Event-Driven Architecture for ChatGPT Apps: Build Scalable Real-Time Systems
Event-driven architecture (EDA) transforms ChatGPT applications from fragile, tightly-coupled systems into resilient, scalable platforms that handle millions of concurrent users. Whether you're processing payment webhooks, coordinating real-time notifications, or orchestrating complex multi-step workflows, adopting event-driven patterns is the difference between a prototype that crashes under load and a production system that scales horizontally.
This comprehensive guide walks you through event-driven architecture fundamentals, implementation patterns, and production-ready code examples for building ChatGPT applications that compete with enterprise platforms. You'll learn how to implement event buses, design event handlers, leverage event sourcing, and monitor distributed event flows—all while maintaining OpenAI Apps SDK compliance.
Why Event-Driven Architecture Matters for ChatGPT Apps
Traditional request-response architectures force MCP servers to wait for slow operations like database writes, third-party API calls, and file processing. Every blocked thread reduces your server's capacity to handle new ChatGPT requests, creating bottlenecks that degrade user experience.
The Core Benefits of Event-Driven Architecture
1. Scalability Through Loose Coupling
Components communicate via events instead of direct function calls. Your payment processor doesn't need to know about your email service—both subscribe to PaymentCompleted events and react independently. This decoupling lets you scale services independently based on load.
2. Resilience and Fault Tolerance When a component fails in traditional architectures, the entire request fails. With event-driven patterns, failed event processing triggers automatic retries, dead-letter queues capture permanently failed events, and the system continues serving new requests without interruption.
3. Asynchronous Processing for Sub-Second Responses ChatGPT users expect instant feedback. Event-driven architecture lets your MCP server respond immediately with "Payment processing..." while background workers handle the heavy lifting. The user sees progress updates via widget state changes without blocking the conversation flow.
4. Audit Trails and Observability Every state change generates an immutable event, creating a complete audit trail. When a user disputes a charge, you can replay their entire interaction history from the event log—critical for compliance in healthcare, legal, and financial ChatGPT applications.
Event-Driven Use Cases for ChatGPT Applications
- Real-Time Notifications: Push booking confirmations to users via WebSocket when events fire
- Multi-Step Workflows: Orchestrate complex processes (order → payment → fulfillment → notification) where each step emits events
- Data Synchronization: Keep ChatGPT widget state in sync with backend databases using change data capture events
- Integration Orchestration: Coordinate between Stripe, Twilio, Salesforce using webhook events
- Background Jobs: Process time-intensive tasks (PDF generation, image processing) asynchronously without blocking ChatGPT responses
Event Architecture Patterns: Event Sourcing vs Event Streaming
Before building an event-driven ChatGPT application, understand the two foundational patterns: event sourcing and event streaming. Both use events as first-class citizens, but they solve different problems.
Event Sourcing: Truth in the Event Log
Event sourcing treats events as the source of truth instead of database records. Rather than storing "User balance: $100", you store the events: AccountCredited($50), AccountDebited($25), AccountCredited($75). Current state is computed by replaying all events.
When to Use Event Sourcing:
- Financial applications requiring complete audit trails
- Systems with complex business logic that changes over time
- Applications needing time-travel debugging (replay to any historical state)
- Compliance requirements mandating immutable records
Event Sourcing Trade-offs:
- Pros: Complete history, audit compliance, temporal queries, easy debugging
- Cons: Increased storage costs, query complexity (no direct state reads), eventual consistency challenges
Event Streaming: Real-Time Data Pipelines
Event streaming focuses on moving data between services in real-time. Services publish events to a central stream (like Apache Kafka or AWS Kinesis), and consumers subscribe to relevant event types. This enables fan-out patterns where one event triggers multiple downstream actions.
When to Use Event Streaming:
- Real-time analytics dashboards tracking ChatGPT usage metrics
- Cross-service data replication (update Elasticsearch indexes from PostgreSQL changes)
- High-throughput systems processing millions of events per second
- Microservices architectures with complex inter-service communication
Event Streaming Trade-offs:
- Pros: Massive horizontal scalability, exactly-once semantics (Kafka), replay capabilities
- Cons: Infrastructure complexity, operational overhead, message ordering challenges
Pub/Sub Pattern for ChatGPT Apps
The publish/subscribe (pub/sub) pattern is the simplest event-driven architecture suitable for most ChatGPT applications. Publishers emit events to named topics/channels, and subscribers receive events from topics they've registered interest in.
Publisher (Payment Service) → Event Topic: "payment.completed" → Subscribers:
- Email Service
- Analytics Service
- Notification Service
This decouples publishers from subscribers—neither knows the other exists, they only know the event contract (schema).
Event Bus Implementation: From In-Memory to Production
Let's build a production-ready event bus supporting in-memory (development), Redis (single-server production), and AWS EventBridge (distributed production) backends.
In-Memory Event Bus (TypeScript)
Perfect for development, testing, and single-process ChatGPT apps with low traffic.
// eventBus.ts - In-Memory Event Bus Implementation
type EventHandler<T = any> = (event: T) => void | Promise<void>;
interface EventMetadata {
eventId: string;
timestamp: number;
source: string;
}
interface Event<T = any> {
type: string;
payload: T;
metadata: EventMetadata;
}
class InMemoryEventBus {
private handlers: Map<string, Set<EventHandler>> = new Map();
private eventLog: Event[] = [];
private maxLogSize: number = 1000;
/**
* Subscribe to events of a specific type
*/
subscribe<T = any>(eventType: string, handler: EventHandler<Event<T>>): () => void {
if (!this.handlers.has(eventType)) {
this.handlers.set(eventType, new Set());
}
this.handlers.get(eventType)!.add(handler);
// Return unsubscribe function
return () => {
this.handlers.get(eventType)?.delete(handler);
if (this.handlers.get(eventType)?.size === 0) {
this.handlers.delete(eventType);
}
};
}
/**
* Publish event to all subscribers
*/
async publish<T = any>(eventType: string, payload: T, source: string = 'unknown'): Promise<void> {
const event: Event<T> = {
type: eventType,
payload,
metadata: {
eventId: this.generateEventId(),
timestamp: Date.now(),
source
}
};
// Store in event log (bounded to prevent memory leaks)
this.eventLog.push(event);
if (this.eventLog.length > this.maxLogSize) {
this.eventLog.shift(); // Remove oldest event
}
// Get handlers (might be none)
const handlers = this.handlers.get(eventType);
if (!handlers || handlers.size === 0) {
console.warn(`No handlers registered for event type: ${eventType}`);
return;
}
// Execute all handlers (async, parallel)
const promises = Array.from(handlers).map(async (handler) => {
try {
await handler(event);
} catch (error) {
console.error(`Event handler error for ${eventType}:`, error);
// Don't throw - one failed handler shouldn't break others
}
});
await Promise.allSettled(promises);
}
/**
* Get recent events (useful for debugging)
*/
getRecentEvents(count: number = 10): Event[] {
return this.eventLog.slice(-count);
}
/**
* Replay events to a specific handler (time-travel debugging)
*/
async replayEvents(eventType: string, handler: EventHandler, fromTimestamp?: number): Promise<void> {
const eventsToReplay = this.eventLog
.filter(e => e.type === eventType)
.filter(e => !fromTimestamp || e.metadata.timestamp >= fromTimestamp);
for (const event of eventsToReplay) {
await handler(event);
}
}
private generateEventId(): string {
return `evt_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
}
}
// Export singleton instance
export const eventBus = new InMemoryEventBus();
Usage Example:
import { eventBus } from './eventBus';
// Subscribe to payment events
const unsubscribe = eventBus.subscribe('payment.completed', async (event) => {
const { userId, amount, currency } = event.payload;
console.log(`Payment received: ${amount} ${currency} from user ${userId}`);
// Send confirmation email
await sendEmail(userId, 'payment-confirmation', { amount, currency });
});
// Publish event from MCP tool handler
await eventBus.publish('payment.completed', {
userId: 'usr_123',
amount: 14900,
currency: 'USD',
subscriptionTier: 'professional'
}, 'stripe-webhook-handler');
// Later: unsubscribe
unsubscribe();
Redis Pub/Sub Event Bus (TypeScript)
For production single-server deployments or distributed systems with low latency requirements.
// redisEventBus.ts - Redis Pub/Sub Event Bus
import Redis from 'ioredis';
interface RedisEventBusConfig {
redisUrl: string;
keyPrefix?: string;
}
class RedisEventBus {
private publisher: Redis;
private subscriber: Redis;
private handlers: Map<string, Set<EventHandler>> = new Map();
private keyPrefix: string;
constructor(config: RedisEventBusConfig) {
this.publisher = new Redis(config.redisUrl);
this.subscriber = new Redis(config.redisUrl);
this.keyPrefix = config.keyPrefix || 'events:';
// Handle incoming messages
this.subscriber.on('message', async (channel, message) => {
try {
const event = JSON.parse(message);
const eventType = channel.replace(this.keyPrefix, '');
const handlers = this.handlers.get(eventType);
if (!handlers) return;
const promises = Array.from(handlers).map(handler =>
handler(event).catch(err =>
console.error(`Handler error for ${eventType}:`, err)
)
);
await Promise.allSettled(promises);
} catch (error) {
console.error('Failed to process Redis message:', error);
}
});
}
async subscribe<T = any>(eventType: string, handler: EventHandler<Event<T>>): Promise<() => Promise<void>> {
if (!this.handlers.has(eventType)) {
this.handlers.set(eventType, new Set());
// Subscribe to Redis channel
await this.subscriber.subscribe(this.keyPrefix + eventType);
}
this.handlers.get(eventType)!.add(handler);
// Return async unsubscribe function
return async () => {
this.handlers.get(eventType)?.delete(handler);
if (this.handlers.get(eventType)?.size === 0) {
await this.subscriber.unsubscribe(this.keyPrefix + eventType);
this.handlers.delete(eventType);
}
};
}
async publish<T = any>(eventType: string, payload: T, source: string = 'unknown'): Promise<void> {
const event: Event<T> = {
type: eventType,
payload,
metadata: {
eventId: `evt_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
timestamp: Date.now(),
source
}
};
const channel = this.keyPrefix + eventType;
await this.publisher.publish(channel, JSON.stringify(event));
// Also store in Redis list for persistence (optional)
await this.publisher.lpush(
`${this.keyPrefix}log:${eventType}`,
JSON.stringify(event)
);
await this.publisher.ltrim(`${this.keyPrefix}log:${eventType}`, 0, 999); // Keep last 1000 events
}
async getRecentEvents(eventType: string, count: number = 10): Promise<Event[]> {
const events = await this.publisher.lrange(
`${this.keyPrefix}log:${eventType}`,
0,
count - 1
);
return events.map(e => JSON.parse(e));
}
async disconnect(): Promise<void> {
await this.publisher.quit();
await this.subscriber.quit();
}
}
// Initialize
export const eventBus = new RedisEventBus({
redisUrl: process.env.REDIS_URL || 'redis://localhost:6379'
});
AWS EventBridge Integration (TypeScript)
For enterprise ChatGPT applications requiring cross-account event routing, SaaS integrations, and guaranteed delivery.
// eventBridgeEventBus.ts - AWS EventBridge Event Bus
import { EventBridgeClient, PutEventsCommand } from '@aws-sdk/client-eventbridge';
interface EventBridgeConfig {
region: string;
eventBusName?: string;
source: string;
}
class EventBridgeEventBus {
private client: EventBridgeClient;
private eventBusName: string;
private source: string;
constructor(config: EventBridgeConfig) {
this.client = new EventBridgeClient({ region: config.region });
this.eventBusName = config.eventBusName || 'default';
this.source = config.source;
}
async publish<T = any>(eventType: string, payload: T, detailType?: string): Promise<void> {
const event = {
type: eventType,
payload,
metadata: {
eventId: `evt_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
timestamp: Date.now(),
source: this.source
}
};
const command = new PutEventsCommand({
Entries: [
{
Source: this.source,
DetailType: detailType || eventType,
Detail: JSON.stringify(event),
EventBusName: this.eventBusName,
Time: new Date()
}
]
});
try {
const response = await this.client.send(command);
if (response.FailedEntryCount && response.FailedEntryCount > 0) {
console.error('EventBridge publish failed:', response.Entries);
throw new Error('Failed to publish event to EventBridge');
}
} catch (error) {
console.error('EventBridge error:', error);
throw error;
}
}
}
// Initialize
export const eventBus = new EventBridgeEventBus({
region: process.env.AWS_REGION || 'us-east-1',
eventBusName: process.env.EVENT_BUS_NAME,
source: 'chatgpt-mcp-server'
});
Event Handler Registry: Type-Safe Event Processing
A handler registry provides type safety, automatic error handling, and centralized event processing logic for ChatGPT MCP servers.
// eventHandlerRegistry.ts - Type-Safe Event Handler Registry
import { eventBus } from './eventBus';
type EventPayload = {
'payment.completed': {
userId: string;
amount: number;
currency: string;
subscriptionTier: string;
};
'user.registered': {
userId: string;
email: string;
source: string;
};
'app.created': {
appId: string;
userId: string;
templateId?: string;
};
'booking.confirmed': {
bookingId: string;
userId: string;
classId: string;
timestamp: number;
};
};
type EventType = keyof EventPayload;
interface EventHandlerConfig<T extends EventType> {
eventType: T;
handler: (payload: EventPayload[T], metadata: EventMetadata) => void | Promise<void>;
retryOnError?: boolean;
maxRetries?: number;
}
class EventHandlerRegistry {
private handlers: Map<string, EventHandlerConfig<any>> = new Map();
private unsubscribers: Map<string, () => void> = new Map();
/**
* Register event handler with automatic retry logic
*/
register<T extends EventType>(config: EventHandlerConfig<T>): void {
const handlerId = `${config.eventType}_${Date.now()}`;
this.handlers.set(handlerId, config);
// Subscribe to event bus
const unsubscribe = eventBus.subscribe(config.eventType, async (event) => {
await this.executeHandler(handlerId, event.payload, event.metadata);
});
this.unsubscribers.set(handlerId, unsubscribe);
}
private async executeHandler(
handlerId: string,
payload: any,
metadata: EventMetadata,
attempt: number = 1
): Promise<void> {
const config = this.handlers.get(handlerId);
if (!config) return;
try {
await config.handler(payload, metadata);
} catch (error) {
console.error(`Handler ${handlerId} failed (attempt ${attempt}):`, error);
if (config.retryOnError && attempt < (config.maxRetries || 3)) {
// Exponential backoff: 1s, 2s, 4s
const delay = Math.pow(2, attempt - 1) * 1000;
await new Promise(resolve => setTimeout(resolve, delay));
await this.executeHandler(handlerId, payload, metadata, attempt + 1);
} else {
// Send to dead-letter queue after max retries
console.error(`Handler ${handlerId} exhausted retries, sending to DLQ`);
await this.sendToDeadLetterQueue(config.eventType, payload, metadata, error);
}
}
}
private async sendToDeadLetterQueue(
eventType: string,
payload: any,
metadata: EventMetadata,
error: any
): Promise<void> {
// Publish to dead-letter topic for manual investigation
await eventBus.publish('dlq.event_processing_failed', {
originalEventType: eventType,
originalPayload: payload,
originalMetadata: metadata,
error: {
message: error.message,
stack: error.stack
},
failedAt: Date.now()
}, 'event-handler-registry');
}
unregisterAll(): void {
this.unsubscribers.forEach(unsubscribe => unsubscribe());
this.handlers.clear();
this.unsubscribers.clear();
}
}
export const registry = new EventHandlerRegistry();
// Register handlers
registry.register({
eventType: 'payment.completed',
handler: async (payload, metadata) => {
console.log(`Processing payment: ${payload.amount} ${payload.currency}`);
// Update user subscription in Firestore
// Send confirmation email
// Log to analytics
},
retryOnError: true,
maxRetries: 3
});
registry.register({
eventType: 'user.registered',
handler: async (payload) => {
// Send welcome email
// Create default app template
// Track in analytics
},
retryOnError: true
});
Async Event Processor with Worker Pool
For CPU-intensive or I/O-heavy event processing, use a worker pool to prevent blocking the main event loop.
// asyncEventProcessor.ts - Worker Pool for Event Processing
import { Worker } from 'worker_threads';
import { eventBus } from './eventBus';
interface WorkerPoolConfig {
maxWorkers: number;
workerScript: string;
}
class AsyncEventProcessor {
private workers: Worker[] = [];
private taskQueue: Array<{ event: Event; resolve: Function; reject: Function }> = [];
private availableWorkers: Worker[] = [];
constructor(config: WorkerPoolConfig) {
// Create worker pool
for (let i = 0; i < config.maxWorkers; i++) {
const worker = new Worker(config.workerScript);
worker.on('message', (result) => {
this.availableWorkers.push(worker);
this.processNextTask();
});
worker.on('error', (error) => {
console.error('Worker error:', error);
this.availableWorkers.push(worker);
this.processNextTask();
});
this.workers.push(worker);
this.availableWorkers.push(worker);
}
}
async processEvent(event: Event): Promise<void> {
return new Promise((resolve, reject) => {
this.taskQueue.push({ event, resolve, reject });
this.processNextTask();
});
}
private processNextTask(): void {
if (this.taskQueue.length === 0 || this.availableWorkers.length === 0) {
return;
}
const worker = this.availableWorkers.pop()!;
const task = this.taskQueue.shift()!;
worker.postMessage(task.event);
const timeout = setTimeout(() => {
task.reject(new Error('Worker timeout'));
this.availableWorkers.push(worker);
this.processNextTask();
}, 30000); // 30s timeout
worker.once('message', (result) => {
clearTimeout(timeout);
task.resolve(result);
});
}
async shutdown(): Promise<void> {
await Promise.all(this.workers.map(w => w.terminate()));
}
}
// Initialize processor
const processor = new AsyncEventProcessor({
maxWorkers: 4,
workerScript: './eventWorker.js'
});
// Subscribe to high-load events
eventBus.subscribe('image.processing.requested', async (event) => {
await processor.processEvent(event);
});
Event Sourcing with PostgreSQL Event Store
Implement event sourcing for audit trails and temporal queries in ChatGPT applications handling sensitive data.
// eventStore.ts - PostgreSQL Event Store Implementation
import { Pool } from 'pg';
interface StoredEvent {
eventId: string;
aggregateId: string;
aggregateType: string;
eventType: string;
payload: any;
metadata: EventMetadata;
version: number;
createdAt: Date;
}
class PostgresEventStore {
private pool: Pool;
constructor(connectionString: string) {
this.pool = new Pool({ connectionString });
this.initializeSchema();
}
private async initializeSchema(): Promise<void> {
await this.pool.query(`
CREATE TABLE IF NOT EXISTS event_store (
event_id VARCHAR(64) PRIMARY KEY,
aggregate_id VARCHAR(64) NOT NULL,
aggregate_type VARCHAR(64) NOT NULL,
event_type VARCHAR(128) NOT NULL,
payload JSONB NOT NULL,
metadata JSONB NOT NULL,
version INTEGER NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(aggregate_id, version)
);
CREATE INDEX IF NOT EXISTS idx_aggregate ON event_store(aggregate_id, version);
CREATE INDEX IF NOT EXISTS idx_event_type ON event_store(event_type, created_at);
`);
}
async append(
aggregateId: string,
aggregateType: string,
events: Array<{ eventType: string; payload: any }>
): Promise<void> {
const client = await this.pool.connect();
try {
await client.query('BEGIN');
// Get current version
const { rows } = await client.query(
'SELECT COALESCE(MAX(version), 0) as version FROM event_store WHERE aggregate_id = $1',
[aggregateId]
);
let version = rows[0].version;
// Append events
for (const event of events) {
version++;
await client.query(
`INSERT INTO event_store (event_id, aggregate_id, aggregate_type, event_type, payload, metadata, version)
VALUES ($1, $2, $3, $4, $5, $6, $7)`,
[
`evt_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
aggregateId,
aggregateType,
event.eventType,
JSON.stringify(event.payload),
JSON.stringify({ timestamp: Date.now() }),
version
]
);
}
await client.query('COMMIT');
} catch (error) {
await client.query('ROLLBACK');
throw error;
} finally {
client.release();
}
}
async getEvents(aggregateId: string): Promise<StoredEvent[]> {
const { rows } = await this.pool.query(
'SELECT * FROM event_store WHERE aggregate_id = $1 ORDER BY version ASC',
[aggregateId]
);
return rows.map(row => ({
eventId: row.event_id,
aggregateId: row.aggregate_id,
aggregateType: row.aggregate_type,
eventType: row.event_type,
payload: row.payload,
metadata: row.metadata,
version: row.version,
createdAt: row.created_at
}));
}
}
export const eventStore = new PostgresEventStore(process.env.DATABASE_URL!);
Event Monitoring with OpenTelemetry
Monitor event flows across distributed systems using OpenTelemetry tracing for ChatGPT app observability.
// eventTracer.ts - OpenTelemetry Event Tracing
import { trace, context, SpanStatusCode } from '@opentelemetry/api';
import { eventBus } from './eventBus';
const tracer = trace.getTracer('event-bus');
// Wrap publish with tracing
const originalPublish = eventBus.publish.bind(eventBus);
eventBus.publish = async function<T = any>(
eventType: string,
payload: T,
source?: string
): Promise<void> {
return tracer.startActiveSpan(`event.publish.${eventType}`, async (span) => {
try {
span.setAttributes({
'event.type': eventType,
'event.source': source || 'unknown',
'event.payload_size': JSON.stringify(payload).length
});
await originalPublish(eventType, payload, source);
span.setStatus({ code: SpanStatusCode.OK });
} catch (error) {
span.setStatus({
code: SpanStatusCode.ERROR,
message: error.message
});
throw error;
} finally {
span.end();
}
});
};
Production Deployment Checklist
Before deploying event-driven ChatGPT applications to production:
- Choose Event Bus: In-memory (dev), Redis (single-server), EventBridge (distributed)
- Implement Idempotency: All handlers must support duplicate event processing
- Add Dead-Letter Queues: Capture permanently failed events for manual review
- Configure Retry Logic: Exponential backoff with max retry limits (3-5 attempts)
- Monitor Event Lag: Alert when consumers fall behind publishers (>1000 events)
- Implement Circuit Breakers: Prevent cascading failures when downstream services fail
- Add Event Versioning: Support schema evolution without breaking existing handlers
- Enable Distributed Tracing: Use OpenTelemetry for cross-service event tracking
- Set Up Alerting: Notify on-call engineers when DLQ receives events
- Document Event Schemas: Maintain JSON Schema definitions for all event types
Conclusion: Build Resilient ChatGPT Applications with Event-Driven Architecture
Event-driven architecture transforms ChatGPT applications from monolithic request-response systems into scalable, resilient platforms capable of handling enterprise workloads. By decoupling components through events, implementing robust error handling with retries and dead-letter queues, and leveraging event sourcing for audit trails, you build ChatGPT apps that compete with industry-leading SaaS products.
The code examples in this guide provide production-ready foundations for in-memory, Redis, and AWS EventBridge event buses—choose the architecture that matches your scale and operational maturity. Combine event-driven patterns with webhook integrations, caching strategies, and background job processing to build ChatGPT applications users trust with mission-critical workflows.
Ready to Build Event-Driven ChatGPT Apps?
MakeAIHQ provides the no-code platform for creating production-ready ChatGPT applications with built-in event bus integration, monitoring, and deployment automation. Join thousands of developers building the next generation of conversational AI applications.
Start Building Free → | View Templates | Read Documentation
Related Resources:
- MCP Server Development Complete Guide
- ChatGPT App Performance Optimization
- MCP Server Webhook Implementation
- Background Jobs Processing Guide
- Database Integration Patterns
External References: