Monitor HuggingFace using OpenTelemetry

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

Install OpenLIT

Open your command line or terminal and run:

Python
Typescript

pip install openlit

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.

Initialize OpenLIT in your Application

Python
Typescript

Zero Code Instrumentation
One-Line Instrumentation

Perfect for existing applications - no code modifications needed:

Via CLI Arguments
Via Environment Variables

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

# 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.

Via Function Parameters
Via Environment Variables

import openlit

openlit.init(otlp_endpoint="YOUR_OTEL_ENDPOINT")

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

One-Line Instrumentation (SDK)

Via Function Parameters
Via Environment Variables

import openlit from "openlit"

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

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.

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

Zero-code observability with the OpenLIT Controller

Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.

​Supported APIs

​Prerequisites

​TypeScript example (local Transformers.js)

​Configuration

​Get started

Quickstart: LLM Observability

Configuration

Destinations

Zero-code observability with the OpenLIT Controller

Supported APIs

Prerequisites

TypeScript example (local Transformers.js)

Configuration

Get started