> ## 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.

# Doubleword

> Trace Doubleword inference API calls with OpenInference and send spans to Arize AX for LLM observability.

[Doubleword](https://doubleword.ai/) is a high-throughput, low-cost LLM inference platform serving open models such as Qwen and DeepSeek across realtime, async, and batch tiers. The endpoint at `https://api.doubleword.ai/v1` mirrors OpenAI's schema, so any OpenAI client works with `base_url` set to Doubleword. Arize AX captures every Doubleword call via the [`openinference-instrumentation-openai`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-openai) package — the same instrumentor that covers OpenAI's hosted API.

## Prerequisites

* Python 3.9+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* A `DOUBLEWORD_API_KEY` from the **API Keys** dashboard at [app.doubleword.ai](https://app.doubleword.ai/)

## Launch Arize

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={null}
pip install arize-otel openinference-instrumentation-openai openai
```

## Configure credentials

```bash theme={null}
export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="doubleword-tracing-example"
export DOUBLEWORD_API_KEY="<your-doubleword-api-key>"
```

## Setup tracing

```python theme={null}
# 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 Doubleword.")
```

## Run Doubleword

```python theme={null}
# 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

# Point the OpenAI client at Doubleword's OpenAI-compatible endpoint.
client = OpenAI(
    base_url="https://api.doubleword.ai/v1",
    api_key=os.environ["DOUBLEWORD_API_KEY"],
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Flash",
    messages=[
        {
            "role": "user",
            "content": "Why is the ocean salty? Answer in two sentences.",
        },
    ],
)

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

### Expected output

```text wrap theme={null}
Arize AX tracing initialized for Doubleword.
The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity.
```

## Verify in Arize

1. Open your Arize AX space and select project **`doubleword-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. The model name on the span will be the Doubleword model identifier you used (e.g. `deepseek-ai/DeepSeek-V4-Flash`).
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={null}
    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={null}
    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={null}
      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={null}
      // 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={null}
      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.** 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.
* **Doubleword 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.
* **`401` from Doubleword.** Use your **Doubleword** API key (from the [Doubleword dashboard](https://app.doubleword.ai/)), not your OpenAI key. They are different services with different credentials.
* **Model not found.** Use a model identifier from the [Doubleword model catalog](https://docs.doubleword.ai/inference-api/models) (e.g. `deepseek-ai/DeepSeek-V4-Flash`). Identifiers are case-sensitive and include the publisher prefix.

## Resources

<CardGroup>
  <Card icon="book-open" href="https://docs.doubleword.ai/" title="Doubleword Documentation" horizontal />

  <Card icon="terminal" href="https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-openai" title="OpenInference OpenAI Instrumentor (used for Doubleword)" horizontal />

  <Card icon="globe" href="https://doubleword.ai/" title="Doubleword Platform" horizontal />
</CardGroup>