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OpenTelemetry instrumentation for LLM applications with distributed tracing
--- name: opentelemetry-llm-59fda80b8116 description: OpenTelemetry instrumentation for LLM applications with distributed tracing allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:agent-debugging-logging, skill-area:observability-instrumentation] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # OpenTelemetry LLM Skill ## Capabilities - Configure OpenTelemetry SDK for LLM apps - Implement LLM-specific instrumentation - Set up trace exporters (Jaeger, OTLP) - Design semantic conventions for LLM - Configure span attributes for AI workloads - Implement context propagation ## Target Processes - llm-observability-monitoring - agent-deployment-pipeline ## Implementation Details ### Core Components 1. **TracerProvider**: SDK configuration 2. **SpanProcessor**: Batch/simple processors 3. **Exporters**: Jaeger, OTLP, Console 4. **Instrumentation**: Auto and manual ### LLM Semantic Conventions - gen_ai.system (OpenAI, Anthropic) - gen_ai.request.model - gen_ai.request.max_tokens - gen_ai.response.finish_reason - gen_ai.usage.prompt_tokens ### Configuration Options - Exporter selection - Sampling strategies - Resource attributes - Span limits - Context propagation ### Best Practices - Consistent attribute naming - Appropriate sampling - Error handling traces - Propagate context across services ### Dependencies - opentelemetry-sdk - opentelemetry-exporter-* - openinference (optional)--- name: opentelemetry-llm-59fda80b8116 description: OpenTelemetry instrumentation for LLM applications with distributed tracing allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:agent-debugging-logging, skill-area:observability-instrumentation] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # OpenTelemetry LLM Skill ## Capabilities - Configure OpenTelemetry SDK for LLM apps - Implement LLM-specific instrumentation - Set up trace exporters (Jaeger, OTLP) - Design semantic conventions for LLM - Configure span attributes for AI workloads - Implement context propagation ## Target Processes - llm-observability-monitoring - agent-deployment-pipeline ## Implementation Details ### Core Components 1. **TracerProvider**: SDK configuration 2. **SpanProcessor**: Batch/simple processors 3. **Exporters**: Jaeger, OTLP, Console 4. **Instrumentation**: Auto and manual ### LLM Semantic Conventions - gen_ai.system (OpenAI, Anthropic) - gen_ai.request.model - gen_ai.request.max_tokens - gen_ai.response.finish_reason - gen_ai.usage.prompt_tokens ### Configuration Options - Exporter selection - Sampling strategies - Resource attributes - Span limits - Context propagation ### Best Practices - Consistent attribute naming - Appropriate sampling - Error handling traces - Propagate context across services ### Dependencies - opentelemetry-sdk - opentelemetry-exporter-* - openinference (optional)
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.