> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langtrace.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# DeepSeek

> Learn how to integrate Langtrace with DeepSeek's family of models

## Introduction

Langtrace provides native support for DeepSeek's family of models. When using DeepSeek models, Langtrace automatically captures essential metrics including:

* Token usage
* Cost
* Latency
* Model hyperparameters

## Prerequisites

Before integrating DeepSeek with Langtrace, ensure you have:

* A Langtrace account with an API key
* DeepSeek API credentials
* Python environment (3.6 or later)

## Installation

1. Install the Langtrace Python SDK:

```bash theme={null}
pip install -U langtrace-python-sdk
```

2. Set up your environment variables:

```bash theme={null}
export LANGTRACE_API_KEY=YOUR_LANGTRACE_API_KEY
```

## Integration

Initialize Langtrace before importing any LLM modules:

<CodeGroup>
  ```python Python theme={null}
  import os
  from langtrace_python_sdk import langtrace  # Must precede any llm module imports
  from openai import OpenAI

  # Initialize Langtrace
  langtrace.init(api_key=os.environ['LANGTRACE_API_KEY'])

  # Initialize DeepSeek client
  client = OpenAI(
      api_key="<DeepSeek API Key>",
      base_url="https://api.deepseek.com"
  )

  # Example: Create a chat completion
  response = client.chat.completions.create(
      model="deepseek-chat",
      messages=[
          {"role": "system", "content": "You are a helpful assistant"},
          {"role": "user", "content": "Hello"},
      ],
      stream=False
  )

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

  ```typescript TypeScript theme={null}
  import * as Langtrace from "@langtrase/typescript-sdk";
  import OpenAI from 'openai';

  // Initialize Langtrace
  Langtrace.init({
      api_key: process.env.LANGTRACE_API_KEY
  });

  // Initialize DeepSeek client
  const client = new OpenAI({
      apiKey: "<DeepSeek API Key>",
      baseURL: "https://api.deepseek.com"
  });

  // Example: Create a chat completion
  async function chatWithDeepSeek() {
      const response = await client.chat.completions.create({
          model: "deepseek-chat",
          messages: [
              { role: "system", content: "You are a helpful assistant" },
              { role: "user", content: "Hello" }
          ],
          stream: false
      });

      console.log(response.choices[0].message.content);
  }
  ```
</CodeGroup>

## Viewing Traces

After integration, you can view your DeepSeek model traces in the Langtrace dashboard. The traces will include:

* Request/response payloads
* Token usage metrics
* Cost information
* Latency measurements
* Model configuration details

## Additional Resources

* [Langtrace Documentation](https://docs.langtrace.ai/introduction)
* [DeepSeek API Documentation](https://platform.deepseek.com/docs)