Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/groq-pack/skills/groq-reference-architecture/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill groq-reference-architecture. Copyright stays with the author (MIT).

Groq Reference Architecture

Overview

Production architecture for applications built on Groq's LPU inference API. It covers four concerns that every serious Groq integration needs: routing requests to the right model by latency/capability/cost, a middleware band (cache, metrics, retry), a multi-provider fallback chain, and a streaming pipeline. The service layer built here is reusable across a chat UI, an API backend, a batch processor, or an agent.

The full layer diagram and how the pieces interact lives in references/architecture.md; the complete, copy-ready TypeScript for every layer is in references/implementation.md.

Prerequisites

• Groq API key — create one at console.groq.com and export it as GROQ_API_KEY. The Groq SDK reads it from the environment; the client is constructed as new Groq({ apiKey: process.env.GROQ_API_KEY }). Never hardcode the key.
• Runtime: Node.js 18+ (for performance.now() and native fetch).
• Packages: groq-sdk and lru-cache (npm install groq-sdk lru-cache).
• Optional backup provider: an OpenAI-compatible key if you extend the fallback chain beyond Groq's own models.

Instructions

Build the service layer in five ordered steps. Each step is one file under src/groq/. The router depends on the registry; the middleware and fallback depend on the client; the streaming pipeline stands alone. Full source for every step (verbatim) is in references/implementation.md.

1. Model Registry (models.ts) — declare a ModelSpec for each model with its tier, context window, speed, cost, and capabilities. Skeleton:

   export const MODELS: Record<string, ModelSpec> = {
     "llama-3.1-8b-instant":     { tier: "speed",   /* fast, cheap */ },
     "llama-3.3-70b-versatile":  { tier: "quality", /* tools + JSON */ },
     "meta-llama/llama-4-scout-17b-16e-instruct": { tier: "vision" },
     "whisper-large-v3-turbo":   { tier: "audio" },
   };

2. Model Router (router.ts) — selectModel(req) maps requirements (maxLatencyMs, needsVision, needsTools, costSensitive) to the cheapest model that satisfies them. Callers pass requirements, never hardcoded ids.

3. Middleware (middleware.ts) — completionWithMiddleware() wraps each call with an LRU cache (deterministic requests only, temperature === 0), latency + token metrics, and a pluggable metrics sink.

4. Fallback Chain (fallback.ts) — completionWithFallback() tries the primary model, drops to a model in a different rate-limit pool on 429/5xx, then returns a graceful-degradation payload instead of throwing.

5. Streaming Pipeline (streaming.ts) — streamCompletion() is an async generator yielding { type: "token" | "done" | "error" } for real-time SSE UIs.

When applying this to an existing repo, Read the current src/ layout and Grep for direct groq.chat.completions.create calls to find code that should route through the middleware and fallback wrappers instead.

Integration Patterns

Output

Applying this skill produces a src/groq/ service layer with six files (client.ts, models.ts, router.ts, middleware.ts, fallback.ts, streaming.ts) plus the service and API layers that consume it. At runtime you get:

• Routed completions — selectModel() returns a ModelSpec; callers never hardcode a model id, so cost/latency policy lives in one place.
• Cached deterministic responses — repeated temperature: 0 calls return from the LRU cache instead of re-billing the API.
• Resilient calls — completionWithFallback() returns a valid completion shape even when Groq is rate-limited, never surfacing a raw 429 to the user.
• Streamed tokens — streamCompletion() yields { type, content } events for SSE, with a terminal done or error event.
• Metrics — every call emits { model, latencyMs, tokens, cached } to your metrics sink (Prometheus, Datadog, or console.log by default).

Error Handling

Examples

A latency-critical chat turn routes to the speed tier and returns one completion:

const model = selectModel({ maxLatencyMs: 80, costSensitive: true });
// → llama-3.1-8b-instant
const res = await completionWithMiddleware(groq, model.id, messages);

Streaming a UI consumes the async generator token-by-token:

for await (const event of streamCompletion(groq, messages)) {
  if (event.type === "token") process.stdout.write(event.content!);
}

Four fully worked examples — latency-critical, quality-with-fallback, streaming, and vision routing — are in references/examples.md.

Resources

• Groq API Documentation
• Groq Models
• Groq Rate Limits
• Groq Pricing

Next Steps

For multi-environment deployment, see the groq-multi-env-setup skill, which extends this service layer with per-environment configuration and secrets handling.