opentelemetry-llm

OpenTelemetry instrumentation for LLM applications with distributed tracing

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

opentelemetry-llm is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

OpenTelemetry instrumentation for LLM applications with distributed tracing

Teams using opentelemetry-llm should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/opentelemetry-llm/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/ai-agents-conversational/skills/opentelemetry-llm/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/opentelemetry-llm/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How opentelemetry-llm Compares

Feature / Agent	opentelemetry-llm	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

OpenTelemetry instrumentation for LLM applications with distributed tracing

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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)

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