home / skills / dexploarer / hyper-forge / deployment-helper
This skill deploys elizaOS agents to production with Docker, monitoring, and scaling guidance for reliable, scalable deployments.
npx playbooks add skill dexploarer/hyper-forge --skill deployment-helperReview the files below or copy the command above to add this skill to your agents.
---
name: deployment-helper
description: Deploy elizaOS agents to production with best practices, monitoring, and scaling. Triggers on "deploy agent", "production setup", or "deploy elizaOS"
allowed-tools: [Write, Read, Bash]
---
# Deployment Helper Skill
Production deployment configurations for elizaOS agents with Docker, monitoring, and scaling.
## Deployment Patterns
### 1. Single Agent Deployment
```typescript
// src/index.ts
import { AgentRuntime } from '@elizaos/core';
import { PGAdapter } from '@elizaos/adapter-postgresql';
import character from './character';
const runtime = new AgentRuntime({
databaseAdapter: new PGAdapter(process.env.DATABASE_URL),
character,
env: process.env
});
await runtime.initialize();
// Health check endpoint
app.get('/health', (req, res) => {
res.json({
status: 'healthy',
agent: character.name,
uptime: process.uptime()
});
});
// Graceful shutdown
process.on('SIGTERM', async () => {
await runtime.stop();
process.exit(0);
});
```
### 2. Docker Deployment
```dockerfile
# Dockerfile
FROM node:20-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build
EXPOSE 3000
CMD ["npm", "start"]
```
```yaml
# docker-compose.yml
version: '3.8'
services:
agent:
build: .
ports:
- "3000:3000"
environment:
- DATABASE_URL=postgresql://user:pass@db:5432/eliza
- OPENAI_API_KEY=${OPENAI_API_KEY}
depends_on:
- db
- redis
restart: unless-stopped
db:
image: postgres:15
volumes:
- pgdata:/var/lib/postgresql/data
environment:
- POSTGRES_DB=eliza
- POSTGRES_USER=user
- POSTGRES_PASSWORD=pass
redis:
image: redis:7-alpine
volumes:
- redisdata:/data
volumes:
pgdata:
redisdata:
```
### 3. Multi-Agent Deployment
```typescript
// agents/coordinator.ts
const agents = [
{ character: agent1, id: 'agent-1' },
{ character: agent2, id: 'agent-2' },
{ character: agent3, id: 'agent-3' }
];
const runtimes = await Promise.all(
agents.map(async ({ character, id }) => {
const runtime = new AgentRuntime({
databaseAdapter: new PGAdapter(DATABASE_URL),
character,
env: process.env
});
await runtime.initialize();
return { id, runtime };
})
);
// Load balancing
function selectAgent(message: string): AgentRuntime {
const hash = hashCode(message);
const index = hash % runtimes.length;
return runtimes[index].runtime;
}
```
## Monitoring
```typescript
// Metrics collection
import { collectDefaultMetrics, register, Counter, Histogram } from 'prom-client';
collectDefaultMetrics();
const messageCounter = new Counter({
name: 'agent_messages_total',
help: 'Total messages processed',
labelNames: ['agent', 'status']
});
const responseTime = new Histogram({
name: 'agent_response_duration_seconds',
help: 'Response time',
buckets: [0.1, 0.5, 1, 2, 5]
});
// Metrics endpoint
app.get('/metrics', async (req, res) => {
res.set('Content-Type', register.contentType);
res.end(await register.metrics());
});
```
## Production Checklist
- [ ] Environment variables configured
- [ ] Database migrations run
- [ ] Health check endpoint working
- [ ] Monitoring configured
- [ ] Logging setup
- [ ] Error tracking (Sentry)
- [ ] Rate limiting enabled
- [ ] HTTPS configured
- [ ] Secrets secured
- [ ] Backup strategy
- [ ] Scaling plan
- [ ] Rollback procedure
This skill helps deploy elizaOS agents to production with practical patterns for single-agent, multi-agent, and containerized setups. It bundles guidance for health checks, graceful shutdowns, metrics, and common production checklist items. Use it to get a reliable, observable, and scalable deployment pipeline for TypeScript-based elizaOS agents.
The skill inspects runtime configuration and provides runnable patterns: a minimal single-agent runtime, Docker and docker-compose manifests, and a coordinator for multi-agent deployments with simple load distribution. It also integrates Prometheus-style metrics collection endpoints and shows how to implement health checks and graceful shutdown. Finally, it lists a production checklist covering migrations, monitoring, secrets, backups, and rollback planning.
How do I scale beyond a single host?
Use container orchestration (Kubernetes, ECS) to run multiple agent replicas, place a load balancer in front, and share state via managed Postgres/Redis. Consider sharding characters across pods for horizontal scaling.
What monitoring should I prioritize first?
Start with health checks, metrics for request count and response latency, and centralized logging. Add error tracking and alerting for high error rates or slow responses before expanding to detailed traces.