> ## 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.

# Portkey Tracing

> How to trace Portkey AI Gateway requests with Phoenix for comprehensive LLM observability

Phoenix provides seamless integration with [Portkey](https://portkey.ai/), the AI Gateway and observability platform that routes to 200+ LLMs with enterprise-grade features including guardrails, caching, and load balancing.

## Install

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

## Setup

Use the register function to connect your application to Phoenix:

```python theme={null}
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-portkey-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)
```

## Run Portkey

By instrumenting Portkey, spans will be created whenever requests are made through the AI Gateway and will be sent to the Phoenix server for collection.

### Basic Usage with OpenAI

```python theme={null}
import os
from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

# Set up your API keys
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["PORTKEY_API_KEY"] = "your-portkey-api-key"  # Optional for self-hosted

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key=os.environ.get("PORTKEY_API_KEY")
    )
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is artificial intelligence?"}]
)

print(response.choices[0].message.content)
```

### Using Portkey SDK Directly

```python theme={null}
from portkey_ai import Portkey

# Initialize Portkey client
portkey = Portkey(
    api_key="your-portkey-api-key",  # Optional for self-hosted
    virtual_key="your-openai-virtual-key"  # Or use provider-specific virtual keys
)

response = portkey.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain machine learning"}]
)

print(response.choices[0].message.content)
```

## Observe

Now that you have tracing setup, all requests through Portkey's AI Gateway will be streamed to your running Phoenix instance for observability and evaluation. You'll be able to see:

* **Request/Response Traces**: Complete visibility into LLM interactions

* **Routing Decisions**: Which provider was selected and why

* **Fallback Events**: When and why fallbacks were triggered

* **Cache Performance**: Hit/miss rates and response times

* **Cost Tracking**: Token usage and costs across providers

* **Latency Metrics**: Response times for each provider and route

## Resources

* [Phoenix OpenInference Instrumentation](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-portkey)