> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-agui.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Arize

> Integrate Agno with Arize Phoenix to send traces and gain insights into your agent's performance.

## Integrating Agno with Arize Phoenix

[Arize Phoenix](https://phoenix.arize.com/) is a powerful platform for monitoring and analyzing AI models. By integrating Agno with Arize Phoenix, you can leverage OpenInference to send traces and gain insights into your agent's performance.

## Prerequisites

1. **Install Dependencies**

   Ensure you have the necessary packages installed:

   ```bash theme={null}
   uv pip install arize-phoenix openai openinference-instrumentation-agno opentelemetry-sdk opentelemetry-exporter-otlp
   ```

2. **Setup Arize Phoenix Account**

   * Create an account at [Arize Phoenix](https://phoenix.arize.com/).
   * Obtain your API key from the Arize Phoenix dashboard.

3. **Set Environment Variables**

   Configure your environment with the Arize Phoenix API key:

   ```bash theme={null}
   export ARIZE_PHOENIX_API_KEY=<your-key>
   ```

## Sending Traces to Arize Phoenix

* ### Example: Using Arize Phoenix with OpenInference

This example demonstrates how to instrument your Agno agent with OpenInference and send traces to Arize Phoenix.

```python theme={null}
import asyncio
import os

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.yfinance import YFinanceTools
from phoenix.otel import register

# Set environment variables for Arize Phoenix
os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('ARIZE_PHOENIX_API_KEY')}"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com"

# Configure the Phoenix tracer
tracer_provider = register(
    project_name="agno-stock-price-agent",  # Default is 'default'
    auto_instrument=True,  # Automatically use the installed OpenInference instrumentation
)

# Create and configure the agent
agent = Agent(
    name="Stock Price Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools()],
    instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
    debug_mode=True,
)

# Use the 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.

<Frame caption="Arize Phoenix Trace">
  <img src="https://mintcdn.com/agno-v2-agui/B_u8TpSpMOBFKmtg/images/arize-phoenix-trace.png?fit=max&auto=format&n=B_u8TpSpMOBFKmtg&q=85&s=2fc12f44ebbf1cad8c864f72e9e09c68" style={{ borderRadius: '10px', width: '100%', maxWidth: '800px' }} alt="arize-agno observability" width="2160" height="1239" data-path="images/arize-phoenix-trace.png" />
</Frame>

* ### Example: Local Collector Setup

For local development, you can run a local collector using

```bash theme={null}
phoenix serve
```

```python theme={null}
import os

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.yfinance import YFinanceTools
from phoenix.otel import register

# Set the local collector endpoint
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

# Configure the Phoenix tracer
tracer_provider = register(
    project_name="agno-stock-price-agent",  # Default is 'default'
    auto_instrument=True,  # Automatically use the installed OpenInference instrumentation
)

# Create and configure the agent
agent = Agent(
    name="Stock Price Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools()],
    instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
    debug_mode=True,
)

# Use the agent
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.

By following these steps, you can effectively integrate Agno with Arize Phoenix, enabling comprehensive observability and monitoring of your AI agents.