Build a chatbot using n8n and natural language tools

Creating your own chatbot doesn’t have to involve complex code or expensive platforms. With the power of n8n and a few natural language processing tools, you can build a fully automated, intelligent n8n chat bot that interacts with users, answers queries, and even connects to APIs and databases. Whether you want to build a support assistant, Slack bot, or a conversational agent powered by AI, n8n gets the job done without breaking the bank.

In this guide, we’ll show you how to build a chatbot using n8n and natural language tools like OpenAI or Hugging Face — step-by-step. Let’s dive in.

Why Build an n8n Chat Bot?

n8n is an open-source automation workflow tool that excels in integrating services, handling APIs, and triggering actions based on input. With its growing community and ability to connect to large language models (LLMs), n8n is quickly becoming a favorite platform for bot builders.

Here’s why n8n is ideal for chatbot creation:

Visual editor: Easily design workflows using drag-and-drop logic.
No-code/low-code: Build complex logic without writing full programs.
Flexible integrations: Connect to Google Sheets, Slack, Telegram, Discord, OpenAI, and more.
Runs anywhere: Self-host it or use n8n's hosted version here.

Tools You’ll Need

To build your chatbot, you’ll combine the strengths of n8n and AI tools. Here’s what you’ll need:

Required:

Self-hosted or cloud-hosted n8n instance
OpenAI, Hugging Face, or other NLP model access (API key required)

Optional Integrations:

Telegram or Slack bot
Webhook to capture inputs from a form or embed on a site
Google Sheets or Notion for storing logs or data

Let’s go step-by-step to build your chatbot.

Step 1: Set Up Your n8n Environment

If you haven’t already installed n8n, follow our self-hosted n8n setup guide or use this easy n8n cloud access to get started quickly.

Once your instance is running:

Launch the n8n editor.
Create a new workflow.
Enable “Activate on Trigger” to make it live upon receiving user input.

Step 2: Choose a Trigger for User Input

This is how users will talk to your chat bot.

Popular triggers:

Telegram node: For chat-based bots
Webhook node: For embedding it on a web front-end or chatbot interface
Slack trigger: Great for internal bots for your team

For example, to set up a webhook:

Add a Webhook node.
Set HTTP Method: POST
Set path as: chatbot-receive
Get the unique URL once saved (e.g., https://yourdomain.com/webhook/chatbot-receive)

Step 3: Process User Messages with NLP

This is where your logic will live. You can use:

Option 1: OpenAI GPT Node

Add the HTTP Request node after your trigger and configure it to send:
- POST request to https://api.openai.com/v1/chat/completions
- Headers: Set Authorization and Content-Type: application/json
- Body parameters:
```
{
"model": "gpt-3.5-turbo",
"messages": [
 { "role": "user", "content": "{{ $json.body.message }}" }
]
}
```
Use n8n variables to pull in the actual message dynamically.

Option 2: Hugging Face API (if alternative to OpenAI preferred)

Use their inference API via HTTP Request. Make sure to format the data appropriately depending on the model.

Step 4: Respond Back to the User

After processing, send back a message using the same channel you received it from.

For a Telegram bot:

Add the Telegram node after the AI processing.
Choose action: Send Message
User chat ID will come from trigger context.

If using Webhook:

You can return the chatbot’s response directly in the Webhook node via the Respond tab.

Set Response Mode: On Received

Return JSON with:

{
"reply": "{{ $node["OpenAI"].json["choices"][0]["message"]["content"] }}"
}

Example Use Case: Chat Support Bot

Let’s say you want to automate basic customer questions from your website.

Workflow Overview:

Step	Node	Function
1	Webhook trigger	Receives input from website
2	OpenAI API	Analyze and respond based on user question
3	Webhook respond	Delivers the response text

Set the response context like:

"I'm a customer support bot. Help the user clearly and concisely about their order, shipping, or account."

Return the message back — and boom, your live chatbot is ready.

Step 5: Add Logging (Optional)

Add a Google Sheets or Notion node after your output to store data like:

User question
AI response
Timestamp
Chat session ID

You’ll then have logs for analytics or future training.

Want to learn more about integrating Google Sheets? Check out our guide on connecting n8n with Google apps.

Tips for a More Powerful n8n Chat Bot

Use conditional logic nodes to create multi-turn conversation logic.
Add memory using [Google Sheets or Notion] nodes to remember conversation states.
Schedule retriggers for inactivity or delayed follow-ups.
Combine with multi-agent AI setups if needed.

With a bit of workflow logic, you can build a virtual assistant, newsletter bot, internal knowledge base, or even voice-based agents.

FAQ

How do I make the chatbot understand more advanced requests?

Connect your workflow to OpenAI's GPT-4 or use tools like LangChain for advanced memory and reasoning.

Can I deploy my n8n chat bot on Telegram or Slack?

Yes! Use the Telegram or Slack trigger + response nodes. Connect tokens via credentials to authenticate your bot.

Is it possible to train my own AI model instead of using OpenAI?

Absolutely. You can use Hugging Face models via API or run custom LLMs locally, then send messages via the n8n HTTP Request node.

What's the cost of running an n8n chat bot?

n8n is free to self-host. Your only potential costs are hosting and any API requests made to paid NLP providers (like OpenAI).

Can I build a multi-agent chatbot with n8n?

Yes, n8n supports multi-agent systems, allowing you to create layered or team-based AI bots with different roles.

Start experimenting with your own n8n chat bot today and automate meaningful conversations with minimal effort. Want to simplify the whole process? Check out this ready-to-use n8n setup and launch your chatbot in minutes.