This article provides a complete, practical walkthrough of the Postgrestool Stickynote Send 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 a ChatGPT-Powered AI Assistant to Query Your Supabase/PostgreSQL Database Using n8n
   
   Meta Description:
   
   Learn how to build an interactive AI assistant using n8n, OpenAI, and Supabase. This no-code workflow enables users to query PostgreSQL databases conversationally through ChatGPT.
   
   Keywords:
   
   n8n workflow, Supabase, OpenAI, ChatGPT database assistant, PostgreSQL, no-code automation, SQL chatbot, AI SQL tool, ChatGPT SQL query, database agent, LangChain, Postgres AI, OpenAI agent, query database with AI, natural language to SQL, 5minAI
   
   Third-Party APIs/Tools Used:
   
   - OpenAI API (for natural language processing and AI responses)
   - Supabase (as a PostgreSQL database backend)
   - n8n (for workflow automation)
   - LangChain (used internally via AI Agent node in n8n)
   
   — — — — — — — — — — — — — — — — — — — — — —
   
   Article:
   
   Conversational Database Interaction With AI: Build an AI SQL Assistant Using n8n, OpenAI, and Supabase
   
   By Mark Shcherbakov – Community: 5minAI
   
   In today’s data-driven world, accessing insights from your database shouldn’t require writing lines of SQL or relying on other team members for reports. What if you could just ask your database a question and get the answer — instantly, in plain English?
   
   That’s the idea behind this powerful no-code n8n workflow that enables you to chat with your PostgreSQL database through an AI-powered assistant built on OpenAI’s GPT models. By connecting n8n, OpenAI, and Supabase, this intelligent automation allows users to query databases, retrieve records, and even explore schema definitions—all through a conversational interface.
   
   Let’s break down how it works and how you can use it.
   
   What You’ll Build
   
   With this workflow, you’ll have your very own AI agent that:
   - Receives incoming chat requests from users
   - Understands natural language questions
   - Analyzes the structure of a Supabase PostgreSQL database
   - Dynamically generates and runs SQL queries
   - Returns results to the user conversationally
   
   Imagine asking questions like:
   - “Give me total orders per customer this month”
   - “Show me all users who signed up via referral”
   - “List columns in the products table”
   
   …and getting instant, AI-generated answers from your live data.
   
   Required Tools & Services:
   
   Before you start, make sure you have:
   - An n8n account (for building and running workflows)
   - A Supabase account with a PostgreSQL project and data
   - An OpenAI API key (GPT-4 support is recommended)
   
   You can also watch the setup video here (20 min):  
   👀 Watch on YouTube → [Chat with Your Database](https://www.youtube.com/watch?v=-GgKzhCNxjk)
   
   How the Workflow Works
   
   This solution is made possible thanks to several key features of n8n and the LangChain integration that allows powerful agent-based reasoning over structured tools like SQL.
   
   Here is a breakdown of the main components:
   
   1. Chat Message Trigger
   The workflow begins when a message is received (via the “When chat message received” node). This could be part of a web app, chatbot interface, or custom frontend where users type natural language inputs.
   
   2. AI Agent with LangChain
   The user prompt is passed into the “AI Agent” node, which uses LangChain’s OpenAIFunctionsAgent and your ChatGPT API (via “OpenAI Chat Model”) to interpret the message.
   
   This AI agent is configured with a system prompt:
   “You are a DB assistant. You need to run queries in the DB aligned with user requests.”
   
   This prompt instructs the language model to think logically, extract information needs, and generate appropriate SQL statements for data retrieval and summarization.
   
   3. Tool Access: Dynamic Query Execution
   The AI assistant can access three tools:
   
   - Run SQL Query → This node executes arbitrary SQL code against the Supabase PostgreSQL database. It supports JSON parsing via the ->> operator and can process query results into useful formats.
     
   - DB Schema → This tool lists all database tables from the information_schema.tables view, giving the assistant a sense of the overall structure.
     
   - Get Table Definition → This more detailed tool pulls column names, data types, nullability, and foreign keys for a specific table, helping the agent understand which fields are available and how they relate.
   
   These tools are connected through LangChain’s function-calling abilities, meaning the AI can decide when and how to use them.
   
   4. OpenAI Language Model
   The underlying intelligence behind this assistant is provided by OpenAI’s API. With the right credentials and proper prompt engineering, the assistant reliably converts human queries into structured SQL statements.
   
   You may want to fine-tune this prompt or use GPT-4 for more nuanced conversations.
   
   5. Supabase Database
   On the backend, this project runs against a Supabase-hosted PostgreSQL database. All queries are securely executed using your own credentials inside n8n (make sure to replace the “username” and “password” placeholders).
   
   Security Note: Be mindful when deploying AI agents with direct access to databases. Always sanitize input, manage permissions, and use role-based access control where appropriate.
   
   Getting Started: Quick Steps
   
   1. Set up your Supabase database with test data.
   2. Create an OpenAI API Key.
   3. Sign in to n8n and replicate the workflow (either from JSON or using the visual editor).
   4. Add your database credentials and OpenAI key as credentials inside n8n.
   5. Start chatting with your data!
   
   Use Cases
   
   This AI SQL chatbot is ideal for:
   - Non-technical team members needing insights from the database
   - Customer support teams querying user information quickly
   - Marketers wanting fast segment analysis
   - Internal dashboards where chat replaces dropdown filters
   
   Conclusion
   
   This conversational database agent is a perfect example of how AI and no-code automation are reshaping data access and analytics. Powered by n8n’s flexible nodes, OpenAI’s reasoning engine, and Supabase’s modern database infrastructure, you now can ask your data anything and get powerful results—instantly.
   
   Ready to talk to your database?
   
   Explore the workflow, customize your query tools, and empower your team to go from insight to action—without writing a single line of SQL.
   
   ➡️ Try the full 5minAI experience: https://www.skool.com/5minai-2861  
   👋 Connect with author Mark Shcherbakov: https://www.linkedin.com/in/marklowcoding/
   
   Unlock answers in your database. With AI—and n8n—it’s conversational.

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.