Ollama

Ollama runs open-weight models locally. The OllamaInstrumentor traces native Ollama chat calls, including messages, responses, tool calls, and token counts, as OpenInference LLM spans in Arize AX.

Llama 3.2 + Ollama Tracing Tutorial (Google Colab)

Prerequisites

Python 3.10+
An Arize AX account (sign up)
Ollama installed and running locally
The llama3.2:1b model pulled: ollama pull llama3.2:1b

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 arize-otel openinference-instrumentation-ollama ollama

Configure credentials

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

Ollama runs locally, so this example needs no provider API key.

Setup tracing

# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.ollama import OllamaInstrumentor

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

OllamaInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for Ollama.")

Import and instrument before importing or calling ollama. In particular, from ollama import chat captured before instrumentation is not traced.

Run Ollama

# example.py
from instrumentation import tracer_provider

import ollama

response = ollama.chat(
    model="llama3.2:1b",
    messages=[
        {
            "role": "user",
            "content": "Why is the ocean salty? Answer in two sentences.",
        },
    ],
)

print(response.message.content)

Expected output

Arize AX tracing initialized for Ollama.
The ocean is salty because rivers carry dissolved minerals to it, and evaporation leaves those salts behind. Over millions of years, this concentrates the salt in seawater.

Verify in Arize AX

Open your Arize AX space and select project ollama-tracing-example.
You should see a new trace within ~30 seconds with a Chat LLM span.
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 AX. Run OllamaInstrumentor().instrument(...) before importing or calling ollama.
Connection refused. Start the daemon with ollama serve and confirm it responds at http://localhost:11434.
Model not found. Pull it with ollama pull llama3.2:1b.
No span for generate or embeddings. This instrumentor traces chat only; generate, embed, and embeddings calls are not supported.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

Llama 3.2 + Ollama Tracing Tutorial (Google Colab)

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run Ollama

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Troubleshooting

Resources

OpenInference Ollama Instrumentor

Ollama Python client

Llama 3.2 + Ollama Tracing Tutorial (Google Colab)

​Prerequisites

​Launch Arize AX

​Install

​Configure credentials

​Setup tracing

​Run Ollama

​Expected output

​Verify in Arize AX

​Check from the skill, CLI, or SDK

​Troubleshooting

​Resources

OpenInference Ollama Instrumentor

Ollama Python client

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run Ollama

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Troubleshooting

Resources