groq-observability
Set up comprehensive observability for Groq integrations with metrics, traces, and alerts.Use when implementing monitoring for Groq operations, setting up dashboards,or configuring alerting for Groq integration health.Trigger with phrases like "groq monitoring", "groq metrics","groq observability", "monitor groq", "groq alerts", "groq tracing".
When & Why to Use This Skill
This Claude skill provides a comprehensive framework for establishing robust observability for Groq integrations. It enables developers to implement full-stack monitoring using Prometheus metrics, OpenTelemetry distributed tracing, and structured logging, ensuring high reliability and performance for AI-powered applications through real-time insights and automated alerting.
Use Cases
- Performance Optimization: Monitor Groq API request latency (P50, P95, P99) and throughput to identify and resolve performance bottlenecks.
- Proactive Incident Management: Configure automated AlertManager rules to trigger notifications for high error rates, service downtime, or rate-limit exhaustion.
- Distributed Tracing: Implement OpenTelemetry to track request flows across microservices, making it easier to debug complex LLM-driven workflows.
- Operational Visibility: Deploy ready-to-use Grafana dashboards to visualize usage trends, success rates, and the overall health of Groq infrastructure.
| name | groq-observability |
|---|---|
| description | | |
| allowed-tools | Read, Write, Edit |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
Groq Observability
Overview
Set up comprehensive observability for Groq integrations.
Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured
Metrics Collection
Key Metrics
| Metric | Type | Description |
|---|---|---|
groq_requests_total |
Counter | Total API requests |
groq_request_duration_seconds |
Histogram | Request latency |
groq_errors_total |
Counter | Error count by type |
groq_rate_limit_remaining |
Gauge | Rate limit headroom |
Prometheus Metrics
import { Registry, Counter, Histogram, Gauge } from 'prom-client';
const registry = new Registry();
const requestCounter = new Counter({
name: 'groq_requests_total',
help: 'Total Groq API requests',
labelNames: ['method', 'status'],
registers: [registry],
});
const requestDuration = new Histogram({
name: 'groq_request_duration_seconds',
help: 'Groq request duration',
labelNames: ['method'],
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
registers: [registry],
});
const errorCounter = new Counter({
name: 'groq_errors_total',
help: 'Groq errors by type',
labelNames: ['error_type'],
registers: [registry],
});
Instrumented Client
async function instrumentedRequest<T>(
method: string,
operation: () => Promise<T>
): Promise<T> {
const timer = requestDuration.startTimer({ method });
try {
const result = await operation();
requestCounter.inc({ method, status: 'success' });
return result;
} catch (error: any) {
requestCounter.inc({ method, status: 'error' });
errorCounter.inc({ error_type: error.code || 'unknown' });
throw error;
} finally {
timer();
}
}
Distributed Tracing
OpenTelemetry Setup
import { trace, SpanStatusCode } from '@opentelemetry/api';
const tracer = trace.getTracer('groq-client');
async function tracedGroqCall<T>(
operationName: string,
operation: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`groq.${operationName}`, async (span) => {
try {
const result = await operation();
span.setStatus({ code: SpanStatusCode.OK });
return result;
} catch (error: any) {
span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
span.recordException(error);
throw error;
} finally {
span.end();
}
});
}
Logging Strategy
Structured Logging
import pino from 'pino';
const logger = pino({
name: 'groq',
level: process.env.LOG_LEVEL || 'info',
});
function logGroqOperation(
operation: string,
data: Record<string, any>,
duration: number
) {
logger.info({
service: 'groq',
operation,
duration_ms: duration,
...data,
});
}
Alert Configuration
Prometheus AlertManager Rules
# groq_alerts.yaml
groups:
- name: groq_alerts
rules:
- alert: GroqHighErrorRate
expr: |
rate(groq_errors_total[5m]) /
rate(groq_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Groq error rate > 5%"
- alert: GroqHighLatency
expr: |
histogram_quantile(0.95,
rate(groq_request_duration_seconds_bucket[5m])
) > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Groq P95 latency > 2s"
- alert: GroqDown
expr: up{job="groq"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Groq integration is down"
Dashboard
Grafana Panel Queries
{
"panels": [
{
"title": "Groq Request Rate",
"targets": [{
"expr": "rate(groq_requests_total[5m])"
}]
},
{
"title": "Groq Latency P50/P95/P99",
"targets": [{
"expr": "histogram_quantile(0.5, rate(groq_request_duration_seconds_bucket[5m]))"
}]
}
]
}
Instructions
Step 1: Set Up Metrics Collection
Implement Prometheus counters, histograms, and gauges for key operations.
Step 2: Add Distributed Tracing
Integrate OpenTelemetry for end-to-end request tracing.
Step 3: Configure Structured Logging
Set up JSON logging with consistent field names.
Step 4: Create Alert Rules
Define Prometheus alerting rules for error rates and latency.
Output
- Metrics collection enabled
- Distributed tracing configured
- Structured logging implemented
- Alert rules deployed
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Missing metrics | No instrumentation | Wrap client calls |
| Trace gaps | Missing propagation | Check context headers |
| Alert storms | Wrong thresholds | Tune alert rules |
| High cardinality | Too many labels | Reduce label values |
Examples
Quick Metrics Endpoint
app.get('/metrics', async (req, res) => {
res.set('Content-Type', registry.contentType);
res.send(await registry.metrics());
});
Resources
Next Steps
For incident response, see groq-incident-runbook.