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
2. An Adaline API key, project ID, and prompt ID

Chat Completions

Complete Chat

Python

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)

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);

Stream Chat

Python

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="")

Next Steps

• Multi-Step Workflows — RAG pipelines, multi-step generation, and conversational agents
• Headers Reference — Complete header documentation

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