rubyllm-opentelemetry

name	rubyllm/opentelemetry
version	0.4.0
description	OpenTelemetry tracing for RubyLLM. Use this skill when you need observability into LLM applications with support for Langfuse, Datadog, Honeycomb, Jaeger, Arize Phoenix, and any OpenTelemetry-compatible backend.

OpenTelemetry RubyLLM Instrumentation v{{ page.version }}

Observability for RubyLLM Applications

Adds OpenTelemetry tracing to RubyLLM. Send traces to any compatible backend (Langfuse, Datadog, Honeycomb, Jaeger, Arize Phoenix).

Gem Version: 0.4.0 GitHub: https://github.com/thoughtbot/opentelemetry-instrumentation-ruby_llm

Installation

gem 'opentelemetry-instrumentation-ruby_llm'
gem 'opentelemetry-sdk'
gem 'opentelemetry-exporter-otlp'

Setup

require 'opentelemetry/sdk'
require 'opentelemetry/exporter/otlp'
require 'opentelemetry/instrumentation/ruby_llm'

OpenTelemetry::SDK.configure do |c|
  c.use 'OpenTelemetry::Instrumentation::RubyLLM'

  c.add_span_processor(
    OpenTelemetry::SDK::Trace::Export::BatchSpanProcessor.new(
      OpenTelemetry::Exporter::OTLP::Exporter.new(
        endpoint: ENV['OTEL_EXPORTER_OTLP_ENDPOINT'],
        headers: ENV['OTEL_EXPORTER_OTLP_HEADERS']
      )
    )
  )

  c.resource = OpenTelemetry::SDK::Resources::Resource.create(
    'service.name' => 'my-llm-app',
    'service.version' => '1.0.0'
  )
end

Backend Configurations

Langfuse

OpenTelemetry::SDK.configure do |c|
  c.use 'OpenTelemetry::Instrumentation::RubyLLM'

  c.add_span_processor(
    OpenTelemetry::SDK::Trace::Export::BatchSpanProcessor.new(
      OpenTelemetry::Exporter::OTLP::Exporter.new(
        endpoint: 'https://cloud.langfuse.com/api/public/otel',
        headers: {
          'Authorization' => "Basic #{ENV['LANGFUSE_PUBLIC_KEY']}:#{ENV['LANGFUSE_SECRET_KEY']}"
        }
      )
    )
  )
end

Datadog

OpenTelemetry::SDK.configure do |c|
  c.use 'OpenTelemetry::Instrumentation::RubyLLM'

  c.add_span_processor(
    OpenTelemetry::SDK::Trace::Export::BatchSpanProcessor.new(
      OpenTelemetry::Exporter::OTLP::Exporter.new(
        endpoint: ENV['DD_TRACE_OTLP_ENDPOINT'] || 'http://localhost:4318'
      )
    )
  )
end

Honeycomb

OpenTelemetry::SDK.configure do |c|
  c.use 'OpenTelemetry::Instrumentation::RubyLLM'

  c.add_span_processor(
    OpenTelemetry::SDK::Trace::Export::BatchSpanProcessor.new(
      OpenTelemetry::Exporter::OTLP::Exporter.new(
        endpoint: 'https://api.honeycomb.io:4318',
        headers: {
          'x-honeycomb-team' => ENV['HONEYCOMB_API_KEY'],
          'x-honeycomb-dataset' => ENV['HONEYCOMB_DATASET']
        }
      )
    )
  )
end

Jaeger (Local Development)

require 'opentelemetry/exporter/jaeger'

OpenTelemetry::SDK.configure do |c|
  c.use 'OpenTelemetry::Instrumentation::RubyLLM'

  c.add_span_processor(
    OpenTelemetry::SDK::Trace::Export::SimpleSpanProcessor.new(
      OpenTelemetry::Exporter::Jaeger::Agent.new(host: 'localhost', port: 6831)
    )
  )
end

Arize Phoenix

OpenTelemetry::SDK.configure do |c|
  c.use 'OpenTelemetry::Instrumentation::RubyLLM'

  c.add_span_processor(
    OpenTelemetry::SDK::Trace::Export::SimpleSpanProcessor.new(
      OpenTelemetry::Exporter::OTLP::Exporter.new(
        endpoint: 'http://localhost:4318/v1/traces'
      )
    )
  )
end

Traced Operations

Chat Completions

Automatically traces Chat#ask.

Span Attributes:

• gen_ai.system — Provider (openai, anthropic, etc.)
• gen_ai.request.model — Requested model
• gen_ai.response.model — Actual model used
• gen_ai.usage.input_tokens — Input tokens
• gen_ai.usage.output_tokens — Output tokens
• gen_ai.operation.name — chat

Tool Calls

Span Attributes:

• gen_ai.tool.name — Tool name
• gen_ai.tool.description — Tool description
• tool.arguments — Tool arguments (JSON)
• tool.result — Tool result

Events: tool_call, tool_result

Embeddings

Span Attributes:

• gen_ai.system, gen_ai.request.model, gen_ai.usage.input_tokens, embedding.dimensions

Image Generation

Span Attributes:

• gen_ai.system, gen_ai.request.model, gen_ai.request.prompt, image.size

Audio Transcription

Span Attributes:

• gen_ai.system, gen_ai.request.model, audio.duration, audio.language

Custom Attributes

RubyLLM::Instrumentation.with(user_id: current_user.id, feature: "chat") do
  RubyLLM.chat.ask("Hello")
end

Sampling

OpenTelemetry::SDK.configure do |c|
  c.use 'OpenTelemetry::Instrumentation::RubyLLM'

  c.sampler = OpenTelemetry::SDK::Trace::Samplers.parent_based(
    root: OpenTelemetry::SDK::Trace::Samplers.trace_id_ratio_based(0.1)
  )
end

Rails Integration

# config/initializers/opentelemetry.rb
Rails.application.reloader.to_prepare do
  OpenTelemetry::SDK.configure do |c|
    c.use 'OpenTelemetry::Instrumentation::RubyLLM'
    c.use_all  # Auto-instrument other gems

    c.add_span_processor(
      OpenTelemetry::SDK::Trace::Export::BatchSpanProcessor.new(
        OpenTelemetry::Exporter::OTLP::Exporter.new
      )
    )
  end
end

See Also

• Main RubyLLM: rubyllm
• Instrumentation: rubyllm-instrumentation
• OpenTelemetry: https://opentelemetry.io
• Source: https://github.com/thoughtbot/opentelemetry-instrumentation-ruby_llm