> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LlamaIndex Workflows Tracing

> How to use the python LlamaIndexInstrumentor to trace LlamaIndex Workflows

[LlamaIndex Workflows](https://www.llamaindex.ai/blog/introducing-workflows-beta-a-new-way-to-create-complex-ai-applications-with-llamaindex) are a subset of the LlamaIndex package specifically designed to support agent development.

<Tip>
  Our LlamaIndexInstrumentor automatically captures traces for LlamaIndex Workflows agents. If you've already enabled that instrumentor, you do not need to complete the steps below.
</Tip>

<Info>
  We recommend using `llama_index >= 0.11.0`
</Info>

## Install

```bash theme={null}
pip install openinference-instrumentation-llama_index
```

## Setup

Initialize the LlamaIndexInstrumentor before your application code. This instrumentor will trace both LlamaIndex Workflows calls, as well as calls to the general LlamaIndex package.

```python theme={null}
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from phoenix.otel import register

tracer_provider = register()
LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)
```

## Run LlamaIndex Workflows

By instrumenting LlamaIndex, spans will be created whenever an agent is invoked and will be sent to the Phoenix server for collection.

## Observe

Now that you have tracing setup, all invocations of chains will be streamed to your running Phoenix for observability and evaluation.

## Resources

* [Example project](https://github.com/Arize-ai/phoenix/tree/main/examples/llamaindex-workflows-research-agent)

* [OpenInference package](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-llama-index)