comet-ml--opik/apps/opik-documentation/documentation/fern/docs-v2/integrations/portkey.mdx

---
description: Start here to integrate Portkey with Opik for enterprise-grade LLM gateway capabilities with advanced routing and fallback features.
headline: Portkey | Opik Documentation
og:description: Learn to integrate Portkey with Opik using the OpenAI SDK wrapper to log all LLM calls for comprehensive observability.
og:site_name: Opik Documentation
og:title: Integrate Portkey with Opik for Enterprise LLM Gateway
title: Observability for Portkey with Opik
---

[Portkey](https://portkey.ai/) is an enterprise-grade AI gateway that provides a unified interface to access 200+ LLMs with advanced features like smart routing, automatic fallbacks, load balancing, and comprehensive observability.

## Gateway Overview

Portkey provides enterprise-grade features for managing LLM API access, including:

- **250+ AI Models**: Single consistent API to connect with models from OpenAI, Anthropic, Google, Azure, AWS, and more
- **Multi-Modal Support**: Language, vision, audio, and image models
- **Advanced Routing**: Fallbacks, load balancing, conditional routing based on metadata, and provider weights
- **Smart Caching**: Simple and semantic caching to reduce latency and cost
- **Security & Governance**: Guardrails, secure key management (virtual keys), role-based access control
- **Compliance**: SOC2, HIPAA, GDPR compliant with data privacy controls
- **Observability**: Request/response logging, latency tracking, cost metrics, error rates, and throughput monitoring

## Account Setup

[Comet](https://www.comet.com/site?from=llm&utm_source=opik&utm_medium=colab&utm_content=portkey&utm_campaign=opik) provides a hosted version of the Opik platform. [Simply create an account](https://www.comet.com/signup?from=llm&utm_source=opik&utm_medium=colab&utm_content=portkey&utm_campaign=opik) and grab your API Key.

> You can also run the Opik platform locally, see the [installation guide](https://www.comet.com/docs/opik/self-host/overview/?from=llm&utm_source=opik&utm_medium=colab&utm_content=portkey&utm_campaign=opik) for more information.

## Getting Started

### Installation

First, ensure you have `opik`, `openai`, and `portkey-ai` packages installed:

```bash
pip install opik openai portkey-ai
```

### Configuring Opik

Configure the Opik Python SDK for your deployment type. See the [Python SDK Configuration guide](/tracing/advanced/sdk_configuration) for detailed instructions on:

- **CLI configuration**: `opik configure`
- **Code configuration**: `opik.configure()`
- **Self-hosted vs Cloud vs Enterprise** setup
- **Configuration files** and environment variables

### Configuring Portkey

You'll need a Portkey API key and virtual keys for your LLM providers. You can get these from the [Portkey dashboard](https://app.portkey.ai/).

Set your API keys as environment variables:

```bash
export PORTKEY_API_KEY="YOUR_PORTKEY_API_KEY"
export PORTKEY_VIRTUAL_KEY="YOUR_PORTKEY_VIRTUAL_KEY"
```

Or set them programmatically:

```python
import os
import getpass

if "PORTKEY_API_KEY" not in os.environ:
    os.environ["PORTKEY_API_KEY"] = getpass.getpass("Enter your Portkey API key: ")

if "PORTKEY_VIRTUAL_KEY" not in os.environ:
    os.environ["PORTKEY_VIRTUAL_KEY"] = getpass.getpass("Enter your Portkey virtual key: ")
```

## Logging LLM Calls

Since Portkey provides an OpenAI-compatible API, we can use the [Opik OpenAI SDK wrapper](/integrations/openai) to automatically log Portkey calls as generations in Opik.

### Simple LLM Call

```python
import os
from opik.integrations.openai import track_openai
from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        api_key=os.environ["PORTKEY_API_KEY"],
        provider="@OPENAI_PROVIDER"
    )
)

# Wrap the client with Opik tracking
client = track_openai(client, project_name="portkey-integration-demo")

# Make a chat completion request
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are a knowledgeable AI assistant."},
        {"role": "user", "content": "What is the largest city in France?"}
    ]
)

# Print the assistant's reply
print(response.choices[0].message.content)
```

## Advanced Usage

### Using with the `@track` decorator

If you have multiple steps in your LLM pipeline, you can use the `@track` decorator to log the traces for each step. If Portkey is called within one of these steps, the LLM call will be associated with that corresponding step:

```python
import os
from opik import track
from opik.integrations.openai import track_openai
from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

# Create an OpenAI client configured for Portkey
client = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        api_key=os.environ["PORTKEY_API_KEY"],
        provider="@OPENAI_PROVIDER"
    )
)

# Wrap the client with Opik tracking
client = track_openai(client, project_name="portkey-integration-demo")

@track
def generate_response(prompt: str):
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a knowledgeable AI assistant."},
            {"role": "user", "content": prompt}
        ]
    )
    return response.choices[0].message.content

@track
def refine_response(initial_response: str):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You enhance and polish text responses."},
            {"role": "user", "content": f"Please improve this response: {initial_response}"}
        ]
    )
    return response.choices[0].message.content

@track(project_name="portkey-integration-demo")
def generate_and_refine(prompt: str):
    # First LLM call: Generate initial response
    initial = generate_response(prompt)
    
    # Second LLM call: Refine the response
    refined = refine_response(initial)
    
    return refined

# Example usage
result = generate_and_refine("Explain quantum computing in simple terms.")
```

The trace will show nested LLM calls with hierarchical spans.

## Further Improvements

If you have suggestions for improving the Portkey integration, please let us know by opening an issue on [GitHub](https://github.com/comet-ml/opik/issues).