1.375e-05, + "cache_read_input_token_cost": 1.1e-06, + }, + } + + for model_name, expected in expected_models.items(): + assert model_name in model_data, f"Missing model entry: {model_name}" + info = model_data[model_name] + assert info["litellm_provider"] == "bedrock_converse" + assert info["max_input_tokens"] == 1000000 + assert info["max_output_tokens"] == 128000 + assert info["bedrock_output_config_effort_ceiling"] == "xhigh" + for key, value in expected.items(): + assert info[key] == value + + +def test_fable_5_geo_multiplier_without_fast_mode(): + """First-party ``inference_geo='us'`` carries the 1.1x premium, but unlike + the Opus line there is no fast-mode variant for Fable 5; a ``fast`` key + here would silently misprice ``speed='fast'`` requests.""" + model_data = _load_root_cost_map() + entry = model_data["claude-fable-5"]["provider_specific_entry"] + assert entry == {"us": 1.1} + + +def test_fable_5_present_in_bundled_backup(): + """The bundled backup is the runtime fallback (and what tests load with + ``LITELLM_LOCAL_MODEL_COST_MAP=True``) — it must carry the same entries as + the root cost map, otherwise the model resolves on one path but not the + other.""" + backup = GetModelCostMap.load_local_model_cost_map() + root = _load_root_cost_map() + for model_name in ( + "claude-fable-5", + "anthropic.claude-fable-5", + "global.anthropic.claude-fable-5", + "us.anthropic.claude-fable-5", + "eu.anthropic.claude-fable-5", + "vertex_ai/claude-fable-5", + "vertex_ai/claude-fable-5@default", + "azure_ai/claude-fable-5", + ): + assert model_name in backup, f"Missing from backup cost map: {model_name}" + assert backup[model_name] == root[model_name], model_name + + +def test_fable_5_registered_for_bedrock_converse(): + assert "anthropic.claude-fable-5" in BEDROCK_CONVERSE_MODELS + + +def test_fable_5_provider_resolves_via_model_info(local_model_cost_map): + info = litellm.get_model_info(model="claude-fable-5") + assert info["litellm_provider"] == "anthropic" + assert info["max_input_tokens"] == 1000000 + assert info["max_output_tokens"] == 128000 + + +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_fable_5_all_variants_carry_adaptive_thinking_flag(cost_map): + """Every Fable 5 entry must advertise ``supports_adaptive_thinking``. + + Adaptive-thinking detection is cost-map driven, so a single variant missing + the flag silently sends the legacy ``thinking.type='enabled'`` shape and the + provider 400s (issue #29188 for the Opus 4.8 equivalent). Fable 5 is even + stricter than Opus 4.8: an explicit ``thinking.type='disabled'`` also 400s, + so adaptive is the only valid thinking shape LiteLLM can emit for it.""" + variants = [k for k in cost_map if "claude-fable-5" in k] + assert variants, "no claude-fable-5 entries found in cost map" + missing = [ + k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True + ] + assert not missing, f"missing supports_adaptive_thinking: {missing}" + + +@pytest.mark.parametrize( + "model", + [ + "claude-fable-5", + "anthropic/claude-fable-5", + "anthropic.claude-fable-5", + "bedrock/us.anthropic.claude-fable-5", + "bedrock/invoke/eu.anthropic.claude-fable-5", + "bedrock/global.anthropic.claude-fable-5", + "vertex_ai/claude-fable-5", + "azure_ai/claude-fable-5", + ], +) +def test_adaptive_thinking_detected_for_fable_5(local_model_cost_map, model): + """Provider-routed ids must resolve to a flagged entry so ``reasoning_effort`` + maps to ``thinking.type='adaptive'`` + ``output_config.effort``.""" + from litellm.llms.anthropic.common_utils import AnthropicModelInfo + + assert AnthropicModelInfo._is_adaptive_thinking_model(model) is True + + +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_sampling_params_flag_on_all_models_that_removed_them(cost_map): + """Fable 5 and Opus 4.7/4.8 reject ``top_p``/``top_k``/``temperature != 1``; + the drop/raise gating is cost-map driven, so every variant must carry an + explicit ``supports_sampling_params: false``. The perplexity route is + exempt: it is OpenAI-compatible and maps sampling params upstream.""" + variants = [ + k + for k in cost_map + if any(v in k for v in ("claude-fable-5", "claude-opus-4-7", "claude-opus-4-8")) + and not k.startswith("perplexity/") + ] + assert variants, "no matching entries found in cost map" + missing = [ + k for k in variants if cost_map[k].get("supports_sampling_params") is not False + ] + assert not missing, f"missing supports_sampling_params=false: {missing}" diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 6a78653ec99..599daea46fc 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -857,6 +857,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "supports_xhigh_reasoning_effort": {"type": "boolean"}, "supports_max_reasoning_effort": {"type": "boolean"}, "supports_adaptive_thinking": {"type": "boolean"}, + "supports_sampling_params": {"type": "boolean"}, "supports_service_tier": {"type": "boolean"}, "supports_preset": {"type": "boolean"}, "supports_output_config": {"type": "boolean"}, From beb460b79dc99a378f848b9a01b0b34655b7fef2 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 10 Jun 2026 08:22:15 +0530 Subject: [PATCH 2/9] fix(proxy): authorize batch files using upload target_model_names (LIT-3593) (#30009) * fix(proxy): authorize batch files using upload target_model_names (LIT-3593) After replace_model_in_jsonl, body.model is a stripped provider id. Reverse-mapping it via resolve_model_name_from_model_id is first-match on model_list and caused false 403s when multiple deployments share the same stripped name. Use target_model_names from the unified file id instead. Co-authored-by: Cursor * fix(proxy): restore resolve_model_name_from_model_id for JSONL fallback path (LIT-3593) Restores the reverse-lookup for the JSONL body.model fallback path so that legacy/pre-target_model_names managed files still map stripped provider IDs back to proxy aliases before auth. Also cleans up redundant `or None`. Co-Authored-By: Claude Sonnet 4.6 * Revert "fix(proxy): restore resolve_model_name_from_model_id for JSONL fallback path (LIT-3593)" This reverts commit 30d2e96f77ef521ccaaf2193fe554980380eb669. --------- Co-authored-by: Cursor Co-authored-by: Claude Sonnet 4.6 (cherry picked from commit 2cd7e874859eade595928b97b73fab5e10620629) --- litellm/proxy/hooks/batch_rate_limiter.py | 29 ++++-- .../proxy/hooks/test_batch_file_validation.py | 99 ++++++++++++++++++- 2 files changed, 116 insertions(+), 12 deletions(-) diff --git a/litellm/proxy/hooks/batch_rate_limiter.py b/litellm/proxy/hooks/batch_rate_limiter.py index 1c14e7d751f..561b6ec8a77 100644 --- a/litellm/proxy/hooks/batch_rate_limiter.py +++ b/litellm/proxy/hooks/batch_rate_limiter.py @@ -227,11 +227,17 @@ class _PROXY_BatchRateLimiter(CustomLogger): # Check if this is a managed file (base64 encoded unified file ID) from litellm.proxy.openai_files_endpoints.common_utils import ( _is_base64_encoded_unified_file_id, + get_models_from_unified_file_id, ) # Managed files require bypassing the HTTP endpoint (which runs access-check hooks) # and calling the managed files hook directly with the user's credentials. is_managed_file = _is_base64_encoded_unified_file_id(file_id) + target_model_names = ( + get_models_from_unified_file_id(is_managed_file) + if is_managed_file + else [] + ) if is_managed_file and user_api_key_dict is not None: file_content = await self._fetch_managed_file_content( file_id=file_id, @@ -256,6 +262,7 @@ class _PROXY_BatchRateLimiter(CustomLogger): await self._enforce_batch_file_model_access( user_api_key_dict=user_api_key_dict, file_content_as_dict=file_content_as_dict, + target_model_names=target_model_names or None, ) input_file_usage = _get_batch_job_input_file_usage( @@ -291,9 +298,13 @@ class _PROXY_BatchRateLimiter(CustomLogger): self, user_api_key_dict: UserAPIKeyAuth, file_content_as_dict: List[dict], + target_model_names: Optional[List[str]] = None, ) -> None: - """Reject the batch if the caller is not authorized for every - ``body.model`` named inside the JSONL. + """Reject the batch if the caller is not authorized for the upload target. + + For managed files, ``target_model_names`` (from the unified file id) is + the proxy alias the file was uploaded for and is used directly for auth. + For legacy/non-managed files, falls back to ``body.model`` values in the JSONL. Reuses ``can_key_call_model`` so the same allowlist semantics (wildcards, access groups, ``all-proxy-models``, team aliases) @@ -302,18 +313,16 @@ class _PROXY_BatchRateLimiter(CustomLogger): from litellm.proxy.auth.auth_checks import can_key_call_model from litellm.proxy.proxy_server import llm_router - models = _get_models_from_batch_input_file_content(file_content_as_dict) - if not models: - return + if target_model_names: + models = target_model_names + else: + models = _get_models_from_batch_input_file_content(file_content_as_dict) + if not models: + return llm_model_list = llm_router.model_list if llm_router is not None else None for model in models: - # body.model may be the provider id after replace_model_in_jsonl; map to proxy model_name for auth. model_to_check = model - if llm_router is not None: - proxy_model_name = llm_router.resolve_model_name_from_model_id(model) - if proxy_model_name is not None: - model_to_check = proxy_model_name try: await can_key_call_model( model=model_to_check, diff --git a/tests/test_litellm/proxy/hooks/test_batch_file_validation.py b/tests/test_litellm/proxy/hooks/test_batch_file_validation.py index f047d625479..a076a09706b 100644 --- a/tests/test_litellm/proxy/hooks/test_batch_file_validation.py +++ b/tests/test_litellm/proxy/hooks/test_batch_file_validation.py @@ -262,7 +262,8 @@ async def test_pre_call_allows_authorized_model_in_batch_file(): @pytest.mark.asyncio async def test_pre_call_allows_stripped_provider_model_when_key_has_proxy_alias(): """After replace_model_in_jsonl, body.model is the provider id (e.g. gpt-5.5). - Auth must check the proxy model_name the key was granted, not the stripped id.""" + Auth must check target_model_names from the unified file id, not reverse-map + the stripped id.""" from litellm.proxy.hooks.batch_rate_limiter import _PROXY_BatchRateLimiter rate_limiter = _PROXY_BatchRateLimiter( @@ -281,7 +282,6 @@ async def test_pre_call_allows_stripped_provider_model_when_key_has_proxy_alias( ) mock_router = MagicMock() mock_router.model_list = [] - mock_router.resolve_model_name_from_model_id.return_value = proxy_alias can_key_call_model = AsyncMock(return_value=True) with ( @@ -294,10 +294,105 @@ async def test_pre_call_allows_stripped_provider_model_when_key_has_proxy_alias( await rate_limiter._enforce_batch_file_model_access( user_api_key_dict=user, file_content_as_dict=file_dict, + target_model_names=[proxy_alias], ) can_key_call_model.assert_awaited_once() assert can_key_call_model.await_args.kwargs["model"] == proxy_alias + mock_router.resolve_model_name_from_model_id.assert_not_called() + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "model_list_order", + [ + [ + "openai/openai/gpt-5.5", + "openai/openai/gpt-5.5-batch", + "us/azure/openai/gpt-5.5", + ], + [ + "us/azure/openai/gpt-5.5", + "openai/openai/gpt-5.5", + "openai/openai/gpt-5.5-batch", + ], + [ + "openai/openai/gpt-5.5-batch", + "us/azure/openai/gpt-5.5", + "openai/openai/gpt-5.5", + ], + ], +) +async def test_pre_call_uses_target_model_names_not_stripped_reverse_lookup( + model_list_order, +): + """LIT-3593: three deployments strip to gpt-5.5; auth must use the upload + target alias from target_model_names, not first-match reverse lookup.""" + from litellm.proxy.hooks.batch_rate_limiter import _PROXY_BatchRateLimiter + + rate_limiter = _PROXY_BatchRateLimiter( + internal_usage_cache=MagicMock(), + parallel_request_limiter=MagicMock(), + ) + batch_alias = "openai/openai/gpt-5.5-batch" + deployment_templates = { + "openai/openai/gpt-5.5": { + "model_name": "openai/openai/gpt-5.5", + "litellm_params": {"model": "openai/gpt-5.5"}, + "model_info": {"id": "openai/openai/gpt-5.5", "mode": "chat"}, + }, + "openai/openai/gpt-5.5-batch": { + "model_name": "openai/openai/gpt-5.5-batch", + "litellm_params": {"model": "openai/gpt-5.5"}, + "model_info": {"id": "openai/openai/gpt-5.5-batch", "mode": "batch"}, + }, + "us/azure/openai/gpt-5.5": { + "model_name": "us/azure/openai/gpt-5.5", + "litellm_params": {"model": "azure/gpt-5.5"}, + "model_info": {"id": "openai/openai/gpt-5.5", "mode": "chat"}, + }, + } + mock_router = MagicMock() + mock_router.model_list = [deployment_templates[name] for name in model_list_order] + + def _resolve(model_id): + for deployment in mock_router.model_list: + actual_model = deployment.get("litellm_params", {}).get("model") + if actual_model == model_id or ( + actual_model and actual_model.endswith(f"/{model_id}") + ): + return deployment.get("model_name") + return None + + mock_router.resolve_model_name_from_model_id.side_effect = _resolve + + file_dict = [ + {"body": {"model": "gpt-5.5", "messages": [{"role": "user", "content": "x"}]}} + ] + user = UserAPIKeyAuth( + api_key="sk-ok", + user_id="alice", + models=[batch_alias], + user_role=LitellmUserRoles.INTERNAL_USER.value, + ) + can_key_call_model = AsyncMock(return_value=True) + + with ( + patch( + "litellm.proxy.auth.auth_checks.can_key_call_model", + new=can_key_call_model, + ), + patch("litellm.proxy.proxy_server.llm_router", mock_router), + ): + await rate_limiter._enforce_batch_file_model_access( + user_api_key_dict=user, + file_content_as_dict=file_dict, + target_model_names=[batch_alias], + ) + + can_key_call_model.assert_awaited_once() + assert can_key_call_model.await_args.kwargs["model"] == batch_alias + mock_router.resolve_model_name_from_model_id.assert_not_called() @pytest.mark.asyncio From 2723601f1e1a6c487b978f6918bde7bbf0638400 Mon Sep 17 00:00:00 2001 From: Kenan Yildirim Date: Tue, 2 Jun 2026 14:52:24 -0400 Subject: [PATCH 3/9] feat(guardrails): capture user and model metadata in CrowdStrike AIDR (cherry picked from commit 6fc715c5bd5b9acd1c73e755da8ecbe808765dfc) --- .../crowdstrike_aidr/crowdstrike_aidr.py | 18 ++- .../guardrail_hooks/test_crowdstrike_aidr.py | 115 ++++++++++++++++++ 2 files changed, 132 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py b/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py index 14d950ecdf4..d1ef165b46e 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py +++ b/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py @@ -312,11 +312,27 @@ class CrowdStrikeAIDRHandler(CustomGuardrail): event_type = "output" hook_name = "apply_guardrail (response)" - ai_guard_payload = { + ai_guard_payload: dict[str, Any] = { "guard_input": guard_input.model_dump(mode="json"), "event_type": event_type, } + model = inputs.get("model") + if model: + ai_guard_payload["model"] = model + + metadata = request_data.get("litellm_metadata", request_data.get("metadata")) + if isinstance(metadata, Mapping): + user_id = metadata.get("user_api_key_user_id") + if user_id: + ai_guard_payload["user_id"] = user_id + + extra_info: dict[str, str] = {} + user_email = metadata.get("user_api_key_user_email") + if user_email: + extra_info["user_name"] = user_email + ai_guard_payload["extra_info"] = extra_info + ai_guard_response = await self._call_crowdstrike_aidr_guard( ai_guard_payload, hook_name ) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py index c58c94cbbc7..39b62b2030a 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py @@ -478,3 +478,118 @@ async def test_apply_guardrail_request_skipped_messages_stay_aligned( assert result["texts"][1] == "" assert result["texts"][2] == "Here is my SSN: " assert result["structured_messages"] == inputs["structured_messages"] + + +@pytest.mark.asyncio +async def test_apply_guardrail_sends_user_id_model_and_extra_info( + crowdstrike_aidr_guardrail: CrowdStrikeAIDRHandler, +) -> None: + inputs: GenericGuardrailAPIInputs = { + "texts": ["Hello"], + "structured_messages": [{"role": "user", "content": "Hello"}], + "model": "gpt-4o", + } + request_data = { + "messages": inputs["structured_messages"], + "model": "gpt-4o", + "litellm_metadata": { + "user_api_key_user_id": "uid-abc", + "user_api_key_user_email": "alice@example.com", + }, + } + guardrail_endpoint = ( + f"{crowdstrike_aidr_guardrail.api_base}/v1/guard_chat_completions" + ) + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + return_value=httpx.Response( + status_code=200, + json={"result": {"blocked": False, "transformed": False}}, + request=httpx.Request(method="POST", url=guardrail_endpoint), + ), + ) as mock_method: + await crowdstrike_aidr_guardrail.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="request", + ) + + payload = mock_method.call_args.kwargs["json"] + assert payload["user_id"] == "uid-abc" + assert payload["model"] == "gpt-4o" + assert payload["extra_info"] == {"user_name": "alice@example.com"} + + +@pytest.mark.asyncio +async def test_apply_guardrail_empty_extra_info_when_no_email( + crowdstrike_aidr_guardrail: CrowdStrikeAIDRHandler, +) -> None: + inputs: GenericGuardrailAPIInputs = { + "texts": ["Hello"], + "structured_messages": [{"role": "user", "content": "Hello"}], + "model": "gemini-flash", + } + request_data = { + "messages": inputs["structured_messages"], + "model": "gemini-flash", + "litellm_metadata": { + "user_api_key_user_id": "uid-no-email", + "user_api_key_user_email": None, + }, + } + guardrail_endpoint = ( + f"{crowdstrike_aidr_guardrail.api_base}/v1/guard_chat_completions" + ) + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + return_value=httpx.Response( + status_code=200, + json={"result": {"blocked": False, "transformed": False}}, + request=httpx.Request(method="POST", url=guardrail_endpoint), + ), + ) as mock_method: + await crowdstrike_aidr_guardrail.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="request", + ) + + payload = mock_method.call_args.kwargs["json"] + assert payload["user_id"] == "uid-no-email" + assert payload["model"] == "gemini-flash" + assert payload["extra_info"] == {} + + +@pytest.mark.asyncio +async def test_apply_guardrail_no_metadata_skips_user_fields( + crowdstrike_aidr_guardrail: CrowdStrikeAIDRHandler, +) -> None: + inputs: GenericGuardrailAPIInputs = { + "texts": ["Hello"], + "structured_messages": [{"role": "user", "content": "Hello"}], + } + request_data = {"messages": inputs["structured_messages"]} + guardrail_endpoint = ( + f"{crowdstrike_aidr_guardrail.api_base}/v1/guard_chat_completions" + ) + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + return_value=httpx.Response( + status_code=200, + json={"result": {"blocked": False, "transformed": False}}, + request=httpx.Request(method="POST", url=guardrail_endpoint), + ), + ) as mock_method: + await crowdstrike_aidr_guardrail.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="request", + ) + + payload = mock_method.call_args.kwargs["json"] + assert "user_id" not in payload + assert "model" not in payload + assert "extra_info" not in payload From c3edd956666666e5ce539780b28e198ecc84a37d Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 8 Jun 2026 17:46:28 -0700 Subject: [PATCH 4/9] fix(guardrails): read CrowdStrike AIDR identity from both metadata bags (#29991) Capture user_id and extra_info from metadata or litellm_metadata. The single-bag read dropped identity whenever a request carried a present litellm_metadata field (null or a user-supplied dict), since /chat/completions routes the authenticated identity into metadata while the guardrail read litellm_metadata first (cherry picked from commit 1bbaf1c39dda367f5a2b4b6b9ab4cac46d71ab14) --- .../crowdstrike_aidr/crowdstrike_aidr.py | 14 +++- .../guardrail_hooks/test_crowdstrike_aidr.py | 79 +++++++++++++++++++ 2 files changed, 91 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py b/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py index d1ef165b46e..248202b644c 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py +++ b/litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py @@ -105,6 +105,16 @@ def _extract_text_from_content(content: object) -> str: return "" +def _merge_metadata_bags(request_data: Mapping[str, Any]) -> Optional[dict[str, Any]]: + merged: dict[str, Any] = {} + present = False + for bag in (request_data.get("metadata"), request_data.get("litellm_metadata")): + if isinstance(bag, Mapping): + present = True + merged.update(bag) + return merged if present else None + + class CrowdStrikeAIDRHandler(CustomGuardrail): """ CrowdStrike AIDR AI Guardrail handler to interact with the CrowdStrike AIDR @@ -321,8 +331,8 @@ class CrowdStrikeAIDRHandler(CustomGuardrail): if model: ai_guard_payload["model"] = model - metadata = request_data.get("litellm_metadata", request_data.get("metadata")) - if isinstance(metadata, Mapping): + metadata = _merge_metadata_bags(request_data) + if metadata is not None: user_id = metadata.get("user_api_key_user_id") if user_id: ai_guard_payload["user_id"] = user_id diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py index 39b62b2030a..607c7b0b32d 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_crowdstrike_aidr.py @@ -593,3 +593,82 @@ async def test_apply_guardrail_no_metadata_skips_user_fields( assert "user_id" not in payload assert "model" not in payload assert "extra_info" not in payload + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "litellm_metadata, metadata", + [ + ( + None, + { + "user_api_key_user_id": "uid-abc", + "user_api_key_user_email": "alice@example.com", + }, + ), + ( + {"trace_id": "t1"}, + { + "user_api_key_user_id": "uid-abc", + "user_api_key_user_email": "alice@example.com", + }, + ), + ( + ["unexpected"], + { + "user_api_key_user_id": "uid-abc", + "user_api_key_user_email": "alice@example.com", + }, + ), + ( + { + "user_api_key_user_id": "uid-abc", + "user_api_key_user_email": "alice@example.com", + }, + {"trace_id": "t1"}, + ), + ], + ids=[ + "identity_in_metadata_llm_none", + "identity_in_metadata_llm_user_dict", + "identity_in_metadata_llm_non_mapping", + "identity_in_litellm_metadata", + ], +) +async def test_apply_guardrail_reads_identity_from_either_metadata_bag( + crowdstrike_aidr_guardrail: CrowdStrikeAIDRHandler, + litellm_metadata, + metadata, +) -> None: + inputs: GenericGuardrailAPIInputs = { + "texts": ["Hello"], + "structured_messages": [{"role": "user", "content": "Hello"}], + "model": "gpt-4o", + } + request_data = { + "messages": inputs["structured_messages"], + "model": "gpt-4o", + "litellm_metadata": litellm_metadata, + "metadata": metadata, + } + guardrail_endpoint = ( + f"{crowdstrike_aidr_guardrail.api_base}/v1/guard_chat_completions" + ) + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + return_value=httpx.Response( + status_code=200, + json={"result": {"blocked": False, "transformed": False}}, + request=httpx.Request(method="POST", url=guardrail_endpoint), + ), + ) as mock_method: + await crowdstrike_aidr_guardrail.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="request", + ) + + payload = mock_method.call_args.kwargs["json"] + assert payload["user_id"] == "uid-abc" + assert payload["extra_info"] == {"user_name": "alice@example.com"} From 655e5318469a57e720734e5490fd7ccda0a257d6 Mon Sep 17 00:00:00 2001 From: Kent Date: Thu, 11 Jun 2026 03:09:29 +0000 Subject: [PATCH 5/9] feat(bedrock_mantle): route Responses API to native OpenAI endpoint (#29490) Backport prerequisite for #29788. Applied as the squash diff of PR #29490, which landed upstream inside the litellm_oss_staging_040626 sync (cb041966bf, #29671) and has no standalone commit to cherry-pick. --- litellm/__init__.py | 3 + litellm/_lazy_imports_registry.py | 5 + .../llms/bedrock_mantle/responses/__init__.py | 0 .../responses/transformation.py | 81 +++++ ...odel_prices_and_context_window_backup.json | 38 +++ litellm/utils.py | 10 + model_prices_and_context_window.json | 38 +++ ...bedrock_mantle_responses_transformation.py | 283 ++++++++++++++++++ 8 files changed, 458 insertions(+) create mode 100644 litellm/llms/bedrock_mantle/responses/__init__.py create mode 100644 litellm/llms/bedrock_mantle/responses/transformation.py create mode 100644 tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py diff --git a/litellm/__init__.py b/litellm/__init__.py index 56d516536e8..6a511948fcb 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1727,6 +1727,9 @@ if TYPE_CHECKING: from .llms.openrouter.responses.transformation import ( OpenRouterResponsesAPIConfig as OpenRouterResponsesAPIConfig, ) + from .llms.bedrock_mantle.responses.transformation import ( + BedrockMantleResponsesAPIConfig as BedrockMantleResponsesAPIConfig, + ) from .llms.gemini.interactions.transformation import ( GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig, ) diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 17eb6609292..9fd4302f362 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -237,6 +237,7 @@ LLM_CONFIG_NAMES = ( "PerplexityResponsesConfig", "DatabricksResponsesAPIConfig", "OpenRouterResponsesAPIConfig", + "BedrockMantleResponsesAPIConfig", "GoogleAIStudioInteractionsConfig", "OpenAIOSeriesConfig", "AnthropicSkillsConfig", @@ -956,6 +957,10 @@ _LLM_CONFIGS_IMPORT_MAP = { ".llms.openrouter.responses.transformation", "OpenRouterResponsesAPIConfig", ), + "BedrockMantleResponsesAPIConfig": ( + ".llms.bedrock_mantle.responses.transformation", + "BedrockMantleResponsesAPIConfig", + ), "GoogleAIStudioInteractionsConfig": ( ".llms.gemini.interactions.transformation", "GoogleAIStudioInteractionsConfig", diff --git a/litellm/llms/bedrock_mantle/responses/__init__.py b/litellm/llms/bedrock_mantle/responses/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/bedrock_mantle/responses/transformation.py b/litellm/llms/bedrock_mantle/responses/transformation.py new file mode 100644 index 00000000000..b63fd0ecdb1 --- /dev/null +++ b/litellm/llms/bedrock_mantle/responses/transformation.py @@ -0,0 +1,81 @@ +""" +Amazon Bedrock Mantle - Responses API backend. + +gpt-5.5 / gpt-5.4 on Mantle are exposed ONLY on the `/openai/v1/responses` +path (not the standard `/v1/responses`). Payloads and SSE follow the OpenAI +Responses spec, so this config inherits OpenAIResponsesAPIConfig and overrides +only the endpoint URL and Bearer authentication. + +Auth: AWS Bedrock API key as Bearer token (BEDROCK_MANTLE_API_KEY or the +standard AWS_BEARER_TOKEN_BEDROCK), NOT SigV4. +""" + +from typing import Optional + +from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + +BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" + +# Checked longest/most-specific first so a full endpoint URL collapses to host +# in one pass and the appended path never doubles. +_BASE_SUFFIXES_TO_STRIP = ( + "/openai/v1/responses", + "/v1/responses", + "/responses", + "/openai/v1", + "/v1", +) + + +class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.BEDROCK_MANTLE + + def get_complete_url( + self, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + region = ( + get_secret_str("BEDROCK_MANTLE_REGION") + or get_secret_str("AWS_REGION") + or BEDROCK_MANTLE_DEFAULT_REGION + ) + base = ( + api_base + or get_secret_str("BEDROCK_MANTLE_API_BASE") + or f"https://bedrock-mantle.{region}.api.aws" + ) + base = base.rstrip("/") + for suffix in _BASE_SUFFIXES_TO_STRIP: + if base.endswith(suffix): + base = base[: -len(suffix)] + break + return f"{base}/openai/v1/responses" + + def validate_environment( + self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] + ) -> dict: + litellm_params = litellm_params or GenericLiteLLMParams() + api_key = ( + litellm_params.api_key + or get_secret_str("BEDROCK_MANTLE_API_KEY") + or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") + ) + if not api_key: + raise ValueError( + "Bedrock Mantle API key is required. Set BEDROCK_MANTLE_API_KEY " + "(or AWS_BEARER_TOKEN_BEDROCK) or pass api_key." + ) + headers["Authorization"] = f"Bearer {api_key}" + return headers + + def supports_native_file_search(self) -> bool: + return False + + def supports_native_websocket(self) -> bool: + return False diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index d74bf429f03..4decaf97ab1 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -41457,6 +41457,44 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "bedrock_mantle/openai.gpt-5.5": { + "input_cost_per_token": 5.5e-06, + "cache_read_input_token_cost": 5.5e-07, + "output_cost_per_token": 3.3e-05, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": ["/v1/responses"], + "supported_modalities": ["text", "image"], + "supported_output_modalities": ["text"], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "bedrock_mantle/openai.gpt-5.4": { + "input_cost_per_token": 2.75e-06, + "cache_read_input_token_cost": 2.75e-07, + "output_cost_per_token": 1.65e-05, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": ["/v1/responses"], + "supported_modalities": ["text", "image"], + "supported_output_modalities": ["text"], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "volcengine/doubao-seed-2-0-pro-260215": { "litellm_provider": "volcengine", "max_input_tokens": 256000, diff --git a/litellm/utils.py b/litellm/utils.py index 5a9dccc089e..7504914d198 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -8816,6 +8816,16 @@ class ProviderConfigManager: return litellm.OpenRouterResponsesAPIConfig() elif litellm.LlmProviders.HOSTED_VLLM == provider: return litellm.HostedVLLMResponsesAPIConfig() + elif litellm.LlmProviders.BEDROCK_MANTLE == provider: + # Only OpenAI gpt frontier models (gpt-5.x, and future gpt-6 etc.) are + # served on the /openai/v1/responses path. gpt-oss and every non-OpenAI + # model on Mantle (nvidia, mistral, google, zai, ...) are chat-completions + # only and 400 on that path, so they fall through to None to keep the + # chat-completions emulation (see litellm/responses/main.py "config is None"). + model_lower = model.lower() if model else "" + if "openai.gpt-" in model_lower and "gpt-oss" not in model_lower: + return litellm.BedrockMantleResponsesAPIConfig() + return None return None @staticmethod diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index a286072bb1e..3a9644e2612 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -41341,6 +41341,44 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "bedrock_mantle/openai.gpt-5.5": { + "input_cost_per_token": 5.5e-06, + "cache_read_input_token_cost": 5.5e-07, + "output_cost_per_token": 3.3e-05, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": ["/v1/responses"], + "supported_modalities": ["text", "image"], + "supported_output_modalities": ["text"], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "bedrock_mantle/openai.gpt-5.4": { + "input_cost_per_token": 2.75e-06, + "cache_read_input_token_cost": 2.75e-07, + "output_cost_per_token": 1.65e-05, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": ["/v1/responses"], + "supported_modalities": ["text", "image"], + "supported_output_modalities": ["text"], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "volcengine/doubao-seed-2-0-pro-260215": { "litellm_provider": "volcengine", "max_input_tokens": 256000, diff --git a/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py b/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py new file mode 100644 index 00000000000..e2133d56f89 --- /dev/null +++ b/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py @@ -0,0 +1,283 @@ +""" +Unit tests for Amazon Bedrock Mantle Responses API configuration. + +Mantle's gpt-5.5 / gpt-5.4 are served ONLY on the non-standard +`/openai/v1/responses` path. These tests lock the URL construction and +Bearer auth that make that routing work. +""" + +import os +import sys + +sys.path.insert(0, os.path.abspath("../../../../..")) + +import pytest + +import litellm +from litellm.llms.bedrock_mantle.responses.transformation import ( + BedrockMantleResponsesAPIConfig, +) +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + + +class TestBedrockMantleResponsesURL: + def test_url_uses_region_from_env(self, monkeypatch): + monkeypatch.setenv("BEDROCK_MANTLE_REGION", "us-east-2") + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + url = cfg.get_complete_url(api_base=None, litellm_params={}) + assert url == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + + def test_url_normalizes_v1_suffix(self, monkeypatch): + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + url = cfg.get_complete_url( + api_base="https://bedrock-mantle.us-east-2.api.aws/v1", + litellm_params={}, + ) + assert url == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + assert "/v1/openai/v1/responses" not in url + url_trailing = cfg.get_complete_url( + api_base="https://bedrock-mantle.us-east-2.api.aws/v1/", + litellm_params={}, + ) + assert ( + url_trailing + == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + ) + + def test_url_does_not_double_openai_v1(self, monkeypatch): + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + url = cfg.get_complete_url( + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1", + litellm_params={}, + ) + assert url == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + + def test_url_full_endpoint_base_not_doubled(self, monkeypatch): + # AWS model card tells users to set OPENAI_BASE_URL to the full endpoint. + # If copied into api_base, it must not be doubled. + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + url = cfg.get_complete_url( + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + litellm_params={}, + ) + assert url == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + assert url.count("/responses") == 1 + + def test_url_region_fallback_to_aws_region(self, monkeypatch): + monkeypatch.delenv("BEDROCK_MANTLE_REGION", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + monkeypatch.setenv("AWS_REGION", "us-west-2") + cfg = BedrockMantleResponsesAPIConfig() + url = cfg.get_complete_url(api_base=None, litellm_params={}) + assert url == "https://bedrock-mantle.us-west-2.api.aws/openai/v1/responses" + + def test_url_region_default_us_east_1(self, monkeypatch): + monkeypatch.delenv("BEDROCK_MANTLE_REGION", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + monkeypatch.delenv("AWS_REGION", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + url = cfg.get_complete_url(api_base=None, litellm_params={}) + assert url == "https://bedrock-mantle.us-east-1.api.aws/openai/v1/responses" + + +class TestBedrockMantleResponsesAuth: + def test_config_api_key_takes_priority(self, monkeypatch): + monkeypatch.setenv("BEDROCK_MANTLE_API_KEY", "env-key") + cfg = BedrockMantleResponsesAPIConfig() + headers = cfg.validate_environment( + headers={}, + model="openai.gpt-5.5", + litellm_params=GenericLiteLLMParams(api_key="config-key"), + ) + assert headers["Authorization"] == "Bearer config-key" + + def test_env_key_fallback(self, monkeypatch): + monkeypatch.setenv("BEDROCK_MANTLE_API_KEY", "env-key") + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + headers = cfg.validate_environment( + headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams() + ) + assert headers["Authorization"] == "Bearer env-key" + + def test_bedrock_bearer_token_fallback(self, monkeypatch): + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + monkeypatch.setenv("AWS_BEARER_TOKEN_BEDROCK", "bearer-key") + cfg = BedrockMantleResponsesAPIConfig() + headers = cfg.validate_environment( + headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams() + ) + assert headers["Authorization"] == "Bearer bearer-key" + + def test_missing_key_raises(self, monkeypatch): + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + with pytest.raises(ValueError, match="Bedrock Mantle API key"): + cfg.validate_environment( + headers={}, + model="openai.gpt-5.5", + litellm_params=GenericLiteLLMParams(), + ) + + def test_custom_llm_provider(self): + cfg = BedrockMantleResponsesAPIConfig() + assert cfg.custom_llm_provider == LlmProviders.BEDROCK_MANTLE + + def test_native_websocket_disabled(self): + # Mantle Responses has no realtime/websocket transport, so the config + # must opt out; otherwise realtime routing would try a socket Mantle + # does not serve. + cfg = BedrockMantleResponsesAPIConfig() + assert cfg.supports_native_websocket() is False + + def test_file_search_routes_to_emulation(self): + # Mantle cannot reach OpenAI's vector stores, so a native file_search + # tool forwarded as-is gets a 400. The config must opt out of native + # file_search so LiteLLM's emulation handles it instead of forwarding. + from litellm.responses.file_search.emulated_handler import ( + should_use_emulated_file_search, + ) + + cfg = BedrockMantleResponsesAPIConfig() + assert cfg.supports_native_file_search() is False + assert ( + should_use_emulated_file_search( + tools=[{"type": "file_search", "vector_store_ids": ["vs_1"]}], + provider_config=cfg, + ) + is True + ) + + +class TestBedrockMantleResponsesRegistry: + def test_registry_returns_config_for_gpt_5_5(self): + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_responses_api_config( + provider="bedrock_mantle", + model="openai.gpt-5.5", + ) + assert isinstance(cfg, BedrockMantleResponsesAPIConfig) + + def test_registry_returns_config_for_gpt_5_4_enum(self): + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_responses_api_config( + provider=LlmProviders.BEDROCK_MANTLE, + model="openai.gpt-5.4", + ) + assert isinstance(cfg, BedrockMantleResponsesAPIConfig) + + def test_registry_returns_none_for_gpt_oss(self): + # Regression guard: gpt-oss must NOT get the native Responses config; it + # keeps the chat-completions emulation path (responses/main.py ~line 1109). + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_responses_api_config( + provider="bedrock_mantle", + model="openai.gpt-oss-120b", + ) + assert cfg is None + + def test_registry_returns_none_for_gpt_oss_safeguard(self): + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_responses_api_config( + provider="bedrock_mantle", + model="openai.gpt-oss-safeguard-20b", + ) + assert cfg is None + + def test_registry_returns_config_for_future_frontier_model(self): + # Forward-compatibility: an unseen OpenAI gpt frontier model (e.g. gpt-6) must + # get the native Responses config without a code change. The gate allow-lists + # the openai.gpt- family (minus gpt-oss), so gpt-6 matches automatically. + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_responses_api_config( + provider="bedrock_mantle", + model="openai.gpt-6", + ) + assert isinstance(cfg, BedrockMantleResponsesAPIConfig) + + @pytest.mark.parametrize( + "model", + [ + "nvidia.nemotron-nano-9b-v2", + "mistral.ministral-3-3b-instruct", + "google.gemma-3-27b-it", + "zai.glm-4.6", + ], + ) + def test_registry_returns_none_for_non_openai_models(self, model): + # Regression for the chat-only families on Mantle. These models 400 on + # /openai/v1/responses and are served on /v1/chat/completions, so the + # registry must NOT hand them the Responses config; they fall through to + # None and keep the chat-completions emulation. + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_responses_api_config( + provider="bedrock_mantle", + model=model, + ) + assert cfg is None + + def test_registry_returns_none_when_model_is_none(self): + # By-id operations (delete/get/cancel) call with model=None; keep returning + # None so those paths are unchanged. + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_responses_api_config( + provider="bedrock_mantle", + model=None, + ) + assert cfg is None + + +@pytest.fixture +def local_cost_map(monkeypatch): + """Force the bundled backup cost map and re-derive the provider model sets. + + ``litellm.model_cost`` is populated once at import time (here, from the + network-fetched ``main`` copy, which lags this branch). ``add_known_models`` + only re-buckets whatever is already in ``model_cost``, so the cost map must + first be reloaded from the local backup before the new keys appear. + """ + original_model_cost = litellm.model_cost + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "true") + litellm.model_cost = litellm.get_model_cost_map(url="") + litellm.get_model_info.cache_clear() + litellm.add_known_models() + try: + yield + finally: + litellm.model_cost = original_model_cost + litellm.get_model_info.cache_clear() + + +class TestBedrockMantleResponsesPricing: + def test_gpt_5_5_pricing_and_mode(self, local_cost_map): + info = litellm.get_model_info("bedrock_mantle/openai.gpt-5.5") + assert info["mode"] == "responses" + assert info["input_cost_per_token"] == pytest.approx(5.5e-06) + assert info["output_cost_per_token"] == pytest.approx(3.3e-05) + assert info["cache_read_input_token_cost"] == pytest.approx(5.5e-07) + assert info["max_input_tokens"] == 272000 + + def test_gpt_5_4_pricing_and_mode(self, local_cost_map): + info = litellm.get_model_info("bedrock_mantle/openai.gpt-5.4") + assert info["mode"] == "responses" + assert info["input_cost_per_token"] == pytest.approx(2.75e-06) + assert info["output_cost_per_token"] == pytest.approx(1.65e-05) + assert info["cache_read_input_token_cost"] == pytest.approx(2.75e-07) + assert info["max_input_tokens"] == 272000 + + def test_models_registered(self, local_cost_map): + assert "bedrock_mantle/openai.gpt-5.5" in litellm.bedrock_mantle_models + assert "bedrock_mantle/openai.gpt-5.4" in litellm.bedrock_mantle_models From ce5604413b27335403cbb607c54a31da6ec82e52 Mon Sep 17 00:00:00 2001 From: Kent Date: Thu, 11 Jun 2026 03:09:30 +0000 Subject: [PATCH 6/9] feat(bedrock_mantle): add SigV4/IAM auth to Responses API route (#29788) Applied as the squash diff of PR #29788 (head 9800b2f17c), which landed upstream inside the litellm_oss_staging_080626 sync (32c88ca74f, #29932) and has no standalone commit to cherry-pick. --- .../llms/base_llm/responses/transformation.py | 20 + .../responses/transformation.py | 120 +++++- litellm/llms/custom_httpx/llm_http_handler.py | 105 +++-- ...bedrock_mantle_responses_transformation.py | 398 +++++++++++++++++- .../custom_httpx/test_llm_http_handler.py | 238 +++++++++++ 5 files changed, 833 insertions(+), 48 deletions(-) diff --git a/litellm/llms/base_llm/responses/transformation.py b/litellm/llms/base_llm/responses/transformation.py index 853eb282758..407d5ad8146 100644 --- a/litellm/llms/base_llm/responses/transformation.py +++ b/litellm/llms/base_llm/responses/transformation.py @@ -62,6 +62,26 @@ class BaseResponsesAPIConfig(ABC): """ return False + def sign_request( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + api_key: Optional[str] = None, + model: Optional[str] = None, + stream: Optional[bool] = None, + fake_stream: Optional[bool] = None, + ) -> Tuple[dict, Optional[bytes]]: + """Sign the request after the body is finalized. + + Default is a no-op (returns headers unchanged, no signed body). Providers + whose endpoint requires request signing (e.g. Bedrock Mantle SigV4) + override this and return the signed body bytes so the handler sends those + exact bytes. + """ + return headers, None + @abstractmethod def get_supported_openai_params(self, model: str) -> list: pass diff --git a/litellm/llms/bedrock_mantle/responses/transformation.py b/litellm/llms/bedrock_mantle/responses/transformation.py index b63fd0ecdb1..df219091074 100644 --- a/litellm/llms/bedrock_mantle/responses/transformation.py +++ b/litellm/llms/bedrock_mantle/responses/transformation.py @@ -4,14 +4,26 @@ Amazon Bedrock Mantle - Responses API backend. gpt-5.5 / gpt-5.4 on Mantle are exposed ONLY on the `/openai/v1/responses` path (not the standard `/v1/responses`). Payloads and SSE follow the OpenAI Responses spec, so this config inherits OpenAIResponsesAPIConfig and overrides -only the endpoint URL and Bearer authentication. +only the endpoint URL and authentication. -Auth: AWS Bedrock API key as Bearer token (BEDROCK_MANTLE_API_KEY or the -standard AWS_BEARER_TOKEN_BEDROCK), NOT SigV4. +Auth: Bearer token (BEDROCK_MANTLE_API_KEY or the standard +AWS_BEARER_TOKEN_BEDROCK, or litellm_params.api_key) when present; otherwise +AWS SigV4 (service name "bedrock") using the standard credential chain (IAM +role / access key / profile / web identity), signed via the shared +BaseAWSLLM._sign_request after the request body is finalized. """ -from typing import Optional +import re +from typing import Optional, Tuple +from botocore.exceptions import ( + CredentialRetrievalError, + NoCredentialsError, + PartialCredentialsError, + ProfileNotFound, +) + +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.secret_managers.main import get_secret_str from litellm.types.router import GenericLiteLLMParams @@ -29,22 +41,44 @@ _BASE_SUFFIXES_TO_STRIP = ( "/v1", ) +# Standard Mantle host: https://bedrock-mantle..api.aws (group 1 = region). +_MANTLE_HOST_RE = re.compile( + r"^https?://bedrock-mantle\.([^/.]+)\.api\.aws", re.IGNORECASE +) + class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): + def __init__(self, aws_signer: Optional[BaseAWSLLM] = None): + super().__init__() + self._aws_signer = aws_signer or BaseAWSLLM() + @property def custom_llm_provider(self) -> LlmProviders: return LlmProviders.BEDROCK_MANTLE + @staticmethod + def _resolve_region(params: dict) -> str: + region = params.get("aws_region_name") + if region: + return region + base = params.get("api_base") or get_secret_str("BEDROCK_MANTLE_API_BASE") + if base: + match = _MANTLE_HOST_RE.match(base.rstrip("/")) + if match: + return match.group(1) + return ( + get_secret_str("BEDROCK_MANTLE_REGION") + or get_secret_str("AWS_REGION_NAME") + or get_secret_str("AWS_REGION") + or BEDROCK_MANTLE_DEFAULT_REGION + ) + def get_complete_url( self, api_base: Optional[str], litellm_params: dict, ) -> str: - region = ( - get_secret_str("BEDROCK_MANTLE_REGION") - or get_secret_str("AWS_REGION") - or BEDROCK_MANTLE_DEFAULT_REGION - ) + region = self._resolve_region({**litellm_params, "api_base": api_base}) base = ( api_base or get_secret_str("BEDROCK_MANTLE_API_BASE") @@ -55,6 +89,11 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): if base.endswith(suffix): base = base[: -len(suffix)] break + # For the standard Mantle host (including the default-region base that + # responses/main.py auto-injects into litellm_params.api_base), pin to the + # single resolved region so aws_region_name wins; preserve custom proxy hosts. + if _MANTLE_HOST_RE.match(base): + base = f"https://bedrock-mantle.{region}.api.aws" return f"{base}/openai/v1/responses" def validate_environment( @@ -66,12 +105,8 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): or get_secret_str("BEDROCK_MANTLE_API_KEY") or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") ) - if not api_key: - raise ValueError( - "Bedrock Mantle API key is required. Set BEDROCK_MANTLE_API_KEY " - "(or AWS_BEARER_TOKEN_BEDROCK) or pass api_key." - ) - headers["Authorization"] = f"Bearer {api_key}" + if api_key: + headers["Authorization"] = f"Bearer {api_key}" return headers def supports_native_file_search(self) -> bool: @@ -79,3 +114,58 @@ class BedrockMantleResponsesAPIConfig(OpenAIResponsesAPIConfig): def supports_native_websocket(self) -> bool: return False + + def sign_request( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + api_key: Optional[str] = None, + model: Optional[str] = None, + stream: Optional[bool] = None, + fake_stream: Optional[bool] = None, + ) -> Tuple[dict, Optional[bytes]]: + bearer = ( + api_key + or get_secret_str("BEDROCK_MANTLE_API_KEY") + or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") + ) + if not bearer: + # SigV4 path. Pin the credential-scope region to the region of the actual + # signing URL (api_base, already region-resolved by get_complete_url) so the + # SigV4 scope and the URL host can never disagree. Resolve from api_base first, + # then fall back to the regular precedence. Also drop any caller Authorization + # so _sign_request's restore-original-Authorization step cannot override the + # SigV4 header. + optional_params = { + **optional_params, + "aws_region_name": self._resolve_region( + {**optional_params, "api_base": api_base} + ), + } + headers = {k: v for k, v in headers.items() if k.lower() != "authorization"} + try: + return self._aws_signer._sign_request( + service_name="bedrock", + headers=headers, + optional_params=optional_params, + request_data=request_data, + api_base=api_base, + api_key=bearer, + model=model, + stream=stream, + fake_stream=fake_stream, + ) + except ( + NoCredentialsError, + PartialCredentialsError, + ProfileNotFound, + CredentialRetrievalError, + ) as e: + raise ValueError( + "Bedrock Mantle auth failed: no Bearer token and no usable AWS " + "credentials. Set BEDROCK_MANTLE_API_KEY (or AWS_BEARER_TOKEN_BEDROCK) " + "or pass api_key for Bearer auth, or provide AWS credentials " + "(IAM role / access key / profile / web identity) for SigV4." + ) from e diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 941fe59e825..cabf6cce7cc 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2315,6 +2315,31 @@ class BaseLLMHTTPHandler: # but never included in the outbound provider payload. request_context["litellm_params"] = dict(litellm_params) + is_stream_request = bool(stream) + if is_stream_request and fake_stream is True: + stream, data = self._prepare_fake_stream_request( + stream=stream, + data=data, + fake_stream=fake_stream, + ) + + # Sign after the body is final (post-transform/normalize/extra_body and post + # fake-stream prep) so signed bytes match what we send. No-op for providers + # that inherit the default sign_request. + headers, signed_body = responses_api_provider_config.sign_request( + headers=headers, + optional_params=dict(litellm_params), + request_data=data, + api_base=api_base, + api_key=litellm_params.api_key, + model=model, + stream=stream, + fake_stream=fake_stream, + ) + body_kwargs: Dict[str, Any] = ( + {"data": signed_body} if signed_body is not None else {"json": data} + ) + ## LOGGING logging_obj.pre_call( input=input, @@ -2327,22 +2352,14 @@ class BaseLLMHTTPHandler: ) try: - if stream: - # For streaming, use stream=True in the request - if fake_stream is True: - stream, data = self._prepare_fake_stream_request( - stream=stream, - data=data, - fake_stream=fake_stream, - ) - + if is_stream_request: response = sync_httpx_client.post( url=api_base, headers=headers, - json=data, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), stream=stream, + **body_kwargs, ) if fake_stream is True: return MockResponsesAPIStreamingIterator( @@ -2367,13 +2384,12 @@ class BaseLLMHTTPHandler: call_type=CallTypes.responses.value, ) else: - # For non-streaming requests response = sync_httpx_client.post( url=api_base, headers=headers, - json=data, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), + **body_kwargs, ) except Exception as e: raise self._handle_error( @@ -2461,6 +2477,28 @@ class BaseLLMHTTPHandler: # but never included in the outbound provider payload. request_context["litellm_params"] = dict(litellm_params) + is_stream_request = bool(stream) + if is_stream_request and fake_stream is True: + stream, data = self._prepare_fake_stream_request( + stream=stream, + data=data, + fake_stream=fake_stream, + ) + + headers, signed_body = responses_api_provider_config.sign_request( + headers=headers, + optional_params=dict(litellm_params), + request_data=data, + api_base=api_base, + api_key=litellm_params.api_key, + model=model, + stream=stream, + fake_stream=fake_stream, + ) + body_kwargs: Dict[str, Any] = ( + {"data": signed_body} if signed_body is not None else {"json": data} + ) + ## LOGGING logging_obj.pre_call( input=input, @@ -2473,22 +2511,14 @@ class BaseLLMHTTPHandler: ) try: - if stream: - # For streaming, we need to use stream=True in the request - if fake_stream is True: - stream, data = self._prepare_fake_stream_request( - stream=stream, - data=data, - fake_stream=fake_stream, - ) - + if is_stream_request: response = await async_httpx_client.post( url=api_base, headers=headers, - json=data, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), stream=stream, + **body_kwargs, ) if fake_stream is True: @@ -2515,13 +2545,12 @@ class BaseLLMHTTPHandler: call_type=CallTypes.responses.value, ) else: - # For non-streaming, proceed as before response = await async_httpx_client.post( url=api_base, headers=headers, - json=data, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), + **body_kwargs, ) except Exception as e: @@ -3998,6 +4027,18 @@ class BaseLLMHTTPHandler: ) data = BaseResponsesAPIConfig.normalize_responses_api_request_dict(data) + headers, signed_body = responses_api_provider_config.sign_request( + headers=headers, + optional_params=dict(litellm_params), + request_data=data, + api_base=url, + api_key=litellm_params.api_key, + model=model, + ) + body_kwargs: Dict[str, Any] = ( + {"data": signed_body} if signed_body is not None else {"json": data} + ) + ## LOGGING logging_obj.pre_call( input=input, @@ -4011,7 +4052,7 @@ class BaseLLMHTTPHandler: try: response = sync_httpx_client.post( - url=url, headers=headers, json=data, timeout=timeout + url=url, headers=headers, timeout=timeout, **body_kwargs ) except Exception as e: @@ -4081,6 +4122,18 @@ class BaseLLMHTTPHandler: ) data = BaseResponsesAPIConfig.normalize_responses_api_request_dict(data) + headers, signed_body = responses_api_provider_config.sign_request( + headers=headers, + optional_params=dict(litellm_params), + request_data=data, + api_base=url, + api_key=litellm_params.api_key, + model=model, + ) + body_kwargs: Dict[str, Any] = ( + {"data": signed_body} if signed_body is not None else {"json": data} + ) + ## LOGGING logging_obj.pre_call( input=input, @@ -4094,7 +4147,7 @@ class BaseLLMHTTPHandler: try: response = await async_httpx_client.post( - url=url, headers=headers, json=data, timeout=timeout + url=url, headers=headers, timeout=timeout, **body_kwargs ) except Exception as e: diff --git a/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py b/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py index e2133d56f89..92b5ca7b10b 100644 --- a/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py +++ b/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py @@ -12,6 +12,11 @@ import sys sys.path.insert(0, os.path.abspath("../../../../..")) import pytest +from botocore.exceptions import ( + ConnectTimeoutError, + PartialCredentialsError, + ProfileNotFound, +) import litellm from litellm.llms.bedrock_mantle.responses.transformation import ( @@ -114,16 +119,15 @@ class TestBedrockMantleResponsesAuth: ) assert headers["Authorization"] == "Bearer bearer-key" - def test_missing_key_raises(self, monkeypatch): + def test_missing_bearer_does_not_raise_in_validate_environment(self, monkeypatch): + # SigV4 may still apply, so validate_environment must defer instead of raising. monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) cfg = BedrockMantleResponsesAPIConfig() - with pytest.raises(ValueError, match="Bedrock Mantle API key"): - cfg.validate_environment( - headers={}, - model="openai.gpt-5.5", - litellm_params=GenericLiteLLMParams(), - ) + headers = cfg.validate_environment( + headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams() + ) + assert "Authorization" not in headers def test_custom_llm_provider(self): cfg = BedrockMantleResponsesAPIConfig() @@ -261,6 +265,386 @@ def local_cost_map(monkeypatch): litellm.get_model_info.cache_clear() +class TestBedrockMantleResponsesSigV4: + def test_bearer_short_circuits_without_credentials(self, monkeypatch): + from unittest.mock import MagicMock + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + + signer = BaseAWSLLM() + signer.get_credentials = MagicMock( + side_effect=AssertionError("get_credentials must not run for bearer auth") + ) + cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer) + + headers, signed_body = cfg.sign_request( + headers={}, + optional_params={}, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key="bearer-from-config", + ) + assert headers["Authorization"] == "Bearer bearer-from-config" + assert signed_body == b'{"input": "hi"}' + signer.get_credentials.assert_not_called() + + def test_bearer_resolved_from_mantle_env_key(self, monkeypatch): + from unittest.mock import MagicMock + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.setenv("BEDROCK_MANTLE_API_KEY", "env-bearer") + + signer = BaseAWSLLM() + signer.get_credentials = MagicMock( + side_effect=AssertionError("get_credentials must not run for bearer auth") + ) + cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer) + + headers, _ = cfg.sign_request( + headers={}, + optional_params={}, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + assert headers["Authorization"] == "Bearer env-bearer" + + def test_bearer_arg_takes_priority_over_mantle_env_key(self, monkeypatch): + # The passed api_key (e.g. litellm_params.api_key) must win over the env + # bearer; a reordered precedence chain would silently use the wrong token. + from unittest.mock import MagicMock + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.setenv("BEDROCK_MANTLE_API_KEY", "env-bearer") + + signer = BaseAWSLLM() + signer.get_credentials = MagicMock( + side_effect=AssertionError("get_credentials must not run for bearer auth") + ) + cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer) + + headers, _ = cfg.sign_request( + headers={}, + optional_params={}, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key="arg-bearer", + ) + assert headers["Authorization"] == "Bearer arg-bearer" + signer.get_credentials.assert_not_called() + + def test_access_key_produces_sigv4_headers(self, monkeypatch): + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + + cfg = BedrockMantleResponsesAPIConfig(aws_signer=BaseAWSLLM()) + headers, signed_body = cfg.sign_request( + headers={}, + optional_params={ + "aws_access_key_id": "AKIAEXAMPLE", + "aws_secret_access_key": "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0", + "aws_session_token": "session-token-test", + "aws_region_name": "us-east-2", + }, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + assert headers["Authorization"].startswith("AWS4-HMAC-SHA256") + assert "Credential=AKIAEXAMPLE/" in headers["Authorization"] + assert "/us-east-2/bedrock/aws4_request" in headers["Authorization"] + assert "X-Amz-Date" in headers + assert headers["X-Amz-Security-Token"] == "session-token-test" + assert signed_body == b'{"input": "hi"}' + + def test_assume_role_path_produces_sigv4_headers(self, monkeypatch): + from unittest.mock import MagicMock + from botocore.credentials import Credentials + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + + signer = BaseAWSLLM() + signer.get_credentials = MagicMock( + return_value=Credentials( + access_key="ASIAEXAMPLE", + secret_key="YXNzdW1lZC1yb2xlLXNlY3JldC1hc3N1bWVk", + token="assumed-session-token", + ) + ) + cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer) + + headers, _ = cfg.sign_request( + headers={}, + optional_params={ + "aws_role_name": "arn:aws:iam::000000000000:role/test-role", + "aws_session_name": "litellm-test", + "aws_region_name": "us-east-2", + }, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + signer.get_credentials.assert_called_once() + call = signer.get_credentials.call_args.kwargs + assert call["aws_role_name"] == "arn:aws:iam::000000000000:role/test-role" + assert call["aws_session_name"] == "litellm-test" + assert headers["Authorization"].startswith("AWS4-HMAC-SHA256") + assert "/us-east-2/bedrock/aws4_request" in headers["Authorization"] + + def test_signed_body_matches_final_data_after_normalize(self, monkeypatch): + """Core regression: the signed bytes must equal the bytes actually sent. + + Sign the *final* data dict and assert the returned signed_body decodes to + exactly that dict, so a later change to the data would break the SigV4 hash. + """ + import json + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + + final_data = {"model": "openai.gpt-5.5", "input": "hi", "max_output_tokens": 16} + cfg = BedrockMantleResponsesAPIConfig(aws_signer=BaseAWSLLM()) + _, signed_body = cfg.sign_request( + headers={}, + optional_params={ + "aws_access_key_id": "AKIAEXAMPLE", + "aws_secret_access_key": "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0", + "aws_region_name": "us-east-2", + }, + request_data=final_data, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + assert signed_body is not None + assert json.loads(signed_body) == final_data + + def test_region_comes_from_optional_params(self, monkeypatch): + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + monkeypatch.delenv("AWS_REGION", raising=False) + monkeypatch.delenv("AWS_REGION_NAME", raising=False) + + cfg = BedrockMantleResponsesAPIConfig(aws_signer=BaseAWSLLM()) + headers, _ = cfg.sign_request( + headers={}, + optional_params={ + "aws_access_key_id": "AKIAEXAMPLE", + "aws_secret_access_key": "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0", + "aws_region_name": "eu-west-1", + }, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.eu-west-1.api.aws/openai/v1/responses", + api_key=None, + ) + assert "/eu-west-1/bedrock/aws4_request" in headers["Authorization"] + + def test_url_region_and_sigv4_region_agree_from_litellm_params(self, monkeypatch): + """Adversarial-review regression: a caller-supplied aws_region_name (no region + env set) must shape BOTH the URL host and the SigV4 credential scope, or the + request is signed for one region and sent to another -> 401. + """ + monkeypatch.delenv("BEDROCK_MANTLE_REGION", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + monkeypatch.delenv("AWS_REGION", raising=False) + monkeypatch.delenv("AWS_REGION_NAME", raising=False) + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + params = { + "aws_region_name": "ap-southeast-2", + "aws_access_key_id": "AKIAEXAMPLE", + "aws_secret_access_key": "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0", + } + cfg = BedrockMantleResponsesAPIConfig(aws_signer=BaseAWSLLM()) + url = cfg.get_complete_url(api_base=None, litellm_params=params) + assert ( + url == "https://bedrock-mantle.ap-southeast-2.api.aws/openai/v1/responses" + ) + + headers, _ = cfg.sign_request( + headers={}, + optional_params=params, + request_data={"input": "hi"}, + api_base=url, + api_key=None, + ) + assert "/ap-southeast-2/bedrock/aws4_request" in headers["Authorization"] + + def test_injected_default_region_base_does_not_override_aws_region_name( + self, monkeypatch + ): + """2nd-round adversarial regression: responses/main.py auto-injects + litellm_params.api_base = https://bedrock-mantle..api.aws/v1 (default + region, ignoring aws_region_name). The config must still pin BOTH the URL host + and the SigV4 scope to aws_region_name, or the IAM deployment 401s. A naive + 'resolve region only when api_base is None' fix would fail this test. + """ + monkeypatch.delenv("BEDROCK_MANTLE_REGION", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + monkeypatch.delenv("AWS_REGION", raising=False) + monkeypatch.delenv("AWS_REGION_NAME", raising=False) + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + injected_base = "https://bedrock-mantle.us-east-1.api.aws/v1" # default region + params = { + "aws_region_name": "us-east-2", # what the caller actually wants + "api_base": injected_base, + "aws_access_key_id": "AKIAEXAMPLE", + "aws_secret_access_key": "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0", + } + cfg = BedrockMantleResponsesAPIConfig(aws_signer=BaseAWSLLM()) + url = cfg.get_complete_url(api_base=injected_base, litellm_params=params) + assert url == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + + headers, _ = cfg.sign_request( + headers={}, + optional_params=params, + request_data={"input": "hi"}, + api_base=url, + api_key=None, + ) + assert "/us-east-2/bedrock/aws4_request" in headers["Authorization"] + assert "us-east-1" not in headers["Authorization"] + + def test_custom_proxy_host_is_preserved(self, monkeypatch): + """A genuinely custom (non-Mantle) api_base host must be preserved, not rewritten + to a bedrock-mantle host. Only standard Mantle hosts are region-pinned. + """ + monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False) + cfg = BedrockMantleResponsesAPIConfig() + url = cfg.get_complete_url( + api_base="https://mantle-proxy.internal.example/openai/v1", + litellm_params={"aws_region_name": "us-east-2"}, + ) + assert url == "https://mantle-proxy.internal.example/openai/v1/responses" + + def test_caller_authorization_does_not_override_sigv4(self, monkeypatch): + """Adversarial-review regression: a caller-supplied Authorization header (e.g. + from extra_headers, surviving the relaxed validate_environment) must not clobber + the SigV4 Authorization that _sign_request would otherwise restore. + """ + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + cfg = BedrockMantleResponsesAPIConfig(aws_signer=BaseAWSLLM()) + headers, _ = cfg.sign_request( + headers={"Authorization": "Bearer stale-caller-token"}, + optional_params={ + "aws_access_key_id": "AKIAEXAMPLE", + "aws_secret_access_key": "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0", + "aws_region_name": "us-east-2", + }, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + assert headers["Authorization"].startswith("AWS4-HMAC-SHA256") + assert "Bearer stale-caller-token" not in headers["Authorization"] + + def test_no_bearer_and_no_credentials_raises_both_paths(self, monkeypatch): + from unittest.mock import MagicMock + from botocore.exceptions import NoCredentialsError + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + + signer = BaseAWSLLM() + signer.get_credentials = MagicMock(side_effect=NoCredentialsError()) + cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer) + + with pytest.raises(ValueError) as exc: + cfg.sign_request( + headers={}, + optional_params={"aws_region_name": "us-east-2"}, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + msg = str(exc.value) + assert "Bearer" in msg + assert "SigV4" in msg or "IAM" in msg + + @pytest.mark.parametrize( + "cred_error", + [ + PartialCredentialsError(provider="env", cred_var="aws_secret_access_key"), + ProfileNotFound(profile="missing-profile"), + ], + ) + def test_partial_credentials_raises_both_paths(self, monkeypatch, cred_error): + from unittest.mock import MagicMock + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + + signer = BaseAWSLLM() + signer.get_credentials = MagicMock(side_effect=cred_error) + cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer) + + with pytest.raises(ValueError) as exc: + cfg.sign_request( + headers={}, + optional_params={"aws_region_name": "us-east-2"}, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + msg = str(exc.value) + assert "Bearer" in msg + assert "SigV4" in msg or "IAM" in msg + + def test_sts_transport_error_is_not_masked_as_credentials(self, monkeypatch): + # An AssumeRole / web-identity flow hits STS over the network, so a transient + # connection error must surface as itself, not be rewritten into the + # "no usable AWS credentials" message that would send the user to fix the + # wrong thing. + from unittest.mock import MagicMock + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False) + monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False) + + signer = BaseAWSLLM() + signer.get_credentials = MagicMock( + side_effect=ConnectTimeoutError( + endpoint_url="https://sts.us-east-2.amazonaws.com" + ) + ) + cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer) + + with pytest.raises(ConnectTimeoutError): + cfg.sign_request( + headers={}, + optional_params={ + "aws_role_name": "arn:aws:iam::000000000000:role/test-role", + "aws_region_name": "us-east-2", + }, + request_data={"input": "hi"}, + api_base="https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses", + api_key=None, + ) + + class TestBedrockMantleResponsesPricing: def test_gpt_5_5_pricing_and_mode(self, local_cost_map): info = litellm.get_model_info("bedrock_mantle/openai.gpt-5.5") diff --git a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py index 07a61c9c104..2305f90deab 100644 --- a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py +++ b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py @@ -629,3 +629,241 @@ async def test_anthropic_post_retry_reserializes_mutated_body(): assert first_sent == prebuilt # attempt 0 used prebuilt assert second_sent == _json.dumps(request_body) # attempt 1 re-serialized assert "MUTATED" in second_sent # ... the mutated body + + +def test_base_responses_config_sign_request_is_noop_by_default(): + """Default responses sign_request must be a no-op: unchanged headers, no signed body. + + Guards the 15 existing responses providers from accidental signing when the + handler starts calling sign_request. + """ + from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig + + cfg = OpenAIResponsesAPIConfig() + headers = {"Authorization": "Bearer sk-existing"} + out_headers, signed_body = cfg.sign_request( + headers=headers, + optional_params={}, + request_data={"input": "hi"}, + api_base="https://api.openai.com/v1/responses", + ) + assert out_headers == {"Authorization": "Bearer sk-existing"} + assert signed_body is None + + +def _make_responses_handler_call(signed_body): + """Drive BaseLLMHTTPHandler.response_api_handler with a fully mocked provider + config + sync client, returning the kwargs the client.post was called with. + + signed_body=None simulates a no-op (non-signing) provider; bytes simulates a + signing provider (e.g. Bedrock Mantle). + """ + from unittest.mock import MagicMock + from litellm.llms.custom_httpx.http_handler import HTTPHandler + from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler + from litellm.types.router import GenericLiteLLMParams + + provider_config = MagicMock() + provider_config.validate_environment.return_value = {} + provider_config.get_complete_url.return_value = ( + "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + ) + provider_config.transform_responses_api_request.return_value = {"input": "hi"} + provider_config.should_fake_stream.return_value = False + provider_config.sign_request.return_value = ({"X-Signed": "1"}, signed_body) + + mock_client = MagicMock(spec=HTTPHandler) + mock_client.post.return_value = MagicMock() + + handler = BaseLLMHTTPHandler() + handler.response_api_handler( + model="openai.gpt-5.5", + input="hi", + responses_api_provider_config=provider_config, + response_api_optional_request_params={}, + custom_llm_provider="bedrock_mantle", + litellm_params=GenericLiteLLMParams(aws_region_name="us-east-2"), + logging_obj=MagicMock(), + client=mock_client, + _is_async=False, + ) + return mock_client.post.call_args.kwargs + + +def test_responses_handler_sends_json_when_not_signed(): + """No-op provider (signed_body is None) -> handler posts json=data, no data= bytes.""" + kwargs = _make_responses_handler_call(signed_body=None) + assert kwargs.get("json") == {"input": "hi"} + assert "data" not in kwargs + + +def test_responses_handler_sends_signed_bytes_when_signed(): + """Signing provider -> handler posts the exact signed bytes via data=, not json=.""" + kwargs = _make_responses_handler_call(signed_body=b'{"input": "hi"}') + assert kwargs.get("data") == b'{"input": "hi"}' + assert "json" not in kwargs + assert kwargs["headers"] == {"X-Signed": "1"} + + +def test_responses_handler_signs_after_fake_stream_prep_strips_stream(): + """Fake-stream signing-order invariant: the bytes SIGNED must equal the bytes SENT. + + In the streaming + fake-stream path the handler first runs + _prepare_fake_stream_request, which pops "stream" out of the body, and only + then calls sign_request. If signing ran before that pop, the signed body + would still carry "stream" while the body sent over the wire would not, + producing a SigV4 payload-hash mismatch (401) for a real Mantle deployment. + We snapshot request_data at sign time and assert "stream" is already gone. + """ + from unittest.mock import MagicMock + from litellm.llms.custom_httpx.http_handler import HTTPHandler + from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler + from litellm.types.llms.openai import ResponsesAPIResponse + from litellm.types.router import GenericLiteLLMParams + + provider_config = MagicMock() + provider_config.validate_environment.return_value = {} + provider_config.get_complete_url.return_value = ( + "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + ) + provider_config.transform_responses_api_request.return_value = { + "input": "hi", + "stream": True, + } + provider_config.should_fake_stream.return_value = True + provider_config.transform_response_api_response.return_value = ResponsesAPIResponse( + id="resp_1", + created_at=0, + output=[], + status="completed", + model="openai.gpt-5.5", + ) + + captured = {} + + def _capture_sign(**kwargs): + captured["request_data"] = dict(kwargs["request_data"]) + return ({"X-Signed": "1"}, b'{"input": "hi"}') + + provider_config.sign_request.side_effect = _capture_sign + + mock_client = MagicMock(spec=HTTPHandler) + mock_client.post.return_value = MagicMock() + + handler = BaseLLMHTTPHandler() + handler.response_api_handler( + model="openai.gpt-5.5", + input="hi", + responses_api_provider_config=provider_config, + response_api_optional_request_params={"stream": True}, + custom_llm_provider="bedrock_mantle", + litellm_params=GenericLiteLLMParams(aws_region_name="us-east-2"), + logging_obj=MagicMock(), + client=mock_client, + _is_async=False, + fake_stream=True, + ) + + assert "stream" not in captured["request_data"] + assert "input" in captured["request_data"] + + post_kwargs = mock_client.post.call_args.kwargs + assert post_kwargs.get("data") == b'{"input": "hi"}' + assert "json" not in post_kwargs + assert "stream" in post_kwargs + + +def _make_compact_handler_call(signed_body, is_async): + """Drive (async_)compact_response_api_handler with a fully mocked provider config + + client, returning the kwargs the client.post was called with. + + signed_body=None simulates a no-op (non-signing) provider; bytes simulates a + signing provider (e.g. Bedrock Mantle SigV4 / bearer). + """ + from unittest.mock import MagicMock + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler + from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler + from litellm.types.router import GenericLiteLLMParams + + compact_url = "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses/compact" + provider_config = MagicMock() + provider_config.validate_environment.return_value = {} + provider_config.get_complete_url.return_value = ( + "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses" + ) + provider_config.transform_compact_response_api_request.return_value = ( + compact_url, + {"model": "openai.gpt-5.5", "input": "hi"}, + ) + provider_config.sign_request.return_value = ({"X-Signed": "1"}, signed_body) + provider_config.transform_compact_response_api_response.return_value = "ok" + + spec = AsyncHTTPHandler if is_async else HTTPHandler + mock_client = MagicMock(spec=spec) + if is_async: + mock_client.post = AsyncMock(return_value=MagicMock()) + else: + mock_client.post.return_value = MagicMock() + + handler = BaseLLMHTTPHandler() + result = handler.compact_response_api_handler( + model="openai.gpt-5.5", + input="hi", + responses_api_provider_config=provider_config, + response_api_optional_request_params={}, + custom_llm_provider="bedrock_mantle", + litellm_params=GenericLiteLLMParams(aws_region_name="us-east-2"), + logging_obj=MagicMock(), + client=mock_client, + _is_async=is_async, + ) + if is_async: + asyncio.run(result) + return provider_config, mock_client.post.call_args.kwargs + + +def test_compact_handler_sends_json_when_not_signed(): + """No-op provider on compact (signed_body is None) -> posts json=data, no data= bytes.""" + provider_config, kwargs = _make_compact_handler_call( + signed_body=None, is_async=False + ) + provider_config.sign_request.assert_called_once() + assert kwargs.get("json") == {"model": "openai.gpt-5.5", "input": "hi"} + assert "data" not in kwargs + + +def test_compact_handler_sends_signed_bytes_when_signed(): + """Signing provider on compact -> posts the signed bytes via data=, not json=. + + Regression for the adversarial-review finding that /responses/compact bypassed + the SigV4 signing hook, so IAM-only Mantle callers sent unsigned bodies. + """ + provider_config, kwargs = _make_compact_handler_call( + signed_body=b'{"model": "openai.gpt-5.5", "input": "hi"}', is_async=False + ) + assert kwargs.get("data") == b'{"model": "openai.gpt-5.5", "input": "hi"}' + assert "json" not in kwargs + assert kwargs["headers"] == {"X-Signed": "1"} + # signing must use the compact endpoint as api_base, not the create URL + assert provider_config.sign_request.call_args.kwargs["api_base"].endswith( + "/openai/v1/responses/compact" + ) + + +def test_async_compact_handler_sends_signed_bytes_when_signed(): + """Async compact must sign identically to sync (same omission in the async twin).""" + provider_config, kwargs = _make_compact_handler_call( + signed_body=b'{"model": "openai.gpt-5.5", "input": "hi"}', is_async=True + ) + assert kwargs.get("data") == b'{"model": "openai.gpt-5.5", "input": "hi"}' + assert "json" not in kwargs + assert kwargs["headers"] == {"X-Signed": "1"} + + +def test_async_compact_handler_sends_json_when_not_signed(): + """Async no-op provider on compact -> posts json=data, no data= bytes.""" + _provider_config, kwargs = _make_compact_handler_call( + signed_body=None, is_async=True + ) + assert kwargs.get("json") == {"model": "openai.gpt-5.5", "input": "hi"} + assert "data" not in kwargs From 24b9655cd42077d8781325838f30325f083a5f78 Mon Sep 17 00:00:00 2001 From: ishaan-berri <155045088+ishaan-berri@users.noreply.github.com> Date: Thu, 11 Jun 2026 04:32:33 +0000 Subject: [PATCH 7/9] fix: completion_cost AttributeError on streaming Anthropic web_search responses (#26153) (#27346) Cherry-picked from staging squash 4a3860df1f. stable/1.88.x predates the Usage.__init__ server_tool_use dict->ServerToolUse coercion that staging carries (it landed via the squashed OSS sync #29932 / 32c88ca74f, not as a standalone commit). The calculate_usage Usage(**returned_usage.model_dump()) round-trip on this line re-serializes server_tool_use to a plain dict, so without that coercion the rebuilt usage holds a dict and the regression test asserting a ServerToolUse type fails. Restored the coercion in litellm/types/utils.py to satisfy the prerequisite -- it matches #27346's own first commit (coerce server_tool_use dict to ServerToolUse in Usage.__init__), which was dropped from the squash only because staging already carried it. --- .../llm_cost_calc/tool_call_cost_tracking.py | 7 +- .../litellm_core_utils/llm_cost_calc/utils.py | 30 +++- .../streaming_chunk_builder_utils.py | 13 +- litellm/llms/anthropic/cost_calculation.py | 14 +- litellm/types/utils.py | 5 +- ...est_tool_call_cost_tracking_dict_safety.py | 88 ++++++++++++ ...streaming_chunk_builder_server_tool_use.py | 130 ++++++++++++++++++ .../test_streaming_chunk_builder_utils.py | 5 +- .../test_cost_calculation_dict_safety.py | 94 +++++++++++++ 9 files changed, 372 insertions(+), 14 deletions(-) create mode 100644 tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking_dict_safety.py create mode 100644 tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_server_tool_use.py create mode 100644 tests/test_litellm/llms/anthropic/test_cost_calculation_dict_safety.py diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py index 8da66d4600d..413ddb71bf8 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py +++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py @@ -6,6 +6,7 @@ from typing import Any, Dict, List, Literal, Optional, Tuple import litellm from litellm.constants import OPENAI_FILE_SEARCH_COST_PER_1K_CALLS +from litellm.litellm_core_utils.llm_cost_calc.utils import _get_web_search_requests from litellm.types.llms.openai import ( FileSearchTool, ResponsesAPIResponse, @@ -339,8 +340,7 @@ class StandardBuiltInToolCostTracking: # and _handle_web_search_cost() is never called. if ( hasattr(usage, "server_tool_use") - and usage.server_tool_use is not None - and usage.server_tool_use.web_search_requests is not None + and _get_web_search_requests(usage.server_tool_use) is not None ): return True return False @@ -352,8 +352,7 @@ class StandardBuiltInToolCostTracking: elif usage is not None: if ( hasattr(usage, "server_tool_use") - and usage.server_tool_use is not None - and usage.server_tool_use.web_search_requests is not None + and _get_web_search_requests(usage.server_tool_use) is not None ): return True elif ( diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 882561ed2e8..54df519c5d3 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,7 +1,7 @@ # What is this? ## Helper utilities for cost_per_token() -from typing import Literal, Optional, Tuple, TypedDict, cast +from typing import Any, Literal, Optional, Tuple, TypedDict, cast import litellm from litellm._logging import verbose_logger @@ -34,6 +34,34 @@ _IMAGE_RESPONSE_CALL_TYPES = frozenset( _VALID_DATA_RESIDENCIES = frozenset(r.value for r in DataResidency) +def _get_token_detail_value(details: object, key: str) -> Optional[int]: + if isinstance(details, dict): + value = details.get(key) + else: + value = getattr(details, key, None) + return value if isinstance(value, int) else None + + +def _get_web_search_requests(server_tool_use: Any) -> Optional[int]: + """ + Tolerantly read ``web_search_requests`` from a ``server_tool_use`` value + that may be ``None``, a ``dict``, a ``ServerToolUse`` pydantic instance, + or any other object supporting attribute access. + + Returns ``None`` when the value cannot be resolved — callers can + distinguish "absent" from "zero" using ``is None``. + + See https://github.com/BerriAI/litellm/issues/26153 — ``stream_chunk_builder`` + historically left this as a plain ``dict``, which broke direct attribute + access in cost calculation. + """ + if server_tool_use is None: + return None + if isinstance(server_tool_use, dict): + return server_tool_use.get("web_search_requests") + return getattr(server_tool_use, "web_search_requests", None) + + def _is_above_128k(tokens: float) -> bool: if tokens > 128000: return True diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index fe7c62c3842..c2a17ae8dcc 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -588,7 +588,18 @@ class ChunkProcessor: hasattr(usage_chunk, "server_tool_use") and usage_chunk.server_tool_use is not None ): - server_tool_use = usage_chunk.server_tool_use + # Coerce dict to ServerToolUse so downstream cost-calc code + # (which accesses .web_search_requests as an attribute) + # doesn't raise AttributeError. Some providers / streaming + # paths leave server_tool_use as a plain dict on the chunk. + if isinstance(usage_chunk.server_tool_use, dict): + server_tool_use = ServerToolUse(**usage_chunk.server_tool_use) + elif isinstance(usage_chunk.server_tool_use, ServerToolUse): + server_tool_use = usage_chunk.server_tool_use + else: + server_tool_use = ServerToolUse.model_validate( + usage_chunk.server_tool_use + ) if ( usage_chunk_dict["prompt_tokens_details"] is not None and getattr( diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py index 3882d8f978c..6a031498dae 100644 --- a/litellm/llms/anthropic/cost_calculation.py +++ b/litellm/llms/anthropic/cost_calculation.py @@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Optional, Tuple from litellm.litellm_core_utils.llm_cost_calc.utils import ( _get_token_base_cost, + _get_web_search_requests, _parse_prompt_tokens_details, calculate_cache_writing_cost, generic_cost_per_token, @@ -110,11 +111,12 @@ def get_cost_for_anthropic_web_search( if model_info is None: return 0.0 - if ( - usage is None - or usage.server_tool_use is None - or usage.server_tool_use.web_search_requests is None - ): + if usage is None: + return 0.0 + web_search_requests = _get_web_search_requests( + getattr(usage, "server_tool_use", None) + ) + if web_search_requests is None: return 0.0 ## Get the cost per web search request @@ -128,5 +130,5 @@ def get_cost_for_anthropic_web_search( return 0.0 ## Calculate the total cost - total_cost = cost_per_web_search_request * usage.server_tool_use.web_search_requests + total_cost = cost_per_web_search_request * web_search_requests return total_cost diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 5574d616fac..e6fd36f792b 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -1570,7 +1570,7 @@ class Usage(SafeAttributeModel, CompletionUsage): completion_tokens_details: Optional[ Union[CompletionTokensDetailsWrapper, dict] ] = None, - server_tool_use: Optional[ServerToolUse] = None, + server_tool_use: Optional[Union[ServerToolUse, dict]] = None, cost: Optional[float] = None, **params, ): @@ -1671,6 +1671,9 @@ class Usage(SafeAttributeModel, CompletionUsage): prompt_tokens_details=_prompt_tokens_details or None, ) + if isinstance(server_tool_use, dict): + server_tool_use = ServerToolUse(**server_tool_use) + if server_tool_use is not None: self.server_tool_use = server_tool_use else: # maintain openai compatibility in usage object if possible diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking_dict_safety.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking_dict_safety.py new file mode 100644 index 00000000000..4eee6b59d34 --- /dev/null +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking_dict_safety.py @@ -0,0 +1,88 @@ +""" +Tests that the cost-tracking call sites tolerate ``server_tool_use`` being +either a ``dict`` or a ``ServerToolUse`` pydantic instance. + +See https://github.com/BerriAI/litellm/issues/26153. +""" + +import os +import sys + +import pytest + +sys.path.insert(0, os.path.abspath("../../../..")) + +from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( + StandardBuiltInToolCostTracking, + _get_web_search_requests, +) +from litellm.types.utils import ModelResponse, ServerToolUse, Usage + + +class _UsageWithDictServerToolUse: + """ + Tiny stand-in that mimics the broken streaming-rebuild shape: + ``server_tool_use`` is a plain dict. + """ + + def __init__(self, server_tool_use): + self.server_tool_use = server_tool_use + self.prompt_tokens_details = None + + +def test_get_web_search_requests_handles_none(): + assert _get_web_search_requests(None) is None + + +def test_get_web_search_requests_handles_dict(): + assert _get_web_search_requests({"web_search_requests": 5}) == 5 + + +def test_get_web_search_requests_handles_dict_missing_key(): + assert _get_web_search_requests({}) is None + + +def test_get_web_search_requests_handles_pydantic(): + stu = ServerToolUse(web_search_requests=7) + assert _get_web_search_requests(stu) == 7 + + +def test_get_web_search_requests_handles_pydantic_with_none_value(): + stu = ServerToolUse() + assert _get_web_search_requests(stu) is None + + +def test_response_object_includes_web_search_call_with_dict_server_tool_use(): + """ + The exact bug: ``usage.server_tool_use`` is a dict and the check in + ``response_object_includes_web_search_call`` used to crash with + ``AttributeError``. + """ + response = ModelResponse() + usage = _UsageWithDictServerToolUse({"web_search_requests": 2}) + + # Must not raise — and must correctly detect the web search call. + result = StandardBuiltInToolCostTracking.response_object_includes_web_search_call( + response_object=response, usage=usage # type: ignore[arg-type] + ) + assert result is True + + +def test_response_object_includes_web_search_call_with_pydantic_server_tool_use(): + response = ModelResponse() + usage = _UsageWithDictServerToolUse(ServerToolUse(web_search_requests=2)) + + result = StandardBuiltInToolCostTracking.response_object_includes_web_search_call( + response_object=response, usage=usage # type: ignore[arg-type] + ) + assert result is True + + +def test_response_object_includes_web_search_call_with_none_server_tool_use(): + response = ModelResponse() + usage = _UsageWithDictServerToolUse(None) + + result = StandardBuiltInToolCostTracking.response_object_includes_web_search_call( + response_object=response, usage=usage # type: ignore[arg-type] + ) + assert result is False diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_server_tool_use.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_server_tool_use.py new file mode 100644 index 00000000000..4e28d5ba7d2 --- /dev/null +++ b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_server_tool_use.py @@ -0,0 +1,130 @@ +""" +Regression tests for https://github.com/BerriAI/litellm/issues/26153 + +``stream_chunk_builder`` used to leave ``usage.server_tool_use`` as a plain +``dict`` when reconstructing a streaming response. Downstream cost-calculation +code (``StandardBuiltInToolCostTracking.response_object_includes_web_search_call`` +and ``get_cost_for_anthropic_web_search``) accesses +``usage.server_tool_use.web_search_requests`` as an attribute, which raised +``AttributeError: 'dict' object has no attribute 'web_search_requests'``. + +These tests reconstruct streaming chunks for an Anthropic-style web_search +response and assert: + +1. ``stream_chunk_builder`` returns ``ServerToolUse`` (not ``dict``) for + ``usage.server_tool_use``. +2. ``completion_cost`` runs end-to-end on the rebuilt response without + raising ``AttributeError``. +""" + +import os +import sys + +import pytest + +sys.path.insert(0, os.path.abspath("../../..")) + +from litellm import completion_cost, stream_chunk_builder +from litellm.types.utils import ( + Delta, + ModelResponseStream, + ServerToolUse, + StreamingChoices, + Usage, +) + + +def _make_text_chunk(text: str) -> ModelResponseStream: + return ModelResponseStream( + id="chatcmpl-test-26153", + created=1700000000, + model="claude-3-haiku-20240307", + object="chat.completion.chunk", + choices=[ + StreamingChoices( + finish_reason=None, + index=0, + delta=Delta(role="assistant", content=text), + ) + ], + ) + + +def _make_finish_chunk_with_usage_dict_server_tool_use() -> ModelResponseStream: + """Final chunk where server_tool_use is a *dict* — reproduces the bug shape.""" + return ModelResponseStream( + id="chatcmpl-test-26153", + created=1700000000, + model="claude-3-haiku-20240307", + object="chat.completion.chunk", + choices=[ + StreamingChoices( + finish_reason="stop", + index=0, + delta=Delta(), + ) + ], + usage=Usage( + prompt_tokens=42, + completion_tokens=11, + total_tokens=53, + # NOTE: passed as a dict on purpose — this is the shape that + # historically slipped through stream_chunk_builder unchanged. + server_tool_use={"web_search_requests": 3}, + ), + ) + + +def test_stream_chunk_builder_coerces_server_tool_use_to_pydantic(): + """ + Regression: stream_chunk_builder must produce ServerToolUse, not dict. + """ + chunks = [ + _make_text_chunk("Otters "), + _make_text_chunk("are great."), + _make_finish_chunk_with_usage_dict_server_tool_use(), + ] + + rebuilt = stream_chunk_builder(chunks) + + assert rebuilt is not None + assert rebuilt.usage is not None # type: ignore[attr-defined] + server_tool_use = rebuilt.usage.server_tool_use # type: ignore[attr-defined] + + assert ( + server_tool_use is not None + ), "server_tool_use should be carried through from the final chunk" + assert isinstance(server_tool_use, ServerToolUse), ( + f"expected ServerToolUse, got {type(server_tool_use).__name__}: " + f"{server_tool_use!r}" + ) + # Attribute access must not raise (this is exactly what was broken). + assert server_tool_use.web_search_requests == 3 + + +def test_completion_cost_does_not_raise_on_streaming_web_search_response(): + """ + Regression: completion_cost(...) must not raise AttributeError when the + response was reconstructed by stream_chunk_builder from a streaming + Anthropic web_search call. + """ + chunks = [ + _make_text_chunk("hello"), + _make_finish_chunk_with_usage_dict_server_tool_use(), + ] + + rebuilt = stream_chunk_builder(chunks) + assert rebuilt is not None + + # The exact dollar amount depends on the model-pricing table; what matters + # for this regression is that it does NOT raise AttributeError on + # `dict has no attribute 'web_search_requests'`. + try: + cost = completion_cost(completion_response=rebuilt) + except AttributeError as e: # pragma: no cover - regression guard + pytest.fail( + "completion_cost raised AttributeError after stream_chunk_builder " + f"(issue #26153 regression): {e}" + ) + + assert isinstance(cost, (int, float)) diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py index e40a0817fd9..35aca525f6c 100644 --- a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py +++ b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py @@ -520,7 +520,10 @@ def test_stream_chunk_builder_anthropic_web_search(): assert usage.prompt_tokens == 50 assert usage.completion_tokens == 27 assert usage.total_tokens == 77 - assert usage.server_tool_use["web_search_requests"] == 2 + # server_tool_use must be a ServerToolUse pydantic so downstream cost-calc + # (which uses attribute access) works. See issue #26153. + assert isinstance(usage.server_tool_use, ServerToolUse) + assert usage.server_tool_use.web_search_requests == 2 def test_sort_chunks_handles_dict_hidden_params_created_at(): diff --git a/tests/test_litellm/llms/anthropic/test_cost_calculation_dict_safety.py b/tests/test_litellm/llms/anthropic/test_cost_calculation_dict_safety.py new file mode 100644 index 00000000000..70fef0162e6 --- /dev/null +++ b/tests/test_litellm/llms/anthropic/test_cost_calculation_dict_safety.py @@ -0,0 +1,94 @@ +""" +Tests that ``get_cost_for_anthropic_web_search`` tolerates ``server_tool_use`` +being either a ``dict`` or a ``ServerToolUse`` pydantic instance. + +See https://github.com/BerriAI/litellm/issues/26153. +""" + +import os +import sys + +import pytest + +sys.path.insert(0, os.path.abspath("../../../..")) + +from litellm.llms.anthropic.cost_calculation import ( + _get_web_search_requests, + get_cost_for_anthropic_web_search, +) +from litellm.types.utils import ModelInfo, ServerToolUse + + +class _UsageWithServerToolUse: + def __init__(self, server_tool_use): + self.server_tool_use = server_tool_use + + +def _make_model_info(cost_per_query: float = 0.01) -> ModelInfo: + info: ModelInfo = { # type: ignore[typeddict-item] + "search_context_cost_per_query": { + "search_context_size_low": cost_per_query, + "search_context_size_medium": cost_per_query, + "search_context_size_high": cost_per_query, + } + } + return info + + +def test_get_web_search_requests_handles_none(): + assert _get_web_search_requests(None) is None + + +def test_get_web_search_requests_handles_dict(): + assert _get_web_search_requests({"web_search_requests": 4}) == 4 + + +def test_get_web_search_requests_handles_dict_missing_key(): + assert _get_web_search_requests({}) is None + + +def test_get_web_search_requests_handles_pydantic(): + assert _get_web_search_requests(ServerToolUse(web_search_requests=2)) == 2 + + +def test_get_cost_for_anthropic_web_search_with_dict_server_tool_use(): + """ + Regression: ``server_tool_use`` was a dict from ``stream_chunk_builder`` and + direct attribute access on it raised ``AttributeError``. + """ + usage = _UsageWithServerToolUse({"web_search_requests": 3}) + info = _make_model_info(cost_per_query=0.01) + + cost = get_cost_for_anthropic_web_search( + model_info=info, usage=usage # type: ignore[arg-type] + ) + + assert cost == pytest.approx(0.03) + + +def test_get_cost_for_anthropic_web_search_with_pydantic_server_tool_use(): + usage = _UsageWithServerToolUse(ServerToolUse(web_search_requests=3)) + info = _make_model_info(cost_per_query=0.01) + + cost = get_cost_for_anthropic_web_search( + model_info=info, usage=usage # type: ignore[arg-type] + ) + + assert cost == pytest.approx(0.03) + + +def test_get_cost_for_anthropic_web_search_with_none_server_tool_use(): + usage = _UsageWithServerToolUse(None) + info = _make_model_info(cost_per_query=0.01) + + cost = get_cost_for_anthropic_web_search( + model_info=info, usage=usage # type: ignore[arg-type] + ) + + assert cost == 0.0 + + +def test_get_cost_for_anthropic_web_search_with_no_usage(): + info = _make_model_info(cost_per_query=0.01) + cost = get_cost_for_anthropic_web_search(model_info=info, usage=None) + assert cost == 0.0 From ba196a493d80dec3438ec1b7b779da2009352bbb Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Thu, 11 Jun 2026 04:32:48 +0000 Subject: [PATCH 8/9] =?UTF-8?q?bump:=20version=201.88.1=20=E2=86=92=201.88?= =?UTF-8?q?.2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index df370a08f01..c7c28583808 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm" -version = "1.88.1" +version = "1.88.2" description = "Library to easily interface with LLM API providers" readme = "README.md" requires-python = ">=3.10, <3.14" @@ -260,7 +260,7 @@ source-exclude = [ profile = "black" [tool.commitizen] -version = "1.88.1" +version = "1.88.2" version_files = [ "pyproject.toml:^version", ] From f116cda53676bd1225e8b31c5971c53ec1e477ff Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Thu, 11 Jun 2026 04:32:52 +0000 Subject: [PATCH 9/9] chore: refresh uv.lock for 1.88.2 --- uv.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/uv.lock b/uv.lock index 988c62d8ada..932318504b6 100644 --- a/uv.lock +++ b/uv.lock @@ -3276,7 +3276,7 @@ wheels = [ [[package]] name = "litellm" -version = "1.88.1" +version = "1.88.2" source = { editable = "." } dependencies = [ { name = "aiohttp" },