liveBook · Manning

OpenLLMetry

As explained in Generative AI in Action

OpenLLMetry is an open-source observability extension that provides instrumentation and telemetry for large language model operations.

OpenLLMetry is an open-source extension of OpenTelemetry for observing large language model applications. It emits standard telemetry while adding LLM-specific visibility across provider calls, APIs, databases, and vector databases. section 11.4.7

OpenLLMetry supports production observability by enabling teams to:

  • Trace LLM executions and inspect interactions with providers such as OpenAI or Anthropic. section 11.4.7
  • Monitor generic operations, including API, database, and vector-database interactions. section 11.4.7
  • Relate completion-token counts to request latency and inspect prompt or response details, subject to privacy and legal controls. section 11.4.7

Instrumenting an Azure OpenAI test

Install the Traceloop SDK, obtain an API key, and call Traceloop.init(api_key=TRACELOOP_API_KEY) before creating an AzureOpenAI client. A load-test script can then send repeated conversational inputs for observability.

How Generative AI in Action covers it

How deep it goes
Explains architecture, supported integrations, installation, initialization, and telemetry risks without detailing OpenTelemetry internals.
Examples
Uses Azure OpenAI load testing, token-latency metrics, and prompt-response telemetry as concrete observability examples.
What it assumes
Assumes a Python LLM application and credentials while teaching instrumentation setup incrementally.
What you can do after
Operate/configure Traceloop SDK instrumentation for LLM applications and inspect standardized telemetry through compatible observability tooling.
How it connects
Connects OpenTelemetry, Traceloop, LLM providers, APIs, databases, and vector databases.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.