vLLM

vLLM is an inference and serving engine for large language models. You can observe vLLM with Arize AX in two complementary ways:

Export vLLM server spans over OTLP with --otlp-traces-endpoint.
Trace your application calls to vLLM’s OpenAI-compatible server with openinference-instrumentation-openai.

Use both paths when possible. vLLM’s server-side OTLP spans show inference-server timing and request metadata. OpenInference OpenAI instrumentation captures the client-side prompt, response, token, model, and latency metadata for calls made through the OpenAI-compatible API.

Prerequisites

Python 3.9+
A running vLLM server
An Arize AX account (sign up)
Your Arize AX Space ID and API Key

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.

Option 1: export vLLM server spans over OTLP

vLLM defaults to OTLP/gRPC. Arize’s direct HTTP endpoint uses OTLP/HTTP with protobuf encoding, so set the exporter protocol before starting vLLM:

export OTEL_SERVICE_NAME="vllm-server"
export OTEL_RESOURCE_ATTRIBUTES="openinference.project.name=vllm-server"
export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_TRACES_HEADERS="space_id=<your-arize-space-id>,api_key=<your-arize-api-key>"

vllm serve <model-name-or-path> \
  --otlp-traces-endpoint "https://otlp.arize.com/v1/traces"

For EU spaces, use:

export OTEL_SERVICE_NAME="vllm-server"
export OTEL_RESOURCE_ATTRIBUTES="openinference.project.name=vllm-server"
export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_TRACES_HEADERS="space_id=<your-arize-space-id>,api_key=<your-arize-api-key>"

vllm serve <model-name-or-path> \
  --otlp-traces-endpoint "https://otlp.eu-west-1a.arize.com/v1/traces"

Current vLLM releases bundle the core OpenTelemetry packages needed for server-side tracing. If you use an older or custom vLLM image and it errors on startup after you add --otlp-traces-endpoint, install matching OpenTelemetry exporter dependencies in the same environment or container image.

Option 2: trace OpenAI-compatible client calls

Install the client-side packages:

pip install arize-otel openinference-instrumentation-openai openai

Configure credentials:

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

Set up tracing before importing and using openai:

# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.openai import OpenAIInstrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for vLLM OpenAI-compatible calls.")

Call the vLLM OpenAI-compatible endpoint:

# example.py
from instrumentation import tracer_provider  # noqa: F401

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="<model-name-served-by-vllm>",
    messages=[
        {
            "role": "user",
            "content": "Name two signals that help debug inference latency.",
        }
    ],
)

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

Verify in Arize

Open your Arize AX space and select project vllm-tracing-example for client-side spans.
If you also enabled vLLM server OTLP export directly, select project vllm-server if you set OTEL_RESOURCE_ATTRIBUTES as shown above.
You should see spans within ~30 seconds.

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.

What Arize captures

OpenInference client spans: prompt, response, model, token usage, latency, errors, and tool call metadata supported by the OpenAI instrumentor.
vLLM server spans: server-side inference and request-processing spans emitted by vLLM’s OpenTelemetry support.

Troubleshooting

No client-side LLM spans. Make sure OpenAIInstrumentor().instrument(...) runs before your OpenAI client sends requests.
No vLLM server spans. Confirm OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf, --otlp-traces-endpoint points to Arize’s OTLP/HTTP traces endpoint, and your OTLP headers contain space_id and api_key.
Server spans appear in the default project. Confirm OTEL_RESOURCE_ATTRIBUTES includes openinference.project.name=vllm-server, or add the project name with an OpenTelemetry Collector resource processor.
Server spans lack prompt or response text. Use the OpenInference OpenAI client path as well; vLLM’s server export may not include the same application-level input and output detail.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

Prerequisites

Launch Arize AX

Option 1: export vLLM server spans over OTLP

Option 2: trace OpenAI-compatible client calls

Verify in Arize

Check from the skill, CLI, or SDK

What Arize captures

Troubleshooting

Resources

vLLM OpenTelemetry example

vLLM OpenAI-compatible server

OpenInference OpenAI Instrumentor

​Prerequisites

​Launch Arize AX

​Option 1: export vLLM server spans over OTLP

​Option 2: trace OpenAI-compatible client calls

​Verify in Arize

​Check from the skill, CLI, or SDK

​What Arize captures

​Troubleshooting

​Resources

vLLM OpenTelemetry example

vLLM OpenAI-compatible server

OpenInference OpenAI Instrumentor

Prerequisites

Launch Arize AX

Option 1: export vLLM server spans over OTLP

Option 2: trace OpenAI-compatible client calls

Verify in Arize

Check from the skill, CLI, or SDK

What Arize captures

Troubleshooting

Resources