exa-observability
Set up comprehensive observability for Exa integrations with metrics, traces, and alerts.Use when implementing monitoring for Exa operations, setting up dashboards,or configuring alerting for Exa integration health.Trigger with phrases like "exa monitoring", "exa metrics","exa observability", "monitor exa", "exa alerts", "exa tracing".
When & Why to Use This Skill
This Claude skill provides a comprehensive framework for setting up observability in Exa integrations. It includes ready-to-use configurations for Prometheus metrics, OpenTelemetry tracing, and structured logging, enabling developers to monitor API health, track latency, and configure proactive alerting for production environments. By standardizing monitoring practices, it ensures the reliability and performance of search-driven applications.
Use Cases
- Real-time Performance Monitoring: Set up Grafana dashboards to track Exa API request volume, success rates, and latency distributions (P50/P95/P99).
- Proactive Incident Alerting: Configure Prometheus AlertManager rules to automatically trigger notifications for high error rates, high latency, or service downtime.
- Distributed Tracing & Debugging: Integrate OpenTelemetry to visualize the end-to-end lifecycle of Exa requests and identify performance bottlenecks in complex workflows.
- Operational Health Auditing: Implement structured JSON logging to maintain a searchable audit trail of Exa operations and monitor rate limit headroom.
| name | exa-observability |
|---|---|
| description | | |
| allowed-tools | Read, Write, Edit |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
Exa Observability
Overview
Set up comprehensive observability for Exa integrations.
Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured
Metrics Collection
Key Metrics
| Metric | Type | Description |
|---|---|---|
exa_requests_total |
Counter | Total API requests |
exa_request_duration_seconds |
Histogram | Request latency |
exa_errors_total |
Counter | Error count by type |
exa_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: 'exa_requests_total',
help: 'Total Exa API requests',
labelNames: ['method', 'status'],
registers: [registry],
});
const requestDuration = new Histogram({
name: 'exa_request_duration_seconds',
help: 'Exa request duration',
labelNames: ['method'],
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
registers: [registry],
});
const errorCounter = new Counter({
name: 'exa_errors_total',
help: 'Exa 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('exa-client');
async function tracedExaCall<T>(
operationName: string,
operation: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`exa.${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: 'exa',
level: process.env.LOG_LEVEL || 'info',
});
function logExaOperation(
operation: string,
data: Record<string, any>,
duration: number
) {
logger.info({
service: 'exa',
operation,
duration_ms: duration,
...data,
});
}
Alert Configuration
Prometheus AlertManager Rules
# exa_alerts.yaml
groups:
- name: exa_alerts
rules:
- alert: ExaHighErrorRate
expr: |
rate(exa_errors_total[5m]) /
rate(exa_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Exa error rate > 5%"
- alert: ExaHighLatency
expr: |
histogram_quantile(0.95,
rate(exa_request_duration_seconds_bucket[5m])
) > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Exa P95 latency > 2s"
- alert: ExaDown
expr: up{job="exa"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Exa integration is down"
Dashboard
Grafana Panel Queries
{
"panels": [
{
"title": "Exa Request Rate",
"targets": [{
"expr": "rate(exa_requests_total[5m])"
}]
},
{
"title": "Exa Latency P50/P95/P99",
"targets": [{
"expr": "histogram_quantile(0.5, rate(exa_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 exa-incident-runbook.