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

---
description: Start here to integrate Opik into your BytePlus-based genai application
  for end-to-end LLM observability, unit testing, and optimization.
headline: BytePlus | Opik Documentation
og:description: Learn to integrate Opik with BytePlus using the OpenAI SDK for seamless
  access to cutting-edge AI models and enterprise-grade security.
og:site_name: Opik Documentation
og:title: Integrate Opik with BytePlus for AI Solutions
title: Observability for BytePlus with Opik
---

[BytePlus](https://www.byteplus.com/) is ByteDance's AI-native enterprise platform offering ModelArk, a comprehensive Platform-as-a-Service (PaaS) solution for deploying and utilizing powerful large language models. It provides access to SkyLark models, DeepSeek V3.1, Kimi-K2, and other cutting-edge AI models with enterprise-grade security and scalability.

This guide explains how to integrate Opik with BytePlus using the OpenAI SDK. BytePlus provides OpenAI-compatible API endpoints that allow you to use the standard OpenAI client with BytePlus models.

## Getting started

First, ensure you have both `opik` and `openai` packages installed:

```bash
pip install opik openai
```

You'll also need a BytePlus API key. Find a guide on creating your BytePlus API keys for model services [here](https://docs.byteplus.com/en/docs/ModelArk/1399008).

## Tracking BytePlus API calls

```python
from opik.integrations.openai import track_openai
from openai import OpenAI

# Initialize the OpenAI client with BytePlus base URL
client = OpenAI(
    base_url="https://ark.ap-southeast.bytepluses.com/api/v3",
    api_key="YOUR_BYTEPLUS_API_KEY"
)
client = track_openai(client)

response = client.chat.completions.create(
    model="kimi-k2-250711",  # You can use any model available on BytePlus
    messages=[
        {"role": "user", "content": "Hello, world!"}
    ],
    temperature=0.7,
    max_tokens=100
)

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

## Advanced Usage

### Using with @track decorator

You can combine the tracked client with Opik's `@track` decorator for comprehensive tracing:

```python
from opik import track
from opik.integrations.openai import track_openai
from openai import OpenAI

client = OpenAI(
    base_url="https://ark.ap-southeast.bytepluses.com/api/v3",
    api_key="YOUR_BYTEPLUS_API_KEY"
)
client = track_openai(client)

@track
def analyze_data_with_ai(query: str):
    """Analyze data using BytePlus AI models."""

    response = client.chat.completions.create(
        model="kimi-k2-250711",
        messages=[
            {"role": "user", "content": query}
        ]
    )

    return response.choices[0].message.content

# Call the tracked function
result = analyze_data_with_ai("Analyze this business data...")
```

## Troubleshooting

### Common Issues

1. **Authentication Errors**: Ensure your API key is correct and has the necessary permissions
2. **Model Not Found**: Verify the model name is available on BytePlus
3. **Rate Limiting**: BytePlus may have rate limits; implement appropriate retry logic
4. **Base URL Issues**: Ensure the base URL is correct for your BytePlus deployment

### Getting Help

- Check the [BytePlus API documentation](https://docs.byteplus.com/en/docs/ModelArk/) for detailed error codes
- Contact BytePlus support for API-specific problems
- Check Opik documentation for tracing and evaluation features

## Next Steps

Once you have BytePlus integrated with Opik, you can:

- [Evaluate your LLM applications](/evaluation/overview) using Opik's evaluation framework
- [Create datasets](/evaluation/advanced/manage_datasets) to test and improve your models
- [Set up feedback collection](/tracing/advanced/annotate_traces) to gather human evaluations
- [Monitor performance](/tracing/concepts) across different models and configurations

For more information about using Opik with OpenAI-compatible APIs, see the [OpenAI integration guide](/integrations/openai).