Monitor ElevenLabs using OpenTelemetry

OpenLIT auto-instruments ElevenLabs’ voice and speech models via OpenTelemetry, offering AI observability with cost tracking across voice generation calls.

OpenLIT uses OpenTelemetry Auto-Instrumentation to help you monitor LLM applications built using Audio and Speech models from ElevenLabs. This includes tracking performance, costs, voice inputs and settings, and how users interact with the application.

Auto-instrumentation means you don’t have to set up monitoring manually for different LLMs, frameworks, or databases. By simply adding OpenLIT in your application, all the necessary monitoring configurations are automatically set up.

The integration is compatible with

• ElevenLabs Python SDK client >= 1.4.0
• ElevenLabs TypeScript SDK client (@elevenlabs/elevenlabs-js or elevenlabs) >= 1.0.0

Supported APIs

SDK	Instrumented methods
TypeScript	ElevenLabsClient.textToSpeech.convert, .stream, .convertWithTimestamps
Python	TextToSpeechClient.convert and AsyncTextToSpeechClient.convert

TypeScript covers more TTS methods than Python. Python instruments .convert only (sync and async).

Prerequisites

• Install the ElevenLabs SDK yourself. OpenLIT does not bundle it as a dependency.
• For TypeScript, call openlit.init() before the ElevenLabs module is first loaded so OpenTelemetry can hook into the SDK at runtime.

TypeScript example

import openlit from "openlit";

openlit.init({ otlpEndpoint: "YOUR_OTEL_ENDPOINT" });

import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";

const client = new ElevenLabsClient({ apiKey: process.env.ELEVENLABS_API_KEY! });
const audio = await client.textToSpeech.convert("voice-id", {
  text: "Hello",
  modelId: "eleven_multilingual_v2", // also accepts model_id / model
});

Configuration

Option	Behavior
captureMessageContent	Gates gen_ai.input.messages / gen_ai.output.messages on spans and in events
disableEvents	Suppresses all inference events
disableMetrics	Global - metrics instruments are not set up
disabledInstrumentors: ['elevenlabs']	Disables this instrumentation

On the Python SDK, inference events are only emitted when capture_message_content is enabled. On the TypeScript SDK, inference events are emitted when disableEvents is false, regardless of captureMessageContent (message fields within the event are still gated by content capture).

Get started

1. Install OpenLIT

   Open your command line or terminal and run:

   Python

   pip install openlit
   

   Typescript

   npm install openlit
   npm install @elevenlabs/elevenlabs-js
   

   The elevenlabs package is also supported.

2. Initialize OpenLIT in your Application

   Python

   Zero Code Instrumentation

   Perfect for existing applications - no code modifications needed:

   Via CLI Arguments

   # Configure via CLI arguments
   openlit-instrument \
     --service-name my-ai-app \
     --environment production \
     --otlp-endpoint YOUR_OTEL_ENDPOINT \
     python your_app.py
   

   Via Environment Variables

   # Configure via environment variables
   export OTEL_SERVICE_NAME=my-ai-app
   export OTEL_DEPLOYMENT_ENVIRONMENT=production
   export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT
   
   # Run with zero code changes
   openlit-instrument python your_app.py
   

   Perfect for: Legacy applications, production systems where code changes need approval, quick testing, or when you want to add observability without touching existing code.

   One-Line Instrumentation

   Via Function Parameters

   import openlit
   
   openlit.init(otlp_endpoint="YOUR_OTEL_ENDPOINT")
   

   Via Environment Variables

   Add the following two lines to your application code:

   import openlit
   
   openlit.init()
   

   Then, configure the your OTLP endpoint using environment variable:

   export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT
   

   Typescript

   One-Line Instrumentation (SDK)

   Via Function Parameters

   import openlit from "openlit"
   
   openlit.init({ otlpEndpoint: "YOUR_OTEL_ENDPOINT" })
   

   Via Environment Variables

   Add the following two lines to your application code:

   import openlit from "openlit"
   
   openlit.init()
   

   Then, configure the your OTLP endpoint using environment variable:

   export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT
   

   Replace: YOUR_OTEL_ENDPOINT with the URL of your OpenTelemetry backend, such as http://127.0.0.1:4318 if you are using OpenLIT and a local OTel Collector.

   To send metrics and traces to other Observability tools, refer to the supported destinations.

   For more advanced configurations and application use cases, visit the OpenLIT Python repository or OpenLIT Typescript repository.

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Quickstart: LLM Observability

Production-ready AI monitoring setup in 2 simple steps with zero code changes

Configuration

Configure the OpenLIT SDK according to you requirements.

Destinations

Send telemetry to Datadog, Grafana, New Relic, and other observability stacks