StandardLoggingPayload Specification

Found under kwargs["standard_logging_object"]. This is a standard payload, logged for every successful and failed response.

StandardLoggingPayload

Field	Type	Description
id	str	Unique identifier
trace_id	str	Trace multiple LLM calls belonging to same overall request
call_type	str	Type of call
response_cost	float	Cost of the response in USD ($)
cost_breakdown	Optional[CostBreakdown]	Detailed cost breakdown object
response_cost_failure_debug_info	StandardLoggingModelCostFailureDebugInformation	Debug information if cost tracking fails
status	StandardLoggingPayloadStatus	Status of the payload
status_fields	StandardLoggingPayloadStatusFields	Typed status fields for easy filtering and analytics
total_tokens	int	Total number of tokens
prompt_tokens	int	Number of prompt tokens
completion_tokens	int	Number of completion tokens
startTime	float	Start time of the call
endTime	float	End time of the call
completionStartTime	float	Time to first token for streaming requests
response_time	float	Total response time. If streaming, this is the time to first token
model_map_information	StandardLoggingModelInformation	Model mapping information
model	str	Model name sent in request
model_id	Optional[str]	Model ID of the deployment used
model_group	Optional[str]	model_group used for the request
api_base	str	LLM API base URL
metadata	StandardLoggingMetadata	Metadata information
cache_hit	Optional[bool]	Whether cache was hit
cache_key	Optional[str]	Optional cache key
saved_cache_cost	float	Cost saved by cache
request_tags	list	List of request tags
end_user	Optional[str]	Optional end user identifier
requester_ip_address	Optional[str]	Optional requester IP address
messages	Optional[Union[str, list, dict]]	Messages sent in the request
response	Optional[Union[str, list, dict]]	LLM response
error_str	Optional[str]	Optional error string
error_information	Optional[StandardLoggingPayloadErrorInformation]	Optional error information
model_parameters	dict	Model parameters
hidden_params	StandardLoggingHiddenParams	Hidden parameters

Cost Breakdown

The cost_breakdown field provides detailed cost breakdown for completion requests as a CostBreakdown object containing:

• input_cost: Cost of input/prompt tokens including cache creation tokens
• output_cost: Cost of output/completion tokens (including reasoning tokens if applicable)
• tool_usage_cost: Cost of built-in tools usage (e.g., web search, code interpreter)
• total_cost: Total cost of input + output + tool usage

Note: This field is populated for all call types. For non-completion calls, input_cost and output_cost may be 0.

The total cost relationship is: response_cost = cost_breakdown.total_cost

CostBreakdown Type

class CostBreakdown(TypedDict, total=False):
    input_cost: float        # Cost of input/prompt tokens in USD
    output_cost: float       # Cost of output/completion tokens in USD (includes reasoning)
    tool_usage_cost: float   # Cost of built-in tools usage in USD
    total_cost: float        # Total cost in USD

StandardLoggingUserAPIKeyMetadata

Field	Type	Description
user_api_key_hash	Optional[str]	Hash of the litellm virtual key
user_api_key_alias	Optional[str]	Alias of the API key
user_api_key_org_id	Optional[str]	Organization ID associated with the key
user_api_key_team_id	Optional[str]	Team ID associated with the key
user_api_key_user_id	Optional[str]	User ID associated with the key
user_api_key_team_alias	Optional[str]	Team alias associated with the key

StandardLoggingMetadata

Inherits from StandardLoggingUserAPIKeyMetadata and adds:

Field	Type	Description
spend_logs_metadata	Optional[dict]	Key-value pairs for spend logging
requester_ip_address	Optional[str]	Requester's IP address
requester_metadata	Optional[dict]	Additional requester metadata
vector_store_request_metadata	Optional[List[StandardLoggingVectorStoreRequest]]	Vector store request metadata
requester_custom_headers	Dict[str, str]	Any custom (x-) headers sent by the client to the proxy.
prompt_management_metadata	Optional[StandardLoggingPromptManagementMetadata]	Prompt management and versioning metadata
mcp_tool_call_metadata	Optional[StandardLoggingMCPToolCall]	MCP (Model Context Protocol) tool call information and cost tracking
applied_guardrails	Optional[List[str]]	List of applied guardrail names
usage_object	Optional[dict]	Raw usage object from the LLM provider
cold_storage_object_key	Optional[str]	S3/GCS object key for cold storage retrieval
guardrail_information	Optional[list[StandardLoggingGuardrailInformation]]	Guardrail information

StandardLoggingVectorStoreRequest

Field	Type	Description
vector_store_id	Optional[str]	ID of the vector store
custom_llm_provider	Optional[str]	Custom LLM provider the vector store is associated with (e.g., bedrock, openai, anthropic)
query	Optional[str]	Query to the vector store
vector_store_search_response	Optional[VectorStoreSearchResponse]	OpenAI format vector store search response
start_time	Optional[float]	Start time of the vector store request
end_time	Optional[float]	End time of the vector store request

StandardLoggingAdditionalHeaders

Field	Type	Description
x_ratelimit_limit_requests	int	Rate limit for requests
x_ratelimit_limit_tokens	int	Rate limit for tokens
x_ratelimit_remaining_requests	int	Remaining requests in rate limit
x_ratelimit_remaining_tokens	int	Remaining tokens in rate limit

StandardLoggingHiddenParams

Field	Type	Description
model_id	Optional[str]	Optional model ID
cache_key	Optional[str]	Optional cache key
api_base	Optional[str]	Optional API base URL
response_cost	Optional[str]	Optional response cost
additional_headers	Optional[StandardLoggingAdditionalHeaders]	Additional headers
batch_models	Optional[List[str]]	Only set for Batches API. Lists the models used for cost calculation
litellm_model_name	Optional[str]	Model name sent in request

StandardLoggingModelInformation

Field	Type	Description
model_map_key	str	Model map key
model_map_value	Optional[ModelInfo]	Optional model information

StandardLoggingModelCostFailureDebugInformation

Field	Type	Description
error_str	str	Error string
traceback_str	str	Traceback string
model	str	Model name
cache_hit	Optional[bool]	Whether cache was hit
custom_llm_provider	Optional[str]	Optional custom LLM provider
base_model	Optional[str]	Optional base model
call_type	str	Call type
custom_pricing	Optional[bool]	Whether custom pricing was used

StandardLoggingPayloadErrorInformation

Field	Type	Description
error_code	Optional[str]	Optional error code (eg. "429")
error_class	Optional[str]	Optional error class (eg. "RateLimitError")
llm_provider	Optional[str]	LLM provider that returned the error (eg. "openai")`

StandardLoggingPayloadStatus

A literal type with two possible values:

• "success"
• "failure"

StandardLoggingGuardrailInformation

Field	Type	Description
guardrail_name	Optional[str]	Guardrail name
guardrail_provider	Optional[str]	Guardrail provider
guardrail_mode	Optional[Union[GuardrailEventHooks, List[GuardrailEventHooks]]]	Guardrail mode
guardrail_request	Optional[dict]	Guardrail request
guardrail_response	Optional[Union[dict, str, List[dict]]]	Guardrail response
guardrail_status	Literal["success", "guardrail_intervened", "guardrail_failed_to_respond"]	Guardrail execution status: success = no violations detected, blocked = content blocked/modified due to policy violations, failure = technical error or API failure
start_time	Optional[float]	Start time of the guardrail
end_time	Optional[float]	End time of the guardrail
duration	Optional[float]	Duration of the guardrail in seconds
masked_entity_count	Optional[Dict[str, int]]	Count of masked entities

StandardLoggingPayloadStatusFields

Typed status fields for easy filtering and analytics.

Field	Type	Description
llm_api_status	StandardLoggingPayloadStatus	Status of the LLM API call: "success" if completed successfully, "failure" if errored
guardrail_status	GuardrailStatus	Status of guardrail execution (see below)

StandardLoggingPayloadStatus

A literal type with two possible values:

• "success" - The LLM API request completed successfully
• "failure" - The LLM API request failed

GuardrailStatus

A literal type with four possible values:

• "success" - Guardrail ran and allowed content through (no violations detected)
• "guardrail_intervened" - Guardrail blocked or modified content due to policy violations
• "guardrail_failed_to_respond" - Guardrail had a technical failure or API error
• "not_run" - No guardrail was executed for this request

Usage Examples

Filter logs for requests where guardrails intervened:

{
  "status_fields": {
    "guardrail_status": "guardrail_intervened"
  }
}

Find guardrail technical failures:

{
  "status_fields": {
    "guardrail_status": "guardrail_failed_to_respond"
  }
}

Get successful LLM requests:

{
  "status_fields": {
    "llm_api_status": "success"
  }
}

Find requests where guardrails ran successfully without intervention:

{
  "status_fields": {
    "guardrail_status": "success",
    "llm_api_status": "success"
  }
}

Find requests where no guardrail was run:

{
  "status_fields": {
    "guardrail_status": "not_run"
  }
}

StandardLoggingPromptManagementMetadata

Used for tracking prompt versioning and management information.

Field	Type	Description
prompt_id	str	Required. Unique identifier for the prompt template or version
prompt_variables	Optional[dict]	Variables/parameters used in the prompt template (e.g., {"user_name": "John", "context": "support"})
prompt_integration	str	Required. Integration or system managing the prompt (e.g., "langfuse", "promptlayer", "custom")

StandardLoggingMCPToolCall

Used to track Model Context Protocol (MCP) tool calls within LiteLLM requests. This provides detailed logging for external tool integrations.

Field	Type	Description
name	str	Required. The name of the tool being called (e.g., "get_weather", "search_database")
arguments	dict	Required. Arguments passed to the tool as key-value pairs
result	Optional[dict]	The response/result returned by the tool execution (populated by custom logging hooks)
mcp_server_name	Optional[str]	Name of the MCP server that handled the tool call (e.g., "weather-service", "database-connector")
mcp_server_logo_url	Optional[str]	URL for the MCP server's logo (used for UI display in LiteLLM dashboard)
namespaced_tool_name	Optional[str]	Fully qualified tool name including server prefix (e.g., "deepwiki-mcp/get_page_content", "github-mcp/create_issue")
mcp_server_cost_info	Optional[MCPServerCostInfo]	Cost tracking information for the tool call

MCPServerCostInfo

Cost tracking structure for MCP server tool calls:

Field	Type	Description
default_cost_per_query	Optional[float]	Default cost in USD for any tool call to this MCP server
tool_name_to_cost_per_query	Optional[Dict[str, float]]	Per-tool cost mapping for granular pricing (e.g., {"search": 0.01, "create": 0.05})

Usage

# Basic MCP tool call metadata
mcp_tool_call = {
    "name": "search_documents",
    "arguments": {
        "query": "machine learning tutorials",
        "limit": 10,
        "filter": "type:pdf"
    },
    "mcp_server_name": "document-search-service",
    "namespaced_tool_name": "docs-mcp/search_documents",
    "mcp_server_cost_info": {
        "default_cost_per_query": 0.02,
        "tool_name_to_cost_per_query": {
            "search_documents": 0.02,
            "get_document": 0.01
        }
    }
}

# optional result field (via custom logging hooks)
mcp_tool_call_with_result = {
    "name": "search_documents",
    "arguments": {
        "query": "machine learning tutorials",
        "limit": 10,
        "filter": "type:pdf"
    },
    "result": {
        "documents": [...],
        "total_found": 42,
        "search_time_ms": 150
    },
    "mcp_server_name": "document-search-service",
    "namespaced_tool_name": "docs-mcp/search_documents",
    "mcp_server_cost_info": {
        "default_cost_per_query": 0.02,
        "tool_name_to_cost_per_query": {
            "search_documents": 0.02,
            "get_document": 0.01
        }
    }
}