PrismML

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Prerequisites

• Python 3.9+
• An Arize AX account (sign up)
• PrismML’s OpenAI-compatible server running locally at http://localhost:8080

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Launch Arize AX

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

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Install

pip install arize-otel openinference-instrumentation-openai openai

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Configure credentials

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

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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 PrismML.")

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Run PrismML

# example.py

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

from openai import OpenAI

# Point the OpenAI client at PrismML's local OpenAI-compatible endpoint.
client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed",
)

response = client.chat.completions.create(
    # The server accepts any model ID and serves the model it loaded.
    model="bonsai",
    messages=[
        {
            "role": "user",
            "content": "Why is the ocean salty? Answer in two sentences.",
        },
    ],
    temperature=0.5,
    top_p=0.85,
)

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

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Expected output

Arize AX tracing initialized for PrismML.
The ocean is salty because rivers carry dissolved minerals into it over time. Water evaporates, but the salt remains and accumulates.

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Verify in Arize AX

1. Open your Arize AX space and select project prismml-tracing-example.
2. You should see a new trace within ~30 seconds containing a ChatCompletion LLM span with the prompt, response, and token usage attached.
3. If no traces appear, see Troubleshooting.

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Check from the skill, CLI, or SDK

npx skills add Arize-ai/arize-skills

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

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))

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Troubleshooting

• No traces in Arize AX. Confirm ARIZE_SPACE_ID and ARIZE_API_KEY are set in the same shell that runs example.py. Enable OpenTelemetry debug logs with export OTEL_LOG_LEVEL=debug and re-run.
• Connection refused or ConnectError to localhost:8080. The PrismML server is not running or is listening on a different port. Start it with PrismML’s server instructions.
• Model not found. The server exposes the model it loaded. Use the model name returned by GET http://localhost:8080/v1/models, or use bonsai as shown in this guide.
• PrismML spans missing but other spans present. OpenAIInstrumentor().instrument(...) must run before any import openai. Make sure instrumentation.py is the first import in your entry point.

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