gen_ai_client_operation_time
GenAI operation durationTechnical Annotations (19)
Configuration Parameters (2)
timeoutrecommended: 60.0http_options.timeoutrecommended: Set in generation config, not client constructorError Signatures (1)
server disconnectedlog patternTechnical References (16)
LLM pipelinescomponentBackend APIcomponentUIcomponentSDKcomponenthttps://api.inference.wandb.ai/v1componentexponential backoffconceptrate limitconceptweave.opcomponent_async_wrappercomponentpostprocess_outputcomponentcontextvarscomponentevent loopconceptevaluation loopconceptgenai.Clientcomponenttypes.HttpOptionscomponentgeneration configcomponentRelated Insights (11)
Distributed agent architectures require trace correlation across multiple context windows and parallel execution paths. Without proper instrumentation, teams lose visibility into subagent activities, making root cause analysis impossible when investigations fail.
LangChain LLM requests timeout after periods of inactivity due to connection pool staleness or dropped connections, requiring server restart to resolve.
LLM provider rate limits cause request failures that aren't retried with appropriate backoff, leading to cascading failures during usage spikes.
High gen_ai_client_token_usage and gen_ai_client_operation_time indicate expensive or slow AI model calls, causing both cost overruns and user-facing latency. Large context windows or inefficient prompt engineering amplify this issue.