Gegenfeld This workflow corresponds to n8n.io template #3680 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "ow1aBANn8lQH0TbA",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "My workflow 4",
"tags": [],
"nodes": [
{
"id": "3be32b55-6c92-45e3-805f-3c582f6bb237",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
760,
320
],
"parameters": {},
"typeVersion": 1.1
},
{
"id": "c0685ca7-91bd-4177-848d-ea7f3a4c160f",
"name": "Chat History",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1100,
520
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "b2e6112c-451e-434f-a732-4c469721f364",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1180,
320
],
"parameters": {},
"typeVersion": 1.7
},
{
"id": "52a55d14-7a46-43fb-9f8c-5fdd099cc4b8",
"name": "Groq Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGroq",
"position": [
920,
520
],
"parameters": {},
"typeVersion": 1
},
{
"id": "d9ff0f6e-e08d-49bc-bd29-1a6602b1562e",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
880,
700
],
"parameters": {
"content": ""
},
"typeVersion": 1
},
{
"id": "56b21c24-9a74-4f61-ad03-2e6c892350d6",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1060,
700
],
"parameters": {
"content": ""
},
"typeVersion": 1
},
{
"id": "63a28a91-68e3-45a5-b236-559db54f87b7",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1240,
700
],
"parameters": {
"content": ""
},
"typeVersion": 1
},
{
"id": "b6dad064-7699-4f07-99c5-cca3a5f3bb82",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
760,
100
],
"parameters": {
"content": ""
},
"typeVersion": 1
},
{
"id": "36b9bf75-cb1d-45ed-8e01-99c272580fae",
"name": "PostgreSQL Schema",
"type": "n8n-nodes-base.postgresTool",
"position": [
1480,
520
],
"parameters": {},
"typeVersion": 2.5
},
{
"id": "71f57073-6857-4b7f-9ee9-e1dc3ce250c5",
"name": "PostgreSQL Definition",
"type": "n8n-nodes-base.postgresTool",
"position": [
1660,
520
],
"parameters": {},
"typeVersion": 2.5
},
{
"id": "95e64a15-03c5-44d9-9a33-4cbd392ab008",
"name": "PostgreSQL",
"type": "n8n-nodes-base.postgresTool",
"position": [
1280,
520
],
"parameters": {},
"typeVersion": 2.5
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "59656397-0d25-47f3-94f3-c00beb3bf8d8",
"connections": {
"PostgreSQL": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Chat History": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Groq Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"PostgreSQL Schema": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"PostgreSQL Definition": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
}
}
}
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
This guide shows you how to deploy a chatbot that lets you query your PostgreSQL database using natural language. You will build a system that accepts chat messages, retains conversation history, constructs dynamic SQL queries, and returns responses generated by an AI model. By…
Source: https://n8n.io/workflows/3680/ — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
teste. Uses chatTrigger, agent, lmChatGroq, memoryBufferWindow. Chat trigger; 24 nodes.
pix-zap. Uses chatTrigger, agent, toolCalculator, toolWikipedia. Chat trigger; 21 nodes.
This workflow enables multimodal file analysis using Google Gemini tools connected to a text-only LLM agent. Users can upload images, videos, audio files, or documents via a chat interface. The workfl
📌 Overview This workflow automates end-to-end appointment scheduling for your business using an AI-powered chatbot. Clients can book, reschedule, or cancel meetings through a simple chat interface — n
Becomex v2. Uses chatTrigger, lmChatOllama, agent, toolWorkflow. Chat trigger; 17 nodes.