This article provides a complete, practical walkthrough of the Http Stickynote Automate Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Building a CBT Chatbot with n8n and Azure OpenAI for LINE Messaging Integration
   
   Meta Description:  
   Explore how a CBT-based AI chatbot for LINE was built using n8n automation, Azure OpenAI, and HTTP integrations. This workflow delivers empathetic responses while maintaining performance and user experience.
   
   Keywords:  
   n8n workflow, CBT chatbot, Azure OpenAI, LINE Messaging API, AI therapy chatbot, mental health chatbot, automation, HTTP request, LangChain, talking therapy, chat interface, webhook integration
   
   Third-party APIs Used:
   1. LINE Messaging API (https://developers.line.biz/)
   2. Azure OpenAI Service (https://learn.microsoft.com/en-us/azure/cognitive-services/openai/)
   
   —
   
   Article:
   
   Creating a Cognitive Behavioral Therapy (CBT) Chatbot with n8n and Azure OpenAI for LINE
   
   As mental health becomes an increasingly important area of focus, so too does the role of responsive, user-friendly AI to support it. Using automation platforms like n8n, healthcare providers and developers can deploy logic-driven mental wellness tools across popular messaging platforms. In this article, we explore a powerful n8n workflow that builds a CBT-based chatbot for LINE using Azure OpenAI, enabling real-time conversations empathically grounded in psychotherapy best practices.
   
   Let’s dive into how this automation works, the technologies used, and the mental wellness goals it helps to address.
   
   What This Chatbot Does:
   This n8n workflow powers a mental health assistant that responds to users via LINE Messenger. Its primary role is to emulate the approach of a Cognitive Behavioral Therapy (CBT) therapist, helping users explore and handle their emotions and thought patterns. While it does not disclose its therapeutic framework to users directly, the chatbot follows clearly defined therapeutic principles under the hood, such as identifying negative thoughts, setting behavioral goals, and encouraging reflective thinking.
   
   Step-by-Step Workflow Breakdown:
   
   1. Incoming Webhook from LINE:
   The chatbot is triggered when a user sends a message to the LINE app. Using the “Webhook” node, the workflow captures the incoming request and fetches the user’s message content and ID from the payload.
   
   2. Loading Animation for User Feedback:
   Before the AI responds, a user-friendly ‘loading’ animation is triggered using a POST request to the LINE Messaging API. This notifies users that the chatbot is processing their input — a small detail that significantly enhances the perceived responsiveness of the system.
   
   3. Message Type Checking:
   n8n then checks if the incoming message is of a supported type — this solution is currently optimized only for text-based communication. If a sticker, image, or video is received, the user will promptly get a reply informing them that only text messages are supported at this time, maintaining a smooth UX even when unsupported inputs are sent.
   
   4. AI Agent – The Heart of the Therapy:
   When a valid text message is received, it is passed to n8n’s LangChain AI Agent node which serves as the interface to Azure OpenAI. Here, a well-crafted system prompt informs the LLM to take on the role of a CBT therapist. This prompt ensures the conversational tone remains empathetic, nonjudgmental, and subtly therapeutic — without directly revealing that therapy is in play.
   
   Key extract from the system prompt:
   “You're a CBT therapist. You'll help the user find the answer to their problems using CBT, but you will not tell them you're using CBT…”
   
   This prompt guides the AI to respond with practical cognitive restructuring techniques, encouraging users to identify unhelpful thoughts, engage in realistic self-assessment, and consider behavior-based solutions — all hallmarks of CBT.
   
   5. Azure OpenAI Language Model:
   The input is sent to Azure OpenAI’s GPT-based chat model using a custom connector. The conversational model chosen is optimized for balanced reasoning and natural responses. Azure's enterprise-grade environment also ensures communication remains private and reliable.
   
   6. Format and Clean AI Output:
   AI responses, though meaningful, might have line breaks or markdown formatting not suitable for JSON structure required by LINE’s API. A “Set” node transforms the response, removing markdown, special tags, and problematic characters. This clean-up ensures smooth delivery and prevents message delivery errors.
   
   7. Final Reply Through LINE Messaging API:
   The final step posts the AI response back to the user via LINE’s reply endpoint using the collected reply token. Notably, this token reply method lets developers deliver messages without consuming broadcast quotas — a smart architectural choice for large deployments.
   
   Defensive Design Choices:
   - Loading animations improve feedback loops and prevent user drop-off during processing.
   - Explicit IF logic provides fallbacks for unsupported message types.
   - System prompts and message formatting keep the tone therapeutic while maintaining message integrity.
   - Modular webhook architecture allows scaling or adapting easily across platforms.
   
   Why It Matters:
   This workflow elegantly overlays AI-powered CBT onto a real messaging ecosystem, turning LINE from a mere chat platform into an extension of therapeutic conversations. In contexts such as mental health support groups, therapy organizations, or self-help tools, such integrations can offer anonymous, consistent, 24/7 support — something human therapists often can't guarantee alone.
   
   Takeaways and Use Cases:
   - This chatbot isn’t designed to replace therapists but supports users with day-to-day cognitive reframing.
   - Developers can reuse this n8n structure with other models, languages, or even switch the front-end messaging app with platforms like WhatsApp, Telegram, or Facebook Messenger.
   - By tweaking the system prompt, the AI could support not only CBT but other modalities such as motivational interviewing or mindfulness coaching.
   
   Conclusion:
   Using n8n, developers can bring together cognitive science, automation, and conversational AI to build user-centric mental health tools. This CBT-style chatbot for LINE is a compelling example of how technology, when thoughtfully applied, can extend psychological support into the digital spaces where people already spend their time.
   
   Whether you're a therapist, mental health nonprofit, or indie developer, this type of automation democratizes access to therapy-inspired conversation without compromising responsibility, empathy, or effectiveness.
   
   —
   
   Looking to build your own chatbot in n8n?  
   Start with this approach and tweak prompts, models, and integrations to match your users' needs.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.