Create a minimal working Groq example. Use when starting a new Groq integration, testing your setup, or learning basic Groq API patterns. Trigger with phrases like "groq hello world", "groq example", "groq quick start", "simple groq code".
npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill groq-hello-worldLoading…
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.
groq-sdk installed (npm install groq-sdk)GROQ_API_KEY environment variable setgroq-install-auth setupimport 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);
async function streamExample() {
const stream = await groq.chat.completions.create({
model: "llama-3.3-70b-versatile",
messages: [
{ role: "user", content: "Explain quantum computing in 3 sentences." },
],
stream: true,
});
for await (const chunk of stream) {
const content = chunk.choices[0]?.delta?.content || "";
process.stdout.write(content);
}
console.log(); // newline
}
from groq import Groq
client = Groq()
completion = client.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?"},
],
)
print(completion.choices[0].message.content)
print(f"Tokens: {completion.usage.total_tokens}")
// Speed tier -- fastest responses (~560 tok/s)
const fast = await groq.chat.completions.create({
model: "llama-3.1-8b-instant",
messages: [{ role: "user", content: "Hello!" }],
});
// Quality tier -- best reasoning (~280 tok/s)
const quality = await groq.chat.completions.create({
model: "llama-3.3-70b-versatile",
messages: [{ role: "user", content: "Explain monads in Haskell." }],
});
// Vision tier -- multimodal understanding
const vision = await groq.chat.completions.create({
model: "meta-llama/llama-4-scout-17b-16e-instruct",
messages: [{
role: "user",
content: [
{ type: "text", text: "Describe this image." },
{ type: "image_url", image_url: { url: "https://example.com/photo.jpg" } },
],
}],
});
| Model ID | Params | Context | Speed | Best For |
|---|---|---|---|---|
llama-3.1-8b-instant | 8B | 128K | ~560 tok/s | Classification, extraction, fast tasks |
llama-3.3-70b-versatile | 70B | 128K | ~280 tok/s | General purpose, reasoning, code |
llama-3.3-70b-specdec | 70B | 128K | Faster | Same quality, speculative decoding |
meta-llama/llama-4-scout-17b-16e-instruct | 17Bx16E | 128K | ~460 tok/s | Vision, multimodal |
meta-llama/llama-4-maverick-17b-128e-instruct | 17Bx128E | 128K | — | Best multimodal quality |
interface ChatCompletion {
id: string; // "chatcmpl-xxx"
object: "chat.completion";
created: number; // Unix timestamp
model: string; // Actual model used
choices: [{
index: number;
message: { role: "assistant"; content: string };
finish_reason: "stop" | "length" | "tool_calls";
}];
usage: {
prompt_tokens: number;
completion_tokens: number;
total_tokens: number;
queue_time: number; // Groq-specific: seconds in queue
prompt_time: number; // Groq-specific: seconds for prompt
completion_time: number; // Groq-specific: seconds for completion
total_time: number; // Groq-specific: total processing seconds
};
}
| Error | Cause | Solution |
|---|---|---|
401 Invalid API Key | Key not set or invalid | Check GROQ_API_KEY env var |
model_not_found | Typo in model ID or deprecated model | Check model list at console.groq.com/docs/models |
429 Rate limit | Free tier: 30 RPM on large models | Wait for retry-after header value |
context_length_exceeded | Prompt + max_tokens > model context | Reduce prompt size or set lower max_tokens |
Proceed to groq-local-dev-loop for development workflow setup.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).