This article provides a complete, practical walkthrough of the Postgrestool Stickynote Automation Triggered 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: Build an AI Agent to Chat with Your Supabase Database Using n8n
   
   Meta Description: Learn how to build a conversational AI agent that connects OpenAI and Supabase using n8n. This workflow allows users to interact with their database using natural language—no SQL knowledge required.
   
   Keywords: n8n, OpenAI, Supabase, PostgreSQL, AI agent, chat with database, no-code automation, AI assistant, conversational SQL, database queries, LangChain, natural language interface
   
   Third-Party APIs Used:
   
   - Supabase (PostgreSQL API)
   - OpenAI (ChatGPT API via LangChain)
   
   Article:
   
   Chat With Your Database: Build an AI Assistant Using n8n, OpenAI, and Supabase
   
   If you've ever wished you could talk to your database like a colleague—asking it questions, retrieving data, or creating summaries on the fly—you're in luck. Thanks to tools like n8n, OpenAI, and Supabase, it's now possible to build a conversational agent that does exactly that, without writing a single line of front-end code.
   
   In this article, we break down an automation workflow designed by Mark Shcherbakov from the 5minAI community. Using n8n, this workflow connects OpenAI with your Supabase-hosted PostgreSQL database to create an AI-powered chatbot that can execute SQL queries based on natural language input. Let’s explore how it works.
   
   What You’ll Need
   
   Before diving into the workflow, a few accounts need to be created or attached:
   
   - n8n: A powerful open-source automation tool. Sign up at https://n8n.io
   - Supabase: A backend-as-a-service platform built on PostgreSQL. Sign up at https://supabase.com
   - OpenAI: For ChatGPT or GPT-4 capabilities. Get your API key at https://openai.com
   
   Once your accounts are ready, you're just a few steps away from querying your own data through natural language.
   
   Overview of the Workflow
   
   The workflow is a conversation-like system that triggers whenever a new chat input is received. Here’s how the major components come together:
   
   1. Receiving the User Prompt
   
   Using a chat trigger node (When Chat Message Received), the system collects the user input—e.g., “Show me the total number of users registered last month”. This input is passed to an agent node built with LangChain’s OpenAI Functions Agent, which interprets the user's intent and plans how to respond.
   
   2. Role of the AI Agent
   
   The AI Agent node acts as the central processing unit of the system. It ingests the text prompt and decides which tools (nodes) to use:
   - Should it run a query directly?
   - Does it need to check the database schema first?
   - Should it analyze or summarize data?
   
   This agent relies on OpenAI’s language model to understand user intent and define a SQL query accordingly.
   
   3. Query Generation and Execution
   
   Once the query is ready, the agent hands it over to a PostgreSQL tool node (Run SQL Query) connected to your Supabase instance. The query is executed, and the results are returned to the AI, completing the request loop.
   
   4. Database Schema Exploration
   
   Two other nodes add intelligence to the agent’s capability:
   - DB Schema: Retrieves the list of all tables in the database using a simple SQL query.
   - Get Table Definition: Returns column names, column types, nullability, and foreign key constraints for any table, helping the AI construct accurate queries even when the user doesn't know the schema.
   
   5. Chat Model Integration
   
   Behind all the reasoning, summarization, and query formation, the "OpenAI Chat Model" node provides the language processing power required to understand and generate human-like conversation. It uses GPT models accessible through your OpenAI API key.
   
   6. Personalization and Prompt Engineering
   
   One sticky note in the workflow reminds you to fine-tune the assistant’s prompt. This allows you to define how your AI should behave—e.g., "You are a helpful database assistant. Always provide concise SQL results and explain any technical terms."
   
   Getting Started: Step-by-Step
   
   The sticky notes embedded within the workflow offer a detailed breakdown of how to begin:
   
   Step 1 – Set Up:
   - Sign up for the required services (n8n, Supabase, OpenAI).
   - Deploy a PostgreSQL database via Supabase and copy credentials.
   
   Step 2 – Configure:
   - Replace database credentials in the relevant n8n nodes.
   - Fine-tune the prompt for the AI Assistant node.
   
   Step 3 – Watch the Video Guide:
   - A 20-minute setup tutorial is available to walk you through the process. [Watch it on YouTube](https://www.youtube.com/watch?v=-GgKzhCNxjk)
   
   Use Cases
   
   This kind of workflow is valuable in many business and technical contexts:
   - Dashboard-free querying: Get insights without logging into your database tools.
   - Customer support: Allow personnel to research data without technical training.
   - Business analytics: Ask business questions like “What’s the average order size last week?” and get data immediately.
   
   Security Note
   
   One important sticky note in the workflow reminds users to replace the default username and password with their actual credentials. Given the nature of database access, proper environment variable management and access controls are essential before going live.
   
   Conclusion
   
   By combining n8n, Supabase, and OpenAI’s powerful language models, this workflow enables a smarter, faster, and more intuitive way to interact with your data—reducing the friction of technical bottlenecks and putting data access in the hands of more users.
   
   Built by Mark Shcherbakov from the 5minAI community, this project not only removes the complexity of SQL but also demonstrates how everyday users can build powerful AI-powered automations with ease.
   
   So whether you’re an entrepreneur looking to automate reporting or a developer interested in conversational UIs, this workflow is your jumpstart into smart database interaction. Now go ask that database a question!

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.