This article provides a complete, practical walkthrough of the Mysqltool 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 an AI-Powered Health Insurance Chatbot with n8n, OpenAI, and Real-Time Data Integration
   
   Meta Description:  
   Discover how to create an intelligent, lead-driven health insurance chatbot using n8n, OpenAI, PostgreSQL, and third-party APIs for dynamic response generation and product recommendations.
   
   Keywords:  
   n8n chatbot, OpenAI assistant, health insurance automation, chatbot with memory, AI in insurance, LangChain, PostgreSQL chatbot memory, dynamic product recommendation, lead-based chatbot, external API integration
   
   Third-Party APIs Used:
   
   1. OpenAI (OpenAI Assistant and LangChain)
   2. PostgreSQL (memory storage via @n8n/n8n-nodes-langchain.memoryPostgresChat)
   3. MySQL (health insurance product lookup query)
   4. HTTP Request to External API (https://integracao-sed-alb-323570099.us-east-1.elb.amazonaws.com/findByNameAndBirthDate)
   5. Knowledge Base API (https://quotation.joov.com.br/widget/info)
   
   Short Article:
   
   Creating a Personalized AI Chatbot for Health Insurance with n8n and OpenAI
   
   As businesses increasingly adopt automation to enhance user experiences, the health insurance industry is turning to AI-powered tools to meet rising consumer expectations. One standout use case is the integration of n8n with OpenAI and real-time data tools to build an intelligent, context-aware chatbot for quoting and guiding users through health insurance options.
   
   In this article, we break down the workflow behind a powerful, personalized chatbot titled "modelo do chatbot", designed using n8n—an open-source workflow automation tool—and OpenAI’s assistant capabilities.
   
   Overview of the Workflow
   
   This n8n workflow acts as the brain behind a conversational lead-capture and quote-recommendation bot. The primary goal is to provide accurate health plan suggestions based on user inputs, using real-time queries and stored session information.
   
   Core Components:
   
   1. Chat Trigger & Lead Data Evaluation
   
   The interaction begins with a "Chat Trigger" node, using LangChain’s @n8n/n8n-nodes-langchain.chatTrigger module. The bot greets users with a friendly message and waits for input. An “If” node checks whether the incoming message includes essential leadData—such as name, age, location, profession, etc.—to determine the proper conversational flow.
   
   2. Leveraging User Data for Personalization
   
   When lead data is present, the "Edit Fields1" node dynamically builds a string embedding the user's name, age, city, profession, and interest (health plan type). This context-rich message is passed to the OpenAI assistant (ID: asst_numdCoMZPQ6GwfiJg5drg9hr), along with a session ID that enables memory persistence.
   
   Key Insight:  
   The chatbot introduces itself using stored user data for a seamless and personalized introduction, enhancing lead engagement from the first interaction.
   
   3. Chat Memory Integration
   
   To simulate a conversational memory, two Postgres memory nodes are employed:
   
   - Postgres Chat Memory: Stores broader session context.
   - Postgres Chat Memory1: Focused on the most recent messages (context window size = 1).
   
   These memory tools are critical in allowing OpenAI to respond contextually across multi-turn conversations and to recall past user queries or shared data.
   
   4. Real-Time Queries and Knowledge Integration
   
   The chatbot is equipped to access and retrieve information dynamically through several integrations, enabling it to act as a real-time assistant.
   
   Third-party integrations include:
   
   - Knowledge Base API – provides plan-related information based on user location and modality.
   - External API – fetches user identity details using name and birth date.
   - MySQL Health Product Lookup – retrieves suitable health insurance products using criteria like location, modality (‘PME’), and age against price brackets.
   
   The MySQL query is particularly powerful, using Lua-style templating with $fromAI() to dynamically inject values parsed from the assistant, such as city, state, holderAge, and applicant details. The query returns the top three plan matches, refined by age-specific pricing logic.
   
   5. Making the AI Response Smart
   
   Responses are shaped by input context, memory, and enriched with real-time data from all tools. For generic cases, or when leadData is not initially captured, the fallback path processes user queries using a different OpenAI assistant (ID: asst_x2qfc7EuoPv7XGOL84ClEZ3L), ensuring users always receive helpful responses—regardless of session maturity.
   
   Conclusion
   
   This workflow is a stellar example of AI-powered automation tailored for lead generation and conversion in the health insurance sector. By combining n8n’s modularity with OpenAI’s conversational capabilities and a robust data retrieval system, the chatbot merges personalization, real-time insights, and intelligent memory into a single, scalable solution.
   
   From verifying leads to recommending personalized insurance plans and accessing updated information from multiple APIs, the system exemplifies how automation can transform traditional processes into user-centric, adaptive, and highly efficient digital experiences.
   
   For businesses looking to automate customer interactions or build intelligent chat systems, this workflow illustrates a versatile and effective blueprint ready for customization and deployment.

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.