AI Automations with n8n and LangChain

Introduction

Automation tools have existed for years — Zapier, Make, IFTTT — but they were limited to simple "if this, then that" logic. In 2026, the combination of n8n (a self-hosted automation platform) and LangChain (an AI orchestration framework) enables something entirely new: intelligent automations that understand context, make decisions, and generate content.

Let's build some real-world examples.

Why n8n Over Zapier?

n8n stands out for several reasons critical to developers:

• Self-hosted — your data never leaves your servers. Essential for companies with privacy requirements.
• Free and open-source — no per-execution pricing. Run millions of automations for the cost of a $5/month server.
• Code nodes — write JavaScript or Python directly in your workflows when visual nodes aren't enough.
• AI-native — built-in nodes for OpenAI, Anthropic, vector databases, and LangChain agents.
• Git-friendly — export workflows as JSON, version-control them, and deploy across environments.

LangChain in 60 Seconds

LangChain is a framework for building applications powered by large language models (LLMs). Its core concepts:

• Chains — sequence of steps where each step's output feeds the next (e.g., summarize → translate → format)
• Agents — LLMs that can use tools (search the web, query a database, call an API) to complete tasks
• Memory — maintain conversation context across multiple interactions
• Retrieval (RAG) — augment LLM responses with your own data from vector databases

Example 1: Intelligent Content Pipeline

Build a workflow that monitors RSS feeds, summarizes new articles, and publishes curated content to your blog:

1. RSS Trigger — monitors tech news feeds every 30 minutes
2. AI Filter — LangChain agent evaluates if the article is relevant to your niche (Flutter, Laravel, AI)
3. AI Summarizer — generates a 200-word summary maintaining the key insights
4. Image Generator — creates a cover image using DALL-E based on the article topic
5. CMS Publisher — posts the curated summary to your blog as a draft via API
6. Slack Notification — alerts you to review and approve before publishing

This workflow runs 24/7 and curates content that would take a human editor 2-3 hours per day.

Example 2: Smart Customer Support

Build an AI-powered support system that handles common questions automatically:

1. Webhook Trigger — receives incoming support emails or chat messages
2. RAG Retrieval — searches your documentation vector database for relevant answers
3. AI Response Generator — crafts a personalized response using the retrieved documentation
4. Confidence Check — if the AI confidence is above 85%, send the response automatically
5. Human Escalation — below 85% confidence, route to a human agent with the AI's draft response as a starting point

Example 3: Code Review Assistant

Automate preliminary code reviews for your team's pull requests:

1. GitHub Trigger — fires when a new PR is opened
2. Fetch Diff — retrieves the changed files and diff
3. AI Analysis — LangChain reviews the code for: security issues, performance concerns, code style violations, and potential bugs
4. Post Comment — adds a structured review comment to the PR with categorized findings

Deploying n8n

The simplest production setup:

docker run -d --name n8n \
  -p 5678:5678 \
  -v n8n_data:/home/node/.n8n \
  -e N8N_BASIC_AUTH_ACTIVE=true \
  -e N8N_BASIC_AUTH_USER=admin \
  -e N8N_BASIC_AUTH_PASSWORD=your-secure-password \
  n8nio/n8n

For production, add PostgreSQL for workflow storage, Redis for queue management, and Nginx for SSL termination.

Conclusion

The combination of n8n and LangChain democratizes intelligent automation. You don't need a machine learning team to build AI-powered workflows — just a clear understanding of your problem, a few API keys, and the visual editor. Start with a single simple automation, prove its value, and scale from there.