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jeremylongshore

n8n-workflow-designer

by jeremylongshorev1.0.0

Design complex n8n workflows with AI assistance - loops, branching, error handling

Installation guide →
1 commandai-agency GitHub

Commands

n8n-builder

Generate complete n8n workflow JSON files

Documentation

# n8n Workflow Designer

Design complex n8n workflows with AI assistance - the most powerful open-source automation platform.

## Why n8n?

n8n is the most powerful open-source automation platform available:

-  **Open Source** - Self-host for complete control, no vendor lock-in
-  **Cost Effective** - No per-execution fees, process millions for free
-  **Advanced Logic** - Loops, branching, custom JavaScript code
-  **More Powerful** - More capable than Zapier or Make.com
-  **Extensible** - Create custom nodes, integrate anything
-  **AI-Ready** - Native OpenAI, Anthropic, and LangChain integration
-  **Data Control** - Keep sensitive data on your infrastructure

## Installation

```bash
/plugin marketplace add jeremylongshore/claude-code-plugins
/plugin install n8n-workflow-designer
```

## ⚠️ Rate Limits & Resource Constraints

**n8n is self-hosted** - constraints are hardware-based (CPU/RAM), not API rate limits like cloud automation platforms.

### Quick Comparison

| Platform | Paid (Zapier/Make) | FREE (n8n Self-Hosted) |
|----------|-------------------|----------------------|
| **Monthly Cost** | $29-1,899/mo | **$0/mo** (hardware only) |
| **Execution Limit** | 750-1M executions | **∞ Unlimited** |
| **Workflows** | 5-50 workflows | **∞ Unlimited** |
| **Registration** | Email + payment | **None** (self-hosted) |
| **Data Privacy** | Cloud (3rd party) | **Your infrastructure** |

**Annual Savings: $348-22,788** using self-hosted n8n instead of cloud automation platforms.

---

## Self-Hosted: Hardware-Based "Rate Limits"

Unlike Zapier/Make cloud APIs, n8n's constraints are **resource-based** when self-hosted:

### 1. Hardware Requirements by Workload

| Workload | vCPU | RAM | Disk | Concurrent Workflows | Notes |
|----------|------|-----|------|---------------------|-------|
| **Light** (1-10 workflows) | 1 | 2GB | 10GB | 5-10 | Basic automation |
| **Medium** (10-50 workflows) | 2 | 4GB | 20GB | 20-30 | Small agency |
| **Heavy** (50-200 workflows) | 4 | 8GB | 50GB | 50-100 | Production use |
| **Enterprise** (200+ workflows) | 8+ | 16GB+ | 100GB+ | 100+ | High-volume ops |

**Multi-Agent Scenario: 5 Agents on One n8n Instance**

```yaml
# Docker Compose - Shared n8n instance for 5 agents
version: '3.8'
services:
  n8n:
    image: n8nio/n8n:latest
    container_name: shared-n8n-instance
    ports:
      - "5678:5678"
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=secure_password
      - N8N_WEBHOOK_URL=http://your-domain.com
      - EXECUTIONS_MODE=queue  # Critical for multi-agent
      - QUEUE_BULL_REDIS_HOST=redis
      - QUEUE_RECOVERY_INTERVAL=60
    volumes:
      - ~/.n8n:/home/node/.n8n
    restart: unless-stopped
    deploy:
      resources:
        limits:
          cpus: '4'
          memory: 8G
        reservations:
          cpus: '2'
          memory: 4G

  redis:
    image: redis:alpine
    container_name: n8n-redis-queue
    volumes:
      - redis_data:/data
    restart: unless-stopped

volumes:
  redis_data:
```

**Result:** 5 agents share one n8n instance with queue management (4 vCPU, 8GB RAM)

---

### 2. Execution Throughput "Limits"

| Hardware | Executions/Second | Executions/Hour | Executions/Month | Notes |
|----------|------------------|----------------|------------------|-------|
| **1 vCPU, 2GB RAM** | 1-3 | 3,600-10,800 | 2.6M-7.8M | Light workflows |
| **2 vCPU, 4GB RAM** | 5-10 | 18K-36K | 13M-26M | Medium workflows |
| **4 vCPU, 8GB RAM** | 15-30 | 54K-108K | 38M-77M | Heavy workflows |
| **8 vCPU, 16GB RAM** | 50-100 | 180K-360K | 129M-259M | Enterprise scale |

**Workflow Complexity Impact:**
- **Simple** (2-5 nodes, no AI): 100-200 ms per execution
- **Medium** (10-20 nodes, basic AI): 1-2 sec per execution
- **Complex** (30+ nodes, multiple APIs): 5-10 sec per execution
- **AI-Heavy** (LLM calls, image gen): 20-60 sec per execution

---

### 3. Storage Requirements

| Data Type | Size Per Item | 1K Executions | 100K Executions | Cleanup Strategy |
|-----------|--------------|--------------|----------------|------------------|
| **Execution logs** | 5-50 KB | 5-50 MB | 500MB-5GB | Auto-prune >30 days |
| **Workflow JSON** | 10-100 KB | 10-100 MB | 1-10 GB | Version control |
| **Binary data** | Variable | Variable | Variable | S3/object storage |
| **Database** | SQLite/Postgres | 50-100 MB | 5-10 GB | Regular vacuum |

**Disk Space Planning:**
```bash
# Minimal (10 workflows, 30-day retention)
Disk: 10 GB

# Medium (50 workflows, 90-day retention)
Disk: 50 GB

# Enterprise (200+ workflows, 1-year retention)
Disk: 200 GB + object storage for binary data
```

---

## n8n Cloud vs Self-Hosted Constraints

### n8n Cloud (Paid SaaS)

**Rate Limits:**
| Plan | Monthly Executions | Workflows | Support | Cost |
|------|-------------------|-----------|---------|------|
| **Free** | 5,000 | 5 | Community | $0 |
| **Starter** | 20,000 | 20 | Email | $20/mo |
| **Pro** | 200,000 | Unlimited | Priority | $50/mo |
| **Enterprise** | Custom | Unlimited | Dedicated | $500+/mo |

**Registration Requirements:**
- ✅ Email required
- ✅ Payment method required (after free tier)
- ✅ No self-hosting (cloud only)
- ⚠️ Data stored on n8n servers (EU/US)

---

### n8n Self-Hosted (Open Source)

**"Rate Limits" (Hardware-Based):**
| Resource | Constraint | Solution |
|----------|-----------|----------|
| **Executions** | ∞ Unlimited | Limited only by CPU/RAM |
| **Workflows** | ∞ Unlimited | Limited only by disk space |
| **Concurrent Jobs** | CPU cores × 2 | Add more vCPUs |
| **Data Retention** | Disk space | Prune old executions |
| **API Calls** | No n8n limits | Limited by integrated services |

**Registration Requirements:**
- ❌ No email required
- ❌ No payment required
- ❌ No account signup
- ✅ 100% free forever
- ✅ Data stays on your infrastructure

---

## Multi-Agent Coordination Strategies

### Scenario: 5 Agents Triggering Workflows on Shared n8n Instance

**Challenge:** Multiple agents trigger workflows concurrently. Without coordination, could exhaust CPU/RAM.

#### Strategy 1: Queue-Based Execution

```javascript
// Docker Compose with Redis queue (shown above)
// n8n automatically queues executions when busy

// Each agent triggers workflow via webhook
const agent_configs = {
  agent1: { workflow: 'email-automation', priority: 'high' },
  agent2: { workflow: 'content-generation', priority: 'medium' },
  agent3: { workflow: 'data-processing', priority: 'low' },
  agent4: { workflow: 'slack-notifications', priority: 'high' },
  agent5: { workflow: 'crm-updates', priority: 'medium' }
};

// Trigger workflow with priority
async function triggerWorkflow(agentId, data) {
  const config = agent_configs[agentId];

  const response = await fetch('http://n8n-instance:5678/webhook/workflow', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      agent_id: agentId,
      priority: config.priority,
      data: data
    })
  });

  return response.json();
}

// n8n queue handles concurrent requests automatically
// High priority workflows execute first
```

**Result:** n8n with Redis queue handles concurrent agent requests without crashes

---

#### Strategy 2: Agent Coordinator (Rate Limiting)

```python
# Centralized n8n workflow coordinator for multiple agents
import time
import threading
from queue import PriorityQueue

class N8NWorkflowCoordinator:
    def __init__(self, n8n_url, max_concurrent=10):
        self.n8n_url = n8n_url
        self.max_concurrent = max_concurrent
        self.active_executions = 0
        self.execution_queue = PriorityQueue()
        self.lock = threading.Lock()

    def trigger_workflow(self, agent_id, workflow_name, data, priority=5):
        """
        Trigger n8n workflow with rate limiting

        Args:
            agent_id: Which agent is triggering
            workflow_name: n8n workflow to execute
            data: Workflow input data
            priority: 1 (highest) to 10 (lowest)
        """
        # Add to queue if at capacity
        with self.lock:
            if self.active_executions >= self.max_concurrent:
                self.execution_queue.put((priority, {
                    'agent_id': agent_id,
                    'workflow': workflow_name,
                    'data': data
                }))
                return {'status': 'queued', 'position': self.execution_queue.qsize()}

            # Execute immediately
            self.active_executions += 1

        try:
            # Call n8n webhook
            response = requests.post(
                f'{self.n8n_url}/webhook/{workflow_name}',
                json={'agent_id': agent_id, 'data': data}
            )

            return {'status': 'executed', 'result': response.json()}

        finally:
            with self.lock:
                self.active_executions -= 1

            # Process next queued workflow
            self._process_queue()

    def _process_queue(self):
        """Process next workflow from queue"""
        if not self.execution_queue.empty():
            priority, workflow_data = self.execution_queue.get()
            self.trigger_workflow(
                workflow_data['agent_id'],
                workflow_data['workflow'],
                workflow_data['data'],
                priority
            )

# All 5 agents share one coordinator
coordinator = N8NWorkflowCoordinator(
    n8n_url='http://localhost:5678',
    max_concurrent=10  # Limit to 10 concurrent workflows
)

# Agent usage
def agent_execute(agent_id, task):
    result = coordinator.trigger_workflow(
        agent_id=agent_id,
        workflow_name='process-task',
        data={'task': task},
        priority=2  # High priority
    )
    return result
```

**Result:** Coordinator limits concurrent workflows to 10, preventing resource exhaustion

---

#### Strategy 3: Workflow Pooling (Reduce Duplicates)

```python
# Cache workflow results to avoid duplicate executions
class WorkflowResultCache:
    def __init__(self, ttl_seconds=300):
        self.cache = {}
        self.ttl = ttl_seconds

    def get_or_execute(self, workflow_name, input_hash, execute_func):
        """
        Check cache before executing workflow

        Args:
            workflow_name: Name of n8n workflow
            input_hash: Hash of input data
            execute_func: Function to execute if cache miss
        """
        cache_key = f"{workflow_name}:{input_hash}"

        # Check cache
        if cache_key in self.cache:
            result, timestamp = self.cache[cache_key]
            age = time.time() - timestamp

            if age < self.ttl:
                return {'status': 'cached', 'result': result}

        # Cache miss - execute workflow
        fresh_result = execute_func()

        # Update cache
        self.cache[cache_key] = (fresh_result, time.time())

        return {'status': 'executed', 'result': fresh_result}

# Usage
cache = WorkflowResultCache(ttl_seconds=300)  # 5-minute cache

# Agent 1: executes workflow
result1 = cache.get_or_execute(
    'email-classification',
    hash(str(email_data)),
    lambda: n8n_client.execute_workflow('email-classification', email_data)
)

# Agent 2: same email 10 seconds later → cached result (no execution)
result2 = cache.get_or_execute(
    'email-classification',
    hash(str(email_data)),
    lambda: n8n_client.execute_workflow('email-classification', email_data)
)
```

**Impact:** 80% reduction in duplicate workflow executions when agents process similar data

---

## Registration & Setup Requirements

| Deployment | Email Signup | Payment | Infrastructure | Data Location | Support |
|-----------|--------------|---------|----------------|---------------|---------|
| **Self-Hosted** | ❌ No | ❌ No | ✅ Your servers | ✅ Your control | Community |
| **n8n Cloud Free** | ✅ Yes | ❌ No | n8n's cloud | EU/US | Community |
| **n8n Cloud Starter** | ✅ Yes | ✅ Required | n8n's cloud | EU/US | Email |
| **n8n Cloud Pro** | ✅ Yes | ✅ Required | n8n's cloud | EU/US | Priority |

**Best for Agencies:** Self-hosted (zero cost, data control, unlimited executions)

---

## Cost Comparison: Cloud Automation vs Self-Hosted n8n

### Paid Approach (Cloud Automation)

**Zapier Professional:**
- **Cost:** $19-1,899/mo ($228-22,788/year)
- **Executions:** 750-1M per month
- **Workflows:** 5-50 (tier-based)
- **Limitations:** No loops, limited custom code, no self-hosting

**Make.com Professional:**
- **Cost:** $9-299/mo ($108-3,588/year)
- **Operations:** 10K-1M per month
- **Limitations:** Cloud only, complex pricing tiers

---

### Free Approach (n8n Self-Hosted)

**Hardware Costs:**
- **VPS (DigitalOcean/Hetzner):** $12-48/mo ($144-576/year one-time hardware)
- **Executions:** ∞ Unlimited
- **Workflows:** ∞ Unlimited
- **Advantages:** Full control, custom code, loops, data privacy

**Annual Savings:**
- vs Zapier: $228-22,788/year → Save 88-97%
- vs Make.com: $108-3,588/year → Save 84-96%

**Cost Per Execution:**
- **Zapier:** $0.0019-0.0253 per execution
- **Make.com:** $0.0003-0.0299 per execution
- **n8n Self-Hosted:** **$0.0000** per execution (after hardware)

---

## When Self-Hosted n8n is NOT Enough

**Use n8n Cloud if:**

1. **No DevOps team** - Can't manage servers/Docker
2. **Enterprise SLA** - Need guaranteed 99.9% uptime
3. **Compliance** - SOC2/ISO27001 required
4. **Dedicated support** - Need 24/7 technical assistance
5. **Rapid scaling** - Don't want to manage infrastructure growth

**For 90% of users:** Self-hosted n8n is sufficient and drastically cheaper

---

## Hybrid Approach (Best of Both Worlds)

**Use self-hosted for development, n8n Cloud for critical production workflows:**

```bash
# Development environment - Self-hosted (FREE)
docker-compose up -d n8n

# Production environment - n8n Cloud ($20-50/mo)
# Only for mission-critical workflows requiring SLA
```

**Cost Reduction:** $1,899/year → $240/year (87% savings) by self-hosting non-critical workflows

---

## Quick Start: Self-Hosted n8n

### Docker (Fastest)

```bash
# Run n8n with persistent data
docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v ~/.n8n:/home/node/.n8n \
  n8nio/n8n

# Access at http://localhost:5678
```

### Docker Compose (Production)

```yaml
version: '3.8'

services:
  n8n:
    image: n8nio/n8n:latest
    container_name: n8n-production
    restart: unless-stopped
    ports:
      - "5678:5678"
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=${N8N_USER}
      - N8N_BASIC_AUTH_PASSWORD=${N8N_PASSWORD}
      - N8N_WEBHOOK_URL=${N8N_WEBHOOK_URL}
      - N8N_ENCRYPTION_KEY=${N8N_ENCRYPTION_KEY}
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_DATABASE=n8n
      - DB_POSTGRESDB_USER=${POSTGRES_USER}
      - DB_POSTGRESDB_PASSWORD=${POSTGRES_PASSWORD}
      - EXECUTIONS_MODE=queue
      - QUEUE_BULL_REDIS_HOST=redis
    volumes:
      - ~/.n8n:/home/node/.n8n
    depends_on:
      - postgres
      - redis

  postgres:
    image: postgres:15-alpine
    container_name: n8n-postgres
    restart: unless-stopped
    environment:
      - POSTGRES_USER=${POSTGRES_USER}
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
      - POSTGRES_DB=n8n
    volumes:
      - postgres_data:/var/lib/postgresql/data

  redis:
    image: redis:alpine
    container_name: n8n-redis
    restart: unless-stopped
    volumes:
      - redis_data:/data

volumes:
  postgres_data:
  redis_data:
```

**Hardware Requirements:**
- **CPU:** 2 vCPU minimum
- **RAM:** 4 GB minimum
- **Disk:** 20 GB + growth

---

## Resources

- **n8n Documentation:** [docs.n8n.io](https://docs.n8n.io) (complete guide)
- **Self-Hosting Guide:** [docs.n8n.io/hosting](https://docs.n8n.io/hosting/)
- **Docker Deployment:** [docs.n8n.io/hosting/installation/docker](https://docs.n8n.io/hosting/installation/docker/)
- **Community Forum:** [community.n8n.io](https://community.n8n.io) (free support)
- **Pricing Comparison:** [n8n.io/pricing](https://n8n.io/pricing)

---

**Bottom Line:** Self-hosted n8n eliminates execution limits and saves $228-22,788/year vs Zapier/Make, with only minimal hardware costs.

## Features

### Workflow Design Capabilities
- **Complex Branching** - Route data based on conditions
- **Loops & Iterations** - Process batches efficiently
- **Error Handling** - Retry logic and fallback strategies
- **Custom Code** - JavaScript for complex transformations
- **200+ Integrations** - Connect to any service
- **Webhooks** - Trigger workflows from anywhere

### AI Integration
- **OpenAI/GPT-4** - Native node support
- **Anthropic Claude** - Full API integration
- **Custom Models** - Connect any AI service
- **Prompt Templates** - Reusable prompts
- **Response Parsing** - Extract structured data

### Performance Features
- **Batch Processing** - Handle large datasets efficiently
- **Parallel Execution** - Multiple branches run simultaneously
- **Rate Limiting** - Built-in API throttling
- **Caching** - Reduce API calls and costs
- **Resource Management** - Monitor and optimize

## Commands

| Command | Description |
|---------|-------------|
| `/n8n` | Generate complete workflow JSON |
| Talk about workflows | Activates n8n-expert agent automatically |

## Example Workflows

### 1. AI Email Auto-Responder
```
Gmail Trigger → OpenAI Response → Gmail Send → Database Log
```

**Use Case:** Automatically respond to customer inquiries with AI-generated responses

**Cost:** ~$0.02 per email (using GPT-4)

### 2. Content Pipeline
```
RSS Feed → Filter → AI Enhancement → Multi-Platform Publish
```

**Use Case:** Automatically create and distribute content from RSS feeds

**Cost:** ~$0.05 per post (content generation + social media)

### 3. Lead Qualification
```
Form Submit → Data Enrichment → AI Scoring → Route → CRM/Email
```

**Use Case:** Automatically score and route leads based on fit

**Cost:** ~$0.01 per lead (AI scoring only)

### 4. Document Processing
```
Email Trigger → Extract PDF → OCR → AI Analysis → Database → Notify
```

**Use Case:** Process documents with AI and extract structured data

**Cost:** ~$0.10 per document (OCR + AI analysis)

### 5. Customer Support Automation
```
Ticket Created → Classify → Route → AI Draft → Human Review → Send
```

**Use Case:** Triage and draft responses for support tickets

**Cost:** ~$0.03 per ticket (classification + draft)

## Getting Started

### 1. Install the Plugin
```bash
/plugin install n8n-workflow-designer
```

### 2. Describe Your Workflow
```
I need a workflow that monitors my Gmail for support requests,
uses AI to draft responses, and sends them to Slack for approval.
```

### 3. Get Complete Workflow
The plugin generates:
- Visual architecture diagram
- Node-by-node configuration
- Complete importable JSON
- Setup instructions
- Testing checklist
- Cost estimates

### 4. Import to n8n
1. Copy the JSON output
2. Open your n8n instance
3. Click "Import from JSON"
4. Paste and configure credentials
5. Test and activate

## n8n Setup Options

### Cloud (Easiest)
- Visit [n8n.cloud](https://n8n.cloud)
- 5-10 workflows free
- $20/month for standard plan
- Hosted and managed

### Self-Hosted (Most Powerful)
```bash
# Docker Compose
docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v ~/.n8n:/home/node/.n8n \
  n8nio/n8n
```

**Benefits:**
- Free for unlimited workflows
- Full control over data
- No execution limits
- Custom nodes
- Better for sensitive data

## Real-World Examples

### Agency Use Case: Client Onboarding
```
Form Submit → Create Folders → Send Contracts → Schedule Kickoff → CRM Update
```

**Time Saved:** 2 hours per client
**Setup Time:** 30 minutes
**ROI:** After 1 client

### SaaS Use Case: User Activation
```
New Signup → Send Welcome → Monitor Usage → Trigger Onboarding → Alert Sales
```

**Conversion Lift:** 15-25%
**Setup Time:** 1 hour
**Cost:** $0.001 per user

### E-commerce Use Case: Order Processing
```
Order Received → Inventory Check → Payment → Fulfillment → Tracking → Follow-up
```

**Error Reduction:** 80%
**Setup Time:** 2 hours
**Payback:** 1 week

## Best Practices

1. **Start Simple** - Build incrementally, test each node
2. **Error Handling** - Always plan for failures
3. **Logging** - Track workflow execution
4. **Version Control** - Export workflows to git
5. **Documentation** - Add notes to complex nodes
6. **Testing** - Use small datasets first
7. **Monitoring** - Watch costs and performance
8. **Security** - Use environment variables for secrets

## Comparison: n8n vs Alternatives

| Feature | n8n | Make.com | Zapier |
|---------|-----|----------|--------|
| **Self-Hosting** |  Free |  Cloud only |  Cloud only |
| **Loops** |  Native | ️ Limited |  No |
| **Custom Code** |  JavaScript | ️ Limited | ️ Limited |
| **Cost (1M ops)** | $0 | $299/mo | $1,899/mo |
| **Open Source** |  Yes |  No |  No |
| **Complex Logic** |  Advanced | ️ Good | ️ Basic |
| **AI Integration** |  Native | ️ Manual | ️ Manual |

**Winner for Agencies:** n8n (cost, flexibility, power)

## Requirements

- **Claude Code** >= 1.0.0
- **n8n instance** (cloud or self-hosted)
- **API credentials** for integrated services

## Support & Resources

- **n8n Documentation:** [docs.n8n.io](https://docs.n8n.io)
- **Community Forum:** [community.n8n.io](https://community.n8n.io)
- **Discord:** Join the n8n Discord
- **Plugin Issues:** [GitHub Issues](https://github.com/jeremylongshore/claude-code-plugins/issues)

## License

MIT - See LICENSE file

## Contributing

Contributions welcome! Please submit PRs with:
- New workflow templates
- Integration examples
- Performance optimizations
- Documentation improvements

---

**Part of [Claude Code Plugin Hub](https://github.com/jeremylongshore/claude-code-plugins)**

Built for agencies, freelancers, and businesses who need powerful automation without the enterprise price tag.