# What is this? ## Common Utility file for Logging handler # Logging function -> log the exact model details + what's being sent | Non-Blocking import copy import datetime import json import os import re import subprocess import sys import time import traceback from datetime import datetime as dt_object from functools import lru_cache from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple, Type, Union, cast, ) from httpx import Response from pydantic import BaseModel import litellm from litellm import ( _custom_logger_compatible_callbacks_literal, json_logs, log_raw_request_response, turn_off_message_logging, ) from litellm._logging import _is_debugging_on, verbose_logger from litellm._uuid import uuid from litellm.batches.batch_utils import _handle_completed_batch from litellm.caching.caching import DualCache, InMemoryCache from litellm.caching.caching_handler import LLMCachingHandler from litellm.constants import ( DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT, DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT, SENTRY_DENYLIST, SENTRY_PII_DENYLIST, ) from litellm.cost_calculator import ( RealtimeAPITokenUsageProcessor, _select_model_name_for_cost_calc, ) from litellm.integrations.agentops import AgentOps from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook from litellm.integrations.arize.arize import ArizeLogger from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.deepeval.deepeval import DeepEvalLogger from litellm.integrations.mlflow import MlflowLogger from litellm.integrations.sqs import SQSLogger from litellm.litellm_core_utils.core_helpers import reconstruct_model_name from litellm.litellm_core_utils.get_litellm_params import get_litellm_params from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( StandardBuiltInToolCostTracking, ) from litellm.litellm_core_utils.logging_utils import truncate_base64_in_messages from litellm.litellm_core_utils.model_param_helper import ModelParamHelper from litellm.litellm_core_utils.redact_messages import ( redact_message_input_output_from_custom_logger, redact_message_input_output_from_logging, ) from litellm.llms.base_llm.ocr.transformation import OCRResponse from litellm.llms.base_llm.search.transformation import SearchResponse from litellm.responses.utils import ResponseAPILoggingUtils from litellm.types.agents import LiteLLMSendMessageResponse from litellm.types.containers.main import ContainerObject from litellm.types.llms.openai import ( AllMessageValues, Batch, FineTuningJob, HttpxBinaryResponseContent, OpenAIFileObject, OpenAIModerationResponse, ResponseAPIUsage, ResponseCompletedEvent, ResponsesAPIResponse, ) from litellm.types.mcp import MCPPostCallResponseObject from litellm.types.prompts.init_prompts import PromptSpec from litellm.types.rerank import RerankResponse from litellm.types.utils import ( CachingDetails, CallTypes, CostBreakdown, CostResponseTypes, CustomPricingLiteLLMParams, DynamicPromptManagementParamLiteral, EmbeddingResponse, GuardrailStatus, ImageResponse, LiteLLMBatch, LiteLLMLoggingBaseClass, LiteLLMRealtimeStreamLoggingObject, ModelResponse, ModelResponseStream, RawRequestTypedDict, StandardBuiltInToolsParams, StandardCallbackDynamicParams, StandardLoggingAdditionalHeaders, StandardLoggingHiddenParams, StandardLoggingMCPToolCall, StandardLoggingMetadata, StandardLoggingModelCostFailureDebugInformation, StandardLoggingModelInformation, StandardLoggingPayload, StandardLoggingPayloadErrorInformation, StandardLoggingPayloadStatus, StandardLoggingPayloadStatusFields, StandardLoggingPromptManagementMetadata, StandardLoggingVectorStoreRequest, TextCompletionResponse, TranscriptionResponse, Usage, ) from litellm.types.videos.main import VideoObject from litellm.utils import _get_base_model_from_metadata, executor, print_verbose from ..integrations.argilla import ArgillaLogger from ..integrations.arize.arize_phoenix import ArizePhoenixLogger from ..integrations.athina import AthinaLogger from ..integrations.azure_sentinel.azure_sentinel import AzureSentinelLogger from ..integrations.azure_storage.azure_storage import AzureBlobStorageLogger from ..integrations.custom_prompt_management import CustomPromptManagement from ..integrations.datadog.datadog import DataDogLogger from ..integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger from ..integrations.datadog.datadog_metrics import DatadogMetricsLogger from ..integrations.dotprompt import DotpromptManager from ..integrations.dynamodb import DyanmoDBLogger from ..integrations.galileo import GalileoObserve from ..integrations.gcs_bucket.gcs_bucket import GCSBucketLogger from ..integrations.gcs_pubsub.pub_sub import GcsPubSubLogger from ..integrations.greenscale import GreenscaleLogger from ..integrations.helicone import HeliconeLogger from ..integrations.humanloop import HumanloopLogger from ..integrations.lago import LagoLogger from ..integrations.langfuse.langfuse import LangFuseLogger from ..integrations.langfuse.langfuse_handler import LangFuseHandler from ..integrations.langfuse.langfuse_prompt_management import LangfusePromptManagement from ..integrations.langsmith import LangsmithLogger from ..integrations.litellm_agent import LiteLLMAgentModelResolver from ..integrations.literal_ai import LiteralAILogger from ..integrations.logfire_logger import LogfireLevel, LogfireLogger from ..integrations.lunary import LunaryLogger from ..integrations.openmeter import OpenMeterLogger from ..integrations.opik.opik import OpikLogger from ..integrations.posthog import PostHogLogger from ..integrations.prompt_layer import PromptLayerLogger from ..integrations.s3 import S3Logger from ..integrations.s3_v2 import S3Logger as S3V2Logger from ..integrations.supabase import Supabase from ..integrations.traceloop import TraceloopLogger from .exception_mapping_utils import _get_response_headers from .initialize_dynamic_callback_params import ( initialize_standard_callback_dynamic_params as _initialize_standard_callback_dynamic_params, ) from .specialty_caches.dynamic_logging_cache import DynamicLoggingCache if TYPE_CHECKING: from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig try: from litellm_enterprise.enterprise_callbacks.callback_controls import ( EnterpriseCallbackControls, ) from litellm_enterprise.enterprise_callbacks.pagerduty.pagerduty import ( PagerDutyAlerting, ) from litellm_enterprise.enterprise_callbacks.send_emails.resend_email import ( ResendEmailLogger, ) from litellm_enterprise.enterprise_callbacks.send_emails.sendgrid_email import ( SendGridEmailLogger, ) from litellm_enterprise.enterprise_callbacks.send_emails.smtp_email import ( SMTPEmailLogger, ) from litellm_enterprise.litellm_core_utils.litellm_logging import ( StandardLoggingPayloadSetup as EnterpriseStandardLoggingPayloadSetup, ) from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger EnterpriseStandardLoggingPayloadSetupVAR: Optional[ Type[EnterpriseStandardLoggingPayloadSetup] ] = EnterpriseStandardLoggingPayloadSetup except Exception as e: verbose_logger.debug( f"[Non-Blocking] Unable to import GenericAPILogger - LiteLLM Enterprise Feature - {str(e)}" ) GenericAPILogger = CustomLogger # type: ignore ResendEmailLogger = CustomLogger # type: ignore SendGridEmailLogger = CustomLogger # type: ignore SMTPEmailLogger = CustomLogger # type: ignore PagerDutyAlerting = CustomLogger # type: ignore EnterpriseCallbackControls = None # type: ignore EnterpriseStandardLoggingPayloadSetupVAR = None _in_memory_loggers: List[Any] = [] _STANDARD_LOGGING_METADATA_KEYS: frozenset = frozenset( StandardLoggingMetadata.__annotations__.keys() ) ### GLOBAL VARIABLES ### # Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys _CUSTOM_PRICING_KEYS: frozenset = frozenset( CustomPricingLiteLLMParams.model_fields.keys() ) sentry_sdk_instance = None capture_exception = None add_breadcrumb = None slack_app = None alerts_channel = None heliconeLogger = None athinaLogger = None promptLayerLogger = None logfireLogger = None weightsBiasesLogger = None customLogger = None langFuseLogger = None openMeterLogger = None lagoLogger = None dataDogLogger = None prometheusLogger = None dynamoLogger = None s3Logger = None greenscaleLogger = None lunaryLogger = None supabaseClient = None deepevalLogger = None callback_list: Optional[List[str]] = [] user_logger_fn = None additional_details: Optional[Dict[str, str]] = {} local_cache: Optional[Dict[str, str]] = {} last_fetched_at = None last_fetched_at_keys = None #### class ServiceTraceIDCache: def __init__(self) -> None: self.cache = InMemoryCache() def get_cache(self, litellm_call_id: str, service_name: str) -> Optional[str]: key_name = "{}:{}".format(service_name, litellm_call_id) response = self.cache.get_cache(key=key_name) return response def set_cache(self, litellm_call_id: str, service_name: str, trace_id: str) -> None: key_name = "{}:{}".format(service_name, litellm_call_id) self.cache.set_cache(key=key_name, value=trace_id) return None in_memory_trace_id_cache = ServiceTraceIDCache() in_memory_dynamic_logger_cache = DynamicLoggingCache() # Cached lazy import for PrometheusLogger # Module-level cache to avoid repeated imports while preserving memory benefits _PrometheusLogger = None def _get_cached_prometheus_logger(): """ Get cached PrometheusLogger class. Lazy imports on first call to avoid loading prometheus.py and utils.py at import time (60MB saved). Subsequent calls use cached class for better performance. """ global _PrometheusLogger if _PrometheusLogger is None: from litellm.integrations.prometheus import PrometheusLogger _PrometheusLogger = PrometheusLogger return _PrometheusLogger class Logging(LiteLLMLoggingBaseClass): global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app custom_pricing: bool = False stream_options = None litellm_request_debug: bool = False def __init__( self, model: str, messages, stream, call_type, start_time, litellm_call_id: str, function_id: str, litellm_trace_id: Optional[str] = None, dynamic_input_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = None, dynamic_success_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = None, dynamic_async_success_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = None, dynamic_failure_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = None, dynamic_async_failure_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = None, applied_guardrails: Optional[List[str]] = None, kwargs: Optional[Dict] = None, log_raw_request_response: bool = False, ): _input: Optional[str] = messages # save original value of messages if messages is not None: if isinstance(messages, str): messages = [ {"role": "user", "content": messages} ] # convert text completion input to the chat completion format elif ( isinstance(messages, list) and len(messages) > 0 and isinstance(messages[0], str) ): new_messages = [] for m in messages: new_messages.append({"role": "user", "content": m}) messages = new_messages self.model = model # Shallow copy of the outer list only (inner message dicts are shared). # Safe because the logging layer does not mutate individual message dicts. _copy_start = time.time() self.messages = copy.copy(messages) if messages is not None else None self.message_copy_duration_ms: float = (time.time() - _copy_start) * 1000 self.callback_duration_ms: float = 0.0 self.stream = stream self.start_time = start_time # log the call start time self.call_type = call_type self.litellm_call_id = litellm_call_id self.litellm_trace_id: str = ( litellm_trace_id if litellm_trace_id else str(uuid.uuid4()) ) self.function_id = function_id self.streaming_chunks: List[Any] = [] # for generating complete stream response self.sync_streaming_chunks: List[Any] = ( [] ) # for generating complete stream response self.log_raw_request_response = log_raw_request_response # Initialize dynamic callbacks self.dynamic_input_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = dynamic_input_callbacks self.dynamic_success_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = dynamic_success_callbacks self.dynamic_async_success_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = dynamic_async_success_callbacks self.dynamic_failure_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = dynamic_failure_callbacks self.dynamic_async_failure_callbacks: Optional[ List[Union[str, Callable, CustomLogger]] ] = dynamic_async_failure_callbacks # Process dynamic callbacks self.process_dynamic_callbacks() ## DYNAMIC LANGFUSE / GCS / logging callback KEYS ## self.standard_callback_dynamic_params: StandardCallbackDynamicParams = ( self.initialize_standard_callback_dynamic_params(kwargs) ) self.standard_built_in_tools_params: StandardBuiltInToolsParams = ( self.initialize_standard_built_in_tools_params(kwargs) ) ## TIME TO FIRST TOKEN LOGGING ## self.completion_start_time: Optional[datetime.datetime] = None self._llm_caching_handler: Optional[LLMCachingHandler] = None # INITIAL LITELLM_PARAMS litellm_params = {} if kwargs is not None: litellm_params = get_litellm_params(**kwargs) litellm_params = scrub_sensitive_keys_in_metadata(litellm_params) self.litellm_params = litellm_params # Initialize cost breakdown field self.cost_breakdown: Optional[CostBreakdown] = None # Init Caching related details self.caching_details: Optional[CachingDetails] = None # Passthrough endpoint guardrails config for field targeting self.passthrough_guardrails_config: Optional[Dict[str, Any]] = None self.model_call_details: Dict[str, Any] = { "litellm_trace_id": litellm_trace_id, "litellm_call_id": litellm_call_id, "input": _input, "litellm_params": litellm_params, "applied_guardrails": applied_guardrails, "model": model, } def process_dynamic_callbacks(self): """ Initializes CustomLogger compatible callbacks in self.dynamic_* callbacks If a callback is in litellm._known_custom_logger_compatible_callbacks, it needs to be intialized and added to the respective dynamic_* callback list. """ # Process input callbacks self.dynamic_input_callbacks = self._process_dynamic_callback_list( self.dynamic_input_callbacks, dynamic_callbacks_type="input" ) # Process failure callbacks self.dynamic_failure_callbacks = self._process_dynamic_callback_list( self.dynamic_failure_callbacks, dynamic_callbacks_type="failure" ) # Process async failure callbacks self.dynamic_async_failure_callbacks = self._process_dynamic_callback_list( self.dynamic_async_failure_callbacks, dynamic_callbacks_type="async_failure" ) # Process success callbacks self.dynamic_success_callbacks = self._process_dynamic_callback_list( self.dynamic_success_callbacks, dynamic_callbacks_type="success" ) # Process async success callbacks self.dynamic_async_success_callbacks = self._process_dynamic_callback_list( self.dynamic_async_success_callbacks, dynamic_callbacks_type="async_success" ) def _process_dynamic_callback_list( self, callback_list: Optional[List[Union[str, Callable, CustomLogger]]], dynamic_callbacks_type: Literal[ "input", "success", "failure", "async_success", "async_failure" ], ) -> Optional[List[Union[str, Callable, CustomLogger]]]: """ Helper function to initialize CustomLogger compatible callbacks in self.dynamic_* callbacks - If a callback is in litellm._known_custom_logger_compatible_callbacks, replace the string with the initialized callback class. - If dynamic callback is a "success" callback that is a known_custom_logger_compatible_callbacks then add it to dynamic_async_success_callbacks - If dynamic callback is a "failure" callback that is a known_custom_logger_compatible_callbacks then add it to dynamic_failure_callbacks """ if callback_list is None: return None processed_list: List[Union[str, Callable, CustomLogger]] = [] for callback in callback_list: if ( isinstance(callback, str) and callback in litellm._known_custom_logger_compatible_callbacks ): callback_class = _init_custom_logger_compatible_class( callback, internal_usage_cache=None, llm_router=None # type: ignore ) if callback_class is not None: processed_list.append(callback_class) # If processing dynamic_success_callbacks, add to dynamic_async_success_callbacks if dynamic_callbacks_type == "success": if self.dynamic_async_success_callbacks is None: self.dynamic_async_success_callbacks = [] self.dynamic_async_success_callbacks.append(callback_class) elif dynamic_callbacks_type == "failure": if self.dynamic_async_failure_callbacks is None: self.dynamic_async_failure_callbacks = [] self.dynamic_async_failure_callbacks.append(callback_class) else: processed_list.append(callback) return processed_list def initialize_standard_callback_dynamic_params( self, kwargs: Optional[Dict] = None ) -> StandardCallbackDynamicParams: """ Initialize the standard callback dynamic params from the kwargs checks if langfuse_secret_key, gcs_bucket_name in kwargs and sets the corresponding attributes in StandardCallbackDynamicParams """ return _initialize_standard_callback_dynamic_params(kwargs) def initialize_standard_built_in_tools_params( self, kwargs: Optional[Dict] = None ) -> StandardBuiltInToolsParams: """ Initialize the standard built-in tools params from the kwargs checks if web_search_options in kwargs or tools and sets the corresponding attribute in StandardBuiltInToolsParams """ return StandardBuiltInToolsParams( web_search_options=StandardBuiltInToolCostTracking._get_web_search_options( kwargs or {} ), file_search=StandardBuiltInToolCostTracking._get_file_search_tool_call( kwargs or {} ), ) def update_environment_variables( self, litellm_params: Dict, optional_params: Dict, model: Optional[str] = None, user: Optional[str] = None, **additional_params, ): self.optional_params = optional_params if model is not None: self.model = model self.user = user self.litellm_params = { **self.litellm_params, **scrub_sensitive_keys_in_metadata(litellm_params), } self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) self.logger_fn = litellm_params.get("logger_fn", None) if _is_debugging_on() or self.litellm_request_debug: verbose_logger.debug(f"self.optional_params: {self.optional_params}") self.model_call_details.update( { "model": self.model, "messages": self.messages, "optional_params": self.optional_params, "litellm_params": self.litellm_params, "start_time": self.start_time, "stream": self.stream, "user": user, "call_type": str(self.call_type), "litellm_call_id": self.litellm_call_id, "completion_start_time": self.completion_start_time, "standard_callback_dynamic_params": self.standard_callback_dynamic_params, **self.optional_params, **additional_params, } ) ## check if stream options is set ## - used by CustomStreamWrapper for easy instrumentation if "stream_options" in additional_params: self.stream_options = additional_params["stream_options"] ## check if custom pricing set ## if any( litellm_params.get(key) is not None for key in _CUSTOM_PRICING_KEYS & litellm_params.keys() ): self.custom_pricing = True if "custom_llm_provider" in self.model_call_details: self.custom_llm_provider = self.model_call_details["custom_llm_provider"] def update_messages(self, messages: List[AllMessageValues]): """ Update the logged value of the messages in the model_call_details Allows pre-call hooks to update the messages before the call is made """ self.messages = messages self.model_call_details["messages"] = messages def should_run_prompt_management_hooks( self, non_default_params: Dict, prompt_id: Optional[str] = None, tools: Optional[List[Dict]] = None, ) -> bool: """ Return True if prompt management hooks should be run """ if prompt_id: return True # Check if model uses litellm_agent prefix (model replacement without prompt_id) model = non_default_params.get("model", "") if isinstance(model, str) and model.startswith("litellm_agent/"): return True if self._should_run_prompt_management_hooks_without_prompt_id( non_default_params=non_default_params, tools=tools, ): return True return False def _should_run_prompt_management_hooks_without_prompt_id( self, non_default_params: Dict, tools: Optional[List[Dict]] = None, ) -> bool: """ Certain prompt management hooks don't need a `prompt_id` to be passed in, they are triggered by dynamic params eg. AnthropicCacheControlHook and BedrockKnowledgeBaseHook both don't require a `prompt_id` to be passed in, they are triggered by dynamic params """ for param in non_default_params: if param in DynamicPromptManagementParamLiteral.list_all_params(): return True ############################################################################# # Check if Vector Store / Knowledge Base hooks should be applied to the prompt ############################################################################# if litellm.vector_store_registry is not None: if litellm.vector_store_registry.get_vector_store_to_run( non_default_params=non_default_params, tools=tools ): return True return False def get_chat_completion_prompt( self, model: str, messages: List[AllMessageValues], non_default_params: Dict, prompt_variables: Optional[dict], prompt_id: Optional[str] = None, prompt_spec: Optional[PromptSpec] = None, prompt_management_logger: Optional[CustomLogger] = None, prompt_label: Optional[str] = None, prompt_version: Optional[int] = None, ) -> Tuple[str, List[AllMessageValues], dict]: custom_logger = ( prompt_management_logger or self.get_custom_logger_for_prompt_management( model=model, non_default_params=non_default_params, prompt_id=prompt_id, prompt_spec=prompt_spec, dynamic_callback_params=self.standard_callback_dynamic_params, ) ) if custom_logger: ( model, messages, non_default_params, ) = custom_logger.get_chat_completion_prompt( model=model, messages=messages, non_default_params=non_default_params or {}, prompt_id=prompt_id, prompt_spec=prompt_spec, prompt_variables=prompt_variables, dynamic_callback_params=self.standard_callback_dynamic_params, prompt_label=prompt_label, prompt_version=prompt_version, ) self.messages = messages return model, messages, non_default_params async def async_get_chat_completion_prompt( self, model: str, messages: List[AllMessageValues], non_default_params: Dict, prompt_variables: Optional[dict], prompt_id: Optional[str] = None, prompt_spec: Optional[PromptSpec] = None, prompt_management_logger: Optional[CustomLogger] = None, tools: Optional[List[Dict]] = None, prompt_label: Optional[str] = None, prompt_version: Optional[int] = None, ) -> Tuple[str, List[AllMessageValues], dict]: custom_logger = ( prompt_management_logger or self.get_custom_logger_for_prompt_management( model=model, tools=tools, non_default_params=non_default_params, prompt_id=prompt_id, prompt_spec=prompt_spec, dynamic_callback_params=self.standard_callback_dynamic_params, ) ) if custom_logger: ( model, messages, non_default_params, ) = await custom_logger.async_get_chat_completion_prompt( model=model, messages=messages, non_default_params=non_default_params or {}, prompt_id=prompt_id, prompt_spec=prompt_spec, prompt_variables=prompt_variables, dynamic_callback_params=self.standard_callback_dynamic_params, litellm_logging_obj=self, tools=tools, prompt_label=prompt_label, prompt_version=prompt_version, ) self.messages = messages return model, messages, non_default_params def _auto_detect_prompt_management_logger( self, prompt_id: str, prompt_spec: Optional[PromptSpec], dynamic_callback_params: StandardCallbackDynamicParams, ) -> Optional[CustomLogger]: """ Auto-detect which prompt management system owns the given prompt_id. This allows a user to just pass prompt_id in the completion call and it will be auto-detected which system owns this prompt. Args: prompt_id: The prompt ID to check dynamic_callback_params: Dynamic callback parameters for should_run_prompt_management checks Returns: A CustomLogger instance if a matching prompt management system is found, None otherwise """ prompt_management_loggers = ( litellm.logging_callback_manager.get_custom_loggers_for_type( callback_type=CustomPromptManagement ) ) for logger in prompt_management_loggers: if isinstance(logger, CustomPromptManagement): try: if logger.should_run_prompt_management( prompt_id=prompt_id, prompt_spec=prompt_spec, dynamic_callback_params=dynamic_callback_params, ): self.model_call_details["prompt_integration"] = ( logger.__class__.__name__ ) return logger except Exception: # If check fails, continue to next logger continue return None def get_custom_logger_for_prompt_management( self, model: str, non_default_params: Dict, tools: Optional[List[Dict]] = None, prompt_id: Optional[str] = None, prompt_spec: Optional[PromptSpec] = None, dynamic_callback_params: Optional[StandardCallbackDynamicParams] = None, ) -> Optional[CustomLogger]: """ Get a custom logger for prompt management based on model name or available callbacks. Args: model: The model name to check for prompt management integration non_default_params: Non-default parameters passed to the completion call tools: Optional tools passed to the completion call prompt_id: Optional prompt ID to auto-detect which system owns this prompt dynamic_callback_params: Dynamic callback parameters for should_run_prompt_management checks Returns: A CustomLogger instance if one is found, None otherwise """ # First check if model starts with a known custom logger compatible callback # This takes precedence for backward compatibility for callback_name in litellm._known_custom_logger_compatible_callbacks: if model.startswith(callback_name): custom_logger = _init_custom_logger_compatible_class( logging_integration=callback_name, internal_usage_cache=None, llm_router=None, ) if custom_logger is not None: self.model_call_details["prompt_integration"] = model.split("/")[0] return custom_logger # If prompt_id is provided, try to auto-detect which system has this prompt if prompt_id and dynamic_callback_params is not None: auto_detected_logger = self._auto_detect_prompt_management_logger( prompt_id=prompt_id, prompt_spec=prompt_spec, dynamic_callback_params=dynamic_callback_params, ) if auto_detected_logger is not None: return auto_detected_logger # Then check for any registered CustomPromptManagement loggers (fallback) prompt_management_loggers = ( litellm.logging_callback_manager.get_custom_loggers_for_type( callback_type=CustomPromptManagement ) ) if prompt_management_loggers: logger = prompt_management_loggers[0] self.model_call_details["prompt_integration"] = logger.__class__.__name__ return logger if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook( non_default_params ): self.model_call_details["prompt_integration"] = ( anthropic_cache_control_logger.__class__.__name__ ) return anthropic_cache_control_logger ######################################################### # Vector Store / Knowledge Base hooks ######################################################### if litellm.vector_store_registry is not None: vector_store_custom_logger = _init_custom_logger_compatible_class( logging_integration="vector_store_pre_call_hook", internal_usage_cache=None, llm_router=None, ) self.model_call_details["prompt_integration"] = ( vector_store_custom_logger.__class__.__name__ ) # Add to global callbacks so post-call hooks are invoked if ( vector_store_custom_logger and vector_store_custom_logger not in litellm.callbacks ): litellm.logging_callback_manager.add_litellm_callback( vector_store_custom_logger ) return vector_store_custom_logger return None def get_custom_logger_for_anthropic_cache_control_hook( self, non_default_params: Dict ) -> Optional[CustomLogger]: if non_default_params.get("cache_control_injection_points", None): custom_logger = _init_custom_logger_compatible_class( logging_integration="anthropic_cache_control_hook", internal_usage_cache=None, llm_router=None, ) return custom_logger return None def _get_raw_request_body(self, data: Optional[Union[dict, str]]) -> dict: if data is None: return {"error": "Received empty dictionary for raw request body"} if isinstance(data, str): try: return json.loads(data) except Exception: return { "error": "Unable to parse raw request body. Got - {}".format(data) } return data def _get_masked_api_base(self, api_base: str) -> str: if "key=" in api_base: # Find the position of "key=" in the string key_index = api_base.find("key=") + 4 # Mask the last 5 characters after "key=" masked_api_base = api_base[:key_index] + "*" * 5 + api_base[-4:] else: masked_api_base = api_base return str(masked_api_base) def _pre_call(self, input, api_key, model=None, additional_args={}): """ Common helper function across the sync + async pre-call function """ self.model_call_details["input"] = input self.model_call_details["api_key"] = api_key self.model_call_details["additional_args"] = additional_args self.model_call_details["log_event_type"] = "pre_api_call" if ( model ): # if model name was changes pre-call, overwrite the initial model call name with the new one self.model_call_details["model"] = model self.model_call_details["litellm_params"]["api_base"] = ( self._get_masked_api_base(additional_args.get("api_base", "")) ) def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915 # Log the exact input to the LLM API litellm.error_logs["PRE_CALL"] = locals() try: self._pre_call( input=input, api_key=api_key, model=model, additional_args=additional_args, ) # User Logging -> if you pass in a custom logging function self._print_llm_call_debugging_log( api_base=additional_args.get("api_base", ""), headers=additional_args.get("headers", {}), additional_args=additional_args, ) # log raw request to provider (like LangFuse) -- if opted in. if ( self.log_raw_request_response is True or log_raw_request_response is True ): _litellm_params = self.model_call_details.get("litellm_params", {}) _metadata = _litellm_params.get("metadata", {}) or {} try: # [Non-blocking Extra Debug Information in metadata] if turn_off_message_logging is True: _metadata["raw_request"] = ( "redacted by litellm. \ 'litellm.turn_off_message_logging=True'" ) else: curl_command = self._get_request_curl_command( api_base=additional_args.get("api_base", ""), headers=additional_args.get("headers", {}), additional_args=additional_args, data=additional_args.get("complete_input_dict", {}), ) _metadata["raw_request"] = str(curl_command) # split up, so it's easier to parse in the UI self.model_call_details["raw_request_typed_dict"] = ( RawRequestTypedDict( raw_request_api_base=str( additional_args.get("api_base") or "" ), raw_request_body=self._get_raw_request_body( additional_args.get("complete_input_dict", {}) ), # NOTE: setting ignore_sensitive_headers to True will cause # the Authorization header to be leaked when calls to the health # endpoint are made and fail. raw_request_headers=self._get_masked_headers( additional_args.get("headers", {}) or {}, ), error=None, ) ) except Exception as e: self.model_call_details["raw_request_typed_dict"] = ( RawRequestTypedDict( error=str(e), ) ) _metadata["raw_request"] = ( "Unable to Log \ raw request: {}".format( str(e) ) ) if getattr(self, "logger_fn", None) and callable(self.logger_fn): try: self.logger_fn( self.model_call_details ) # Expectation: any logger function passed in by the user should accept a dict object except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( str(e) ) ) self.model_call_details["api_call_start_time"] = datetime.datetime.now() # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made callbacks = litellm.input_callback + (self.dynamic_input_callbacks or []) for callback in callbacks: try: if callback == "supabase" and supabaseClient is not None: verbose_logger.debug("reaches supabase for logging!") model = self.model_call_details["model"] messages = self.model_call_details["input"] verbose_logger.debug(f"supabaseClient: {supabaseClient}") supabaseClient.input_log_event( model=model, messages=messages, end_user=self.model_call_details.get("user", "default"), litellm_call_id=self.litellm_params["litellm_call_id"], print_verbose=print_verbose, ) elif callback == "sentry" and add_breadcrumb: try: details_to_log = copy.deepcopy(self.model_call_details) except Exception: details_to_log = self.model_call_details if litellm.turn_off_message_logging: # make a copy of the _model_Call_details and log it details_to_log.pop("messages", None) details_to_log.pop("input", None) details_to_log.pop("prompt", None) add_breadcrumb( category="litellm.llm_call", message=f"Model Call Details pre-call: {details_to_log}", level="info", ) elif isinstance(callback, CustomLogger): # custom logger class callback.log_pre_api_call( model=self.model, messages=self.messages, kwargs=self.model_call_details, ) elif ( callable(callback) and customLogger is not None ): # custom logger functions customLogger.log_input_event( model=self.model, messages=self.messages, kwargs=self.model_call_details, print_verbose=print_verbose, callback_func=callback, ) except Exception as e: verbose_logger.exception( "litellm.Logging.pre_call(): Exception occured - {}".format( str(e) ) ) verbose_logger.debug( f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" ) if capture_exception: # log this error to sentry for debugging capture_exception(e) except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( str(e) ) ) verbose_logger.error( f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" ) if capture_exception: # log this error to sentry for debugging capture_exception(e) def _print_llm_call_debugging_log( self, api_base: str, headers: dict, additional_args: dict, ): """ Internal debugging helper function Prints the RAW curl command sent from LiteLLM """ if _is_debugging_on() or self.litellm_request_debug: if json_logs: masked_headers = self._get_masked_headers(headers) if self.litellm_request_debug: verbose_logger.warning( # .warning ensures this shows up in all environments "POST Request Sent from LiteLLM", extra={"api_base": {api_base}, **masked_headers}, ) else: verbose_logger.debug( "POST Request Sent from LiteLLM", extra={"api_base": {api_base}, **masked_headers}, ) else: headers = additional_args.get("headers", {}) if headers is None: headers = {} data = additional_args.get("complete_input_dict", {}) api_base = str(additional_args.get("api_base", "")) curl_command = self._get_request_curl_command( api_base=api_base, headers=headers, additional_args=additional_args, data=data, ) if self.litellm_request_debug: verbose_logger.warning( f"\033[92m{curl_command}\033[0m\n" ) # .warning ensures this shows up in all environments else: verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") def _get_request_body(self, data: dict) -> str: return str(data) def _get_request_curl_command( self, api_base: str, headers: Optional[dict], additional_args: dict, data: dict ) -> str: masked_api_base = self._get_masked_api_base(api_base) if headers is None: headers = {} curl_command = "\n\nPOST Request Sent from LiteLLM:\n" curl_command += "curl -X POST \\\n" curl_command += f"{masked_api_base} \\\n" masked_headers = self._get_masked_headers(headers) formatted_headers = " ".join( [f"-H '{k}: {v}'" for k, v in masked_headers.items()] ) curl_command += ( f"{formatted_headers} \\\n" if formatted_headers.strip() != "" else "" ) curl_command += f"-d '{self._get_request_body(data)}'\n" if additional_args.get("request_str", None) is not None: # print the sagemaker / bedrock client request curl_command = "\nRequest Sent from LiteLLM:\n" request_str = additional_args.get("request_str", "") curl_command += request_str elif api_base == "": curl_command = str(self.model_call_details) return curl_command def _get_masked_headers( self, headers: dict, ignore_sensitive_headers: bool = False ) -> dict: """ Internal debugging helper function Masks the headers of the request sent from LiteLLM """ return _get_masked_values( headers, ignore_sensitive_values=ignore_sensitive_headers ) def post_call( self, original_response, input=None, api_key=None, additional_args={} ): # Log the exact result from the LLM API, for streaming - log the type of response received litellm.error_logs["POST_CALL"] = locals() if isinstance(original_response, dict): original_response = json.dumps(original_response) try: self.model_call_details["input"] = input self.model_call_details["api_key"] = api_key self.model_call_details["original_response"] = original_response self.model_call_details["additional_args"] = additional_args self.model_call_details["log_event_type"] = "post_api_call" if self.litellm_request_debug: attr = "warning" else: attr = "debug" if json_logs: callattr = getattr(verbose_logger, attr) callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details ) ), ) else: callattr = getattr(verbose_logger, attr) callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details ) ) ) if getattr(self, "logger_fn", None) and callable(self.logger_fn): try: self.logger_fn( self.model_call_details ) # Expectation: any logger function passed in by the user should accept a dict object except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( str(e) ) ) original_response = redact_message_input_output_from_logging( model_call_details=( self.model_call_details if hasattr(self, "model_call_details") else {} ), result=original_response, ) # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made callbacks = litellm.input_callback + (self.dynamic_input_callbacks or []) for callback in callbacks: try: if callback == "sentry" and add_breadcrumb: verbose_logger.debug("reaches sentry breadcrumbing") try: details_to_log = copy.deepcopy(self.model_call_details) except Exception: details_to_log = self.model_call_details if litellm.turn_off_message_logging: # make a copy of the _model_Call_details and log it details_to_log.pop("messages", None) details_to_log.pop("input", None) details_to_log.pop("prompt", None) add_breadcrumb( category="litellm.llm_call", message=f"Model Call Details post-call: {details_to_log}", level="info", ) elif isinstance(callback, CustomLogger): # custom logger class callback.log_post_api_call( kwargs=self.model_call_details, response_obj=None, start_time=self.start_time, end_time=None, ) except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {}".format( str(e) ) ) verbose_logger.debug( f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" ) if capture_exception: # log this error to sentry for debugging capture_exception(e) except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( str(e) ) ) async def async_post_mcp_tool_call_hook( self, kwargs: dict, response_obj: Any, start_time: datetime.datetime, end_time: datetime.datetime, ): """ Post MCP Tool Call Hook Use this to modify the MCP tool call response before it is returned to the user. """ from litellm.types.llms.base import HiddenParams from litellm.types.mcp import MCPPostCallResponseObject callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_success_callbacks, global_callbacks=litellm.success_callback, ) post_mcp_tool_call_response_obj: MCPPostCallResponseObject = ( MCPPostCallResponseObject( mcp_tool_call_response=response_obj, hidden_params=HiddenParams() ) ) for callback in callbacks: try: if isinstance(callback, CustomLogger): response: Optional[MCPPostCallResponseObject] = ( await callback.async_post_mcp_tool_call_hook( kwargs=kwargs, response_obj=post_mcp_tool_call_response_obj, start_time=start_time, end_time=end_time, ) ) ###################################################################### # if any of the callbacks modify the response, use the modified response # current implementation returns the first modified response ###################################################################### if response is not None: response_obj = self._parse_post_mcp_call_hook_response( response=response ) except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( str(e) ) ) return response_obj def _parse_post_mcp_call_hook_response( self, response: Optional[MCPPostCallResponseObject] ) -> Any: """ Parse the response from the post_mcp_tool_call_hook 1. Unpack the mcp_tool_call_response 2. save the updated response_cost to the model_call_details """ if response is None: return None self.model_call_details["response_cost"] = response.hidden_params.response_cost return response.mcp_tool_call_response def get_response_ms(self) -> float: return ( self.model_call_details.get("end_time", datetime.datetime.now()) - self.model_call_details.get("start_time", datetime.datetime.now()) ).total_seconds() * 1000 def set_cost_breakdown( self, input_cost: float, output_cost: float, total_cost: float, cost_for_built_in_tools_cost_usd_dollar: float, additional_costs: Optional[dict] = None, original_cost: Optional[float] = None, discount_percent: Optional[float] = None, discount_amount: Optional[float] = None, margin_percent: Optional[float] = None, margin_fixed_amount: Optional[float] = None, margin_total_amount: Optional[float] = None, ) -> None: """ Helper method to store cost breakdown in the logging object. Args: input_cost: Cost of input/prompt tokens output_cost: Cost of output/completion tokens cost_for_built_in_tools_cost_usd_dollar: Cost of built-in tools total_cost: Total cost of request additional_costs: Free-form additional costs dict (e.g., {"azure_model_router_flat_cost": 0.00014}) original_cost: Cost before discount discount_percent: Discount percentage (0.05 = 5%) discount_amount: Discount amount in USD margin_percent: Margin percentage applied (0.10 = 10%) margin_fixed_amount: Fixed margin amount in USD margin_total_amount: Total margin added in USD """ self.cost_breakdown = CostBreakdown( input_cost=input_cost, output_cost=output_cost, total_cost=total_cost, tool_usage_cost=cost_for_built_in_tools_cost_usd_dollar, ) # Store additional costs if provided (free-form dict for extensibility) if ( additional_costs and isinstance(additional_costs, dict) and len(additional_costs) > 0 ): self.cost_breakdown["additional_costs"] = additional_costs # Store discount information if provided if original_cost is not None: self.cost_breakdown["original_cost"] = original_cost if discount_percent is not None: self.cost_breakdown["discount_percent"] = discount_percent if discount_amount is not None: self.cost_breakdown["discount_amount"] = discount_amount # Store margin information if provided if margin_percent is not None: self.cost_breakdown["margin_percent"] = margin_percent if margin_fixed_amount is not None: self.cost_breakdown["margin_fixed_amount"] = margin_fixed_amount if margin_total_amount is not None: self.cost_breakdown["margin_total_amount"] = margin_total_amount def _response_cost_calculator( self, result: Union[ ModelResponse, ModelResponseStream, EmbeddingResponse, ImageResponse, TranscriptionResponse, TextCompletionResponse, HttpxBinaryResponseContent, RerankResponse, Batch, FineTuningJob, ResponsesAPIResponse, ResponseCompletedEvent, OpenAIFileObject, LiteLLMRealtimeStreamLoggingObject, OpenAIModerationResponse, "SearchResponse", ], cache_hit: Optional[bool] = None, litellm_model_name: Optional[str] = None, router_model_id: Optional[str] = None, ) -> Optional[float]: """ Calculate response cost using result + logging object variables. used for consistent cost calculation across response headers + logging integrations. """ if cache_hit is None: cache_hit = self.model_call_details.get("cache_hit", False) if cache_hit is True: return 0.0 if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"): hidden_params = getattr(result, "_hidden_params", {}) if ( "response_cost" in hidden_params and hidden_params["response_cost"] is not None ): # use cost if already calculated return hidden_params["response_cost"] elif ( router_model_id is None and "model_id" in hidden_params ): # use model_id if not already set router_model_id = hidden_params["model_id"] ## RESPONSE COST ## custom_pricing = use_custom_pricing_for_model( litellm_params=( self.litellm_params if hasattr(self, "litellm_params") else None ) ) prompt = "" # use for tts cost calc _input = self.model_call_details.get("input", None) if _input is not None and isinstance(_input, str): prompt = _input if cache_hit is None: cache_hit = self.model_call_details.get("cache_hit", False) try: response_cost_calculator_kwargs = { "response_object": result, "model": litellm_model_name or self.model, "cache_hit": cache_hit, "custom_llm_provider": self.model_call_details.get( "custom_llm_provider", None ), "base_model": _get_base_model_from_metadata( model_call_details=self.model_call_details ), "call_type": self.call_type, "optional_params": self.optional_params, "custom_pricing": custom_pricing, "prompt": prompt, "standard_built_in_tools_params": self.standard_built_in_tools_params, "router_model_id": router_model_id, "litellm_logging_obj": self, "service_tier": ( self.optional_params.get("service_tier") if self.optional_params else None ), } except Exception as e: # error creating kwargs for cost calculation debug_info = StandardLoggingModelCostFailureDebugInformation( error_str=str(e), traceback_str=_get_traceback_str_for_error(str(e)), ) verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) self.model_call_details["response_cost_failure_debug_information"] = ( debug_info ) return None try: response_cost = litellm.response_cost_calculator( **response_cost_calculator_kwargs ) verbose_logger.debug(f"response_cost: {response_cost}") return response_cost except Exception as e: # error calculating cost debug_info = StandardLoggingModelCostFailureDebugInformation( error_str=str(e), traceback_str=_get_traceback_str_for_error(str(e)), model=response_cost_calculator_kwargs["model"], cache_hit=response_cost_calculator_kwargs["cache_hit"], custom_llm_provider=response_cost_calculator_kwargs[ "custom_llm_provider" ], base_model=response_cost_calculator_kwargs["base_model"], call_type=response_cost_calculator_kwargs["call_type"], custom_pricing=response_cost_calculator_kwargs["custom_pricing"], ) verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) self.model_call_details["response_cost_failure_debug_information"] = ( debug_info ) return None async def _response_cost_calculator_async( self, result: Union[ ModelResponse, ModelResponseStream, EmbeddingResponse, ImageResponse, TranscriptionResponse, TextCompletionResponse, HttpxBinaryResponseContent, RerankResponse, Batch, FineTuningJob, ], cache_hit: Optional[bool] = None, ) -> Optional[float]: return self._response_cost_calculator(result=result, cache_hit=cache_hit) def should_run_logging( self, event_type: Literal[ "async_success", "sync_success", "async_failure", "sync_failure" ], stream: bool = False, ) -> bool: try: if self.model_call_details.get(f"has_logged_{event_type}", False) is True: return False return True except Exception: return True def has_run_logging( self, event_type: Literal[ "async_success", "sync_success", "async_failure", "sync_failure" ], ) -> None: if self.stream is not None and self.stream is True: """ Ignore check on stream, as there can be multiple chunks """ return self.model_call_details[f"has_logged_{event_type}"] = True return def should_run_callback( self, callback: litellm.CALLBACK_TYPES, litellm_params: dict, event_hook: str ) -> bool: if litellm.global_disable_no_log_param: return True if litellm_params.get("no-log", False) is True: # proxy cost tracking cal backs should run if not ( isinstance(callback, CustomLogger) and "_PROXY_" in callback.__class__.__name__ ): verbose_logger.debug( f"no-log request, skipping logging for {event_hook} event" ) return False # Check for dynamically disabled callbacks via headers if ( EnterpriseCallbackControls is not None and EnterpriseCallbackControls.is_callback_disabled_dynamically( callback=callback, litellm_params=litellm_params, standard_callback_dynamic_params=self.standard_callback_dynamic_params, ) ): verbose_logger.debug( f"Callback {callback} disabled via x-litellm-disable-callbacks header for {event_hook} event" ) return False return True def _update_completion_start_time(self, completion_start_time: datetime.datetime): self.completion_start_time = completion_start_time self.model_call_details["completion_start_time"] = self.completion_start_time def normalize_logging_result(self, result: Any) -> Any: """ Some endpoints return a different type of result than what is expected by the logging system. This function is used to normalize the result to the expected type. """ logging_result = result if self.call_type == CallTypes.arealtime.value and isinstance(result, list): combined_usage_object = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results( results=result ) logging_result = ( RealtimeAPITokenUsageProcessor.create_logging_realtime_object( usage=combined_usage_object, results=result, ) ) elif ( self.call_type == CallTypes.llm_passthrough_route.value or self.call_type == CallTypes.allm_passthrough_route.value ) and isinstance(result, Response): from litellm.utils import ProviderConfigManager provider_config = ProviderConfigManager.get_provider_passthrough_config( provider=self.model_call_details.get("custom_llm_provider", ""), model=self.model, ) if provider_config is not None: logging_result = provider_config.logging_non_streaming_response( model=self.model, custom_llm_provider=self.model_call_details.get( "custom_llm_provider", "" ), httpx_response=result, request_data=self.model_call_details.get("request_data", {}), logging_obj=self, endpoint=self.model_call_details.get("endpoint", ""), ) return logging_result def _process_hidden_params_and_response_cost( self, logging_result, start_time, end_time, ): hidden_params = getattr(logging_result, "_hidden_params", {}) if hidden_params: if self.model_call_details.get("litellm_params") is not None: self.model_call_details["litellm_params"].setdefault("metadata", {}) if self.model_call_details["litellm_params"]["metadata"] is None: self.model_call_details["litellm_params"]["metadata"] = {} self.model_call_details["litellm_params"]["metadata"]["hidden_params"] = getattr(logging_result, "_hidden_params", {}) # type: ignore if self.model_call_details.get("cache_hit") is True: self.model_call_details["response_cost"] = 0.0 elif "response_cost" in hidden_params: self.model_call_details["response_cost"] = hidden_params["response_cost"] elif self.model_call_details.get("response_cost") is not None: # Preserve response_cost if already calculated (e.g., by pass-through # handlers like Gemini/Vertex which call completion_cost directly) pass else: self.model_call_details["response_cost"] = self._response_cost_calculator( result=logging_result ) self.model_call_details["standard_logging_object"] = ( self._build_standard_logging_payload(logging_result, start_time, end_time) ) if ( standard_logging_payload := self.model_call_details.get( "standard_logging_object" ) ) is not None: emit_standard_logging_payload(standard_logging_payload) def _build_standard_logging_payload( self, init_response_obj: Any, start_time: Any, end_time: Any ) -> Any: """Build StandardLoggingPayload and accumulate its construction time.""" _start = time.time() payload = get_standard_logging_object_payload( kwargs=self.model_call_details, init_response_obj=init_response_obj, start_time=start_time, end_time=end_time, logging_obj=self, status="success", standard_built_in_tools_params=self.standard_built_in_tools_params, ) self.callback_duration_ms += (time.time() - _start) * 1000 return payload def _transform_usage_objects(self, result): if isinstance(result, ResponsesAPIResponse): result = result.model_copy() transformed_usage = ( ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( result.usage ) ) setattr(result, "usage", transformed_usage) if ( standard_logging_payload := self.model_call_details.get( "standard_logging_object" ) ) is not None: response_dict = ( result.model_dump() if hasattr(result, "model_dump") else dict(result) ) # Ensure usage is properly included with transformed chat format if transformed_usage is not None: response_dict["usage"] = ( transformed_usage.model_dump() if hasattr(transformed_usage, "model_dump") else dict(transformed_usage) ) standard_logging_payload["response"] = response_dict elif isinstance(result, TranscriptionResponse): from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( TranscriptionUsageObjectTransformation, ) result = result.model_copy() transformed_usage = TranscriptionUsageObjectTransformation.transform_transcription_usage_object(result.usage) # type: ignore setattr(result, "usage", transformed_usage) return result def _success_handler_helper_fn( self, result=None, start_time=None, end_time=None, cache_hit=None, standard_logging_object: Optional[StandardLoggingPayload] = None, ): try: if start_time is None: start_time = self.start_time if end_time is None: end_time = datetime.datetime.now() if self.completion_start_time is None: self.completion_start_time = end_time self.model_call_details["completion_start_time"] = ( self.completion_start_time ) self.model_call_details["log_event_type"] = "successful_api_call" self.model_call_details["end_time"] = end_time self.model_call_details["cache_hit"] = cache_hit if self.call_type == CallTypes.anthropic_messages.value: result = self._handle_anthropic_messages_response_logging(result=result) elif ( self.call_type == CallTypes.generate_content.value or self.call_type == CallTypes.agenerate_content.value ): result = self._handle_non_streaming_google_genai_generate_content_response_logging( result=result ) elif ( self.call_type == CallTypes.asend_message.value or self.call_type == CallTypes.send_message.value ): result = self._handle_a2a_response_logging(result=result) logging_result = self.normalize_logging_result(result=result) if ( standard_logging_object is None and result is not None and self.stream is not True ): if self._is_recognized_call_type_for_logging( logging_result=logging_result ): self._process_hidden_params_and_response_cost( logging_result=logging_result, start_time=start_time, end_time=end_time, ) elif isinstance(result, dict) or isinstance(result, list): self.model_call_details["standard_logging_object"] = ( self._build_standard_logging_payload( result, start_time, end_time ) ) if ( standard_logging_payload := self.model_call_details.get( "standard_logging_object" ) ) is not None: emit_standard_logging_payload(standard_logging_payload) elif standard_logging_object is not None: self.model_call_details["standard_logging_object"] = ( standard_logging_object ) else: self.model_call_details["response_cost"] = None result = self._transform_usage_objects(result=result) if ( litellm.max_budget and self.stream is False and result is not None and isinstance(result, dict) and "content" in result ): time_diff = (end_time - start_time).total_seconds() float_diff = float(time_diff) litellm._current_cost += litellm.completion_cost( model=self.model, prompt="", completion=getattr(result, "content", ""), total_time=float_diff, standard_built_in_tools_params=self.standard_built_in_tools_params, ) return start_time, end_time, result except Exception as e: raise Exception(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}") def _is_recognized_call_type_for_logging( self, logging_result: Any, ): """ Returns True if the call type is recognized for logging (eg. ModelResponse, ModelResponseStream, etc.) """ if ( isinstance(logging_result, ModelResponse) or isinstance(logging_result, ModelResponseStream) or isinstance(logging_result, EmbeddingResponse) or isinstance(logging_result, ImageResponse) or isinstance(logging_result, TranscriptionResponse) or isinstance(logging_result, TextCompletionResponse) or isinstance(logging_result, HttpxBinaryResponseContent) # tts or isinstance(logging_result, RerankResponse) or isinstance(logging_result, FineTuningJob) or isinstance(logging_result, LiteLLMBatch) or isinstance(logging_result, ResponsesAPIResponse) or isinstance(logging_result, OpenAIFileObject) or isinstance(logging_result, LiteLLMRealtimeStreamLoggingObject) or isinstance(logging_result, OpenAIModerationResponse) or isinstance(logging_result, OCRResponse) # OCR or isinstance(logging_result, SearchResponse) # Search API or isinstance(logging_result, dict) and logging_result.get("object") == "vector_store.search_results.page" or isinstance(logging_result, dict) and logging_result.get("object") == "search" # Search API (dict format) or isinstance(logging_result, VideoObject) or isinstance(logging_result, ContainerObject) or isinstance(logging_result, LiteLLMSendMessageResponse) # A2A or (self.call_type == CallTypes.call_mcp_tool.value) ): return True return False def _flush_passthrough_collected_chunks_helper( self, raw_bytes: List[bytes], provider_config: "BasePassthroughConfig", ) -> Optional["CostResponseTypes"]: all_chunks = provider_config._convert_raw_bytes_to_str_lines(raw_bytes) complete_streaming_response = provider_config.handle_logging_collected_chunks( all_chunks=all_chunks, litellm_logging_obj=self, model=self.model, custom_llm_provider=self.model_call_details.get("custom_llm_provider", ""), endpoint=self.model_call_details.get("endpoint", ""), ) return complete_streaming_response def flush_passthrough_collected_chunks( self, raw_bytes: List[bytes], provider_config: "BasePassthroughConfig", ): """ Flush collected chunks from the logging object This is used to log the collected chunks once streaming is done on passthrough endpoints 1. Decode the raw bytes to string lines 2. Get the complete streaming response from the provider config 3. Log the complete streaming response (trigger success handler) This is used for passthrough endpoints """ complete_streaming_response = self._flush_passthrough_collected_chunks_helper( raw_bytes=raw_bytes, provider_config=provider_config, ) if complete_streaming_response is not None: self.success_handler(result=complete_streaming_response) return async def async_flush_passthrough_collected_chunks( self, raw_bytes: List[bytes], provider_config: "BasePassthroughConfig", ): complete_streaming_response = self._flush_passthrough_collected_chunks_helper( raw_bytes=raw_bytes, provider_config=provider_config, ) if complete_streaming_response is not None: await self.async_success_handler(result=complete_streaming_response) return def success_handler( # noqa: PLR0915 self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs ): verbose_logger.debug( f"Logging Details LiteLLM-Success Call: Cache_hit={cache_hit}" ) if not self.should_run_logging( event_type="sync_success" ): # prevent double logging return start_time, end_time, result = self._success_handler_helper_fn( start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit, standard_logging_object=kwargs.get("standard_logging_object", None), ) litellm_params = self.model_call_details.get("litellm_params", {}) is_sync_request = ( litellm_params.get(CallTypes.acompletion.value, False) is not True and litellm_params.get(CallTypes.aresponses.value, False) is not True and litellm_params.get(CallTypes.aembedding.value, False) is not True and litellm_params.get(CallTypes.aimage_generation.value, False) is not True and litellm_params.get(CallTypes.atranscription.value, False) is not True ) try: ## BUILD COMPLETE STREAMED RESPONSE complete_streaming_response: Optional[ Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse] ] = None if "complete_streaming_response" in self.model_call_details: return # break out of this. complete_streaming_response = self._get_assembled_streaming_response( result=result, start_time=start_time, end_time=end_time, is_async=False, streaming_chunks=self.sync_streaming_chunks, ) if complete_streaming_response is not None: verbose_logger.debug( "Logging Details LiteLLM-Success Call streaming complete" ) self.model_call_details["complete_streaming_response"] = ( complete_streaming_response ) self.model_call_details["response_cost"] = ( self._response_cost_calculator(result=complete_streaming_response) ) ## STANDARDIZED LOGGING PAYLOAD self.model_call_details["standard_logging_object"] = ( self._build_standard_logging_payload( complete_streaming_response, start_time, end_time ) ) if ( standard_logging_payload := self.model_call_details.get( "standard_logging_object" ) ) is not None: # Only emit for sync requests (async_success_handler handles async) if is_sync_request: emit_standard_logging_payload(standard_logging_payload) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_success_callbacks, global_callbacks=litellm.success_callback, ) ## REDACT MESSAGES ## result = redact_message_input_output_from_logging( model_call_details=( self.model_call_details if hasattr(self, "model_call_details") else {} ), result=result, ) ## LOGGING HOOK ## for callback in callbacks: if isinstance(callback, CustomGuardrail): from litellm.types.guardrails import GuardrailEventHooks if ( callback.should_run_guardrail( data=self.model_call_details, event_type=GuardrailEventHooks.logging_only, ) is not True ): continue self.model_call_details, result = callback.logging_hook( kwargs=self.model_call_details, result=result, call_type=self.call_type, ) elif isinstance(callback, CustomLogger): self.model_call_details, result = callback.logging_hook( kwargs=self.model_call_details, result=result, call_type=self.call_type, ) self.has_run_logging(event_type="sync_success") for callback in callbacks: try: should_run = self.should_run_callback( callback=callback, litellm_params=litellm_params, event_hook="success_handler", ) if not should_run: continue if callback == "promptlayer" and promptLayerLogger is not None: print_verbose("reaches promptlayer for logging!") promptLayerLogger.log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) if callback == "supabase" and supabaseClient is not None: print_verbose("reaches supabase for logging!") kwargs = self.model_call_details # this only logs streaming once, complete_streaming_response exists i.e when stream ends if self.stream: if "complete_streaming_response" not in kwargs: continue else: print_verbose("reaches supabase for streaming logging!") result = kwargs["complete_streaming_response"] model = kwargs["model"] messages = kwargs["messages"] optional_params = kwargs.get("optional_params", {}) litellm_params = kwargs.get("litellm_params", {}) supabaseClient.log_event( model=model, messages=messages, end_user=optional_params.get("user", "default"), response_obj=result, start_time=start_time, end_time=end_time, litellm_call_id=( current_call_id if ( current_call_id := litellm_params.get( "litellm_call_id" ) ) is not None else str(uuid.uuid4()) ), print_verbose=print_verbose, ) if callback == "wandb" and weightsBiasesLogger is not None: print_verbose("reaches wandb for logging!") weightsBiasesLogger.log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) if callback == "logfire" and logfireLogger is not None: verbose_logger.debug("reaches logfire for success logging!") kwargs = {} for k, v in self.model_call_details.items(): if ( k != "original_response" ): # copy.deepcopy raises errors as this could be a coroutine kwargs[k] = v # this only logs streaming once, complete_streaming_response exists i.e when stream ends if self.stream: if "complete_streaming_response" not in kwargs: continue else: print_verbose("reaches logfire for streaming logging!") result = kwargs["complete_streaming_response"] logfireLogger.log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, level=LogfireLevel.INFO.value, # type: ignore ) if callback == "lunary" and lunaryLogger is not None: print_verbose("reaches lunary for logging!") model = self.model kwargs = self.model_call_details input = kwargs.get("messages", kwargs.get("input", None)) type = ( "embed" if self.call_type == CallTypes.embedding.value else "llm" ) # this only logs streaming once, complete_streaming_response exists i.e when stream ends if self.stream: if "complete_streaming_response" not in kwargs: continue else: result = kwargs["complete_streaming_response"] lunaryLogger.log_event( type=type, kwargs=kwargs, event="end", model=model, input=input, user_id=kwargs.get("user", None), # user_props=self.model_call_details.get("user_props", None), extra=kwargs.get("optional_params", {}), response_obj=result, start_time=start_time, end_time=end_time, run_id=self.litellm_call_id, print_verbose=print_verbose, ) if callback == "helicone" and heliconeLogger is not None: print_verbose("reaches helicone for logging!") model = self.model messages = self.model_call_details["input"] kwargs = self.model_call_details # this only logs streaming once, complete_streaming_response exists i.e when stream ends if self.stream: if "complete_streaming_response" not in kwargs: continue else: print_verbose("reaches helicone for streaming logging!") result = kwargs["complete_streaming_response"] heliconeLogger.log_success( model=model, messages=messages, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, kwargs=kwargs, ) if callback == "langfuse": global langFuseLogger print_verbose("reaches langfuse for success logging!") kwargs = {} for k, v in self.model_call_details.items(): if ( k != "original_response" ): # copy.deepcopy raises errors as this could be a coroutine kwargs[k] = v # this only logs streaming once, complete_streaming_response exists i.e when stream ends if self.stream: verbose_logger.debug( f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}" ) if complete_streaming_response is None: continue else: print_verbose("reaches langfuse for streaming logging!") result = kwargs["complete_streaming_response"] langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( globalLangfuseLogger=langFuseLogger, standard_callback_dynamic_params=self.standard_callback_dynamic_params, in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, ) if langfuse_logger_to_use is not None: _response = langfuse_logger_to_use.log_event_on_langfuse( kwargs=kwargs, response_obj=result, start_time=start_time, end_time=end_time, user_id=kwargs.get("user", None), ) if _response is not None and isinstance(_response, dict): _trace_id = _response.get("trace_id", None) if _trace_id is not None: in_memory_trace_id_cache.set_cache( litellm_call_id=self.litellm_call_id, service_name="langfuse", trace_id=_trace_id, ) if callback == "greenscale" and greenscaleLogger is not None: kwargs = {} for k, v in self.model_call_details.items(): if ( k != "original_response" ): # copy.deepcopy raises errors as this could be a coroutine kwargs[k] = v # this only logs streaming once, complete_streaming_response exists i.e when stream ends if self.stream: verbose_logger.debug( f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}" ) if complete_streaming_response is None: continue else: print_verbose( "reaches greenscale for streaming logging!" ) result = kwargs["complete_streaming_response"] greenscaleLogger.log_event( kwargs=kwargs, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) if callback == "athina" and athinaLogger is not None: deep_copy = {} for k, v in self.model_call_details.items(): deep_copy[k] = v athinaLogger.log_event( kwargs=deep_copy, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) if callback == "traceloop": deep_copy = {} for k, v in self.model_call_details.items(): if k != "original_response": deep_copy[k] = v traceloopLogger.log_event( kwargs=deep_copy, response_obj=result, start_time=start_time, end_time=end_time, user_id=kwargs.get("user", None), print_verbose=print_verbose, ) if callback == "s3": global s3Logger if s3Logger is None: s3Logger = S3Logger() if self.stream: if "complete_streaming_response" in self.model_call_details: print_verbose( "S3Logger Logger: Got Stream Event - Completed Stream Response" ) s3Logger.log_event( kwargs=self.model_call_details, response_obj=self.model_call_details[ "complete_streaming_response" ], start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) else: print_verbose( "S3Logger Logger: Got Stream Event - No complete stream response as yet" ) else: s3Logger.log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) if callback == "openmeter" and is_sync_request: global openMeterLogger if openMeterLogger is None: print_verbose("Instantiates openmeter client") openMeterLogger = OpenMeterLogger() if self.stream and complete_streaming_response is None: openMeterLogger.log_stream_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) else: if self.stream and complete_streaming_response: self.model_call_details["complete_response"] = ( self.model_call_details.get( "complete_streaming_response", {} ) ) result = self.model_call_details["complete_response"] openMeterLogger.log_success_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) if ( isinstance(callback, CustomLogger) and is_sync_request and self.call_type != CallTypes.pass_through.value # pass-through endpoints call async_log_success_event ): # custom logger class if self.stream and complete_streaming_response is None: callback.log_stream_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) else: if self.stream and complete_streaming_response: self.model_call_details["complete_response"] = ( self.model_call_details.get( "complete_streaming_response", {} ) ) result = self.model_call_details["complete_response"] callback.log_success_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) if ( callable(callback) is True and is_sync_request and customLogger is not None ): # custom logger functions print_verbose( "success callbacks: Running Custom Callback Function - {}".format( callback ) ) customLogger.log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, callback_func=callback, ) except Exception as e: print_verbose( f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging with integrations {traceback.format_exc()}" ) print_verbose( f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" ) if capture_exception: # log this error to sentry for debugging capture_exception(e) # Track callback logging failures in Prometheus try: self._handle_callback_failure(callback=callback) except Exception: pass except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {}".format( str(e) ), ) async def async_success_handler( # noqa: PLR0915 self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs ): """ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. """ print_verbose( "Logging Details LiteLLM-Async Success Call, cache_hit={}".format(cache_hit) ) if not self.should_run_logging( event_type="async_success" ): # prevent double logging return ## CALCULATE COST FOR BATCH JOBS if self.call_type == CallTypes.aretrieve_batch.value and isinstance( result, LiteLLMBatch ): litellm_params = self.litellm_params or {} litellm_metadata = litellm_params.get("litellm_metadata") or {} if ( litellm_metadata.get("batch_ignore_default_logging", False) is True ): # polling job will query these frequently, don't spam db logs return from litellm.proxy.openai_files_endpoints.common_utils import ( _is_base64_encoded_unified_file_id, ) # check if file id is a unified file id is_base64_unified_file_id = _is_base64_encoded_unified_file_id(result.id) batch_cost = kwargs.get("batch_cost", None) batch_usage = kwargs.get("batch_usage", None) batch_models = kwargs.get("batch_models", None) has_explicit_batch_data = all( x is not None for x in (batch_cost, batch_usage, batch_models) ) should_compute_batch_data = ( not is_base64_unified_file_id or not has_explicit_batch_data and result.status == "completed" ) if has_explicit_batch_data: result._hidden_params["response_cost"] = batch_cost result._hidden_params["batch_models"] = batch_models result.usage = batch_usage elif should_compute_batch_data: ( response_cost, batch_usage, batch_models, ) = await _handle_completed_batch( batch=result, custom_llm_provider=self.custom_llm_provider, litellm_params=self.litellm_params, ) result._hidden_params["response_cost"] = response_cost result._hidden_params["batch_models"] = batch_models result.usage = batch_usage start_time, end_time, result = self._success_handler_helper_fn( start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit, standard_logging_object=kwargs.get("standard_logging_object", None), ) ## BUILD COMPLETE STREAMED RESPONSE if "async_complete_streaming_response" in self.model_call_details: return # break out of this. complete_streaming_response: Optional[ Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse] ] = self._get_assembled_streaming_response( result=result, start_time=start_time, end_time=end_time, is_async=True, streaming_chunks=self.streaming_chunks, ) if complete_streaming_response is not None: print_verbose("Async success callbacks: Got a complete streaming response") self.model_call_details["async_complete_streaming_response"] = ( complete_streaming_response ) try: if self.model_call_details.get("cache_hit", False) is True: self.model_call_details["response_cost"] = 0.0 else: # check if base_model set on azure _get_base_model_from_metadata( model_call_details=self.model_call_details ) # base_model defaults to None if not set on model_info self.model_call_details["response_cost"] = ( self._response_cost_calculator( result=complete_streaming_response ) ) verbose_logger.debug( f"Model={self.model}; cost={self.model_call_details['response_cost']}" ) except litellm.NotFoundError: verbose_logger.warning( f"Model={self.model} not found in completion cost map. Setting 'response_cost' to None" ) self.model_call_details["response_cost"] = None ## STANDARDIZED LOGGING PAYLOAD self.model_call_details["standard_logging_object"] = ( self._build_standard_logging_payload( complete_streaming_response, start_time, end_time ) ) # print standard logging payload if ( standard_logging_payload := self.model_call_details.get( "standard_logging_object" ) ) is not None: emit_standard_logging_payload(standard_logging_payload) elif self.call_type == "pass_through_endpoint": print_verbose( "Async success callbacks: Got a pass-through endpoint response" ) self.model_call_details["async_complete_streaming_response"] = result # Only set response_cost to None if not already calculated by # pass-through handlers (e.g. Gemini/Vertex handlers already # compute cost via completion_cost) if self.model_call_details.get("response_cost") is None: self.model_call_details["response_cost"] = None # Only build standard_logging_object if not already built by # _success_handler_helper_fn if self.model_call_details.get("standard_logging_object") is None: ## STANDARDIZED LOGGING PAYLOAD self.model_call_details["standard_logging_object"] = ( self._build_standard_logging_payload(result, start_time, end_time) ) # print standard logging payload if ( standard_logging_payload := self.model_call_details.get( "standard_logging_object" ) ) is not None: emit_standard_logging_payload(standard_logging_payload) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_async_success_callbacks, global_callbacks=litellm._async_success_callback, ) result = redact_message_input_output_from_logging( model_call_details=( self.model_call_details if hasattr(self, "model_call_details") else {} ), result=result, ) ## LOGGING HOOK ## for callback in callbacks: if isinstance(callback, CustomGuardrail): from litellm.types.guardrails import GuardrailEventHooks if ( callback.should_run_guardrail( data=self.model_call_details, event_type=GuardrailEventHooks.logging_only, ) is not True ): continue self.model_call_details, result = await callback.async_logging_hook( kwargs=self.model_call_details, result=result, call_type=self.call_type, ) elif isinstance(callback, CustomLogger): result = redact_message_input_output_from_custom_logger( result=result, litellm_logging_obj=self, custom_logger=callback ) self.model_call_details, result = await callback.async_logging_hook( kwargs=self.model_call_details, result=result, call_type=self.call_type, ) self.has_run_logging(event_type="async_success") for callback in callbacks: # check if callback can run for this request litellm_params = self.model_call_details.get("litellm_params", {}) should_run = self.should_run_callback( callback=callback, litellm_params=litellm_params, event_hook="async_success_handler", ) if not should_run: continue try: if callback == "openmeter" and openMeterLogger is not None: if self.stream is True: if ( "async_complete_streaming_response" in self.model_call_details ): await openMeterLogger.async_log_success_event( kwargs=self.model_call_details, response_obj=self.model_call_details[ "async_complete_streaming_response" ], start_time=start_time, end_time=end_time, ) else: await openMeterLogger.async_log_stream_event( # [TODO]: move this to being an async log stream event function kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) else: await openMeterLogger.async_log_success_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) if isinstance(callback, CustomLogger): # custom logger class model_call_details: Dict = self.model_call_details ################################## # call redaction hook for custom logger model_call_details = callback.redact_standard_logging_payload_from_model_call_details( model_call_details=model_call_details ) ################################## if self.stream is True: if "async_complete_streaming_response" in model_call_details: await callback.async_log_success_event( kwargs=model_call_details, response_obj=model_call_details[ "async_complete_streaming_response" ], start_time=start_time, end_time=end_time, ) else: await callback.async_log_stream_event( # [TODO]: move this to being an async log stream event function kwargs=model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) else: await callback.async_log_success_event( kwargs=model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) if callable(callback): # custom logger functions global customLogger if customLogger is None: customLogger = CustomLogger() if self.stream: if ( "async_complete_streaming_response" in self.model_call_details ): await customLogger.async_log_event( kwargs=self.model_call_details, response_obj=self.model_call_details[ "async_complete_streaming_response" ], start_time=start_time, end_time=end_time, print_verbose=print_verbose, callback_func=callback, ) else: await customLogger.async_log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, callback_func=callback, ) if callback == "dynamodb": global dynamoLogger if dynamoLogger is None: dynamoLogger = DyanmoDBLogger() if self.stream: if ( "async_complete_streaming_response" in self.model_call_details ): print_verbose( "DynamoDB Logger: Got Stream Event - Completed Stream Response" ) await dynamoLogger._async_log_event( kwargs=self.model_call_details, response_obj=self.model_call_details[ "async_complete_streaming_response" ], start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) else: print_verbose( "DynamoDB Logger: Got Stream Event - No complete stream response as yet" ) else: await dynamoLogger._async_log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) except Exception: verbose_logger.error( f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" ) self._handle_callback_failure(callback=callback) pass def _handle_callback_failure(self, callback: Any): """ Handle callback logging failures by incrementing Prometheus metrics. Works for both sync and async contexts since Prometheus counter increment is synchronous. Args: callback: The callback that failed """ try: callback_name = self._get_callback_name(callback) all_callbacks = litellm.logging_callback_manager._get_all_callbacks() for callback_obj in all_callbacks: if hasattr(callback_obj, "increment_callback_logging_failure"): callback_obj.increment_callback_logging_failure(callback_name=callback_name) # type: ignore break # Only increment once except Exception as e: verbose_logger.debug(f"Error in _handle_callback_failure: {str(e)}") def _failure_handler_helper_fn( self, exception, traceback_exception, start_time=None, end_time=None ): if start_time is None: start_time = self.start_time if end_time is None: end_time = datetime.datetime.now() # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions if not hasattr(self, "model_call_details"): self.model_call_details = {} self.model_call_details["log_event_type"] = "failed_api_call" self.model_call_details["exception"] = exception self.model_call_details["traceback_exception"] = traceback_exception self.model_call_details["end_time"] = end_time self.model_call_details.setdefault("original_response", None) self.model_call_details["response_cost"] = 0 if hasattr(exception, "headers") and isinstance(exception.headers, dict): self.model_call_details.setdefault("litellm_params", {}) metadata = ( self.model_call_details["litellm_params"].get("metadata", {}) or {} ) metadata.update(exception.headers) ## STANDARDIZED LOGGING PAYLOAD self.model_call_details["standard_logging_object"] = ( get_standard_logging_object_payload( kwargs=self.model_call_details, init_response_obj={}, start_time=start_time, end_time=end_time, logging_obj=self, status="failure", error_str=str(exception), original_exception=exception, standard_built_in_tools_params=self.standard_built_in_tools_params, ) ) return start_time, end_time async def special_failure_handlers(self, exception: Exception): """ Custom events, emitted for specific failures. Currently just for router model group rate limit error """ from litellm.types.router import RouterErrors litellm_params: dict = self.model_call_details.get("litellm_params") or {} metadata = litellm_params.get("metadata") or {} ## BASE CASE ## check if rate limit error for model group size 1 is_base_case = False if metadata.get("model_group_size") is not None: model_group_size = metadata.get("model_group_size") if isinstance(model_group_size, int) and model_group_size == 1: is_base_case = True ## check if special error ## if ( RouterErrors.no_deployments_available.value not in str(exception) and is_base_case is False ): return ## get original model group ## model_group = metadata.get("model_group") or None for callback in litellm._async_failure_callback: if isinstance(callback, CustomLogger): # custom logger class await callback.log_model_group_rate_limit_error( exception=exception, original_model_group=model_group, kwargs=self.model_call_details, ) # type: ignore def failure_handler( # noqa: PLR0915 self, exception, traceback_exception, start_time=None, end_time=None ): verbose_logger.debug( f"Logging Details LiteLLM-Failure Call: {litellm.failure_callback}" ) if not self.should_run_logging( event_type="sync_failure" ): # prevent double logging return litellm_params = self.model_call_details.get("litellm_params", {}) is_sync_request = ( litellm_params.get(CallTypes.acompletion.value, False) is not True and litellm_params.get(CallTypes.aresponses.value, False) is not True and litellm_params.get(CallTypes.aembedding.value, False) is not True and litellm_params.get(CallTypes.aimage_generation.value, False) is not True and litellm_params.get(CallTypes.atranscription.value, False) is not True ) try: start_time, end_time = self._failure_handler_helper_fn( exception=exception, traceback_exception=traceback_exception, start_time=start_time, end_time=end_time, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_failure_callbacks, global_callbacks=litellm.failure_callback, ) result = None # result sent to all loggers, init this to None incase it's not created result = redact_message_input_output_from_logging( model_call_details=( self.model_call_details if hasattr(self, "model_call_details") else {} ), result=result, ) self.has_run_logging(event_type="sync_failure") for callback in callbacks: try: should_run = self.should_run_callback( callback=callback, litellm_params=litellm_params, event_hook="failure_handler", ) if not should_run: continue if callback == "lunary" and lunaryLogger is not None: print_verbose("reaches lunary for logging error!") model = self.model input = self.model_call_details["input"] _type = ( "embed" if self.call_type == CallTypes.embedding.value else "llm" ) lunaryLogger.log_event( kwargs=self.model_call_details, type=_type, event="error", user_id=self.model_call_details.get("user", "default"), model=model, input=input, error=traceback_exception, run_id=self.litellm_call_id, start_time=start_time, end_time=end_time, print_verbose=print_verbose, ) if callback == "sentry": print_verbose("sending exception to sentry") if capture_exception: capture_exception(exception) else: print_verbose( f"capture exception not initialized: {capture_exception}" ) elif callback == "supabase" and supabaseClient is not None: print_verbose("reaches supabase for logging!") print_verbose(f"supabaseClient: {supabaseClient}") supabaseClient.log_event( model=self.model if hasattr(self, "model") else "", messages=self.messages, end_user=self.model_call_details.get("user", "default"), response_obj=result, start_time=start_time, end_time=end_time, litellm_call_id=self.model_call_details["litellm_call_id"], print_verbose=print_verbose, ) if ( callable(callback) and customLogger is not None ): # custom logger functions customLogger.log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, callback_func=callback, ) if ( isinstance(callback, CustomLogger) and is_sync_request ): # custom logger class callback.log_failure_event( start_time=start_time, end_time=end_time, response_obj=result, kwargs=self.model_call_details, ) if callback == "langfuse": global langFuseLogger verbose_logger.debug("reaches langfuse for logging failure") kwargs = {} for k, v in self.model_call_details.items(): if ( k != "original_response" ): # copy.deepcopy raises errors as this could be a coroutine kwargs[k] = v # this only logs streaming once, complete_streaming_response exists i.e when stream ends langfuse_logger_to_use = LangFuseHandler.get_langfuse_logger_for_request( globalLangfuseLogger=langFuseLogger, standard_callback_dynamic_params=self.standard_callback_dynamic_params, in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, ) _response = langfuse_logger_to_use.log_event_on_langfuse( start_time=start_time, end_time=end_time, response_obj=None, user_id=kwargs.get("user", None), status_message=str(exception), level="ERROR", kwargs=self.model_call_details, ) if _response is not None and isinstance(_response, dict): _trace_id = _response.get("trace_id", None) if _trace_id is not None: in_memory_trace_id_cache.set_cache( litellm_call_id=self.litellm_call_id, service_name="langfuse", trace_id=_trace_id, ) if callback == "traceloop": traceloopLogger.log_event( start_time=start_time, end_time=end_time, response_obj=None, user_id=self.model_call_details.get("user", None), print_verbose=print_verbose, status_message=str(exception), level="ERROR", kwargs=self.model_call_details, ) if callback == "logfire" and logfireLogger is not None: verbose_logger.debug("reaches logfire for failure logging!") kwargs = {} for k, v in self.model_call_details.items(): if ( k != "original_response" ): # copy.deepcopy raises errors as this could be a coroutine kwargs[k] = v kwargs["exception"] = exception logfireLogger.log_event( kwargs=kwargs, response_obj=result, start_time=start_time, end_time=end_time, level=LogfireLevel.ERROR.value, # type: ignore print_verbose=print_verbose, ) except Exception as e: print_verbose( f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {str(e)}" ) print_verbose( f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" ) if capture_exception: # log this error to sentry for debugging capture_exception(e) except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {}".format( str(e) ) ) async def async_failure_handler( self, exception, traceback_exception, start_time=None, end_time=None ): """ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. """ await self.special_failure_handlers(exception=exception) if not self.should_run_logging( event_type="async_failure" ): # prevent double logging return start_time, end_time = self._failure_handler_helper_fn( exception=exception, traceback_exception=traceback_exception, start_time=start_time, end_time=end_time, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_async_failure_callbacks, global_callbacks=litellm._async_failure_callback, ) result = None # result sent to all loggers, init this to None incase it's not created self.has_run_logging(event_type="async_failure") for callback in callbacks: try: litellm_params = self.model_call_details.get("litellm_params", {}) should_run = self.should_run_callback( callback=callback, litellm_params=litellm_params, event_hook="async_failure_handler", ) if not should_run: continue if isinstance(callback, CustomLogger): # custom logger class await callback.async_log_failure_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, ) # type: ignore if ( callable(callback) and customLogger is not None ): # custom logger functions await customLogger.async_log_event( kwargs=self.model_call_details, response_obj=result, start_time=start_time, end_time=end_time, print_verbose=print_verbose, callback_func=callback, ) except Exception as e: verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure \ logging {}\nCallback={}".format( str(e), callback ) ) # Track callback logging failures in Prometheus self._handle_callback_failure(callback=callback) def _get_trace_id(self, service_name: Literal["langfuse"]) -> Optional[str]: """ For the given service (e.g. langfuse), return the trace_id actually logged. Used for constructing the url in slack alerting. Returns: - str: The logged trace id - None: If trace id not yet emitted. """ trace_id: Optional[str] = None if service_name == "langfuse": trace_id = in_memory_trace_id_cache.get_cache( litellm_call_id=self.litellm_call_id, service_name=service_name ) return trace_id def _get_callback_object(self, service_name: Literal["langfuse"]) -> Optional[Any]: """ Return dynamic callback object. Meant to solve issue when doing key-based/team-based logging """ global langFuseLogger if service_name == "langfuse": if langFuseLogger is None or ( ( self.standard_callback_dynamic_params.get("langfuse_public_key") is not None and self.standard_callback_dynamic_params.get("langfuse_public_key") != langFuseLogger.public_key ) or ( self.standard_callback_dynamic_params.get("langfuse_public_key") is not None and self.standard_callback_dynamic_params.get("langfuse_public_key") != langFuseLogger.public_key ) or ( self.standard_callback_dynamic_params.get("langfuse_host") is not None and self.standard_callback_dynamic_params.get("langfuse_host") != langFuseLogger.langfuse_host ) ): return LangFuseLogger( langfuse_public_key=self.standard_callback_dynamic_params.get( "langfuse_public_key" ), langfuse_secret=self.standard_callback_dynamic_params.get( "langfuse_secret" ), langfuse_host=self.standard_callback_dynamic_params.get( "langfuse_host" ), ) return langFuseLogger return None def handle_sync_success_callbacks_for_async_calls( self, result: Any, start_time: datetime.datetime, end_time: datetime.datetime, cache_hit: Optional[Any] = None, ) -> None: """ Handles calling success callbacks for Async calls. Why: Some callbacks - `langfuse`, `s3` are sync callbacks. We need to call them in the executor. """ if self._should_run_sync_callbacks_for_async_calls() is False: return executor.submit( self.success_handler, result, start_time, end_time, cache_hit, ) def _should_run_sync_callbacks_for_async_calls(self) -> bool: """ Returns: - bool: True if sync callbacks should be run for async calls. eg. `langfuse`, `s3` """ _combined_sync_callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_success_callbacks, global_callbacks=litellm.success_callback, ) _filtered_success_callbacks = self._remove_internal_custom_logger_callbacks( _combined_sync_callbacks ) _filtered_success_callbacks = self._remove_internal_litellm_callbacks( _filtered_success_callbacks ) return len(_filtered_success_callbacks) > 0 def get_combined_callback_list( self, dynamic_success_callbacks: Optional[List], global_callbacks: List ) -> List: if dynamic_success_callbacks is None: return list(global_callbacks) return list(set(dynamic_success_callbacks + global_callbacks)) def _remove_internal_litellm_callbacks(self, callbacks: List) -> List: """ Creates a filtered list of callbacks, excluding internal LiteLLM callbacks. Args: callbacks: List of callback functions/strings to filter Returns: List of filtered callbacks with internal ones removed """ filtered = [ cb for cb in callbacks if not self._is_internal_litellm_proxy_callback(cb) ] verbose_logger.debug(f"Filtered callbacks: {filtered}") return filtered def _get_callback_name(self, cb) -> str: """ Helper to get the name of a callback function Args: cb: The callback object/function/string to get the name of Returns: The name of the callback """ if isinstance(cb, str): return cb if hasattr(cb, "__name__"): return cb.__name__ if hasattr(cb, "__func__"): return cb.__func__.__name__ if hasattr(cb, "__class__"): return cb.__class__.__name__ return str(cb) def _is_internal_litellm_proxy_callback(self, cb) -> bool: """Helper to check if a callback is internal""" INTERNAL_PREFIXES = [ "_PROXY", "_service_logger.ServiceLogging", "sync_deployment_callback_on_success", ] if isinstance(cb, str): return False if not callable(cb): return True cb_name = self._get_callback_name(cb) return any(prefix in cb_name for prefix in INTERNAL_PREFIXES) def _remove_internal_custom_logger_callbacks(self, callbacks: List) -> List: """ Removes internal custom logger callbacks from the list. """ _new_callbacks = [] for _c in callbacks: if isinstance(_c, CustomLogger): continue elif ( isinstance(_c, str) and _c in litellm._known_custom_logger_compatible_callbacks ): continue _new_callbacks.append(_c) return _new_callbacks def _get_assembled_streaming_response( self, result: Union[ ModelResponse, TextCompletionResponse, ModelResponseStream, ResponseCompletedEvent, Any, ], start_time: datetime.datetime, end_time: datetime.datetime, is_async: bool, streaming_chunks: List[Any], ) -> Optional[Union[ModelResponse, TextCompletionResponse, ResponsesAPIResponse]]: if self.stream is not True: return None if isinstance(result, ModelResponse): return result elif isinstance(result, TextCompletionResponse): return result elif isinstance(result, ResponseCompletedEvent): ## return unified Usage object if isinstance(result.response.usage, ResponseAPIUsage): transformed_usage = ( ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( result.response.usage ) ) # Set as dict instead of Usage object so model_dump() serializes it correctly setattr( result.response, "usage", ( transformed_usage.model_dump() if hasattr(transformed_usage, "model_dump") else dict(transformed_usage) ), ) return result.response else: return None return None def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse: """ Handles logging for Anthropic messages responses. Args: result: The response object from the model call Returns: The the response object from the model call - For Non-streaming responses, we need to transform the response to a ModelResponse object. - For streaming responses, anthropic_messages handler calls success_handler with a assembled ModelResponse. """ import httpx if self.stream and isinstance(result, ModelResponse): return result elif isinstance(result, ModelResponse): return result httpx_response = self.model_call_details.get("httpx_response", None) if httpx_response and isinstance(httpx_response, httpx.Response): result = litellm.AnthropicConfig().transform_response( raw_response=httpx_response, model_response=litellm.ModelResponse(), model=self.model, messages=[], logging_obj=self, optional_params={}, api_key="", request_data={}, encoding=litellm.encoding, json_mode=False, litellm_params={}, ) else: from litellm.types.llms.anthropic import AnthropicResponse pydantic_result = AnthropicResponse.model_validate(result) import httpx result = litellm.AnthropicConfig().transform_parsed_response( completion_response=pydantic_result.model_dump(), raw_response=httpx.Response( status_code=200, headers={}, ), model_response=litellm.ModelResponse(), json_mode=None, ) return result def _handle_non_streaming_google_genai_generate_content_response_logging( self, result: Any ) -> ModelResponse: """ Handles logging for Google GenAI generate content responses. """ import httpx httpx_response = self.model_call_details.get("httpx_response", None) if httpx_response is None: raise ValueError("Google GenAI Generate Content: httpx_response is None") dict_result = httpx_response.json() result = litellm.VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( completion_response=dict_result, model_response=litellm.ModelResponse(), model=self.model, logging_obj=self, raw_response=httpx.Response( status_code=200, headers={}, ), ) return result def _handle_a2a_response_logging(self, result: Any) -> Any: """ Handles logging for A2A (Agent-to-Agent) responses. Adds usage from model_call_details to the result if available. Uses Pydantic's model_copy to avoid modifying the original response. Args: result: The LiteLLMSendMessageResponse from the A2A call Returns: The response object with usage added if available """ # Get usage from model_call_details (set by asend_message) usage = self.model_call_details.get("usage") if usage is None: return result # Deep copy result and add usage result_copy = result.model_copy(deep=True) result_copy.usage = ( usage.model_dump() if hasattr(usage, "model_dump") else dict(usage) ) return result_copy def _get_masked_values( sensitive_object: dict, ignore_sensitive_values: bool = False, mask_all_values: bool = False, unmasked_length: int = 4, number_of_asterisks: Optional[int] = 4, ) -> dict: """ Internal debugging helper function Masks the headers of the request sent from LiteLLM Args: masked_length: Optional length for the masked portion (number of *). If set, will use exactly this many * regardless of original string length. The total length will be unmasked_length + masked_length. """ sensitive_keywords = [ "authorization", "token", "key", "secret", "vertex_credentials", ] return { k: ( # If ignore_sensitive_values is True, or if this key doesn't contain sensitive keywords, return original value v if ignore_sensitive_values or not any( sensitive_keyword in k.lower() for sensitive_keyword in sensitive_keywords ) else ( # Apply masking to sensitive keys ( v[: unmasked_length // 2] + "*" * number_of_asterisks + v[-unmasked_length // 2 :] ) if ( isinstance(v, str) and len(v) > unmasked_length and number_of_asterisks is not None ) else ( ( v[: unmasked_length // 2] + "*" * (len(v) - unmasked_length) + v[-unmasked_length // 2 :] ) if (isinstance(v, str) and len(v) > unmasked_length) else ("*****" if isinstance(v, str) else v) ) ) ) for k, v in sensitive_object.items() } def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 """ Globally sets the callback client """ global sentry_sdk_instance, capture_exception, add_breadcrumb, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger try: for callback in callback_list: if callback == "sentry": try: import sentry_sdk except ImportError: print_verbose("Package 'sentry_sdk' is missing. Installing it...") subprocess.check_call( [sys.executable, "-m", "pip", "install", "sentry_sdk"] ) import sentry_sdk from sentry_sdk.scrubber import EventScrubber sentry_sdk_instance = sentry_sdk sentry_trace_rate = ( os.environ.get("SENTRY_API_TRACE_RATE") if "SENTRY_API_TRACE_RATE" in os.environ else "1.0" ) sentry_sample_rate = ( os.environ.get("SENTRY_API_SAMPLE_RATE") if "SENTRY_API_SAMPLE_RATE" in os.environ else "1.0" ) sentry_sdk_instance.init( dsn=os.environ.get("SENTRY_DSN"), traces_sample_rate=float(sentry_trace_rate), # type: ignore sample_rate=float( sentry_sample_rate if sentry_sample_rate else 1.0 ), send_default_pii=False, # Prevent sending Personal Identifiable Information event_scrubber=EventScrubber( denylist=SENTRY_DENYLIST, pii_denylist=SENTRY_PII_DENYLIST ), environment=os.environ.get("SENTRY_ENVIRONMENT", "production"), ) capture_exception = sentry_sdk_instance.capture_exception add_breadcrumb = sentry_sdk_instance.add_breadcrumb elif callback == "slack": try: from slack_bolt import App except ImportError: print_verbose("Package 'slack_bolt' is missing. Installing it...") subprocess.check_call( [sys.executable, "-m", "pip", "install", "slack_bolt"] ) from slack_bolt import App slack_app = App( token=os.environ.get("SLACK_API_TOKEN"), signing_secret=os.environ.get("SLACK_API_SECRET"), ) alerts_channel = os.environ["SLACK_API_CHANNEL"] print_verbose(f"Initialized Slack App: {slack_app}") elif callback == "traceloop": traceloopLogger = TraceloopLogger() elif callback == "athina": athinaLogger = AthinaLogger() print_verbose("Initialized Athina Logger") elif callback == "helicone": heliconeLogger = HeliconeLogger() elif callback == "lunary": lunaryLogger = LunaryLogger() elif callback == "promptlayer": promptLayerLogger = PromptLayerLogger() elif callback == "langfuse": langFuseLogger = LangFuseLogger( langfuse_public_key=None, langfuse_secret=None, langfuse_host=None ) elif callback == "openmeter": openMeterLogger = OpenMeterLogger() elif callback == "datadog": dataDogLogger = DataDogLogger() elif callback == "dynamodb": dynamoLogger = DyanmoDBLogger() elif callback == "s3": s3Logger = S3Logger() elif callback == "wandb": from litellm.integrations.weights_biases import WeightsBiasesLogger weightsBiasesLogger = WeightsBiasesLogger() elif callback == "logfire": logfireLogger = LogfireLogger() elif callback == "supabase": print_verbose("instantiating supabase") supabaseClient = Supabase() elif callback == "greenscale": greenscaleLogger = GreenscaleLogger() print_verbose("Initialized Greenscale Logger") elif callable(callback): customLogger = CustomLogger() except Exception as e: raise e return None def _init_custom_logger_compatible_class( # noqa: PLR0915 logging_integration: _custom_logger_compatible_callbacks_literal, internal_usage_cache: Optional[DualCache], llm_router: Optional[ Any ], # expect litellm.Router, but typing errors due to circular import custom_logger_init_args: Optional[dict] = {}, ) -> Optional[CustomLogger]: """ Initialize a custom logger compatible class """ try: custom_logger_init_args = custom_logger_init_args or {} if logging_integration == "agentops": # Add AgentOps initialization for callback in _in_memory_loggers: if isinstance(callback, AgentOps): return callback # type: ignore agentops_logger = AgentOps() _in_memory_loggers.append(agentops_logger) return agentops_logger # type: ignore elif logging_integration == "lago": for callback in _in_memory_loggers: if isinstance(callback, LagoLogger): return callback # type: ignore lago_logger = LagoLogger() _in_memory_loggers.append(lago_logger) return lago_logger # type: ignore elif logging_integration == "openmeter": for callback in _in_memory_loggers: if isinstance(callback, OpenMeterLogger): return callback # type: ignore _openmeter_logger = OpenMeterLogger() _in_memory_loggers.append(_openmeter_logger) return _openmeter_logger # type: ignore elif logging_integration == "posthog": for callback in _in_memory_loggers: if isinstance(callback, PostHogLogger): return callback # type: ignore _posthog_logger = PostHogLogger() _in_memory_loggers.append(_posthog_logger) return _posthog_logger # type: ignore elif logging_integration == "braintrust": from litellm.integrations.braintrust_logging import BraintrustLogger for callback in _in_memory_loggers: if isinstance(callback, BraintrustLogger): return callback # type: ignore braintrust_logger = BraintrustLogger() _in_memory_loggers.append(braintrust_logger) return braintrust_logger # type: ignore elif logging_integration == "langsmith": for callback in _in_memory_loggers: if isinstance(callback, LangsmithLogger): return callback # type: ignore _langsmith_logger = LangsmithLogger() _in_memory_loggers.append(_langsmith_logger) return _langsmith_logger # type: ignore elif logging_integration == "argilla": for callback in _in_memory_loggers: if isinstance(callback, ArgillaLogger): return callback # type: ignore _argilla_logger = ArgillaLogger() _in_memory_loggers.append(_argilla_logger) return _argilla_logger # type: ignore elif logging_integration == "literalai": for callback in _in_memory_loggers: if isinstance(callback, LiteralAILogger): return callback # type: ignore _literalai_logger = LiteralAILogger() _in_memory_loggers.append(_literalai_logger) return _literalai_logger # type: ignore elif logging_integration == "litellm_agent": for callback in _in_memory_loggers: if isinstance(callback, LiteLLMAgentModelResolver): return callback # type: ignore _litellm_agent_resolver = LiteLLMAgentModelResolver() _in_memory_loggers.append(_litellm_agent_resolver) return _litellm_agent_resolver # type: ignore elif logging_integration == "prometheus": PrometheusLogger = _get_cached_prometheus_logger() for callback in _in_memory_loggers: if isinstance(callback, PrometheusLogger): return callback # type: ignore _prometheus_logger = PrometheusLogger() _in_memory_loggers.append(_prometheus_logger) return _prometheus_logger # type: ignore elif logging_integration == "datadog": for callback in _in_memory_loggers: if isinstance(callback, DataDogLogger): return callback # type: ignore _datadog_logger = DataDogLogger() _in_memory_loggers.append(_datadog_logger) return _datadog_logger # type: ignore elif logging_integration == "datadog_metrics": for callback in _in_memory_loggers: if isinstance(callback, DatadogMetricsLogger): return callback # type: ignore _datadog_metrics_logger = DatadogMetricsLogger() _in_memory_loggers.append(_datadog_metrics_logger) return _datadog_metrics_logger # type: ignore elif logging_integration == "datadog_llm_observability": _datadog_llm_obs_logger = DataDogLLMObsLogger() _in_memory_loggers.append(_datadog_llm_obs_logger) return _datadog_llm_obs_logger # type: ignore elif logging_integration == "azure_sentinel": for callback in _in_memory_loggers: if isinstance(callback, AzureSentinelLogger): return callback # type: ignore _azure_sentinel_logger = AzureSentinelLogger() _in_memory_loggers.append(_azure_sentinel_logger) return _azure_sentinel_logger # type: ignore elif logging_integration == "gcs_bucket": for callback in _in_memory_loggers: if isinstance(callback, GCSBucketLogger): return callback # type: ignore _gcs_bucket_logger = GCSBucketLogger() _in_memory_loggers.append(_gcs_bucket_logger) return _gcs_bucket_logger # type: ignore elif logging_integration == "s3_v2": for callback in _in_memory_loggers: if isinstance(callback, S3V2Logger): return callback # type: ignore _s3_v2_logger = S3V2Logger() _in_memory_loggers.append(_s3_v2_logger) return _s3_v2_logger # type: ignore elif logging_integration == "aws_sqs": for callback in _in_memory_loggers: if isinstance(callback, SQSLogger): return callback # type: ignore _aws_sqs_logger = SQSLogger() _in_memory_loggers.append(_aws_sqs_logger) return _aws_sqs_logger # type: ignore elif logging_integration == "azure_storage": for callback in _in_memory_loggers: if isinstance(callback, AzureBlobStorageLogger): return callback # type: ignore _azure_storage_logger = AzureBlobStorageLogger() _in_memory_loggers.append(_azure_storage_logger) return _azure_storage_logger # type: ignore elif logging_integration == "opik": for callback in _in_memory_loggers: if isinstance(callback, OpikLogger): return callback # type: ignore _opik_logger = OpikLogger() _in_memory_loggers.append(_opik_logger) return _opik_logger # type: ignore elif logging_integration == "arize": from litellm.integrations.opentelemetry import ( OpenTelemetry, OpenTelemetryConfig, ) arize_config = ArizeLogger.get_arize_config() if arize_config.endpoint is None: raise ValueError( "No valid endpoint found for Arize, please set 'ARIZE_ENDPOINT' to your GRPC endpoint or 'ARIZE_HTTP_ENDPOINT' to your HTTP endpoint" ) otel_config = OpenTelemetryConfig( exporter=arize_config.protocol, endpoint=arize_config.endpoint, service_name=arize_config.project_name, ) os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( f"space_id={arize_config.space_key or arize_config.space_id},api_key={arize_config.api_key}" ) for callback in _in_memory_loggers: if ( isinstance(callback, ArizeLogger) and callback.callback_name == "arize" ): return callback # type: ignore _arize_otel_logger = ArizeLogger(config=otel_config, callback_name="arize") _in_memory_loggers.append(_arize_otel_logger) return _arize_otel_logger # type: ignore elif logging_integration == "arize_phoenix": from litellm.integrations.opentelemetry import ( OpenTelemetry, OpenTelemetryConfig, ) arize_phoenix_config = ArizePhoenixLogger.get_arize_phoenix_config() otel_config = OpenTelemetryConfig( exporter=arize_phoenix_config.protocol, endpoint=arize_phoenix_config.endpoint, headers=arize_phoenix_config.otlp_auth_headers, ) if arize_phoenix_config.project_name: existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "") # Add openinference.project.name attribute if existing_attrs: os.environ["OTEL_RESOURCE_ATTRIBUTES"] = ( f"{existing_attrs},openinference.project.name={arize_phoenix_config.project_name}" ) else: os.environ["OTEL_RESOURCE_ATTRIBUTES"] = ( f"openinference.project.name={arize_phoenix_config.project_name}" ) # Set Phoenix project name from environment variable phoenix_project_name = os.environ.get("PHOENIX_PROJECT_NAME", None) if phoenix_project_name: existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "") # Add openinference.project.name attribute if existing_attrs: os.environ["OTEL_RESOURCE_ATTRIBUTES"] = ( f"{existing_attrs},openinference.project.name={phoenix_project_name}" ) else: os.environ["OTEL_RESOURCE_ATTRIBUTES"] = ( f"openinference.project.name={phoenix_project_name}" ) # auth can be disabled on local deployments of arize phoenix if arize_phoenix_config.otlp_auth_headers is not None: os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( arize_phoenix_config.otlp_auth_headers ) for callback in _in_memory_loggers: if ( isinstance(callback, ArizePhoenixLogger) and callback.callback_name == "arize_phoenix" ): return callback # type: ignore _arize_phoenix_otel_logger = ArizePhoenixLogger( config=otel_config, callback_name="arize_phoenix" ) _in_memory_loggers.append(_arize_phoenix_otel_logger) return _arize_phoenix_otel_logger # type: ignore elif logging_integration == "levo": from litellm.integrations.levo.levo import LevoLogger from litellm.integrations.opentelemetry import ( OpenTelemetry, OpenTelemetryConfig, ) levo_config = LevoLogger.get_levo_config() otel_config = OpenTelemetryConfig( exporter=levo_config.protocol, endpoint=levo_config.endpoint, headers=levo_config.otlp_auth_headers, ) # Check if LevoLogger instance already exists for callback in _in_memory_loggers: if ( isinstance(callback, LevoLogger) and callback.callback_name == "levo" ): return callback # type: ignore _levo_otel_logger = LevoLogger(config=otel_config, callback_name="levo") _in_memory_loggers.append(_levo_otel_logger) return _levo_otel_logger # type: ignore elif logging_integration == "otel": from litellm.integrations.opentelemetry import OpenTelemetry for callback in _in_memory_loggers: if type(callback) is OpenTelemetry: return callback # type: ignore otel_logger = OpenTelemetry( **_get_custom_logger_settings_from_proxy_server( callback_name=logging_integration ) ) _in_memory_loggers.append(otel_logger) # Auto-initialize Arize Phoenix if Phoenix env vars are configured # This allows users to get nested traces in both OTEL and Phoenix # by only specifying "otel" in callbacks _maybe_auto_initialize_arize_phoenix(_in_memory_loggers) return otel_logger # type: ignore elif logging_integration == "galileo": for callback in _in_memory_loggers: if isinstance(callback, GalileoObserve): return callback # type: ignore galileo_logger = GalileoObserve() _in_memory_loggers.append(galileo_logger) return galileo_logger # type: ignore elif logging_integration == "cloudzero": from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger for callback in _in_memory_loggers: if isinstance(callback, CloudZeroLogger): return callback # type: ignore cloudzero_logger = CloudZeroLogger() _in_memory_loggers.append(cloudzero_logger) return cloudzero_logger # type: ignore elif logging_integration == "focus": from litellm.integrations.focus.focus_logger import FocusLogger for callback in _in_memory_loggers: if isinstance(callback, FocusLogger): return callback # type: ignore focus_logger = FocusLogger() _in_memory_loggers.append(focus_logger) return focus_logger # type: ignore elif logging_integration == "deepeval": for callback in _in_memory_loggers: if isinstance(callback, DeepEvalLogger): return callback # type: ignore deepeval_logger = DeepEvalLogger() _in_memory_loggers.append(deepeval_logger) return deepeval_logger # type: ignore elif logging_integration == "logfire": if "LOGFIRE_TOKEN" not in os.environ: raise ValueError("LOGFIRE_TOKEN not found in environment variables") from litellm.integrations.opentelemetry import ( OpenTelemetry, OpenTelemetryConfig, ) logfire_base_url = os.getenv( "LOGFIRE_BASE_URL", "https://logfire-api.pydantic.dev" ) otel_config = OpenTelemetryConfig( exporter="otlp_http", endpoint=f"{logfire_base_url.rstrip('/')}/v1/traces", headers=f"Authorization={os.getenv('LOGFIRE_TOKEN')}", ) for callback in _in_memory_loggers: # Use exact type check to avoid matching ArizePhoenixLogger (subclass) if type(callback) is OpenTelemetry: return callback # type: ignore _otel_logger = OpenTelemetry(config=otel_config) _in_memory_loggers.append(_otel_logger) return _otel_logger # type: ignore elif logging_integration == "dynamic_rate_limiter": from litellm.proxy.hooks.dynamic_rate_limiter import ( _PROXY_DynamicRateLimitHandler, ) for callback in _in_memory_loggers: if isinstance(callback, _PROXY_DynamicRateLimitHandler): return callback # type: ignore if internal_usage_cache is None: raise Exception( "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( internal_usage_cache ) ) dynamic_rate_limiter_obj = _PROXY_DynamicRateLimitHandler( internal_usage_cache=internal_usage_cache ) if llm_router is not None and isinstance(llm_router, litellm.Router): dynamic_rate_limiter_obj.update_variables(llm_router=llm_router) _in_memory_loggers.append(dynamic_rate_limiter_obj) return dynamic_rate_limiter_obj # type: ignore elif logging_integration == "dynamic_rate_limiter_v3": from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( _PROXY_DynamicRateLimitHandlerV3, ) for callback in _in_memory_loggers: if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): return callback # type: ignore if internal_usage_cache is None: raise Exception( "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( internal_usage_cache ) ) dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3( internal_usage_cache=internal_usage_cache ) if llm_router is not None and isinstance(llm_router, litellm.Router): dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router) _in_memory_loggers.append(dynamic_rate_limiter_obj_v3) return dynamic_rate_limiter_obj_v3 # type: ignore elif logging_integration == "langtrace": if "LANGTRACE_API_KEY" not in os.environ: raise ValueError("LANGTRACE_API_KEY not found in environment variables") from litellm.integrations.opentelemetry import ( OpenTelemetry, OpenTelemetryConfig, ) otel_config = OpenTelemetryConfig( exporter="otlp_http", endpoint="https://langtrace.ai/api/trace", ) os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( f"api_key={os.getenv('LANGTRACE_API_KEY')}" ) for callback in _in_memory_loggers: if ( isinstance(callback, OpenTelemetry) and callback.callback_name == "langtrace" ): return callback # type: ignore _otel_logger = OpenTelemetry(config=otel_config, callback_name="langtrace") _in_memory_loggers.append(_otel_logger) return _otel_logger # type: ignore elif logging_integration == "mlflow": for callback in _in_memory_loggers: if isinstance(callback, MlflowLogger): return callback # type: ignore _mlflow_logger = MlflowLogger() _in_memory_loggers.append(_mlflow_logger) return _mlflow_logger # type: ignore elif logging_integration == "langfuse": for callback in _in_memory_loggers: if isinstance(callback, LangfusePromptManagement): return callback langfuse_logger = LangfusePromptManagement() _in_memory_loggers.append(langfuse_logger) return langfuse_logger # type: ignore elif logging_integration == "langfuse_otel": from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger for callback in _in_memory_loggers: if ( isinstance(callback, LangfuseOtelLogger) and callback.callback_name == "langfuse_otel" ): return callback # type: ignore # Allow LangfuseOtelLogger to initialize its own config safely # This prevents startup crashes if LANGFUSE keys are not in env (e.g. for dynamic usage) _otel_logger = LangfuseOtelLogger( config=None, callback_name="langfuse_otel" ) _in_memory_loggers.append(_otel_logger) return _otel_logger # type: ignore elif logging_integration == "weave_otel": from litellm.integrations.opentelemetry import OpenTelemetryConfig from litellm.integrations.weave.weave_otel import ( WeaveOtelLogger, get_weave_otel_config, ) weave_otel_config = get_weave_otel_config() otel_config = OpenTelemetryConfig( exporter=weave_otel_config.protocol, endpoint=weave_otel_config.endpoint, headers=weave_otel_config.otlp_auth_headers, ) for callback in _in_memory_loggers: if ( isinstance(callback, WeaveOtelLogger) and callback.callback_name == "weave_otel" ): return callback # type: ignore _otel_logger = WeaveOtelLogger( config=otel_config, callback_name="weave_otel" ) _in_memory_loggers.append(_otel_logger) return _otel_logger # type: ignore elif logging_integration == "pagerduty": for callback in _in_memory_loggers: if isinstance(callback, PagerDutyAlerting): return callback pagerduty_logger = PagerDutyAlerting(**custom_logger_init_args) _in_memory_loggers.append(pagerduty_logger) return pagerduty_logger # type: ignore elif logging_integration == "anthropic_cache_control_hook": for callback in _in_memory_loggers: if isinstance(callback, AnthropicCacheControlHook): return callback anthropic_cache_control_hook = AnthropicCacheControlHook() _in_memory_loggers.append(anthropic_cache_control_hook) return anthropic_cache_control_hook # type: ignore elif logging_integration == "vector_store_pre_call_hook": from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import ( VectorStorePreCallHook, ) for callback in _in_memory_loggers: if isinstance(callback, VectorStorePreCallHook): return callback vector_store_pre_call_hook = VectorStorePreCallHook() _in_memory_loggers.append(vector_store_pre_call_hook) return vector_store_pre_call_hook # type: ignore elif logging_integration == "gcs_pubsub": for callback in _in_memory_loggers: if isinstance(callback, GcsPubSubLogger): return callback _gcs_pubsub_logger = GcsPubSubLogger() _in_memory_loggers.append(_gcs_pubsub_logger) return _gcs_pubsub_logger # type: ignore elif logging_integration == "generic_api": for callback in _in_memory_loggers: if isinstance(callback, GenericAPILogger): return callback generic_api_logger = GenericAPILogger() _in_memory_loggers.append(generic_api_logger) return generic_api_logger # type: ignore elif logging_integration == "resend_email": for callback in _in_memory_loggers: if isinstance(callback, ResendEmailLogger): return callback resend_email_logger = ResendEmailLogger() _in_memory_loggers.append(resend_email_logger) return resend_email_logger # type: ignore elif logging_integration == "sendgrid_email": for callback in _in_memory_loggers: if isinstance(callback, SendGridEmailLogger): return callback sendgrid_email_logger = SendGridEmailLogger() _in_memory_loggers.append(sendgrid_email_logger) return sendgrid_email_logger # type: ignore elif logging_integration == "smtp_email": for callback in _in_memory_loggers: if isinstance(callback, SMTPEmailLogger): return callback smtp_email_logger = SMTPEmailLogger() _in_memory_loggers.append(smtp_email_logger) return smtp_email_logger # type: ignore elif logging_integration == "humanloop": for callback in _in_memory_loggers: if isinstance(callback, HumanloopLogger): return callback humanloop_logger = HumanloopLogger() _in_memory_loggers.append(humanloop_logger) return humanloop_logger # type: ignore elif logging_integration == "dotprompt": for callback in _in_memory_loggers: if isinstance(callback, DotpromptManager): return callback dotprompt_logger = DotpromptManager() _in_memory_loggers.append(dotprompt_logger) return dotprompt_logger # type: ignore elif logging_integration == "bitbucket": from litellm.integrations.bitbucket.bitbucket_prompt_manager import ( BitBucketPromptManager, ) for callback in _in_memory_loggers: if isinstance(callback, BitBucketPromptManager): return callback # Get global BitBucket config bitbucket_config = getattr(litellm, "global_bitbucket_config", None) if bitbucket_config is None: raise ValueError( "BitBucket configuration not found. Please set litellm.global_bitbucket_config first." ) bitbucket_logger = BitBucketPromptManager(bitbucket_config=bitbucket_config) _in_memory_loggers.append(bitbucket_logger) return bitbucket_logger # type: ignore elif logging_integration == "gitlab": from litellm.integrations.gitlab.gitlab_prompt_manager import ( GitLabPromptManager, ) for callback in _in_memory_loggers: if isinstance(callback, GitLabPromptManager): return callback # Get global BitBucket config gitlab_config = getattr(litellm, "global_gitlab_config", None) if gitlab_config is None: raise ValueError( "Gitlab configuration not found. Please set litellm.global_gitlab_config first." ) gitlab_logger = GitLabPromptManager(gitlab_config=gitlab_config) _in_memory_loggers.append(gitlab_logger) return gitlab_logger # type: ignore return None except Exception as e: verbose_logger.exception( f"[Non-Blocking Error] Error initializing custom logger: {e}" ) return None return None def _maybe_auto_initialize_arize_phoenix(_in_memory_loggers: list) -> None: """ Auto-initialize ArizePhoenixLogger when Phoenix env vars are detected. Called during ``otel`` callback setup so that users get nested traces in both their OTEL collector *and* Arize Phoenix by only listing ``"otel"`` in ``callbacks``. If no Phoenix env vars are set, this is a no-op. """ phoenix_env_vars = ( "PHOENIX_API_KEY", "PHOENIX_COLLECTOR_HTTP_ENDPOINT", "PHOENIX_COLLECTOR_ENDPOINT", ) if not any(os.environ.get(v) for v in phoenix_env_vars): return # Already registered — nothing to do if any( isinstance(cb, ArizePhoenixLogger) and cb.callback_name == "arize_phoenix" for cb in _in_memory_loggers ): return try: from litellm.integrations.opentelemetry import OpenTelemetryConfig arize_phoenix_config = ArizePhoenixLogger.get_arize_phoenix_config() otel_config = OpenTelemetryConfig( exporter=arize_phoenix_config.protocol, endpoint=arize_phoenix_config.endpoint, headers=arize_phoenix_config.otlp_auth_headers, ) phoenix_logger = ArizePhoenixLogger( config=otel_config, callback_name="arize_phoenix" ) _in_memory_loggers.append(phoenix_logger) # Register as a litellm callback so it receives success/failure events litellm.logging_callback_manager.add_litellm_callback(phoenix_logger) verbose_logger.info( "Auto-initialized Arize Phoenix logger alongside otel " "(endpoint=%s)", arize_phoenix_config.endpoint, ) except Exception as e: verbose_logger.warning( "Failed to auto-initialize Arize Phoenix logger: %s", str(e) ) def get_custom_logger_compatible_class( # noqa: PLR0915 logging_integration: _custom_logger_compatible_callbacks_literal, ) -> Optional[CustomLogger]: try: if logging_integration == "lago": for callback in _in_memory_loggers: if isinstance(callback, LagoLogger): return callback elif logging_integration == "openmeter": for callback in _in_memory_loggers: if isinstance(callback, OpenMeterLogger): return callback elif logging_integration == "braintrust": from litellm.integrations.braintrust_logging import BraintrustLogger for callback in _in_memory_loggers: if isinstance(callback, BraintrustLogger): return callback elif logging_integration == "galileo": for callback in _in_memory_loggers: if isinstance(callback, GalileoObserve): return callback elif logging_integration == "cloudzero": from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger for callback in _in_memory_loggers: if isinstance(callback, CloudZeroLogger): return callback elif logging_integration == "focus": from litellm.integrations.focus.focus_logger import FocusLogger for callback in _in_memory_loggers: if isinstance(callback, FocusLogger): return callback elif logging_integration == "deepeval": for callback in _in_memory_loggers: if isinstance(callback, DeepEvalLogger): return callback elif logging_integration == "langsmith": for callback in _in_memory_loggers: if isinstance(callback, LangsmithLogger): return callback elif logging_integration == "argilla": for callback in _in_memory_loggers: if isinstance(callback, ArgillaLogger): return callback elif logging_integration == "literalai": for callback in _in_memory_loggers: if isinstance(callback, LiteralAILogger): return callback elif logging_integration == "litellm_agent": for callback in _in_memory_loggers: if isinstance(callback, LiteLLMAgentModelResolver): return callback elif logging_integration == "prometheus": PrometheusLogger = _get_cached_prometheus_logger() for callback in _in_memory_loggers: if isinstance(callback, PrometheusLogger): return callback elif logging_integration == "datadog": for callback in _in_memory_loggers: if isinstance(callback, DataDogLogger): return callback elif logging_integration == "datadog_metrics": for callback in _in_memory_loggers: if isinstance(callback, DatadogMetricsLogger): return callback elif logging_integration == "datadog_llm_observability": for callback in _in_memory_loggers: if isinstance(callback, DataDogLLMObsLogger): return callback elif logging_integration == "azure_sentinel": for callback in _in_memory_loggers: if isinstance(callback, AzureSentinelLogger): return callback elif logging_integration == "gcs_bucket": for callback in _in_memory_loggers: if isinstance(callback, GCSBucketLogger): return callback elif logging_integration == "s3_v2": for callback in _in_memory_loggers: if isinstance(callback, S3V2Logger): return callback elif logging_integration == "aws_sqs": for callback in _in_memory_loggers: if isinstance(callback, SQSLogger): return callback _aws_sqs_logger = SQSLogger() _in_memory_loggers.append(_aws_sqs_logger) return _aws_sqs_logger # type: ignore elif logging_integration == "azure_storage": for callback in _in_memory_loggers: if isinstance(callback, AzureBlobStorageLogger): return callback elif logging_integration == "opik": for callback in _in_memory_loggers: if isinstance(callback, OpikLogger): return callback elif logging_integration == "langfuse": for callback in _in_memory_loggers: if isinstance(callback, LangfusePromptManagement): return callback elif logging_integration == "otel": from litellm.integrations.opentelemetry import OpenTelemetry for callback in _in_memory_loggers: # Use exact type check to avoid matching ArizePhoenixLogger (subclass) if type(callback) is OpenTelemetry: return callback elif logging_integration == "arize": if "ARIZE_API_KEY" not in os.environ: raise ValueError("ARIZE_API_KEY not found in environment variables") for callback in _in_memory_loggers: if ( isinstance(callback, ArizeLogger) and callback.callback_name == "arize" ): return callback elif logging_integration == "logfire": if "LOGFIRE_TOKEN" not in os.environ: raise ValueError("LOGFIRE_TOKEN not found in environment variables") from litellm.integrations.opentelemetry import OpenTelemetry for callback in _in_memory_loggers: # Use exact type check to avoid matching ArizePhoenixLogger (subclass) if type(callback) is OpenTelemetry: return callback # type: ignore elif logging_integration == "dynamic_rate_limiter": from litellm.proxy.hooks.dynamic_rate_limiter import ( _PROXY_DynamicRateLimitHandler, ) for callback in _in_memory_loggers: if isinstance(callback, _PROXY_DynamicRateLimitHandler): return callback # type: ignore elif logging_integration == "dynamic_rate_limiter_v3": from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( _PROXY_DynamicRateLimitHandlerV3, ) for callback in _in_memory_loggers: if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): return callback # type: ignore elif logging_integration == "langtrace": from litellm.integrations.opentelemetry import OpenTelemetry if "LANGTRACE_API_KEY" not in os.environ: raise ValueError("LANGTRACE_API_KEY not found in environment variables") for callback in _in_memory_loggers: if ( isinstance(callback, OpenTelemetry) and callback.callback_name == "langtrace" ): return callback elif logging_integration == "mlflow": for callback in _in_memory_loggers: if isinstance(callback, MlflowLogger): return callback elif logging_integration == "pagerduty": for callback in _in_memory_loggers: if isinstance(callback, PagerDutyAlerting): return callback elif logging_integration == "anthropic_cache_control_hook": for callback in _in_memory_loggers: if isinstance(callback, AnthropicCacheControlHook): return callback elif logging_integration == "vector_store_pre_call_hook": from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import ( VectorStorePreCallHook, ) for callback in _in_memory_loggers: if isinstance(callback, VectorStorePreCallHook): return callback elif logging_integration == "gcs_pubsub": for callback in _in_memory_loggers: if isinstance(callback, GcsPubSubLogger): return callback elif logging_integration == "generic_api": for callback in _in_memory_loggers: if isinstance(callback, GenericAPILogger): return callback elif logging_integration == "resend_email": for callback in _in_memory_loggers: if isinstance(callback, ResendEmailLogger): return callback elif logging_integration == "sendgrid_email": for callback in _in_memory_loggers: if isinstance(callback, SendGridEmailLogger): return callback elif logging_integration == "smtp_email": for callback in _in_memory_loggers: if isinstance(callback, SMTPEmailLogger): return callback return None except Exception as e: verbose_logger.exception( f"[Non-Blocking Error] Error getting custom logger: {e}" ) return None def _get_custom_logger_settings_from_proxy_server(callback_name: str) -> Dict: """ Get the settings for a custom logger from the proxy server config.yaml Proxy server config.yaml defines callback_settings as: callback_settings: otel: message_logging: False """ if litellm.callback_settings: return dict(litellm.callback_settings.get(callback_name, {})) return {} def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool: """ Check if the model uses custom pricing Returns True if any of `SPECIAL_MODEL_INFO_PARAMS` are present in `litellm_params` or `model_info` """ if litellm_params is None: return False # Check litellm_params using set intersection (only check keys that exist in both) matching_keys = _CUSTOM_PRICING_KEYS & litellm_params.keys() for key in matching_keys: if litellm_params.get(key) is not None: return True # Check model_info metadata: dict = litellm_params.get("metadata", {}) or {} model_info: dict = metadata.get("model_info", {}) or {} if model_info: matching_keys = _CUSTOM_PRICING_KEYS & model_info.keys() for key in matching_keys: if model_info.get(key) is not None: return True return False def is_valid_sha256_hash(value: str) -> bool: # Check if the value is a valid SHA-256 hash (64 hexadecimal characters) return bool(re.fullmatch(r"[a-fA-F0-9]{64}", value)) class StandardLoggingPayloadSetup: @staticmethod def cleanup_timestamps( start_time: Union[dt_object, float], end_time: Union[dt_object, float], completion_start_time: Union[dt_object, float], ) -> Tuple[float, float, float]: """ Convert datetime objects to floats Args: start_time: Union[dt_object, float] end_time: Union[dt_object, float] completion_start_time: Union[dt_object, float] Returns: Tuple[float, float, float]: A tuple containing the start time, end time, and completion start time as floats. """ if isinstance(start_time, datetime.datetime): start_time_float = start_time.timestamp() elif isinstance(start_time, float): start_time_float = start_time else: raise ValueError( f"start_time is required, got={start_time} of type {type(start_time)}" ) if isinstance(end_time, datetime.datetime): end_time_float = end_time.timestamp() elif isinstance(end_time, float): end_time_float = end_time else: raise ValueError( f"end_time is required, got={end_time} of type {type(end_time)}" ) if isinstance(completion_start_time, datetime.datetime): completion_start_time_float = completion_start_time.timestamp() elif isinstance(completion_start_time, float): completion_start_time_float = completion_start_time else: completion_start_time_float = end_time_float return start_time_float, end_time_float, completion_start_time_float @staticmethod def append_system_prompt_messages( kwargs: Optional[Dict] = None, messages: Optional[Any] = None ): """ Append system prompt messages to the messages """ if kwargs is not None: if kwargs.get("system") is not None and isinstance( kwargs.get("system"), str ): if messages is None: return [{"role": "system", "content": kwargs.get("system")}] elif isinstance(messages, list): if len(messages) == 0: return [{"role": "system", "content": kwargs.get("system")}] # check for duplicates if messages[0].get("role") == "system" and messages[0].get( "content" ) == kwargs.get("system"): return messages messages = [ {"role": "system", "content": kwargs.get("system")} ] + messages elif isinstance(messages, str): messages = [ {"role": "system", "content": kwargs.get("system")}, {"role": "user", "content": messages}, ] return messages return messages @staticmethod def merge_litellm_metadata(litellm_params: dict) -> dict: """ Merge both litellm_metadata and metadata from litellm_params. litellm_metadata contains model-related fields, metadata contains user API key fields. We need both for complete standard logging payload. Args: litellm_params: Dictionary containing metadata and litellm_metadata Returns: dict: Merged metadata with user API key fields taking precedence """ merged_metadata: dict = {} # Start with metadata (user API key fields) - but skip non-serializable objects if litellm_params.get("metadata") and isinstance( litellm_params.get("metadata"), dict ): for key, value in litellm_params["metadata"].items(): # Skip non-serializable objects like UserAPIKeyAuth if key == "user_api_key_auth": continue merged_metadata[key] = value # Then merge litellm_metadata (model-related fields) - this will NOT overwrite existing keys if litellm_params.get("litellm_metadata") and isinstance( litellm_params.get("litellm_metadata"), dict ): for key, value in litellm_params["litellm_metadata"].items(): if ( key not in merged_metadata ): # Don't overwrite existing keys from metadata merged_metadata[key] = value return merged_metadata @staticmethod def get_standard_logging_metadata( metadata: Optional[Dict[str, Any]], litellm_params: Optional[dict] = None, prompt_integration: Optional[str] = None, applied_guardrails: Optional[List[str]] = None, mcp_tool_call_metadata: Optional[StandardLoggingMCPToolCall] = None, vector_store_request_metadata: Optional[ List[StandardLoggingVectorStoreRequest] ] = None, usage_object: Optional[dict] = None, proxy_server_request: Optional[dict] = None, start_time: Optional[dt_object] = None, response_id: Optional[str] = None, ) -> StandardLoggingMetadata: """ Clean and filter the metadata dictionary to include only the specified keys in StandardLoggingMetadata. Args: metadata (Optional[Dict[str, Any]]): The original metadata dictionary. Returns: StandardLoggingMetadata: A StandardLoggingMetadata object containing the cleaned metadata. Note: - If the input metadata is None or not a dictionary, an empty StandardLoggingMetadata object is returned. - If 'user_api_key' is present in metadata and is a valid SHA256 hash, it's stored as 'user_api_key_hash'. """ prompt_management_metadata: Optional[ StandardLoggingPromptManagementMetadata ] = None if litellm_params is not None: prompt_id = cast(Optional[str], litellm_params.get("prompt_id", None)) prompt_variables = cast( Optional[dict], litellm_params.get("prompt_variables", None) ) if prompt_id is not None and prompt_integration is not None: prompt_management_metadata = StandardLoggingPromptManagementMetadata( prompt_id=prompt_id, prompt_variables=prompt_variables, prompt_integration=prompt_integration, ) # Initialize with default values clean_metadata = StandardLoggingMetadata( user_api_key_hash=None, user_api_key_alias=None, user_api_key_spend=None, user_api_key_max_budget=None, user_api_key_budget_reset_at=None, user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_project_id=None, user_api_key_user_id=None, user_api_key_team_alias=None, user_api_key_user_email=None, user_api_key_end_user_id=None, user_api_key_request_route=None, spend_logs_metadata=None, requester_ip_address=None, user_agent=None, requester_metadata=None, prompt_management_metadata=prompt_management_metadata, applied_guardrails=applied_guardrails, mcp_tool_call_metadata=mcp_tool_call_metadata, vector_store_request_metadata=vector_store_request_metadata, usage_object=usage_object, requester_custom_headers=None, cold_storage_object_key=None, user_api_key_auth_metadata=None, team_alias=None, team_id=None, ) if isinstance(metadata, dict): for key in metadata.keys() & _STANDARD_LOGGING_METADATA_KEYS: clean_metadata[key] = metadata[key] # type: ignore user_api_key = metadata.get("user_api_key") if ( user_api_key and isinstance(user_api_key, str) and is_valid_sha256_hash(user_api_key) ): clean_metadata["user_api_key_hash"] = user_api_key _potential_requester_metadata = metadata.get( "metadata", None ) # check if user passed metadata in the sdk request - e.g. metadata for langsmith logging - https://docs.litellm.ai/docs/observability/langsmith_integration#set-langsmith-fields if ( clean_metadata["requester_metadata"] is None and _potential_requester_metadata is not None and isinstance(_potential_requester_metadata, dict) ): clean_metadata["requester_metadata"] = _potential_requester_metadata if ( EnterpriseStandardLoggingPayloadSetupVAR and proxy_server_request is not None ): clean_metadata = EnterpriseStandardLoggingPayloadSetupVAR.apply_enterprise_specific_metadata( standard_logging_metadata=clean_metadata, proxy_server_request=proxy_server_request, ) # Generate cold storage object key if cold storage is configured if start_time is not None and response_id is not None: cold_storage_object_key = ( StandardLoggingPayloadSetup._generate_cold_storage_object_key( start_time=start_time, response_id=response_id, team_alias=clean_metadata.get("user_api_key_team_alias"), ) ) if cold_storage_object_key: clean_metadata["cold_storage_object_key"] = cold_storage_object_key return clean_metadata @staticmethod def get_usage_from_response_obj( response_obj: Optional[dict], combined_usage_object: Optional[Usage] = None ) -> Usage: ## BASE CASE ## if combined_usage_object is not None: return combined_usage_object if response_obj is None: return Usage( prompt_tokens=0, completion_tokens=0, total_tokens=0, ) usage = response_obj.get("usage", None) or {} if usage is None or ( not isinstance(usage, dict) and not isinstance(usage, Usage) ): return Usage( prompt_tokens=0, completion_tokens=0, total_tokens=0, ) elif isinstance(usage, Usage): return usage elif isinstance(usage, ResponseAPIUsage): return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( usage ) elif isinstance(usage, dict): if ResponseAPILoggingUtils._is_response_api_usage(usage): return ( ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( usage ) ) return Usage(**usage) raise ValueError(f"usage is required, got={usage} of type {type(usage)}") @staticmethod def get_usage_as_dict( response_obj: Optional[dict], combined_usage_object: Optional[Usage] = None, ) -> dict: """ Like get_usage_from_response_obj but returns a plain dict, skipping the Pydantic Usage construction on the hot path. """ _empty: dict = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} if combined_usage_object is not None: return combined_usage_object.model_dump() if not response_obj: return _empty _raw = response_obj.get("usage", None) if _raw is None: return _empty if isinstance(_raw, ResponseAPIUsage): return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( _raw ).model_dump() if isinstance(_raw, dict): if ResponseAPILoggingUtils._is_response_api_usage(_raw): return ( ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( _raw ).model_dump() ) return _raw if isinstance(_raw, Usage): return _raw.model_dump() return _empty @staticmethod def get_model_cost_information( base_model: Optional[str], custom_pricing: Optional[bool], custom_llm_provider: Optional[str], init_response_obj: Union[Any, BaseModel, dict], api_base: Optional[str] = None, ) -> StandardLoggingModelInformation: model_cost_name = _select_model_name_for_cost_calc( model=None, completion_response=init_response_obj, # type: ignore base_model=base_model, custom_pricing=custom_pricing, ) if model_cost_name is None: model_cost_information = StandardLoggingModelInformation( model_map_key="", model_map_value=None ) else: try: _model_cost_information = litellm.get_model_info( model=model_cost_name, custom_llm_provider=custom_llm_provider, api_base=api_base, ) model_cost_information = StandardLoggingModelInformation( model_map_key=model_cost_name, model_map_value=_model_cost_information, ) except Exception: verbose_logger.debug( # keep in debug otherwise it will trigger on every call "Model={} is not mapped in model cost map. Defaulting to None model_cost_information for standard_logging_payload".format( model_cost_name ) ) model_cost_information = StandardLoggingModelInformation( model_map_key=model_cost_name, model_map_value=None ) return model_cost_information @staticmethod def get_final_response_obj( response_obj: dict, init_response_obj: Union[Any, BaseModel, dict], kwargs: dict ) -> Optional[Union[dict, str, list]]: """ Get final response object after redacting the message input/output from logging """ if response_obj: final_response_obj: Optional[Union[dict, str, list]] = response_obj elif isinstance(init_response_obj, list) or isinstance(init_response_obj, str): final_response_obj = init_response_obj else: final_response_obj = {} modified_final_response_obj = redact_message_input_output_from_logging( model_call_details=kwargs, result=final_response_obj, ) if modified_final_response_obj is not None and isinstance( modified_final_response_obj, BaseModel ): final_response_obj = modified_final_response_obj.model_dump() else: final_response_obj = modified_final_response_obj return final_response_obj @staticmethod def get_additional_headers( additiona_headers: Optional[dict], ) -> Optional[StandardLoggingAdditionalHeaders]: if additiona_headers is None: return None additional_logging_headers: StandardLoggingAdditionalHeaders = {} for key in StandardLoggingAdditionalHeaders.__annotations__.keys(): _key = key.lower() _key = _key.replace("_", "-") if _key in additiona_headers: try: additional_logging_headers[key] = int(additiona_headers[_key]) # type: ignore except (ValueError, TypeError): verbose_logger.debug( f"Could not convert {additiona_headers[_key]} to int for key {key}." ) return additional_logging_headers @staticmethod def get_hidden_params( hidden_params: Optional[dict], ) -> StandardLoggingHiddenParams: clean_hidden_params = StandardLoggingHiddenParams( model_id=None, cache_key=None, api_base=None, response_cost=None, additional_headers=None, litellm_overhead_time_ms=None, batch_models=None, litellm_model_name=None, usage_object=None, ) if hidden_params is not None: for key in StandardLoggingHiddenParams.__annotations__.keys(): if key in hidden_params: if key == "additional_headers": clean_hidden_params["additional_headers"] = ( StandardLoggingPayloadSetup.get_additional_headers( hidden_params[key] ) ) else: clean_hidden_params[key] = hidden_params[key] # type: ignore return clean_hidden_params @staticmethod def strip_trailing_slash(api_base: Optional[str]) -> Optional[str]: if api_base: if api_base.endswith("//"): return api_base.rstrip("/") if api_base[-1] == "/": return api_base[:-1] return api_base @staticmethod def _generate_cold_storage_object_key( start_time: dt_object, response_id: str, team_alias: Optional[str] = None, ) -> Optional[str]: """ Generate cold storage object key in the same format as S3Logger. Args: start_time: The start time of the request response_id: The response ID team_alias: Optional team alias for team-based prefixing Returns: Optional[str]: The generated object key or None if cold storage not configured """ # Generate object key in same format as S3Logger from litellm.integrations.s3 import get_s3_object_key # Only generate object key if cold storage is configured cold_storage_custom_logger = litellm.cold_storage_custom_logger if cold_storage_custom_logger is None: return None try: # Generate file name in same format as litellm.utils.get_logging_id s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}" # Get the actual s3_path from the configured cold storage logger instance s3_path = "" # default value # Try to get the actual logger instance from the logger name try: custom_logger = litellm.logging_callback_manager.get_active_custom_logger_for_callback_name( cold_storage_custom_logger ) if ( custom_logger and hasattr(custom_logger, "s3_path") and getattr(custom_logger, "s3_path") ): s3_path = getattr(custom_logger, "s3_path") except Exception: # If any error occurs in getting the logger instance, use default empty s3_path pass s3_object_key = get_s3_object_key( s3_path=s3_path, # Use actual s3_path from logger configuration prefix="", # Don't split by team alias for cold storage start_time=start_time, s3_file_name=s3_file_name, ) return s3_object_key except Exception: # If any error occurs in generating the key, return None return None @staticmethod def get_error_information( original_exception: Optional[Exception], traceback_str: Optional[str] = None, ) -> StandardLoggingPayloadErrorInformation: from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG # Check for 'code' first (used by ProxyException), then fall back to 'status_code' (used by LiteLLM exceptions) # Ensure error_code is always a string for Prisma Python JSON field compatibility error_code_attr = getattr(original_exception, "code", None) if error_code_attr is not None and str(error_code_attr) not in ("", "None"): error_status: str = str(error_code_attr) else: status_code_attr = getattr(original_exception, "status_code", None) error_status = str(status_code_attr) if status_code_attr is not None else "" error_class: str = ( str(original_exception.__class__.__name__) if original_exception else "" ) _llm_provider_in_exception = getattr(original_exception, "llm_provider", "") # Get traceback information (first 100 lines) traceback_info = traceback_str or "" if original_exception: tb = getattr(original_exception, "__traceback__", None) if tb: tb_lines = traceback.format_tb(tb) traceback_info += "".join( tb_lines[:MAXIMUM_TRACEBACK_LINES_TO_LOG] ) # Limit to first 100 lines # Get additional error details error_message = str(original_exception) return StandardLoggingPayloadErrorInformation( error_code=error_status, error_class=error_class, llm_provider=_llm_provider_in_exception, traceback=traceback_info, error_message=error_message if original_exception else "", ) @staticmethod def get_response_time( start_time_float: float, end_time_float: float, completion_start_time_float: float, stream: bool, ) -> float: """ Get the response time for the LLM response Args: start_time_float: float - start time of the LLM call end_time_float: float - end time of the LLM call completion_start_time_float: float - time to first token of the LLM response (for streaming responses) stream: bool - True when a stream response is returned Returns: float: The response time for the LLM response """ if stream is True: return completion_start_time_float - start_time_float else: return end_time_float - start_time_float @staticmethod def _get_standard_logging_payload_trace_id( logging_obj: Logging, litellm_params: dict, ) -> str: """ Returns the `litellm_trace_id` for this request This helps link sessions when multiple requests are made in a single session """ dynamic_litellm_session_id = litellm_params.get("litellm_session_id") dynamic_litellm_trace_id = litellm_params.get("litellm_trace_id") # Note: we recommend using `litellm_session_id` for session tracking # `litellm_trace_id` is an internal litellm param if dynamic_litellm_session_id: return str(dynamic_litellm_session_id) elif dynamic_litellm_trace_id: return str(dynamic_litellm_trace_id) # Fallback: use metadata.session_id or metadata.trace_id for call chaining metadata = litellm_params.get("metadata") or {} metadata_session_id = metadata.get("session_id") metadata_trace_id = metadata.get("trace_id") if metadata_session_id: return str(metadata_session_id) if metadata_trace_id: return str(metadata_trace_id) return logging_obj.litellm_trace_id @staticmethod def _get_user_agent_tags(proxy_server_request: dict) -> Optional[List[str]]: """ Return the user agent tags from the proxy server request for spend tracking """ if litellm.disable_add_user_agent_to_request_tags is True: return None user_agent_tags: Optional[List[str]] = None headers = proxy_server_request.get("headers", {}) if headers is not None and isinstance(headers, dict): if "user-agent" in headers: user_agent = headers["user-agent"] if user_agent is not None: if user_agent_tags is None: user_agent_tags = [] user_agent_part: Optional[str] = None if "/" in user_agent: user_agent_part = user_agent.split("/")[0] if user_agent_part is not None: user_agent_tags.append("User-Agent: " + user_agent_part) if user_agent is not None: user_agent_tags.append("User-Agent: " + user_agent) return user_agent_tags @staticmethod def _get_extra_header_tags(proxy_server_request: dict) -> Optional[List[str]]: """ Extract additional header tags for spend tracking based on config. """ extra_headers: List[str] = ( getattr(litellm, "extra_spend_tag_headers", None) or [] ) if not extra_headers: return None headers = proxy_server_request.get("headers", {}) if not isinstance(headers, dict): return None header_tags = [] for header_name in extra_headers: header_value = headers.get(header_name) if header_value: header_tags.append(f"{header_name}: {header_value}") return header_tags if header_tags else None @staticmethod def _get_request_tags( litellm_params: dict, proxy_server_request: dict ) -> List[str]: # check for 'tags' in both 'metadata' and 'litellm_metadata' metadata = litellm_params.get("metadata") or {} litellm_metadata = litellm_params.get("litellm_metadata") or {} if metadata.get("tags", []): request_tags = metadata.get("tags", []).copy() elif litellm_metadata.get("tags", []): request_tags = litellm_metadata.get("tags", []).copy() else: request_tags = [] user_agent_tags = StandardLoggingPayloadSetup._get_user_agent_tags( proxy_server_request ) additional_header_tags = StandardLoggingPayloadSetup._get_extra_header_tags( proxy_server_request ) if user_agent_tags is not None: request_tags.extend(user_agent_tags) if additional_header_tags is not None: request_tags.extend(additional_header_tags) return request_tags def _get_status_fields( status: StandardLoggingPayloadStatus, guardrail_information: Optional[List[dict]], error_str: Optional[str], ) -> "StandardLoggingPayloadStatusFields": """ Determine status fields based on request status and guardrail information. Args: status: Overall request status ("success" or "failure") guardrail_information: Guardrail information from metadata error_str: Error string if any Returns: StandardLoggingPayloadStatusFields with llm_api_status and guardrail_status """ # Mapping for legacy guardrail status values to new GuardrailStatus values GUARDRAIL_STATUS_MAP: Dict[str, GuardrailStatus] = { "success": "success", "blocked": "guardrail_intervened", # legacy "guardrail_intervened": "guardrail_intervened", # direct "failure": "guardrail_failed_to_respond", # legacy "guardrail_failed_to_respond": "guardrail_failed_to_respond", # direct "not_run": "not_run", } # Set LLM API status llm_api_status: StandardLoggingPayloadStatus = status ######################################################### # Map - guardrail_information.guardrail_status to guardrail_status ######################################################### guardrail_status: GuardrailStatus = "not_run" if guardrail_information and isinstance(guardrail_information, list): for information in guardrail_information: if isinstance(information, dict): raw_status = information.get("guardrail_status", "not_run") if raw_status != "not_run": guardrail_status = GUARDRAIL_STATUS_MAP.get(raw_status, "not_run") break return StandardLoggingPayloadStatusFields( llm_api_status=llm_api_status, guardrail_status=guardrail_status ) def _extract_response_obj_and_hidden_params( init_response_obj: Union[Any, BaseModel, dict], original_exception: Optional[Exception], ) -> Tuple[dict, Optional[dict]]: """Extract response_obj and hidden_params from init_response_obj.""" hidden_params: Optional[dict] = None if init_response_obj is None: response_obj = {} elif isinstance(init_response_obj, BaseModel): response_obj = init_response_obj.model_dump() hidden_params = getattr(init_response_obj, "_hidden_params", None) elif isinstance(init_response_obj, dict): response_obj = init_response_obj else: response_obj = {} if original_exception is not None and hidden_params is None: response_headers = _get_response_headers(original_exception) if response_headers is not None: hidden_params = dict( StandardLoggingHiddenParams( additional_headers=StandardLoggingPayloadSetup.get_additional_headers( dict(response_headers) ), model_id=None, cache_key=None, api_base=None, response_cost=None, litellm_overhead_time_ms=None, batch_models=None, litellm_model_name=None, usage_object=None, ) ) return response_obj, hidden_params def get_standard_logging_object_payload( kwargs: Optional[dict], init_response_obj: Union[Any, BaseModel, dict], start_time: dt_object, end_time: dt_object, logging_obj: Logging, status: StandardLoggingPayloadStatus, error_str: Optional[str] = None, original_exception: Optional[Exception] = None, standard_built_in_tools_params: Optional[StandardBuiltInToolsParams] = None, ) -> Optional[StandardLoggingPayload]: try: kwargs = kwargs or {} response_obj, hidden_params = _extract_response_obj_and_hidden_params( init_response_obj, original_exception ) # standardize this function to be used across, s3, dynamoDB, langfuse logging litellm_params = kwargs.get("litellm_params", {}) or {} proxy_server_request = litellm_params.get("proxy_server_request") or {} # Merge both litellm_metadata and metadata to get complete metadata metadata: dict = StandardLoggingPayloadSetup.merge_litellm_metadata( litellm_params ) completion_start_time = kwargs.get("completion_start_time", end_time) call_type = kwargs.get("call_type") cache_hit = kwargs.get("cache_hit", False) # Extract usage as a plain dict, avoiding Pydantic round-trip usage_dict = StandardLoggingPayloadSetup.get_usage_as_dict( response_obj=response_obj, combined_usage_object=cast( Optional[Usage], kwargs.get("combined_usage_object") ), ) id = response_obj.get("id", kwargs.get("litellm_call_id")) _model_id = metadata.get("model_info", {}).get("id", "") _model_group = metadata.get("model_group", "") request_tags = StandardLoggingPayloadSetup._get_request_tags( litellm_params=litellm_params, proxy_server_request=proxy_server_request ) # cleanup timestamps ( start_time_float, end_time_float, completion_start_time_float, ) = StandardLoggingPayloadSetup.cleanup_timestamps( start_time=start_time, end_time=end_time, completion_start_time=completion_start_time, ) response_time = StandardLoggingPayloadSetup.get_response_time( start_time_float=start_time_float, end_time_float=end_time_float, completion_start_time_float=completion_start_time_float, stream=kwargs.get("stream", False), ) # clean up litellm hidden params clean_hidden_params = StandardLoggingPayloadSetup.get_hidden_params( hidden_params ) # clean up litellm metadata clean_metadata = StandardLoggingPayloadSetup.get_standard_logging_metadata( metadata=metadata, litellm_params=litellm_params, prompt_integration=kwargs.get("prompt_integration", None), applied_guardrails=kwargs.get("applied_guardrails", None), mcp_tool_call_metadata=kwargs.get("mcp_tool_call_metadata", None), vector_store_request_metadata=kwargs.get( "vector_store_request_metadata", None ), usage_object=usage_dict, proxy_server_request=proxy_server_request, start_time=start_time, response_id=id, ) _request_body = proxy_server_request.get("body", {}) end_user_id = clean_metadata["user_api_key_end_user_id"] or _request_body.get( "user", None ) # maintain backwards compatibility with old request body check saved_cache_cost: float = 0.0 if cache_hit is True: id = f"{id}_cache_hit{time.time()}" # do not duplicate the request id saved_cache_cost = ( logging_obj._response_cost_calculator( result=init_response_obj, cache_hit=False # type: ignore ) or 0.0 ) ## Get model cost information ## base_model = _get_base_model_from_metadata(model_call_details=kwargs) custom_pricing = use_custom_pricing_for_model(litellm_params=litellm_params) model_cost_information = StandardLoggingPayloadSetup.get_model_cost_information( base_model=base_model, custom_pricing=custom_pricing, custom_llm_provider=kwargs.get("custom_llm_provider"), init_response_obj=init_response_obj, api_base=litellm_params.get("api_base"), ) response_cost: float = kwargs.get("response_cost", 0) or 0.0 error_information = StandardLoggingPayloadSetup.get_error_information( original_exception=original_exception, ) ## get final response object ## final_response_obj = StandardLoggingPayloadSetup.get_final_response_obj( response_obj=response_obj, init_response_obj=init_response_obj, kwargs=kwargs, ) stream: Optional[bool] = None if ( kwargs.get("complete_streaming_response") is not None or kwargs.get("async_complete_streaming_response") is not None ) and kwargs.get("stream") is True: stream = True # Reconstruct full model name with provider prefix for logging # This ensures Bedrock models like "us.anthropic.claude-3-5-sonnet-20240620-v1:0" # are logged as "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0" custom_llm_provider = cast(Optional[str], kwargs.get("custom_llm_provider")) model_name = reconstruct_model_name( kwargs.get("model", "") or "", custom_llm_provider, metadata ) payload: StandardLoggingPayload = StandardLoggingPayload( id=str(id), trace_id=StandardLoggingPayloadSetup._get_standard_logging_payload_trace_id( logging_obj=logging_obj, litellm_params=litellm_params, ), call_type=call_type or "", cache_hit=cache_hit, stream=stream, status=status, status_fields=_get_status_fields( status=status, guardrail_information=metadata.get( "standard_logging_guardrail_information", None ), error_str=error_str, ), custom_llm_provider=custom_llm_provider, saved_cache_cost=saved_cache_cost, startTime=start_time_float, endTime=end_time_float, completionStartTime=completion_start_time_float, response_time=response_time, model=model_name, metadata=clean_metadata, cache_key=clean_hidden_params["cache_key"], response_cost=response_cost, cost_breakdown=logging_obj.cost_breakdown, total_tokens=usage_dict.get("total_tokens", 0), prompt_tokens=usage_dict.get("prompt_tokens", 0), completion_tokens=usage_dict.get("completion_tokens", 0), request_tags=request_tags, end_user=end_user_id or "", api_base=StandardLoggingPayloadSetup.strip_trailing_slash( litellm_params.get("api_base", "") ) or "", model_group=_model_group, model_id=_model_id, requester_ip_address=clean_metadata.get("requester_ip_address", None), user_agent=clean_metadata.get("user_agent", None), messages=truncate_base64_in_messages( StandardLoggingPayloadSetup.append_system_prompt_messages( kwargs=kwargs, messages=kwargs.get("messages") ) ), response=final_response_obj, model_parameters=ModelParamHelper.get_standard_logging_model_parameters( kwargs.get("optional_params", None) or {} ), hidden_params=clean_hidden_params, model_map_information=model_cost_information, error_str=error_str, error_information=error_information, response_cost_failure_debug_info=kwargs.get( "response_cost_failure_debug_information" ), guardrail_information=metadata.get( "standard_logging_guardrail_information", None ), standard_built_in_tools_params=standard_built_in_tools_params, ) # emit_standard_logging_payload(payload) - Moved to success_handler to prevent double emitting return payload except Exception as e: verbose_logger.exception( "Error creating standard logging object - {}".format(str(e)) ) return None def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): print(json.dumps(payload, indent=4)) # noqa def get_standard_logging_metadata( metadata: Optional[Dict[str, Any]], ) -> StandardLoggingMetadata: """ Clean and filter the metadata dictionary to include only the specified keys in StandardLoggingMetadata. Args: metadata (Optional[Dict[str, Any]]): The original metadata dictionary. Returns: StandardLoggingMetadata: A StandardLoggingMetadata object containing the cleaned metadata. Note: - If the input metadata is None or not a dictionary, an empty StandardLoggingMetadata object is returned. - If 'user_api_key' is present in metadata and is a valid SHA256 hash, it's stored as 'user_api_key_hash'. """ # Initialize with default values clean_metadata = StandardLoggingMetadata( user_api_key_hash=None, user_api_key_alias=None, user_api_key_spend=None, user_api_key_max_budget=None, user_api_key_budget_reset_at=None, user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_project_id=None, user_api_key_user_id=None, user_api_key_user_email=None, user_api_key_team_alias=None, spend_logs_metadata=None, requester_ip_address=None, user_agent=None, requester_metadata=None, user_api_key_end_user_id=None, prompt_management_metadata=None, applied_guardrails=None, mcp_tool_call_metadata=None, vector_store_request_metadata=None, usage_object=None, requester_custom_headers=None, user_api_key_request_route=None, cold_storage_object_key=None, user_api_key_auth_metadata=None, team_alias=None, team_id=None, ) if isinstance(metadata, dict): # Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields for key in StandardLoggingMetadata.__annotations__.keys(): if key in metadata: clean_metadata[key] = metadata[key] # type: ignore if metadata.get("user_api_key") is not None: if is_valid_sha256_hash(str(metadata.get("user_api_key"))): clean_metadata["user_api_key_hash"] = metadata.get( "user_api_key" ) # this is the hash return clean_metadata def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]): if litellm_params is None: litellm_params = {} metadata = litellm_params.get("metadata", {}) or {} ## Extract provider-specific callable values (like langfuse_masking_function) ## Store them separately so only the intended logger can access them ## This prevents callables from leaking to other logging integrations if "langfuse_masking_function" in metadata: masking_fn = metadata.pop("langfuse_masking_function", None) if callable(masking_fn): litellm_params["_langfuse_masking_function"] = masking_fn litellm_params["metadata"] = metadata ## check user_api_key_metadata for sensitive logging keys cleaned_user_api_key_metadata = {} if "user_api_key_metadata" in metadata and isinstance( metadata["user_api_key_metadata"], dict ): for k, v in metadata["user_api_key_metadata"].items(): if k == "logging": # prevent logging user logging keys cleaned_user_api_key_metadata[k] = ( "scrubbed_by_litellm_for_sensitive_keys" ) else: cleaned_user_api_key_metadata[k] = v metadata["user_api_key_metadata"] = cleaned_user_api_key_metadata litellm_params["metadata"] = metadata return litellm_params # integration helper function def modify_integration(integration_name, integration_params): global supabaseClient if integration_name == "supabase": if "table_name" in integration_params: Supabase.supabase_table_name = integration_params["table_name"] @lru_cache(maxsize=16) def _get_traceback_str_for_error(error_str: str) -> str: """ function wrapped with lru_cache to limit the number of times `traceback.format_exc()` is called """ return traceback.format_exc() from decimal import Decimal # used for unit testing from typing import Any, Dict, List, Optional, Union def create_dummy_standard_logging_payload() -> StandardLoggingPayload: # First create the nested objects with proper typing model_info = StandardLoggingModelInformation( model_map_key="gpt-3.5-turbo", model_map_value=None ) metadata = StandardLoggingMetadata( # type: ignore user_api_key_hash=str("test_hash"), user_api_key_alias=str("test_alias"), user_api_key_team_id=str("test_team"), user_api_key_user_id=str("test_user"), user_api_key_team_alias=str("test_team_alias"), user_api_key_org_id=None, spend_logs_metadata=None, requester_ip_address=str("127.0.0.1"), requester_metadata=None, user_api_key_end_user_id=str("test_end_user"), ) hidden_params = StandardLoggingHiddenParams( model_id=None, cache_key=None, api_base=None, response_cost=None, additional_headers=None, litellm_overhead_time_ms=None, batch_models=None, litellm_model_name=None, usage_object=None, ) # Convert numeric values to appropriate types response_cost = Decimal("0.1") start_time = Decimal("1234567890.0") end_time = Decimal("1234567891.0") completion_start_time = Decimal("1234567890.5") saved_cache_cost = Decimal("0.0") # Create messages and response with proper typing messages: List[Dict[str, str]] = [{"role": "user", "content": "Hello, world!"}] response: Dict[str, List[Dict[str, Dict[str, str]]]] = { "choices": [{"message": {"content": "Hi there!"}}] } # Main payload initialization return StandardLoggingPayload( # type: ignore id=str("test_id"), call_type=str("completion"), stream=bool(False), response_cost=response_cost, response_cost_failure_debug_info=None, status=str("success"), total_tokens=int( DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT + DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT ), prompt_tokens=int(DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT), completion_tokens=int(DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT), startTime=start_time, endTime=end_time, completionStartTime=completion_start_time, model_map_information=model_info, model=str("gpt-3.5-turbo"), model_id=str("model-123"), model_group=str("openai-gpt"), custom_llm_provider=str("openai"), api_base=str("https://api.openai.com"), metadata=metadata, cache_hit=bool(False), cache_key=None, saved_cache_cost=saved_cache_cost, request_tags=[], end_user=None, requester_ip_address=str("127.0.0.1"), messages=messages, response=response, error_str=None, model_parameters={"stream": True}, hidden_params=hidden_params, )