fireflies-observability

name	fireflies-observability
description	Set up comprehensive observability for Fireflies.ai integrations with metrics, traces, and alerts. Use when implementing monitoring for Fireflies.ai operations, setting up dashboards, or configuring alerting for Fireflies.ai integration health. Trigger with phrases like "fireflies monitoring", "fireflies metrics", "fireflies observability", "monitor fireflies", "fireflies alerts", "fireflies tracing".
allowed-tools	Read, Write, Edit
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

Fireflies.ai Observability

Overview

Set up comprehensive observability for Fireflies.ai integrations.

Prerequisites

• Prometheus or compatible metrics backend
• OpenTelemetry SDK installed
• Grafana or similar dashboarding tool
• AlertManager configured

Metrics Collection

Key Metrics

Metric	Type	Description
fireflies_requests_total	Counter	Total API requests
fireflies_request_duration_seconds	Histogram	Request latency
fireflies_errors_total	Counter	Error count by type
fireflies_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: 'fireflies_requests_total',
  help: 'Total Fireflies.ai API requests',
  labelNames: ['method', 'status'],
  registers: [registry],
});

const requestDuration = new Histogram({
  name: 'fireflies_request_duration_seconds',
  help: 'Fireflies.ai request duration',
  labelNames: ['method'],
  buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
  registers: [registry],
});

const errorCounter = new Counter({
  name: 'fireflies_errors_total',
  help: 'Fireflies.ai 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('fireflies-client');

async function tracedFireflies.aiCall<T>(
  operationName: string,
  operation: () => Promise<T>
): Promise<T> {
  return tracer.startActiveSpan(`fireflies.${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: 'fireflies',
  level: process.env.LOG_LEVEL || 'info',
});

function logFireflies.aiOperation(
  operation: string,
  data: Record<string, any>,
  duration: number
) {
  logger.info({
    service: 'fireflies',
    operation,
    duration_ms: duration,
    ...data,
  });
}

Alert Configuration

Prometheus AlertManager Rules

# fireflies_alerts.yaml
groups:
  - name: fireflies_alerts
    rules:
      - alert: Fireflies.aiHighErrorRate
        expr: |
          rate(fireflies_errors_total[5m]) /
          rate(fireflies_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Fireflies.ai error rate > 5%"

      - alert: Fireflies.aiHighLatency
        expr: |
          histogram_quantile(0.95,
            rate(fireflies_request_duration_seconds_bucket[5m])
          ) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Fireflies.ai P95 latency > 2s"

      - alert: Fireflies.aiDown
        expr: up{job="fireflies"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Fireflies.ai integration is down"

Dashboard

Grafana Panel Queries

{
  "panels": [
    {
      "title": "Fireflies.ai Request Rate",
      "targets": [{
        "expr": "rate(fireflies_requests_total[5m])"
      }]
    },
    {
      "title": "Fireflies.ai Latency P50/P95/P99",
      "targets": [{
        "expr": "histogram_quantile(0.5, rate(fireflies_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

• Prometheus Best Practices
• OpenTelemetry Documentation
• Fireflies.ai Observability Guide

Next Steps

For incident response, see fireflies-incident-runbook.