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AI Agent with n8n: Step-by-Step Integration Tutorial [2026]

Agente IA con n8n: flujo visual de automatización con nodos de IA, herramientas y webhooks

n8n is an open-source automation platform with visual workflow editor, over 400 native integrations, and the ability to connect any LLM on the market. For teams that want to build an AI agent without writing hundreds of lines of code, n8n offers the perfect balance between flexibility and accessibility. You can run it self-hosted on your server (free) or use its cloud with free tier. In this guide we build an AI agent with n8n step by step, from installation to a functional customer support agent. For a complete view of the agent ecosystem, check our complete guide to AI agents.

Why n8n for AI Agents

There are many ways to build an AI agent: code frameworks like LangChain, closed platforms like ChatGPT GPTs, or automation tools like Zapier. What makes n8n different is the combination of several factors that no alternative brings together.

First, it's visual. You build the agent flow by dragging nodes and connecting them on a canvas, not writing code. Second, it's open-source with over 40,000 stars on GitHub: you can audit it, modify it, and host it on your infrastructure with total control over data. Third, it's LLM agnostic: connect OpenAI (GPT-4o), Anthropic (Claude), Google Gemini, or local models from the same node. Fourth, it has native AI nodes: the AI Agent node, memory nodes, tools nodes, and vector store nodes, all integrated in the visual editor. Fifth, the over 400 integrations cover CRMs, databases, messaging channels, payment gateways, and any service with REST API.

Compared to LangChain, n8n eliminates the code barrier: you don't need Python or JavaScript to set up a functional agent. Compared to ChatGPT GPTs, it offers real integrations with external systems, not just uploaded documents. Compared to Zapier, it's open-source and doesn't scale in price with execution volume. If you want to explore more free options, check our guide on free AI agent.

Architecture of an AI Agent in n8n

The typical flow of an AI agent with n8n follows this visual structure:

Trigger (Webhook, WhatsApp, Email) -> AI Agent Node -> LLM (OpenAI / Anthropic) + Tools (HTTP Request, Database, Code) + Memory (Buffer / Vector Store) -> Response -> Output Node (WhatsApp, Email, Slack).

The AI Agent node is the central piece. It orchestrates the LLM, tools, and memory in a ReAct loop: the model reasons about the user query, decides which tool to invoke, receives the result, reasons again, and composes the final response. n8n handles executing the tools; the LLM handles reasoning. Understanding this pattern is key: we explain it in detail in how AI agents work.

Tutorial: Create a Support Agent in n8n

We're going to build a customer support AI agent that queries orders, answers frequently asked questions, and escalates to a human when it cannot resolve.

1. Install n8n

The fastest way is with Docker:

docker run -it --rm -p 5678:5678 n8nio/n8n

Access localhost:5678 in your browser. If you prefer to avoid local installation, n8n cloud offers a free tier with limited executions. For production, deploy on a VPS with Docker Compose and a persistent volume to not lose workflows.

2. Create the Workflow with the AI Agent Node

In the visual editor, add a trigger node. For an AI agent accessible via API, use the Webhook node. For an agent connected to WhatsApp, use the trigger corresponding to your provider (like the 360dialog or Twilio node).

Next, add the "AI Agent" node. Select the agent type: Tools Agent, which implements the ReAct pattern. Connect an LLM: in the node submenu, select OpenAI GPT-4o or Anthropic Claude and enter your API key.

3. Configure the System Prompt

The system prompt defines your agent's behavior. Example for support:

"You are a support agent for [company]. Your goal is to resolve queries about order status and returns. Use the 'query_order' tool when the user asks about an order. If you cannot resolve the query, tell the user that a human agent will assist them shortly. Always respond in English, with professional and concise tone."

Set temperature to 0.1-0.3 for consistent responses and establish a reasonable token limit to control costs. If you need to go deeper into how to design effective prompts for agents, check the guide to create your own AI agent.

4. Add Tools

Tools are nodes connected to the AI Agent node that the LLM can invoke. Each tool needs a descriptive name and a clear description of when to use it:

  • HTTP Request Node: connect to your orders API. Name: "query_order". Description: "Use this tool when the user asks about order status. Requires order number."
  • PostgreSQL or MySQL Node: direct query to your customer database to get history, contact data, or account information.
  • Code Node: custom logic in JavaScript for calculations, date formatting, data validation, or any transformation the LLM should not do alone.

The LLM reads the names and descriptions of each tool to decide which to invoke on each conversation turn. Poorly written descriptions produce incorrect invocations.

5. Configure Memory

Without memory, your agent forgets everything at the next message. Add a memory node connected to the AI Agent:

  • Buffer Memory: stores the last N messages of the conversation. Simple and sufficient for most customer support cases. It's short-term memory, within the same session.
  • Vector Store Memory (Pinecone, Qdrant, or Supabase): stores long-term information as embeddings. Useful when the agent needs to remember data from previous sessions or access an extensive knowledge base.

For a basic support agent, buffer memory with 10-20 message window is sufficient. If you need an agent on WhatsApp with context between sessions, check our guide on AI agent for WhatsApp.

Limitations and When to Scale

n8n is the best free option for technical teams, but it has clear limits. The self-hosted version requires server maintenance: updates, backups, monitoring. Complex agents with multiple LLM calls can be slow if the server doesn't have sufficient resources. It doesn't include integrated analytics to measure resolution rates or satisfaction. And WhatsApp integration requires configuring a BSP and managing the WhatsApp Business API on your own.

When your volume exceeds 1,000 daily conversations or you need analytics and a managed WhatsApp integration, it's time to evaluate dedicated platforms. Check AI agent vs chatbot to understand the key differences.

Conclusion

n8n is the best free and visual tool to build a functional AI agent without relying on code. Its open-source architecture, over 400 integrations, and native AI nodes make it the reference option for teams that want total control without license cost. To go deeper, check what is an AI agent, how to create your agent from scratch, and the options to build an AI agent with ChatGPT.

GuruSup takes the concept of AI agent with n8n to the next level: a managed platform with native WhatsApp integration, performance analytics, intelligent human escalation, and no need to maintain infrastructure. Try GuruSup for free and deploy your customer support agent in days, not weeks.

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