Line Chatbot AI Replies with Groq & Llama3 in n8n

Opening Problem Statement

Meet Mai, a customer support agent running a Line official account for her growing e-commerce store. Mai finds herself overwhelmed when messages flood in during sales promotions. The challenge? Handling complex customer queries that sometimes require lengthy, nuanced replies powered by AI. Previously, her chatbot kept breaking with JSON errors whenever AI responses were long or complex, frustrating customers and forcing Mai to intervene manually. This not only wasted hours daily but risked losing customer engagement and sales.

Mai needed an automation that could accept any message from Line, send it to a powerful AI model, and reply back with accurate, context-aware responses—without the risk of technical failures.

What This Automation Does

Using this n8n workflow, Mai gets an intelligent Line chatbot running effortlessly. Here’s what it accomplishes:

• Receives incoming Line messages via a Webhook listening to the Line Messaging API.
• Extracts and formats message content, sender ID, and message ID for processing.
• Sends the user’s message to Groq’s AI assistant using the Llama-3.3-70b-versatile model for powerful, nuanced responses.
• Receives and parses the AI-generated reply from Groq, ensuring no JSON errors occur even with long responses.
• Replies directly back to the user on Line using the replyToken and formatted AI message.
• Maintains a smooth, error-free conversation loop enabling better customer engagement and support automation.

This results in substantial time saved on customer support, better user satisfaction, and a scalable chatbot solution.

Prerequisites ⚙️

• n8n account (cloud or self-hosted) 🔌
• Line Developer account with Messaging API access and a Channel Access Token 📧
• Groq account with API Key to access AI models 🔑
• Generic HTTP Header Auth credentials set up in n8n for both Groq and Line API nodes 🔐
• Basic familiarity with JSON and HTTP requests helps but isn’t mandatory

Step-by-Step Guide

1. Set up the Line Messaging API Webhook in n8n

Navigate to your n8n editor and add a Webhook node:

• Rename it to Line: Messaging API.
• Set HTTP Method to POST and enter a unique path, e.g., befed026-573c-4d3a-9113-046ea8ae5930.
• Save and copy the webhook URL generated.
• Head to your Line Developer Console → Messaging API settings and configure the webhook URL so incoming messages trigger n8n.

Outcome: n8n listens for incoming Line messages in real-time.

Common mistake: Forgetting to enable webhook in Line or typing the wrong URL path causes no messages to reach n8n.

2. Extract Incoming Message Data Using the Set Node

Add a Set node next and name it Get Messages. Connect it to the webhook node.

• Configure to extract body.events[0].message.text, body.events[0].message.id, and body.events[0].source.userId from the webhook payload.
• These fields will prepare the message for AI processing.

Outcome: Clean, focused data from the incoming message is ready for the AI request.

Common mistake: Incorrect JSON path expressions can yield empty fields causing AI API errors.

3. Call Groq AI Assistant with HTTP Request Node

Insert an HTTP Request node named Groq AI Assistant.

• Set URL to https://api.groq.com/openai/v1/chat/completions.
• Select POST method.
• Set authentication type to HTTP Header Auth and select your Groq credentials.
• Paste this JSON body to send the AI request (with dynamic content):

{
  "messages": [
    {
      "role": "user",
      "content": "{{ $json.body.events[0].message.text }}"
    }
  ],
  "model": "llama-3.3-70b-versatile",
  "temperature": 1,
  "max_completion_tokens": 2500,
  "top_p": 1
}

Outcome: The AI generates a rich, context-aware response based on user input.

Common mistake: Forgetting to properly inject the message text or misconfiguring authentication leads to request failures.

4. Reply to User on Line with HTTP Request Node

Add another HTTP Request node Line: Reply Message connected to the Groq AI node:

• Use URL https://api.line.me/v2/bot/message/reply and method POST.
• Set authentication with your Line Channel Access Token credentials.
• Use this JSON body to reply with AI content dynamically:

{
  "replyToken":"{{ $('Line: Messaging API').item.json.body.events[0].replyToken }}",
  "messages":[
    {
      "type":"text",
      "text": {{ JSON.stringify($('Groq AI Assistant').item.json.choices[0].message.content) }}
    }
  ]
}

Outcome: User receives the AI-generated reply instantly in Line chat.

Common mistake: Typos in node names inside expressions cause broken replies.

Customizations ✏️

• Change AI Model: In Groq AI Assistant node, replace llama-3.3-70b-versatile with other supported Groq models for different styles or deeper conversations.
• Adjust Max Tokens: Modify max_completion_tokens parameter based on your chatbot needs and Line’s message length limits.
• Add Message Logging: Insert a Google Sheets node (not in this workflow by default) to save user queries and AI replies for analytics or training purposes.
• Multi-language Support: Enhance the Groq AI Assistant payload to include system instructions for language translation or specific reply tone.
• Rich Media Replies: Modify Line reply payload to include images, buttons, or quick replies instead of plain text.

Troubleshooting 🔧

Problem: “401 Unauthorized” from Groq AI Assistant

Cause: Incorrect or expired API key in Groq HTTP Request node.

Solution: Check your Groq credentials in n8n and update the HTTP Header Auth node with a valid API key.

Problem: “ReplyToken is invalid” Error from Line API

Cause: Using expired or reused replyToken.

Solution: Ensure replyToken is used immediately after receiving it from the webhook. Check node dependencies and avoid delays in the workflow.

Pre-Production Checklist ✅

• Verify webhook URL is correctly set in Line Developer Console.
• Test sending sample messages from Line to trigger n8n workflow.
• Check Groq API credentials and test AI response manually if needed.
• Confirm authentication headers in all HTTP Request nodes.
• Validate JSON paths for incoming message extraction.
• Run test end-to-end chat exchange to ensure replies work flawlessly.

Deployment Guide

Activate the workflow in n8n by toggling it to active.

Monitor webhook call logs in the Line Developer Console and the n8n execution logs in n8n editor for errors.

Set up alerting mechanism via n8n if needed by adding nodes for error notifications (Slack, email, etc.).

FAQs

• Can I use another AI provider instead of Groq?
  Yes, you can replace the Groq HTTP Request node with another AI provider API but you must adjust authentication and payload accordingly.
• Does this workflow consume a lot of API credits?
  AI API usage depends on your Groq plan and the number of messages processed, so keep an eye on quota limits.
• Is my customer’s data safe?
  Data is handled securely through HTTPS and API keys. Ensure to comply with privacy policies and not log sensitive information unnecessarily.
• Can this handle high volumes of messages?
  This workflow handles real-time chat but for very high volumes, consider scaling n8n instance and Groq plan accordingly.

Conclusion

By building this Line Chatbot powered by Groq’s Llama3 AI model in n8n, you have a robust, scalable solution to intelligently reply to customer messages without the headaches of JSON errors or manual intervention. Mai now saves hours daily, provides timely and accurate support, and elevates her customer experience.

Next up, you might explore automating multi-channel support by linking WhatsApp or Facebook Messenger, adding sentiment analysis to tune replies, or integrating CRM updates for richer customer profiles.

Start transforming your chatbot today—your customers will thank you!