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

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

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 safe...

Best for

Engineering automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.executeworkflowtrigger, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.toolthink, @n8n/n8n-nodes-langchain.lmchatopenrouter, @n8n/n8n-nodes-langchain.memorypostgreschat, n8n-nodes-base.telegram, @n8n/n8n-nodes-langchain.openai, n8n-nodes-base.telegramtrigger

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Paul.

Original n8n.io source

1.1 Workflow description

Title: Natural language database queries with dual-agent AI & PostgreSQL integration
Workflow name: Natural language database queries with dual-agent AI & PostgreSQL integration

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!

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When Executed by Another Workflow

Type / Role: n8n-nodes-base.executeWorkflowTrigger - executeWorkflowTrigger
Config choices: Version 1.1

Block 2 - Query agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 3 - Think

Type / Role: @n8n/n8n-nodes-langchain.toolThink - toolThink
Config choices: Version 1

Block 4 - OpenRouter Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices: Version 1

Block 5 - Postgres Chat Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryPostgresChat - memoryPostgresChat
Config choices: Version 1.3

Block 6 - OpenRouter Chat Model1

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices: Version 1

Block 7 - Think1

Type / Role: @n8n/n8n-nodes-langchain.toolThink - toolThink
Config choices: Version 1

Block 8 - Download File1

Type / Role: n8n-nodes-base.telegram - telegram
Config choices: Version 1.2

Block 9 - Transcribe1

Type / Role: @n8n/n8n-nodes-langchain.openAi - openAi
Config choices: Version 1.6

Block 10 - Telegram Trigger1

Type / Role: n8n-nodes-base.telegramTrigger - telegramTrigger
Config choices: Version 1.1

Block 11 - Voice or Text

Type / Role: n8n-nodes-base.switch - switch
Config choices: Version 3.2

Block 12 - Text

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 13 - Merge

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.1

Block 14 - Main agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 15 - SEND MESSAGE

Type / Role: n8n-nodes-base.telegram - telegram
Config choices: Version 1.2

Block 16 - CALL QUERY AGENT

Type / Role: @n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices: Version 2.2

Block 17 - ACCES DATABASE WITH DYNAMIC QUERYS

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 18 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 19 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 20 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 21 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 22 - Sticky Note4

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

3. Summary Table

Workflow	Natural language database queries with dual-agent AI & PostgreSQL integration
Complexity	advanced
Nodes	22
Categories	Engineering, AI RAG
Author	Paul
Published	17 Jun 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/5012/5012.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Engineering workflows AI RAG workflows

Frequently asked questions

What does Natural language database queries with dual-agent AI & PostgreSQL integration do?

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 safe...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI RAG use case.

Paul

21 workflows

Nodes

n8n-nodes-base.executeworkflowtrigger @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.toolthink @n8n/n8n-nodes-langchain.lmchatopenrouter @n8n/n8n-nodes-langchain.memorypostgreschat n8n-nodes-base.telegram @n8n/n8n-nodes-langchain.openai n8n-nodes-base.telegramtrigger

Complexity

advanced

Published 17 Jun 2025

Likes 0

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Install path: /data/workflows/5012/5012.json

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

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Description:

🔧 How It Works

⚡ Setup Instructions

1️⃣ Prepare Your Database

2️⃣ Customize the Templates

3️⃣ Configure the Agents

4️⃣ Test & Deploy

🎯 Why Use This Workflow?

🚨 Critical Features

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When Executed by Another Workflow

Block 2 - Query agent

Block 3 - Think

Block 4 - OpenRouter Chat Model

Block 5 - Postgres Chat Memory

Block 6 - OpenRouter Chat Model1

Block 7 - Think1

Block 8 - Download File1

Block 9 - Transcribe1

Block 10 - Telegram Trigger1

Block 11 - Voice or Text

Block 12 - Text

Block 13 - Merge

Block 14 - Main agent

Block 15 - SEND MESSAGE

Block 16 - CALL QUERY AGENT

Block 17 - ACCES DATABASE WITH DYNAMIC QUERYS

Block 18 - Sticky Note

Block 19 - Sticky Note1

Block 20 - Sticky Note2

Block 21 - Sticky Note3

Block 22 - Sticky Note4

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions