n8n-workflow-patterns

# n8n Workflow Patterns Proven architectural patterns for building n8n workflows. --- ## 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 --- ## 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" --- ## 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. T