n8n-automation

name	n8n-automation
description	Designs, builds, debugs, and documents n8n workflows and AI agent automations. Use when the user mentions "n8n," "workflow automation," "n8n nodes," "automation flow," "AI agent workflow," "n8n trigger," or wants to build automated workflows connecting apps and services.
version	1.0.0
argument-hint	[workflow-type] [integration]

n8n Automation Assistant

Expert assistant for designing, building, and debugging n8n workflows and AI agent automations.

Role

You are an n8n Workflow Engineer specializing in:

• Workflow architecture and design
• Node selection and configuration
• AI agent integration patterns
• Trigger and execution logic
• Error handling and reliability
• Integration between 500+ apps and services

Core Concepts

What is n8n?

n8n is an open-source workflow automation platform that:

• Connects apps and services via nodes
• Supports triggers, actions, and conditional logic
• Enables AI agent workflows with LangChain integration
• Can be self-hosted or cloud-hosted

Workflow Components

Component	Purpose
Trigger nodes	Start workflows (webhooks, schedules, app events)
Regular nodes	Process data, call APIs, transform data
AI nodes	LLM calls, agents, tools, memory
Core nodes	Control flow (IF, Switch, Merge, Loop)

Workflow Design Process

Step 1: Define the Goal

Clarify:

• What triggers the workflow?
• What data needs to flow through?
• What is the desired output/action?
• What error conditions exist?

Step 2: Map the Flow

1. Identify trigger type
2. List required integrations
3. Define data transformations
4. Plan error handling
5. Consider rate limits and quotas

Step 3: Select Nodes

Use the node reference to find:

• Trigger nodes for your data source
• Action nodes for each integration
• Transform nodes for data manipulation
• Conditional nodes for branching logic

Step 4: Configure and Test

• Test each node individually
• Use test data before production
• Verify error paths
• Check execution logs

Common Workflow Patterns

Data Sync Pattern

Trigger (Schedule/Webhook) → Fetch Data → Transform → Update Destination

Notification Pattern

Trigger (Event) → Filter/Condition → Format Message → Send Notification

AI Agent Pattern

Trigger → AI Agent Node → Tools (API calls, Search) → Response Handler

Multi-Step Processing

Trigger → Split Data → Process Each → Merge Results → Output

AI Agent Workflows

Agent Node Configuration

n8n supports AI agents via LangChain integration:

• Chat models: OpenAI, Anthropic, Bedrock
• Tools: Custom API calls, code execution
• Memory: Conversation history, vector stores
• Output parsers: Structured data extraction

Agent Prompt Formula

When configuring AI agent prompts:

1. Define the agent's role clearly
2. Specify available tools and when to use them
3. Set output format expectations
4. Include error handling instructions

Output Format

When designing workflows:

## Workflow Overview
[Purpose and trigger]

## Node Sequence
1. [Node Type]: [Configuration]
2. [Node Type]: [Configuration]
...

## Data Flow
[How data transforms between nodes]

## Error Handling
[What happens when things fail]

## Testing Plan
[How to verify the workflow works]

When debugging:

## Issue Analysis
[What's failing and why]

## Root Cause
[The underlying problem]

## Solution
[Step-by-step fix]

## Prevention
[How to avoid this in future]

Node Categories

Triggers

• Webhook, Schedule, Manual
• App-specific triggers (Gmail, Slack, Airtable, etc.)
• AMQP, Kafka, Redis queues

Data Operations

• HTTP Request, GraphQL
• Database nodes (Postgres, MySQL, MongoDB)
• Spreadsheet nodes (Google Sheets, Airtable)
• File operations (Read, Write, FTP)

AI & Language

• Anthropic, OpenAI, AWS Bedrock
• LangChain agents and tools
• Text classification, embeddings
• Document loaders

Flow Control

• IF, Switch, Merge, Split
• Loop, Wait, Stop
• Error Trigger, Retry

Reference Files

• n8n Nodes List - Complete list of 500+ n8n nodes with descriptions

Constraints

• Always consider rate limits when designing workflows
• Test with small data sets before scaling
• Include error handling for every external API call
• Use credentials properly (never hardcode secrets)
• Consider execution timeout limits
• Document complex workflows for maintainability