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

# Groq

> Integrate Groq LPU models through the Adaline Proxy for automatic telemetry and observability.

# Groq

Integrate Groq models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes. Groq uses an OpenAI-compatible API.

## Supported Models

**Chat Models**

| Model                                           | Description                              |
| ----------------------------------------------- | ---------------------------------------- |
| `openai/gpt-oss-120b`                           | OpenAI GPT OSS 120B on Groq LPU          |
| `openai/gpt-oss-20b`                            | OpenAI GPT OSS 20B on Groq LPU           |
| `openai/gpt-oss-safeguard-20b`                  | OpenAI GPT OSS Safeguard 20B on Groq LPU |
| `moonshotai/kimi-k2-instruct`                   | Moonshot Kimi K2                         |
| `moonshotai/kimi-k2-instruct-0905`              | Moonshot Kimi K2 September 2025 snapshot |
| `meta-llama/llama-4-maverick-17b-128e-instruct` | Llama 4 Maverick                         |
| `meta-llama/llama-4-scout-17b-16e-instruct`     | Llama 4 Scout                            |
| `meta-llama/llama-guard-4-12b`                  | Llama Guard 4 (safety)                   |
| `qwen/qwen3-32b`                                | Qwen 3 32B                               |
| `deepseek-r1-distill-llama-70b`                 | DeepSeek R1 Distill 70B                  |
| `llama-3.3-70b-versatile`                       | Llama 3.3 70B                            |
| `llama-3.1-8b-instant`                          | Llama 3.1 8B, ultra-fast inference       |
| `gemma2-9b-it`                                  | Google Gemma 2 9B                        |

## Proxy Base URL

```
https://gateway.adaline.ai/v1/groq/
```

## Prerequisites

1. A [Groq API key](https://console.groq.com/keys)
2. An [Adaline API key](/docs/admin/create-api-keys), project ID, and prompt ID

## Chat Completions

### Complete Chat

<CodeGroup>
  ```python Python theme={null}
  from groq import Groq

  client = Groq(
      api_key="your-groq-api-key",
      base_url="https://gateway.adaline.ai/v1/groq/"
  )

  headers = {
      "adaline-api-key": "your-adaline-api-key",
      "adaline-project-id": "your-project-id",
      "adaline-prompt-id": "your-prompt-id"
  }

  response = client.chat.completions.create(
      model="llama3-8b-8192",
      messages=[
          {"role": "system", "content": "You are a helpful assistant."},
          {"role": "user", "content": "What is the speed of light?"}
      ],
      extra_headers=headers
  )

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

  ```typescript TypeScript theme={null}
  import OpenAI from "openai";

  // Groq is OpenAI-compatible
  const client = new OpenAI({
    apiKey: "your-groq-api-key",
    baseURL: "https://gateway.adaline.ai/v1/groq/",
  });

  const response = await client.chat.completions.create(
    {
      model: "llama3-8b-8192",
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "What is the speed of light?" },
      ],
    },
    {
      headers: {
        "adaline-api-key": "your-adaline-api-key",
        "adaline-project-id": "your-project-id",
        "adaline-prompt-id": "your-prompt-id",
      },
    }
  );

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

### Stream Chat

<CodeGroup>
  ```python Python theme={null}
  from groq import Groq

  client = Groq(
      api_key="your-groq-api-key",
      base_url="https://gateway.adaline.ai/v1/groq/"
  )

  headers = {
      "adaline-api-key": "your-adaline-api-key",
      "adaline-project-id": "your-project-id",
      "adaline-prompt-id": "your-prompt-id"
  }

  stream = client.chat.completions.create(
      model="llama3-8b-8192",
      messages=[
          {"role": "system", "content": "You are a helpful assistant."},
          {"role": "user", "content": "Explain how neural networks work."}
      ],
      stream=True,
      extra_headers=headers
  )

  for chunk in stream:
      if chunk.choices[0].delta.content is not None:
          print(chunk.choices[0].delta.content, end="")
  ```
</CodeGroup>

## Next Steps

* [Multi-Step Workflows](/docs/integrations/examples/multi-step-workflows) — RAG pipelines, multi-step generation, and conversational agents
* [Headers Reference](/docs/reference/proxy/headers) — Complete header documentation

***

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