> ## 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.

# LlamaIndex

> Trace LlamaIndex applications with OpenInference and send spans to Arize AX for LLM observability.

[LlamaIndex](https://github.com/run-llama/llama_index) is a data framework for building LLM applications — RAG pipelines, agents, query engines. Arize AX captures every LlamaIndex run — LLM calls, retrievals, embeddings, and the agent/query-engine spans that wrap them — via the [`openinference-instrumentation-llama-index`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-llama-index) package.

<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/llamaindex/llamaindex-tracing.ipynb" title="LlamaIndex 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)

## 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-llama-index \
  llama-index llama-index-llms-openai
```

## Configure credentials

```bash theme={null}
export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="llamaindex-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.llama_index import LlamaIndexInstrumentor

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

LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for LlamaIndex.")
```

## Run LlamaIndex

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

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

from llama_index.llms.openai import OpenAI

# OpenAI reads OPENAI_API_KEY from the environment.
llm = OpenAI(model="gpt-5.5")

response = llm.complete(
    "Why is the ocean salty? Answer in two sentences."
)

print(str(response))
```

### Expected output

```text wrap theme={null}
Arize AX tracing initialized for LlamaIndex.
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 **`llamaindex-tracing-example`**.
2. You should see a new trace within \~30 seconds containing an `OpenAI.complete` LLM span (LlamaIndex's wrapper) 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](/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](/api-clients/python/version-8/client-resources/spans) · [TypeScript](/api-clients/typescript/version-1/client-resources/spans) · [Go](/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.
* **LlamaIndex spans missing but other spans present.** `LlamaIndexInstrumentor().instrument(...)` must run before any `llama_index` import. Make sure `instrumentation.py` is the first import in your entry point.
* **`401` from OpenAI.** Verify `OPENAI_API_KEY` is set and has access to `gpt-5.5`. Swap for a model your key can call.
* **`ModuleNotFoundError: llama_index.llms.openai`.** Modern LlamaIndex packages providers as separate sub-packages. Install `llama-index-llms-openai` (already in the install command above).

## Resources

<CardGroup>
  <Card icon="book-open" href="https://docs.llamaindex.ai/en/stable/" title="LlamaIndex Documentation" horizontal />

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

  <Card icon="github" href="https://github.com/Arize-ai/tutorials/tree/main/python/llm/tracing/llamaindex" title="Arize AX LlamaIndex Tutorials" horizontal />
</CardGroup>