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

# Arize Phoenix

> 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 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

1. **Install Dependencies**

   Ensure you have the necessary packages installed:

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

2. **Setup Arize Phoenix Account**

   * Create an account at [Arize Phoenix](https://app.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 PHOENIX_API_KEY=<your-key>
   ```

## Sending Traces to Arize Phoenix

### Example: Using Arize Phoenix with OpenInference

Instrument your Agno agent with OpenInference and send traces to Arize Phoenix.

```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 environment variables for Arize Phoenix
os.environ["PHOENIX_API_KEY"] = os.getenv("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 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="https://mintcdn.com/agno-v2-fix-typo/rJBfOT41imdsRZuY/images/arize-phoenix-trace.png?fit=max&auto=format&n=rJBfOT41imdsRZuY&q=85&s=805f835eaa7d83e499c91bf19e4ee9b9" 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.