> ## 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.

# LiteLLM

> Trace LiteLLM Python SDK calls with OpenInference and send spans to Arize AX for LLM observability.

[LiteLLM](https://github.com/BerriAI/litellm) lets you call 100+ LLM providers — OpenAI, Anthropic, Bedrock, Vertex AI, Together, Groq, and more — through a single OpenAI-compatible interface. Arize AX captures every LiteLLM call — chat completions, embeddings, image generation, retries, and the underlying provider calls — via the [`openinference-instrumentation-litellm`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-litellm) package. The instrumentor wraps `completion()`, `acompletion()`, `completion_with_retries()`, `embedding()`, `aembedding()`, `image_generation()`, and `aimage_generation()`.

<Note>
  This guide covers the **LiteLLM Python SDK** (`litellm.completion(...)`) — the in-process library. If you're using the **LiteLLM Proxy** (a standalone server that exposes an OpenAI-compatible API on a port), your client is just an OpenAI client pointed at the proxy URL; follow the [OpenAI tracing guide](/docs/ax/integrations/llm-providers/openai/openai-tracing) and set `base_url` to your proxy.
</Note>

<CardGroup>
  <Card horizontal icon="https://storage.googleapis.com/arize-phoenix-assets/assets/images/phoenix-docs-images/gc.ico" href="http://colab.research.google.com/github/Arize-ai/tutorials/blob/main/python/llm/tracing/litellm/litellm-tracing.ipynb" title="LiteLLM Tracing Tutorial (Google Colab)" />
</CardGroup>

## Prerequisites

* Python 3.10+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* An `OPENAI_API_KEY` from the [OpenAI Platform](https://platform.openai.com/api-keys) (or another provider key — LiteLLM auto-routes based on the model string)

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

## Configure credentials

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

## Setup tracing

```python theme={null}
# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.litellm import LiteLLMInstrumentor

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

LiteLLMInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for LiteLLM.")
```

## Run LiteLLM

```python theme={null}
# example.py

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

import litellm

# litellm reads OPENAI_API_KEY from the environment for openai/* models.
response = litellm.completion(
    model="gpt-5.5",
    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 LiteLLM.
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 AX

1. Open your Arize AX space and select project **`litellm-tracing-example`**.
2. You should see a new trace within \~30 seconds containing a `completion` LLM span (LiteLLM's wrapper around the underlying provider call) with the prompt, response, and token usage attached.
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 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.
* **LiteLLM spans missing but other spans present.** `LiteLLMInstrumentor().instrument(...)` must run before any `import litellm`. Make sure `instrumentation.py` is the first import in your entry point.
* **`401` from the underlying provider.** LiteLLM picks the provider from the `model` string (`openai/...`, `anthropic/...`, `groq/...`). Make sure the matching key (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, etc.) is set.
* **Other LLM providers.** Switch the `model` string to a different provider — `litellm.completion(model="anthropic/claude-sonnet-4-6", ...)`, `litellm.completion(model="groq/llama-3.3-70b-versatile", ...)`, etc. The same `LiteLLMInstrumentor` covers every provider LiteLLM routes to.
* **Using the LiteLLM Proxy instead.** When the client talks to a proxy on a port, the in-process `LiteLLMInstrumentor` doesn't see the call — the client is making a plain OpenAI HTTP request. Use the [OpenAI tracing guide](/docs/ax/integrations/llm-providers/openai/openai-tracing) and set `base_url` to your proxy URL.

## Resources

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

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

  <Card icon="github" href="https://github.com/BerriAI/litellm" title="LiteLLM GitHub" horizontal />
</CardGroup>