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Clarify Arize Phoenix observability positioning #755

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Clarify Arize Phoenix observability positioning #755

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12 changes: 7 additions & 5 deletions observability/arize.mdx
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Original file line number Diff line number Diff line change
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---
title: Arize
description: Integrate Agno with Arize Phoenix to send traces and gain insights into your agent's performance.
title: Arize Phoenix
description: Integrate Agno with Arize Phoenix to send traces and evaluate your agent's performance.
---

## Integrating Agno with Arize Phoenix

[Arize Phoenix](https://arize.com/phoenix/) is a platform for monitoring and analyzing AI models. By integrating Agno with Arize Phoenix, you can use OpenInference to send traces and gain insights into your agent's performance.
[Arize Phoenix](https://arize.com/phoenix/) is the open-source observability and evaluation platform from [Arize AI](https://arize.com/?utm_source=agno-docs&utm_medium=partner&utm_campaign=partner-docs&utm_content=observability-arize) for tracing, evaluating, and debugging LLM applications and AI agents. By integrating Agno with Arize Phoenix, you can use OpenInference to send traces and understand your agent's runtime behavior.

Phoenix is a good fit for local development, OSS workflows, and self-hosted experimentation. Teams that need the full-featured platform for production AI observability and evaluation can use [Arize AX](https://arize.com/products/ax/), available as managed cloud or enterprise self-hosted deployment. For examples of how traces turn into evaluation workflows, see Arize's [agent evaluation guide](https://arize.com/guides/ai-agent-handbook/agent-evaluation/) and [LLM evaluation guide](https://arize.com/resources/llm-evaluation/).

## Prerequisites

Expand Down Expand Up @@ -67,7 +69,7 @@ agent = Agent(
agent.print_response("What is the current price of Tesla?")
```

Now go to the [Phoenix Cloud](https://app.phoenix.arize.com) and view the traces created by your agent. You can visualize the execution flow, monitor performance, and debug issues directly from the Arize Phoenix dashboard.
Now open your Phoenix instance and view the traces created by your agent. You can visualize the execution flow, monitor performance, and debug issues directly from the Arize Phoenix dashboard.
<Frame caption="Arize Phoenix Trace">
<img
src="/images/arize-phoenix-trace.png"
Expand Down Expand Up @@ -116,4 +118,4 @@ agent.print_response("What is the current price of Tesla?")
## Notes

- **Environment Variables**: Ensure your environment variables are correctly set for the API key and collector endpoint.
- **Local Development**: Use `phoenix serve` to start a local collector for development purposes.
- **Local Development**: Use `phoenix serve` to start a local collector for development purposes.