N8n Workflow Patterns

# n8n Workflow Patterns

Proven architectural patterns for building n8n workflows.

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## The 6 Core Patterns

Based on analysis of real workflow usage:

1. **[Webhook Processing](webhook_processing.md)** (Most Common)
   - Receive HTTP requests → Process → Output
   - Pattern: Webhook → Validate → Transform → Respond/Notify

2. **[HTTP API Integration](http_api_integration.md)**
   - Fetch from REST APIs → Transform → Store/Use
   - Pattern: Trigger → HTTP Request → Transform → Action → Error Handler

3. **[Database Operations](database_operations.md)**
   - Read/Write/Sync database data
   - Pattern: Schedule → Query → Transform → Write → Verify

4. **[AI Agent Workflow](ai_agent_workflow.md)**
   - AI agents with tools and memory
   - Pattern: Trigger → AI Agent (Model + Tools + Memory) → Output

5. **[Scheduled Tasks](scheduled_tasks.md)**
   - Recurring automation workflows
   - Pattern: Schedule → Fetch → Process → Deliver → Log

6. **Batch Processing** (below)
   - Process large datasets in chunks with API rate limits
   - Pattern: Prepare → SplitInBatches → Process per batch → Accumulate → Aggregate

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## Pattern Selection Guide

### When to use each pattern:

**Webhook Processing** - Use when:
- Receiving data from external systems
- Building integrations (Slack commands, form submissions, GitHub webhooks)
- Need instant response to events
- Example: "Receive Stripe payment webhook → Update database → Send confirmation"

**HTTP API Integration** - Use when:
- Fetching data from external APIs
- Synchronizing with third-party services
- Building data pipelines
- Example: "Fetch GitHub issues → Transform → Create Jira tickets"

**Database Operations** - Use when:
- Syncing between databases
- Running database queries on schedule
- ETL workflows
- Example: "Read Postgres records → Transform → Write to MySQL"

**AI Agent Workflow** - Use when:
- Building conversational AI
- Need AI with tool access
- Multi-step reasoning tasks
- Example: "Chat with AI that can search docs, query database, send emails"

**Scheduled Tasks** - Use when:
- Recurring reports or summaries
- Periodic data fetching
- Maintenance tasks
- Example: "Daily: Fetch analytics → Generate report → Email team"

**Batch Processing** - Use when:
- Processing large datasets that exceed API batch limits
- Need to accumulate results across multiple API calls
- Nested loops (e.g., multiple categories × paginated API calls per category)
- Example: "Fetch products for 4 markets × 1000 per API call → Aggregate all results"

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## Common Workflow Components

All patterns share these building blocks:

### 1. Triggers
- **Webhook** - HTTP endpoint (instant)
- **Schedule** - Cron-based timing (periodic)
- **Manual** - Click to execute (testing)
- **Polling** - Check for changes (intervals)

### 2. Data Sources
- **HTTP Request** - REST APIs
- **Database nodes** - Postgres, MySQL, MongoDB
- **Service nodes** - Slack, Google Sheets, etc.
- **Code** - Custom JavaScript/Python

### 3. Transformation
- **Set** - Map/transform fields
- **Code** - Complex logic
- **IF/Switch** - Conditional routing
- **Merge** - Combine data streams

### 4. Outputs
- **HTTP Request** - Call APIs
- **Database** - Write data
- **Communication** - Email, Slack, Discord
- **Storage** - Files, cloud storage

### 5. Error Handling
- **Error Trigger** - Cat