opentelemetry-llm
OpenTelemetry instrumentation for LLM applications with distributed tracing
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技能内容
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
- TracerProvider: SDK configuration
- SpanProcessor: Batch/simple processors
- Exporters: Jaeger, OTLP, Console
- 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)
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原文件路径
library/specializations/ai-agents-conversational/skills/opentelemetry-llm/SKILL.md