home / skills / jeremylongshore / claude-code-plugins-plus-skills / retellai-observability

This skill helps you implement end-to-end observability for Retell AI integrations by configuring metrics, tracing, logs, and alerts.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill retellai-observability

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---
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 <[email protected]>
---

# 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
| Metric | Type | Description |
|--------|------|-------------|
| `retellai_requests_total` | Counter | Total API requests |
| `retellai_request_duration_seconds` | Histogram | Request latency |
| `retellai_errors_total` | Counter | Error count by type |
| `retellai_rate_limit_remaining` | Gauge | Rate limit headroom |

### Prometheus Metrics

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```yaml
# 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

```json
{
  "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
| 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
```typescript
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', registry.contentType);
  res.send(await registry.metrics());
});
```

## Resources
- [Prometheus Best Practices](https://prometheus.io/docs/practices/naming/)
- [OpenTelemetry Documentation](https://opentelemetry.io/docs/)
- [Retell AI Observability Guide](https://docs.retellai.com/observability)

## Next Steps
For incident response, see `retellai-incident-runbook`.

Overview

This skill sets up comprehensive observability for Retell AI integrations, covering metrics, traces, logs, dashboards, and alerts. It provides ready-to-use Prometheus metrics, OpenTelemetry tracing patterns, structured logging guidance, and Prometheus AlertManager rules. Use it to make Retell AI behavior measurable and alertable in production.

How this skill works

The skill instruments Retell AI client calls with Prometheus counters, histograms, and gauges to capture request volume, latency, errors, and rate-limit headroom. It adds OpenTelemetry spans around operations for distributed tracing and recommends structured JSON logging for consistent context. It also supplies example Prometheus alerting rules and Grafana panel queries to visualize health and performance.

When to use it

  • When integrating Retell AI into services that need production monitoring
  • When you need end-to-end tracing for AI requests across microservices
  • When you want alerting on error rate, high latency, or service outages
  • When building dashboards to track request volume and latency percentiles
  • When onboarding SRE or incident response for Retell AI integrations

Best practices

  • Instrument every client request with a timer and increment success/error counters
  • Keep label cardinality low—use fixed label sets like method and status, avoid user IDs
  • Record latency with a histogram and use histogram_quantile for P50/P95/P99
  • Propagate trace context in HTTP headers for full distributed traces
  • Log in structured JSON with consistent fields: service, operation, duration_ms, and error

Example use cases

  • Expose retellai_requests_total and retellai_request_duration_seconds to Prometheus for service-level SLOs
  • Wrap Retell AI calls in tracedRetellAICall to capture spans and exceptions end-to-end
  • Create Grafana panels for request rate and P95 latency backed by histogram_quantile queries
  • Configure AlertManager rules to fire on error rate >5%, P95 latency >2s, or downstream integration down
  • Add a /metrics endpoint that serves the Prometheus registry for scraping

FAQ

Which backends are required?

Use Prometheus or a compatible metrics backend, an OpenTelemetry collector or SDK for traces, and Grafana for dashboards. AlertManager handles alerts.

How do I avoid alert storms from transient spikes?

Add a for: duration to alerts (for example 5m) and tune thresholds to match typical traffic patterns. Consider alert deduplication and mute windows.