Litellm oss staging 050626

README.md

@@ -407,7 +407,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
 ### Run in Developer Mode
 #### Services
 1. Setup .env file in root
-2. Run dependant services `docker-compose up db prometheus`
+2. Run dependent services `docker-compose up db prometheus`
 
 #### Backend
 1. (In root) create virtual environment `python -m venv .venv`

litellm-proxy-extras/litellm_proxy_extras/migrations/20260605182307_add_timeout_to_mcp_server_table/migration.sql

@@ -0,0 +1,3 @@
+-- AlterTable
+ALTER TABLE "LiteLLM_MCPServerTable" ADD COLUMN     "timeout" DOUBLE PRECISION;
+

litellm-proxy-extras/litellm_proxy_extras/schema.prisma

@@ -330,6 +330,7 @@ model LiteLLM_MCPServerTable {
   byok_description      String[] @default([])
   byok_api_key_help_url String?
   source_url            String?
+  timeout               Float?
   // BYOM submission lifecycle
   approval_status  String?   @default("active")
   submitted_by     String?

litellm/__init__.py

@@ -612,6 +612,7 @@ def identify(event_details):
 galadriel_models: Set = set()
 nvidia_nim_models: Set = set()
 nvidia_riva_models: Set = set()
+soniox_models: Set = set()
 sambanova_models: Set = set()
 sambanova_embedding_models: Set = set()
 novita_models: Set = set()
@@ -844,6 +845,8 @@ def add_known_models(model_cost_map: Optional[Dict] = None):
             nvidia_nim_models.add(key)
         elif value.get("litellm_provider") == "nvidia_riva":
             nvidia_riva_models.add(key)
+        elif value.get("litellm_provider") == "soniox":
+            soniox_models.add(key)
         elif value.get("litellm_provider") == "sambanova":
             sambanova_models.add(key)
         elif value.get("litellm_provider") == "sambanova-embedding-models":
@@ -1009,6 +1012,7 @@ def add_known_models(model_cost_map: Optional[Dict] = None):
     | galadriel_models
     | nvidia_nim_models
     | nvidia_riva_models
+    | soniox_models
     | sambanova_models
     | azure_text_models
     | novita_models
@@ -1109,6 +1113,7 @@ def add_known_models(model_cost_map: Optional[Dict] = None):
     "galadriel": galadriel_models,
     "nvidia_nim": nvidia_nim_models,
     "nvidia_riva": nvidia_riva_models,
+    "soniox": soniox_models,
     "sambanova": sambanova_models | sambanova_embedding_models,
     "novita": novita_models,
     "nebius": nebius_models | nebius_embedding_models,

litellm/_lazy_imports_registry.py

@@ -321,6 +321,7 @@
     "LemonadeChatConfig",
     "SnowflakeEmbeddingConfig",
     "AmazonNovaChatConfig",
+    "SonioxAudioTranscriptionConfig",
 )
 
 # Types that support lazy loading via _lazy_import_types
@@ -1195,6 +1196,10 @@
         ".llms.amazon_nova.chat.transformation",
         "AmazonNovaChatConfig",
     ),
+    "SonioxAudioTranscriptionConfig": (
+        ".llms.soniox.audio_transcription.transformation",
+        "SonioxAudioTranscriptionConfig",
+    ),
 }
 
 # Import map for utils module lazy imports

litellm/experimental_mcp_client/client.py

@@ -556,7 +556,9 @@ async def _call_tool_operation(session: ClientSession):
             )
             return tool_result
         except asyncio.CancelledError:
-            verbose_logger.warning("MCP client tool call was cancelled")
+            verbose_logger.warning(
+                f"MCP client tool call timed out after {self.timeout}s for {self.server_url}"
+            )
             raise
         except Exception as e:
             import traceback

litellm/integrations/langfuse/langfuse_prompt_management.py

@@ -102,6 +102,12 @@ def langfuse_client_init(
     if Version(langfuse.version.__version__) >= Version("2.6.0"):
         parameters["sdk_integration"] = "litellm"
 
+    from ...llms.custom_httpx.http_handler import _get_httpx_client
+
+    if Version(langfuse.version.__version__) >= Version("2.7.3"):
+        http_client = _get_httpx_client()
+        parameters["httpx_client"] = http_client.client
+
     client = Langfuse(**parameters)
 
     return client

litellm/integrations/openmeter.py

@@ -65,7 +65,15 @@ def _common_logic(self, kwargs: dict, response_obj):
                 "total_tokens": response_obj["usage"].get("total_tokens"),
             }
 
-        user_param = kwargs.get("user", None)  # end-user passed in via 'user' param
+        # OPENMETER_TRUST_REQUEST_USER (default "true"): when set to "false",
+        # the request-supplied `user` field is ignored and the subject is
+        # resolved solely from the key-bound user_api_key_user_id. Proxies
+        # serving multi-tenant traffic enable this to prevent clients from
+        # forging attribution by setting `user` in the request body.
+        trust_request_user = (
+            os.getenv("OPENMETER_TRUST_REQUEST_USER", "true").lower() != "false"
+        )
+        user_param = kwargs.get("user", None) if trust_request_user else None
 
         # If no user provided directly, try to get it from token user_id
         if user_param is None:

litellm/integrations/websearch_interception/ARCHITECTURE.md

@@ -244,6 +244,9 @@ search_tools:
   - search_tool_name: "my-tavily-tool"
     litellm_params:
       search_provider: "tavily"
+  - search_tool_name: "my-you-com-tool"
+    litellm_params:
+      search_provider: "you_com"
 ```
 
 ---

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -659,6 +659,11 @@ def _get_openai_compatible_provider_info(  # noqa: PLR0915
             or get_secret_str("NVIDIA_RIVA_API_KEY")
             or get_secret_str("NVIDIA_NIM_API_KEY")
         )
+    elif custom_llm_provider == "soniox":
+        api_base = (
+            api_base or get_secret_str("SONIOX_API_BASE") or "https://api.soniox.com"
+        )
+        dynamic_api_key = api_key or get_secret_str("SONIOX_API_KEY")
     elif custom_llm_provider == "cerebras":
         api_base = (
             api_base or get_secret("CEREBRAS_API_BASE") or "https://api.cerebras.ai/v1"

litellm/litellm_core_utils/get_supported_openai_params.py

@@ -341,6 +341,11 @@ def get_supported_openai_params(  # noqa: PLR0915
             return ElevenLabsAudioTranscriptionConfig().get_supported_openai_params(
                 model=model
             )
+    elif custom_llm_provider == "soniox":
+        if request_type == "transcription":
+            return litellm.SonioxAudioTranscriptionConfig().get_supported_openai_params(
+                model=model
+            )
     elif custom_llm_provider in litellm._custom_providers:
         if request_type == "chat_completion":
             provider_config = litellm.ProviderConfigManager.get_provider_chat_config(

litellm/litellm_core_utils/litellm_logging.py

@@ -3503,7 +3503,9 @@ def _get_assembled_streaming_response(
         else:
             return None
 
-    def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse:
+    def _handle_anthropic_messages_response_logging(
+        self, result: Any
+    ) -> Union[ModelResponse, ResponsesAPIResponse]:
         """
         Handles logging for Anthropic messages responses.
 
@@ -3522,6 +3524,15 @@ def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelRespo
             return result
         elif isinstance(result, ModelResponse):
             return result
+        elif isinstance(
+            result,
+            (ResponseCompletedEvent, ResponseIncompleteEvent, ResponseFailedEvent),
+        ):
+            # anthropic_messages() can route to OpenAI Responses API; in that path
+            # the assembled streaming result is one of these terminal events rather than
+            # a ModelResponse. Return the inner response so downstream handlers
+            # (_transform_usage_objects, normalize_logging_result) can process it.
+            return result.response
 
         httpx_response = self.model_call_details.get("httpx_response", None)
         if httpx_response and isinstance(httpx_response, httpx.Response):

litellm/llms/moonshot/chat/transformation.py

@@ -134,11 +134,15 @@ def map_openai_params(
 
         ##########################################
         # temperature limitations
-        # 1. `temperature` on KIMI API is [0, 1] but OpenAI is [0, 2]
-        # 2. If temperature < 0.3 and n > 1, KIMI will raise an exception.
+        # 1. reasoning models (kimi-k2.5, kimi-k2.6, ...) reject every temperature
+        #    except 1, so the param is dropped and the model's default is used
+        # 2. `temperature` on KIMI API is [0, 1] but OpenAI is [0, 2]
+        # 3. If temperature < 0.3 and n > 1, KIMI will raise an exception.
         #       If we enter this condition, we set the temperature to 0.3 as suggested by Moonshot AI
         ##########################################
-        if "temperature" in optional_params:
+        if supports_reasoning(model=model, custom_llm_provider="moonshot"):
+            optional_params.pop("temperature", None)
+        elif "temperature" in optional_params:
             if optional_params["temperature"] > 1:
                 optional_params["temperature"] = 1
             if optional_params["temperature"] < 0.3 and optional_params.get("n", 1) > 1:

litellm/llms/openai_like/providers.json

@@ -115,6 +115,15 @@
       "max_completion_tokens": "max_tokens"
     }
   },
+  "neosantara": {
+    "base_url": "https://api.neosantara.xyz/v1",
+    "api_key_env": "NEOSANTARA_API_KEY",
+    "api_base_env": "NEOSANTARA_API_BASE",
+    "param_mappings": {
+      "max_completion_tokens": "max_tokens"
+    },
+    "supported_endpoints": ["/v1/chat/completions", "/v1/responses"]
+  },
   "tensormesh": {
     "base_url": "https://serverless.tensormesh.ai/v1",
     "api_key_env": "TENSORMESH_INFERENCE_API_KEY",

litellm/llms/soniox/__init__.py

@@ -0,0 +1 @@
+"""Soniox LLM provider implementation."""

litellm/llms/soniox/audio_transcription/__init__.py

@@ -0,0 +1 @@
+"""Soniox audio transcription implementation."""