retellai-migration-deep-dive

name	retellai-migration-deep-dive
description	Execute Retell AI major re-architecture and migration strategies with strangler fig pattern. Use when migrating to or from Retell AI, performing major version upgrades, or re-platforming existing integrations to Retell AI. Trigger with phrases like "migrate retellai", "retellai migration", "switch to retellai", "retellai replatform", "retellai upgrade major".
allowed-tools	Read, Write, Edit, Bash(npm:*), Bash(node:*), Bash(kubectl:*)
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

Retell AI Migration Deep Dive

Overview

Comprehensive guide for migrating to or from Retell AI, or major version upgrades.

Prerequisites

• Current system documentation
• Retell AI SDK installed
• Feature flag infrastructure
• Rollback strategy tested

Migration Types

Type	Complexity	Duration	Risk
Fresh install	Low	Days	Low
From competitor	Medium	Weeks	Medium
Major version	Medium	Weeks	Medium
Full replatform	High	Months	High

Pre-Migration Assessment

Step 1: Current State Analysis

# Document current implementation
find . -name "*.ts" -o -name "*.py" | xargs grep -l "retellai" > retellai-files.txt

# Count integration points
wc -l retellai-files.txt

# Identify dependencies
npm list | grep retellai
pip freeze | grep retellai

Step 2: Data Inventory

interface MigrationInventory {
  dataTypes: string[];
  recordCounts: Record<string, number>;
  dependencies: string[];
  integrationPoints: string[];
  customizations: string[];
}

async function assessRetell AIMigration(): Promise<MigrationInventory> {
  return {
    dataTypes: await getDataTypes(),
    recordCounts: await getRecordCounts(),
    dependencies: await analyzeDependencies(),
    integrationPoints: await findIntegrationPoints(),
    customizations: await documentCustomizations(),
  };
}

Migration Strategy: Strangler Fig Pattern

Phase 1: Parallel Run
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   System    │ ──▶ │  Retell AI   │
│   (100%)    │     │   (0%)      │
└─────────────┘     └─────────────┘

Phase 2: Gradual Shift
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   (50%)     │ ──▶ │   (50%)     │
└─────────────┘     └─────────────┘

Phase 3: Complete
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   (0%)      │ ──▶ │   (100%)    │
└─────────────┘     └─────────────┘

Implementation Plan

Phase 1: Setup (Week 1-2)

# Install Retell AI SDK
npm install @retellai/sdk

# Configure credentials
cp .env.example .env.retellai
# Edit with new credentials

# Verify connectivity
node -e "require('@retellai/sdk').ping()"

Phase 2: Adapter Layer (Week 3-4)

// src/adapters/retellai.ts
interface ServiceAdapter {
  create(data: CreateInput): Promise<Resource>;
  read(id: string): Promise<Resource>;
  update(id: string, data: UpdateInput): Promise<Resource>;
  delete(id: string): Promise<void>;
}

class Retell AIAdapter implements ServiceAdapter {
  async create(data: CreateInput): Promise<Resource> {
    const retellaiData = this.transform(data);
    return retellaiClient.create(retellaiData);
  }

  private transform(data: CreateInput): Retell AIInput {
    // Map from old format to Retell AI format
  }
}

Phase 3: Data Migration (Week 5-6)

async function migrateRetell AIData(): Promise<MigrationResult> {
  const batchSize = 100;
  let processed = 0;
  let errors: MigrationError[] = [];

  for await (const batch of oldSystem.iterateBatches(batchSize)) {
    try {
      const transformed = batch.map(transform);
      await retellaiClient.batchCreate(transformed);
      processed += batch.length;
    } catch (error) {
      errors.push({ batch, error });
    }

    // Progress update
    console.log(`Migrated ${processed} records`);
  }

  return { processed, errors };
}

Phase 4: Traffic Shift (Week 7-8)

// Feature flag controlled traffic split
function getServiceAdapter(): ServiceAdapter {
  const retellaiPercentage = getFeatureFlag('retellai_migration_percentage');

  if (Math.random() * 100 < retellaiPercentage) {
    return new Retell AIAdapter();
  }

  return new LegacyAdapter();
}

Rollback Plan

# Immediate rollback
kubectl set env deployment/app RETELLAI_ENABLED=false
kubectl rollout restart deployment/app

# Data rollback (if needed)
./scripts/restore-from-backup.sh --date YYYY-MM-DD

# Verify rollback
curl https://app.yourcompany.com/health | jq '.services.retellai'

Post-Migration Validation

async function validateRetell AIMigration(): Promise<ValidationReport> {
  const checks = [
    { name: 'Data count match', fn: checkDataCounts },
    { name: 'API functionality', fn: checkApiFunctionality },
    { name: 'Performance baseline', fn: checkPerformance },
    { name: 'Error rates', fn: checkErrorRates },
  ];

  const results = await Promise.all(
    checks.map(async c => ({ name: c.name, result: await c.fn() }))
  );

  return { checks: results, passed: results.every(r => r.result.success) };
}

Instructions

Step 1: Assess Current State

Document existing implementation and data inventory.

Step 2: Build Adapter Layer

Create abstraction layer for gradual migration.

Step 3: Migrate Data

Run batch data migration with error handling.

Step 4: Shift Traffic

Gradually route traffic to new Retell AI integration.

Output

• Migration assessment complete
• Adapter layer implemented
• Data migrated successfully
• Traffic fully shifted to Retell AI

Error Handling

Issue	Cause	Solution
Data mismatch	Transform errors	Validate transform logic
Performance drop	No caching	Add caching layer
Rollback triggered	Errors spiked	Reduce traffic percentage
Validation failed	Missing data	Check batch processing

Examples

Quick Migration Status

const status = await validateRetell AIMigration();
console.log(`Migration ${status.passed ? 'PASSED' : 'FAILED'}`);
status.checks.forEach(c => console.log(`  ${c.name}: ${c.result.success}`));

Resources

• Strangler Fig Pattern
• Retell AI Migration Guide

Flagship+ Skills

For advanced troubleshooting, see retellai-advanced-troubleshooting.