> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Ollama

> Trace Ollama chat calls with OpenInference and send local-model spans to Arize AX.

[Ollama](https://ollama.com/) runs open-weight models locally. The
[`OllamaInstrumentor`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-ollama)
traces native Ollama chat calls, including messages, responses, tool calls, and
token counts, as OpenInference LLM spans in Arize AX.

<CardGroup>
  <Card horizontal icon="https://storage.googleapis.com/arize-phoenix-assets/assets/images/phoenix-docs-images/gc.ico" href="https://colab.research.google.com/github/Arize-ai/tutorials_python/blob/main/Arize_Tutorials/Tracing/Arize_Tutorial_Llama32_Instrumentation.ipynb" title="Llama 3.2 + Ollama Tracing Tutorial (Google Colab)" />
</CardGroup>

## Prerequisites

* Python 3.10+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* [Ollama](https://ollama.com/download) installed and running locally
* The `llama3.2:1b` model pulled: `ollama pull llama3.2:1b`

## Launch Arize AX

1. Sign in to your [Arize AX account](https://app.arize.com/).
2. From **Space Settings**, copy your **Space ID** and **API Key**. You will set them as `ARIZE_SPACE_ID` and `ARIZE_API_KEY` below.

## Install

```bash theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
pip install arize-otel openinference-instrumentation-ollama ollama
```

## Configure credentials

```bash theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
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

```python theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
# 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

```python theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
# 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

```text wrap theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
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

1. Open your Arize AX space and select project **`ollama-tracing-example`**.
2. You should see a new trace within \~30 seconds with a `Chat` LLM span.
3. If no traces appear, see [Troubleshooting](#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.

<Tabs>
  <Tab title="Arize skill (agent)">
    Install the [Arize Skills](https://github.com/Arize-ai/arize-skills) plugin and let your coding agent check for you:

    ```bash theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
    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.
  </Tab>

  <Tab title="AX CLI">
    Export recent spans for your project — any rows mean traces are landing:

    ```bash theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
    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](/docs/api-clients/cli/spans).
  </Tab>

  <Tab title="SDK">
    Query the project's spans and check that at least one came back.

    <CodeGroup>
      ```python Python theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
      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"
      )
      ```

      ```typescript TypeScript theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
      // 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",
      );
      ```

      ```go Go theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
      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))
      ```
    </CodeGroup>

    SDK span references: [Python](/docs/api-clients/python/version-8/client-resources/spans) · [TypeScript](/docs/api-clients/typescript/version-1/client-resources/spans) · [Go](/docs/api-clients/go/version-2/client-resources/spans).
  </Tab>
</Tabs>

## 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.

## Resources

<CardGroup>
  <Card icon="terminal" href="https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-ollama" title="OpenInference Ollama Instrumentor" horizontal />

  <Card icon="github" href="https://github.com/ollama/ollama-python" title="Ollama Python client" horizontal />
</CardGroup>