jeremylongshore

retellai-observability

@jeremylongshore/retellai-observability
jeremylongshore
1,004
123 forks
Updated 1/18/2026
View on GitHub

Set up comprehensive observability for Retell AI integrations with metrics, traces, and alerts. Use when implementing monitoring for Retell AI operations, setting up dashboards, or configuring alerting for Retell AI integration health. Trigger with phrases like "retellai monitoring", "retellai metrics", "retellai observability", "monitor retellai", "retellai alerts", "retellai tracing".

Installation

$skills install @jeremylongshore/retellai-observability
Claude Code
Cursor
Copilot
Codex
Antigravity

Details

Repositoryjeremylongshore/claude-code-plugins-plus-skills
Pathplugins/saas-packs/retellai-pack/skills/retellai-observability/SKILL.md
Branchmain
Scoped Name@jeremylongshore/retellai-observability

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions

name: retellai-observability description: | Set up comprehensive observability for Retell AI integrations with metrics, traces, and alerts. Use when implementing monitoring for Retell AI operations, setting up dashboards, or configuring alerting for Retell AI integration health. Trigger with phrases like "retellai monitoring", "retellai metrics", "retellai observability", "monitor retellai", "retellai alerts", "retellai tracing". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Retell AI Observability

Overview

Set up comprehensive observability for Retell AI integrations.

Prerequisites

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

Metrics Collection

Key Metrics

MetricTypeDescription
retellai_requests_totalCounterTotal API requests
retellai_request_duration_secondsHistogramRequest latency
retellai_errors_totalCounterError count by type
retellai_rate_limit_remainingGaugeRate limit headroom

Prometheus Metrics

import { Registry, Counter, Histogram, Gauge } from 'prom-client';

const registry = new Registry();

const requestCounter = new Counter({
  name: 'retellai_requests_total',
  help: 'Total Retell AI API requests',
  labelNames: ['method', 'status'],
  registers: [registry],
});

const requestDuration = new Histogram({
  name: 'retellai_request_duration_seconds',
  help: 'Retell 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: 'retellai_errors_total',
  help: 'Retell 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('retellai-client');

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

function logRetell AIOperation(
  operation: string,
  data: Record<string, any>,
  duration: number
) {
  logger.info({
    service: 'retellai',
    operation,
    duration_ms: duration,
    ...data,
  });
}

Alert Configuration

Prometheus AlertManager Rules

# retellai_alerts.yaml
groups:
  - name: retellai_alerts
    rules:
      - alert: Retell AIHighErrorRate
        expr: |
          rate(retellai_errors_total[5m]) /
          rate(retellai_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Retell AI error rate > 5%"

      - alert: Retell AIHighLatency
        expr: |
          histogram_quantile(0.95,
            rate(retellai_request_duration_seconds_bucket[5m])
          ) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Retell AI P95 latency > 2s"

      - alert: Retell AIDown
        expr: up{job="retellai"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Retell AI integration is down"

Dashboard

Grafana Panel Queries

{
  "panels": [
    {
      "title": "Retell AI Request Rate",
      "targets": [{
        "expr": "rate(retellai_requests_total[5m])"
      }]
    },
    {
      "title": "Retell AI Latency P50/P95/P99",
      "targets": [{
        "expr": "histogram_quantile(0.5, rate(retellai_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

IssueCauseSolution
Missing metricsNo instrumentationWrap client calls
Trace gapsMissing propagationCheck context headers
Alert stormsWrong thresholdsTune alert rules
High cardinalityToo many labelsReduce 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 retellai-incident-runbook.

More by jeremylongshore

View all
rabbitmq-queue-setup
1,004

Rabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.

model-evaluation-suite
1,004

evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".

neural-network-builder
1,004

building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").

oauth-callback-handler
1,004

Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.