Monitor HuggingFace using OpenTelemetry

Instrument Hugging Face Transformers, the Inference API, and Transformers.js with OpenTelemetry for AI observability into tokens, cost, and inference calls.

OpenLIT uses OpenTelemetry Auto-Instrumentation to help you monitor LLM applications built using models from HuggingFace. This includes tracking performance, token usage, costs, 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

• HuggingFace Transformers Python SDK client >= 4.48.0
• HuggingFace Inference TypeScript SDK (@huggingface/inference) >= 2.0.0
• Transformers.js (@huggingface/transformers >= 3 or @xenova/transformers) for local inference

Supported APIs

SDK	Instrumented surface	Inference type
Python	TextGenerationPipeline.__call__	Local (transformers)
TypeScript	@huggingface/inference chat completions	Remote (Inference API)
TypeScript	Transformers.js pipeline calls (@huggingface/transformers / @xenova/transformers)	Local

Local text-generation is reported as the chat operation to match the Python SDK. Other local Transformers.js pipelines (summarization, translation, fill-mask, question-answering, classification) are reported as text_completion, and feature-extraction / sentence-similarity as embeddings.

Prerequisites

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

TypeScript example (local Transformers.js)

import openlit from "openlit";

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

import { pipeline } from "@huggingface/transformers";

const generator = await pipeline("text-generation", "Xenova/distilgpt2");
const output = await generator("OpenTelemetry makes observability", {
  max_new_tokens: 32,
  temperature: 0.7,
});

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: ['transformers']	Disables the local Transformers.js instrumentation
disabledInstrumentors: ['huggingface']	Disables the remote Inference API 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
   # Remote Inference API
   npm install @huggingface/inference
   # or local inference with Transformers.js
   npm install @huggingface/transformers
   

   The older @xenova/transformers package is also supported for local inference.

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