Fast LLM inference, speech and batch processing via Groq's OpenAI-compatible API.
Groq ships in the w6w first-party pack. It declares 16 actions, 2 health checks, and the host runs its code in a sandbox that never sees the credential.
io.w6w.groqGroq runs open-weight models (Llama, Qwen, GPT-OSS, Kimi, Whisper, and its own Compound agentic models) on purpose-built LPU hardware, exposed through an OpenAI-compatible surface under /openai/v1 rather than OpenAI’s own /v1 — the appeal is inference speed and cost, not a different model catalog.
Chat completions cover the core case, including Groq-specific controls with no OpenAI equivalent: a service_tier latency/availability trade-off (flex fails fast instead of queueing), per-model reasoning-effort and reasoning-format controls for the reasoning models, and Compound-model web search / custom tool configuration. A handful of OpenAI-shaped chat parameters (frequency_penalty, logprobs, logit_bias, and anything beyond n=1) are accepted but documented as having no effect on any current model, so this app leaves them out rather than exposing dead controls.
Whisper covers audio transcription and translation, with a Groq-specific convenience OpenAI’s own endpoint lacks: either a base64 file OR a plain URL (including a data URL) can be sent in the same request. Text-to-speech, the Batch API (chat-completions requests only, processed over a 24h-7d window against a purpose-built batch file upload — Groq’s Files API exists only to feed batches, unlike OpenAI’s multi-purpose one), and the newer, intentionally stateless Responses (beta) endpoint round out the surface. Embeddings, reranking and fine-tuning appear in Groq’s raw API schema but have no documentation page as of writing and are left out entirely rather than guessed at.
A fit for any workflow step that wants an LLM call — especially one where response latency matters more than picking a specific frontier model.
Three routes to the same 16 actions. The Workflow tab is generated from Groq's own manifest and carries its real ids, so it is copy-pasteable; the Code and CLI examples are the same call for any action on any app, so every app-specific value in them is a blank you fill in.
batch-create Create and execute a batch of chat-completion requests from an uploaded file.
response-create Generate a model response via Groq's Responses API. Beta: no server-side conversation state — every call is stateless, unlike OpenAI's `previous_response_id` chaining.
A workflow step names the app and the action, and the editor fills in the
connection when you pick one. This is the Step shape from the
workflow spec, carrying Groq's real ids.
{
"manifestVersion": "2",
"name": "groq-example",
"steps": [
{
"id": "audio-speech",
"uses": {
"app": "io.w6w.groq",
"action": "audio-speech",
"connection": "conn_YOUR_CONNECTION_ID"
},
"with": {
"model": "<model>",
"input": "<input>",
"voice": "<voice>"
}
}
]
}audio-speech audio-transcribe audio-translate batch-create batch-get +11 more actions available
Every app-specific value here is a blank you have to fill in. An
app action is reached through the connection that authenticates it, so the
address is a connection id, not the app id — and connections belong to your account,
so a public page cannot know yours. Create one for Groq, then fill in
the three blanks: conn_YOUR_CONNECTION_ID, the action key, and the
parameters that action declares. The call itself is real — the shape is transcribed
from the studio's own snippet builder, which prints the same kind of blanks — but
nothing in it is specific to Groq. The Workflow tab is where this app's
real ids are.
npm install @w6w/sdkyarn add @w6w/sdkpnpm add @w6w/sdkdeno add npm:@w6w/sdkimport { W6wClient, isActionRun } from "@w6w/sdk";
// Reads W6W_BASE_URL and W6W_TOKEN from the environment when omitted.
const client = new W6wClient();
const envelope = await client.run({
urn: "conn_YOUR_CONNECTION_ID",
action: "audio-speech",
payload: {
model: "<value>",
input: "<value>",
voice: "<value>",
// responseFormat: "<value>",
// sampleRate: "<value>",
// speed: "<value>",
},
});
if (isActionRun(envelope)) console.log(envelope.value); npm install -g @w6w/cli w6w run conn_YOUR_CONNECTION_ID --action audio-speech --payload '{"model":"<value>","input":"<value>","voice":"<value>"}' Give an AI agent Groq — without giving it Groq's credentials. One MCP endpoint exposes every app, function and workflow the caller is entitled to, as tools it can discover and run. Access is granted per team while we onboard.
One tool call{
"name": "w6w_invoke",
"arguments": {
"ref": "app:io.w6w.groq#audio-speech",
"input": {
"model": "<model>",
"input": "<input>",
"voice": "<voice>"
}
}
}
Every tool names its target with a single ref. The
app: form above doesn't name a connection at all — the
host resolves which of the caller's Groq connections to sign
with, and refuses rather than guesses when the answer is ambiguous.
The token is attached host-side, at the moment of the call. It is never a tool argument, never in the model's context, and never in a transcript — so a prompt injection has nothing to exfiltrate.
Tools are derived per end user from what that person has actually connected and is entitled to — not one shared bot identity carrying the union of everyone's access.
Multi-step work runs on the workflow engine and returns a run handle the agent can poll — retries, branching and state survive the conversation that started them.
Groq's declared health checks are on the surface too, so an agent can tell "the vendor is down" from "your credential expired" before it burns a retry on either.
The MCP surface is part of the hosted platform. Groq itself is MIT, and the runtime that executes it is source-available (FSL).
Groq declares its own checks, so its health is a property of the app rather than something the host guesses at.
Atlassian Statuspage rollup for groqstatus.com. Most components are per-model; only `API` and `Website` feed this check's top-level state. Unauthenticated and unsigned.
Requests-per-minute and tokens-per-minute remaining, read off the `x-ratelimit-*` headers.