OpenLLMetry & Traceloop

OpenLLMetry is Traceloop’s open-source observability package that auto-instruments 20+ LLM providers and frameworks with OpenTelemetry. The Traceloop SDK (traceloop-sdk) is the one-line Traceloop.init() wrapper that activates those instrumentors — the two are the same integration, so this page covers both. Arize AX ingests their spans by converting them to OpenInference with the openinference-instrumentation-openllmetry span processor.

Prerequisites

Python 3.9+
An Arize AX account (sign up)
An OPENAI_API_KEY from the OpenAI Platform

Launch Arize AX

Sign in to your Arize AX account.
From Space Settings, copy your Space ID and API Key. You will set them as ARIZE_SPACE_ID and ARIZE_API_KEY below.

Install

pip install traceloop-sdk openinference-instrumentation-openllmetry arize-otel opentelemetry-exporter-otlp-proto-grpc openai

Configure credentials

OpenAI

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="openllmetry-tracing-example"
export OPENAI_API_KEY="<your-openai-api-key>"

OpenLLMetry auto-instruments any provider it supports the same way — swap the OpenAI packages and client below for another supported provider and the wiring is unchanged.

Setup tracing

# instrumentation.py
import os

import grpc
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from openinference.instrumentation.openllmetry import OpenInferenceSpanProcessor
from traceloop.sdk import Traceloop

project_name = os.environ.get("ARIZE_PROJECT_NAME", "openllmetry-tracing-example")

# Export spans to Arize AX over OTLP/gRPC.
arize_exporter = OTLPSpanExporter(
    endpoint="otlp.arize.com:443",
    headers={
        "authorization": f"Bearer {os.environ['ARIZE_API_KEY']}",
        "arize-space-id": os.environ["ARIZE_SPACE_ID"],
        "arize-interface": "python",
        "user-agent": "arize-python",
    },
    compression=grpc.Compression.Gzip,
)

# Traceloop/OpenLLMetry emits OpenTelemetry spans. OpenInferenceSpanProcessor
# converts them to OpenInference before the exporter ships them to Arize AX, so
# it must run before the exporting processor in the list below.
Traceloop.init(
    app_name=project_name,
    disable_batch=True,
    processor=[OpenInferenceSpanProcessor(), SimpleSpanProcessor(arize_exporter)],
    resource_attributes={
        "openinference.project.name": project_name,
        "model_id": project_name,
    },
)

print("Arize AX tracing initialized for OpenLLMetry.")

Prefer to wire up individual instrumentors instead of the Traceloop SDK? Install the specific OpenLLMetry instrumentor (e.g. opentelemetry-instrumentation-openai), then instrument it against a tracer provider that carries the OpenInferenceSpanProcessor — the conversion step is the same.

Run OpenLLMetry

# example.py

# Importing instrumentation first runs Traceloop.init() and activates
# OpenLLMetry's auto-instrumentation before the OpenAI client is used.
from instrumentation import project_name  # noqa: F401

from openai import OpenAI

# The client reads OPENAI_API_KEY from the environment.
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "user", "content": "Write a haiku about observability."},
    ],
)

print(response.choices[0].message.content)

Expected output

Arize AX tracing initialized for OpenLLMetry.
Logs whisper softly,
metrics rise like morning mist —
truth in every span.

Verify in Arize AX

Open your Arize AX space and select project openllmetry-tracing-example.
You should see a new trace within ~30 seconds containing an openai.chat span with openinference.span.kind set to LLM, the prompt and response, and token usage attached.
If no traces appear, confirm ARIZE_SPACE_ID, ARIZE_API_KEY, and OPENAI_API_KEY are exported in the shell that ran the example.

Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

Arize skill (agent)
AX CLI
SDK

Install the Arize Skills plugin and let your coding agent check for you:

npx skills add Arize-ai/arize-skills

Then prompt your agent:

Use the arize-trace skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.

Export recent spans for your project — any rows mean traces are landing:

ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
  --limit 5 --stdout | jq 'length'

A non-zero count confirms spans reached Arize AX. Run ax auth login first if you have not authenticated. See the ax spans reference.

Query the project’s spans and check that at least one came back.

import os
from arize import ArizeClient

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
resp = client.spans.list(
    project=os.environ["ARIZE_PROJECT_NAME"],
    space=os.environ["ARIZE_SPACE_ID"],
    limit=5,
)
count = len(resp.spans)
print(
    f"{count} span(s) found" if count else "No spans yet — recheck setup"
)

// Reads ARIZE_API_KEY from the environment.
import { listSpans } from "@arizeai/ax-client";

const { data: spans } = await listSpans({
  project: process.env.ARIZE_PROJECT_NAME!,
  space: process.env.ARIZE_SPACE_ID!,
  limit: 5,
});
const count = spans.length;
console.log(
  count ? `${count} span(s) found` : "No spans yet — recheck setup",
);

client, err := arize.NewClient(
    arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
)
if err != nil {
    log.Fatal(err)
}
resp, err := client.Spans.List(ctx, spans.ListRequest{
    Project: os.Getenv("ARIZE_PROJECT_NAME"),
    Space:   os.Getenv("ARIZE_SPACE_ID"),
    Limit:   5,
})
if err != nil {
    log.Fatal(err)
}
fmt.Printf("%d span(s) found\n", len(resp.Spans))

SDK span references: Python · TypeScript · Go.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run OpenLLMetry

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Resources

​Prerequisites

​Launch Arize AX

​Install

​Configure credentials

​Setup tracing

​Run OpenLLMetry

​Expected output

​Verify in Arize AX

​Check from the skill, CLI, or SDK

​Resources

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run OpenLLMetry

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Resources