> ## 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.

# Fireworks AI

> Trace Fireworks AI OpenAI-compatible API calls with OpenInference and send spans to Arize AX for LLM observability.

[Fireworks AI](https://fireworks.ai/) provides fast model inference through an OpenAI-compatible API. Set the OpenAI client's `base_url` to Fireworks and Arize AX captures each completion through [`openinference-instrumentation-openai`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-openai).

<Note>
  This page uses Fireworks' OpenAI-compatible API. There is no separate Fireworks-specific OpenInference instrumentor required.
</Note>

## Prerequisites

* Python 3.9+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* A `FIREWORKS_API_KEY` from Fireworks 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={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
pip install arize-otel openinference-instrumentation-openai openai
```

## 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="fireworks-ai-tracing-example"
export FIREWORKS_API_KEY="<your-fireworks-api-key>"
export FIREWORKS_MODEL="<your-fireworks-model-id>"
```

## 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.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 Fireworks AI.")
```

## Run Fireworks AI

```python theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
# 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.fireworks.ai/inference/v1",
    api_key=os.environ["FIREWORKS_API_KEY"],
)

response = client.chat.completions.create(
    model=os.environ["FIREWORKS_MODEL"],
    messages=[
        {
            "role": "user",
            "content": "Name two signals an LLM trace should capture.",
        },
    ],
)

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

### Expected output

```text wrap theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
Arize AX tracing initialized for Fireworks AI.
An LLM trace should capture the input prompt and generated response, along with operational signals such as latency, token counts, model name, and errors.
```

## Verify in Arize

1. Open your Arize AX space and select project **`fireworks-ai-tracing-example`**.
2. You should see a new trace within \~30 seconds containing a `ChatCompletion` LLM span with prompt, response, model, token, and latency metadata.
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.** Confirm `ARIZE_SPACE_ID` and `ARIZE_API_KEY` are set in the same shell that runs `example.py`.
* **Fireworks spans missing but other spans present.** `OpenAIInstrumentor().instrument(...)` must run before any `import openai`.
* **`401` from Fireworks.** Use your Fireworks API key, not your OpenAI or Arize API key.
* **Model not found.** Confirm `FIREWORKS_MODEL` matches a model identifier from the Fireworks model catalog.

## Resources

<CardGroup>
  <Card icon="book-open" href="https://docs.fireworks.ai/tools-sdks/openai-compatibility" title="Fireworks OpenAI Compatibility" horizontal />

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

  <Card icon="scale-balanced" href="https://arize.com/resources/llm-evaluation/" title="LLM Evaluation Guide" horizontal />

  <Card icon="robot" href="https://arize.com/guides/ai-agent-handbook/agent-evaluation/" title="Agent Evaluation Guide" horizontal />
</CardGroup>