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Natural language database queries with dual-agent AI & PostgreSQL integration

PaulPaul
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2/3/2026
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AI Database Assistant with Smart Query's & PostgreSQL Integration

Description:

🚀 Transform Your Database into an Intelligent AI Assistant

This workflow creates a smart database assistant that safely handles natural language queries without crashing your system. Features dual-agent architecture with built-in query limits and PostgreSQL optimization – perfect for commercial applications!

✅ Ideal for:

  • SaaS developers building database search features 🔍
  • Database administrators providing safe AI access 🛡️
  • Business teams needing user-friendly data queries 📊
  • Anyone wanting ChatGPT-like database interaction 🤖

🔧 How It Works

1️⃣ User asks a question – "Show me top 10 popular products" 2️⃣ Main AI Agent – Interprets the request and ensures safety limits 3️⃣ SQL Sub-Agent – Generates precise PostgreSQL queries 4️⃣ Database executes – Returns formatted, limited results safely

⚡ Setup Instructions

1️⃣ Prepare Your Database

  • Ensure PostgreSQL is accessible from n8n
  • Note your table structure and column names
  • Set up database connection credentials

2️⃣ Customize the Templates

  • Replace [YOUR_TABLE_NAME] with your actual table name
  • Update [YOUR_FIELDS] with your column names
  • Modify examples to match your use case
  • Important: Keep all LIMIT clauses intact!

3️⃣ Configure the Agents

  • Copy Main Agent system message to your primary AI node
  • Copy Sub-Agent system message to your SQL generator node
  • Connect the sub-workflow between both agents

4️⃣ Test & Deploy

  • Test with sample queries like "Show me 5 recent items"
  • Verify query limits work (max 50 results)
  • Deploy and monitor performance

🎯 Why Use This Workflow?

✔️ System Protection – Built-in limits prevent crashes from large queries ✔️ Natural Language – Users ask questions in plain English ✔️ Commercial Ready – Generic templates work with any database ✔️ Dual-Agent Safety – Smart interpretation + precise SQL generation ✔️ PostgreSQL Optimized – Handles complex schemas and data types

🚨 Critical Features

  • Query Limits: Default 10, maximum 50 results (can be modified)
  • Error Prevention: No unlimited data retrieval
  • Smart Routing: Natural language → Safe SQL → Formatted results
  • Customizable: Works with any PostgreSQL database schema

🔗 Start building your AI database assistant today – safe, smart, and scalable!

Natural Language Database Queries with Dual-Agent AI (PostgreSQL Integration)

This n8n workflow empowers users to query a PostgreSQL database using natural language via a Telegram bot. It leverages a dual-agent AI system (powered by LangChain and OpenAI/OpenRouter) to interpret user requests, generate SQL queries, execute them against the database, and return the results in a human-readable format.

What it does

  1. Listens for Telegram Messages: The workflow is triggered by incoming messages to a configured Telegram bot.
  2. Initial Message Processing: The received message content is extracted and prepared for AI processing.
  3. Routes AI Agent: Depending on the user's input, the workflow routes the request to an AI agent.
  4. AI Agent (LangChain): An AI agent, configured with a chat model (OpenAI or OpenRouter) and PostgreSQL chat memory, interprets the natural language query.
    • It uses a "Call n8n Workflow Tool" to interact with the PostgreSQL database.
    • It also utilizes a "Think Tool" for internal reasoning and planning.
  5. PostgreSQL Query Generation & Execution: The AI agent, through the "Call n8n Workflow Tool", triggers a sub-workflow (not fully defined in this JSON but implied by the tool's purpose) that:
    • Translates the natural language query into a valid SQL query.
    • Executes the SQL query against the configured PostgreSQL database.
    • Retrieves the results from the database.
  6. Merges Results: The results from the PostgreSQL query and the AI agent's response are merged.
  7. Sends Response to Telegram: The final, human-readable response (including database query results or an appropriate message) is sent back to the user via the Telegram bot.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n.
  • Telegram Bot: A Telegram bot token and chat ID.
  • PostgreSQL Database: Access to a PostgreSQL database, including host, port, user, password, and database name.
  • OpenAI API Key or OpenRouter API Key: An API key for either OpenAI or OpenRouter to power the AI chat model.
  • LangChain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Telegram Trigger & Telegram Node: Set up your Telegram Bot API credentials.
    • Postgres Chat Memory Node: Configure your PostgreSQL database credentials.
    • OpenAI Node or OpenRouter Chat Model Node: Provide your respective API key.
  3. Configure AI Agent:
    • In the "AI Agent" node, select your preferred Chat Model (OpenAI or OpenRouter).
    • Ensure the "Postgres Chat Memory" node is correctly linked for conversational context.
    • The "Call n8n Workflow Tool" will need to be configured to point to a sub-workflow that handles the actual PostgreSQL query generation and execution. This sub-workflow is not part of this JSON and needs to be created separately. It should accept natural language queries and return database results.
  4. Activate the Workflow: Once all credentials and configurations are set, activate the workflow.
  5. Interact via Telegram: Send natural language queries to your Telegram bot. The AI will interpret them, query your PostgreSQL database, and respond with the results.

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