Cerebras

Cerebras Inference exposes an OpenAI-compatible API for high-speed LLM inference. Set the OpenAI client’s base_url to Cerebras and use openinference-instrumentation-openai to send request, response, token, latency, and error spans to Arize AX.

This is OpenAI SDK instrumentation pointed at Cerebras’ OpenAI-compatible endpoint. It is not a separate Cerebras-specific instrumentor.

Prerequisites

Python 3.9+
An Arize AX account (sign up)
A CEREBRAS_API_KEY from Cerebras

Launch Arize

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 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="cerebras-tracing-example"
export CEREBRAS_API_KEY="<your-cerebras-api-key>"
export CEREBRAS_MODEL="<your-cerebras-model-id>"

Setup tracing

# 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 Cerebras.")

Run Cerebras

# example.py

# Importing instrumentation first ensures tracing is set up
# before `openai` is imported.
from instrumentation import tracer_provider

import os

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key=os.environ["CEREBRAS_API_KEY"],
)

response = client.chat.completions.create(
    model=os.environ["CEREBRAS_MODEL"],
    messages=[
        {
            "role": "user",
            "content": "Summarize why fast inference still needs observability.",
        },
    ],
)

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

Expected output

Arize AX tracing initialized for Cerebras.
Fast inference still needs observability because teams must understand quality, cost, latency, errors, and prompt behavior after models are deployed.

Verify in Arize

Open your Arize AX space and select project cerebras-tracing-example.
You should see a new trace within ~30 seconds containing a ChatCompletion LLM span with the model name, prompt, response, latency, and token metadata.
If no traces appear, see Troubleshooting.

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.

Troubleshooting

No traces in Arize. Confirm ARIZE_SPACE_ID and ARIZE_API_KEY are set in the same shell that runs example.py.
Cerebras spans missing but other spans present. OpenAIInstrumentor().instrument(...) must run before any import openai.
401 from Cerebras. Use your Cerebras API key, not your OpenAI or Arize API key.
Model not found. Confirm CEREBRAS_MODEL matches a model available in your Cerebras account.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

Prerequisites

Launch Arize

Install

Configure credentials

Setup tracing

Run Cerebras

Expected output

Verify in Arize

Check from the skill, CLI, or SDK

Troubleshooting

Resources

Cerebras Inference Documentation

OpenInference OpenAI Instrumentor

LLM Evaluation Guide

Agent Evaluation Guide

​Prerequisites

​Launch Arize

​Install

​Configure credentials

​Setup tracing

​Run Cerebras

​Expected output

​Verify in Arize

​Check from the skill, CLI, or SDK

​Troubleshooting

​Resources

Cerebras Inference Documentation

OpenInference OpenAI Instrumentor

LLM Evaluation Guide

Agent Evaluation Guide

Prerequisites

Launch Arize

Install

Configure credentials

Setup tracing

Run Cerebras

Expected output

Verify in Arize

Check from the skill, CLI, or SDK

Troubleshooting

Resources