Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/groq-hello-world/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill groq-hello-world. Copyright stays with the author (MIT).

Groq Hello World

Overview

Build a minimal chat completion with Groq's LPU inference API. Groq uses an OpenAI-compatible endpoint, so the API shape is familiar -- but responses arrive 10-50x faster than GPU-based providers. This skill gets you from an installed SDK to a working, verified request; deeper variants (streaming, Python, model selection) live in references/.

Prerequisites

• groq-sdk installed (npm install groq-sdk)
• GROQ_API_KEY environment variable set
• Completed groq-install-auth setup

Instructions

Use Write to create the example file, then run it to confirm your key and SDK work. Start with the single basic request below; reach for the reference variants only once this succeeds.

Step 1: Basic Chat Completion (TypeScript)

import Groq from "groq-sdk";

const groq = new Groq();

async function main() {
  const completion = await groq.chat.completions.create({
    model: "llama-3.3-70b-versatile",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "What is Groq's LPU and why is it fast?" },
    ],
  });

  console.log(completion.choices[0].message.content);
  console.log(`Tokens: ${completion.usage?.total_tokens}`);
}

main().catch(console.error);

Step 2: Go deeper (references)

Once Step 1 returns text, extend it with the moved-out variants:

• Streaming tokens as they generate, plus the Python equivalent and a model-selection cheat sheet — references/examples.md.
• Full model catalog (IDs, params, context, speed) and the complete response interface — references/models-and-response.md.

Output

A successful run prints the assistant's reply text followed by the total token count, e.g.:

Groq's LPU (Language Processing Unit) is a deterministic, single-core
inference chip... [assistant response continues]
Tokens: 142

The underlying API returns an OpenAI-compatible ChatCompletion object: the text is at choices[0].message.content, and usage carries token counts plus four Groq-specific timing fields (queue_time, prompt_time, completion_time, total_time). Full response shape: references/models-and-response.md.

Error Handling

Examples

• Minimal request — the TypeScript block in Step 1 above is the canonical hello-world; run it as-is after setting GROQ_API_KEY.
• Streaming a response — see references/examples.md for the stream: true loop that writes tokens to stdout as they arrive.
• Python equivalent — the same request in Python: references/examples.md.
• Choosing a model per task (speed vs. quality vs. vision) — references/examples.md.

Resources

• Groq Text Generation Docs
• Groq Models Reference
• Groq API Reference
• Next: proceed to groq-local-dev-loop for development workflow setup.