exa-observability

jeremylongshore's avatarfrom jeremylongshore

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".

⭐939stars🔀114forks📁View on GitHub🕐Updated Jan 11, 2026

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.
nameexa-observability
description|
allowed-toolsRead, Write, Edit
version1.0.0
licenseMIT
authorJeremy 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.