> ## 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.

# Prompt Flow Tracing

> Create flows using Microsoft PromptFlow and send their traces to Phoenix

This integration will allow you to trace [Microsoft PromptFlow](https://github.com/microsoft/promptflow) flows and send their traces into[`arize-phoenix`](https://github.com/Arize-ai/phoenix).

## Install

```bash theme={null}
pip install promptflow
```

## Setup

Set up the OpenTelemetry endpoint to point to Phoenix and use Prompt flow's `setup_exporter_from_environ` to start tracing any further flows and LLM calls.

```swift theme={null}
import os
from opentelemetry.sdk.environment_variables import OTEL_EXPORTER_OTLP_ENDPOINT
from promptflow.tracing._start_trace import setup_exporter_from_environ

endpoint = f"{os.environ["PHOENIX_COLLECTOR_ENDPOINT]}/v1/traces" # replace with your Phoenix endpoint if self-hosting
os.environ[OTEL_EXPORTER_OTLP_ENDPOINT] = endpoint
setup_exporter_from_environ()
```

## Run PromptFlow

Proceed with creating Prompt flow flows as usual. See this [example notebook](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-promptflow/examples/chat_flow_example_to_phoenix.ipynb) for inspiration.

## Observe

You should see the spans render in Phoenix as shown in the below screenshots.

## Resources

* [Example Notebook](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-promptflow/examples/chat_flow_example_to_phoenix.ipynb)