This workflow follows the Agent → OpenAI Chat recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"updatedAt": "2026-04-04T20:52:16.458Z",
"createdAt": "2026-01-09T23:20:53.320Z",
"id": "F9OWClEBtwYHjHTv",
"name": "Chat Question Suggestions With Persistence",
"active": true,
"isArchived": false,
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "chat_suggestions",
"responseMode": "responseNode",
"options": {}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
-144,
256
],
"id": "150560c9-b6bf-40e8-9d3b-cf9427e30c52",
"name": "Webhook"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "7bdf57f2-c6ca-43ca-a584-60495cf70e2d",
"name": "book_id",
"value": "={{ $json.body.book_id }}",
"type": "string"
},
{
"id": "316af802-b37f-4d88-89e5-69c167410869",
"name": "locale",
"value": "={{ $json.body.locale }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
80,
256
],
"id": "59a3061a-c67b-424a-b559-59f3cd5a533d",
"name": "Edit Fields"
},
{
"parameters": {
"promptType": "define",
"text": "=book title: {{ $('Load Book').item.json.title}}\n\nsummary: {{$('Load Book').item.json.summary }}\n\n",
"options": {
"systemMessage": "=You are an executive thinking partner.\n You respond in the following language: {{$('Edit Fields').item.json.locale == 'de'? 'German' : 'English' }}\nYour only input is a book title and summary.\n\nBased solely on the implications, themes, and promises suggested by the title and summary, generate 2\u20133 sharp questions that a high-performing individual would naturally ask. \n\nAssume the reader is:\n- Experienced\n- Time-constrained\n- Focused on leverage, clarity, and outcomes\n\nYour questions must:\n- Be short, direct, and high-signal\n\n\nDo NOT:\n- Explain the questions\n- Add preamble or commentary\n\nOutput format:\nReturn ONLY a JSON array of strings.\n\nExample output:\n[\n \"What is meant by [key phrase in book title]?\",\n \"What are the key takeaways from this book?\",\n \"Where could I apply the principles to my business?\"\n]\n"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
976,
0
],
"id": "3bbc1ed2-9bd0-49ee-9578-d02002be98f1",
"name": "AI Agent"
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"builtInTools": {},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
1056,
224
],
"id": "416b2a68-67d7-4e1b-bd47-907c669cfdeb",
"name": "OpenAI",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.5,
"position": [
1328,
112
],
"id": "5c6debe8-fc0e-43e0-86bf-a17a4adfa74f",
"name": "Respond to Webhook"
},
{
"parameters": {
"operation": "get",
"tableId": "books",
"filters": {
"conditions": [
{
"keyName": "id",
"keyValue": "={{ $json.book_id }}"
}
]
}
},
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
304,
256
],
"id": "b9a79999-fe19-4aea-b6c3-3532b6575dbf",
"name": "Load Book",
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"operation": "getAll",
"tableId": "chat_suggestions",
"matchType": "allFilters",
"filters": {
"conditions": [
{
"keyName": "book_id",
"condition": "eq",
"keyValue": "={{ $json.id }}"
},
{
"keyName": "locale",
"condition": "eq",
"keyValue": "={{ $('Edit Fields').item.json.locale }}"
}
]
}
},
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
528,
256
],
"id": "2878f446-a007-447e-b45e-15e5a9325256",
"name": "Load Suggestions",
"alwaysOutputData": true,
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 3
},
"conditions": [
{
"id": "76d6027e-ff87-47c2-b251-65d340567f19",
"leftValue": "={{ $json.isEmpty() }}",
"rightValue": "",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.if",
"typeVersion": 2.3,
"position": [
752,
256
],
"id": "f7195611-3650-44f9-80e4-38e57d353a9a",
"name": "If Suggestions Empty"
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.5,
"position": [
1328,
400
],
"id": "32302a5e-8c37-4981-8d17-448f869e2b0f",
"name": "Respond to Webhook1"
},
{
"parameters": {
"tableId": "chat_suggestions",
"fieldsUi": {
"fieldValues": [
{
"fieldId": "suggestion",
"fieldValue": "={{ $json.question }}"
},
{
"fieldId": "book_id",
"fieldValue": "={{ $('Load Book').item.json.id }}"
},
{
"fieldId": "locale",
"fieldValue": "={{ $('Edit Fields').item.json.locale }}"
}
]
}
},
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
1776,
112
],
"id": "ec1fe71b-f138-4d67-aff5-173f09fa5c65",
"name": "Store Suggestions",
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "const raw = items[0].json.output;\n\n// Parse the stringified JSON array\nlet parsed;\ntry {\n parsed = JSON.parse(raw);\n} catch (e) {\n throw new Error(\"Failed to parse output as JSON array\");\n}\n\n// Ensure it's an array\nif (!Array.isArray(parsed)) {\n throw new Error(\"Parsed output is not an array\");\n}\n\n// Return one item per entry\nreturn parsed.map(question => ({\n json: {\n question\n }\n}));\n"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1552,
112
],
"id": "b4914a54-4c60-4f82-a258-f4a39f6eb514",
"name": "Parse Response"
},
{
"parameters": {
"jsCode": "// Extract suggestion strings from all incoming items\nconst suggestions = items.map(item => item.json.suggestion);\n\n// Encode as a JSON string (array of strings)\nconst encoded = JSON.stringify(suggestions, null, 2);\n\n// Return a single item matching the required format\nreturn [\n {\n json: {\n output: encoded\n }\n }\n];\n"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1040,
400
],
"id": "20fcb9eb-e0a5-4e65-b07e-4131cce462a9",
"name": "PreProcess Suggestions"
},
{
"parameters": {
"content": "## \ud83d\uddc2\ufe0f Chat Question Suggestions (with persistence)\n\n### Purpose\nGenerate and cache 2\u20133 high-signal executive questions for a book, returning them instantly on repeat requests.\n\n### How it works\n- Accepts a `book_id` via webhook \n- Loads the book (title + summary) from Supabase \n- Checks for existing chat suggestions for that book \n - **If found** \u2192 returns them immediately \n - **If missing** \u2192 asks an AI agent to generate new questions \n- Parses the AI response into individual questions \n- Persists the questions to Supabase for future requests \n- Responds with a single `output` field containing a JSON-encoded array of strings (LLM-friendly format)\n\n### Why this exists\n- Keeps chat UIs fast and deterministic \n- Avoids regenerating suggestions on every page load \n- Ensures consistent, high-quality starter questions per book \n\n",
"height": 512,
"width": 768,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-208,
-336
],
"id": "0407f240-b923-49fb-9f61-19dfcbd2564b",
"name": "Sticky Note"
},
{
"parameters": {
"content": "## Output\n```json\n{\n \"output\": \"[\\\"Question 1\\\", \\\"Question 2\\\", \\\"Question 3\\\"]\"\n}\n",
"height": 352,
"width": 672,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
1296,
-80
],
"id": "e1503ce7-18cf-4465-821e-303d99257812",
"name": "Sticky Note1"
},
{
"parameters": {
"content": "## Input\n```json\n{ \n \"book_id\": \"uuid\" \n}\n```",
"height": 224,
"width": 752,
"color": 7
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-192,
496
],
"id": "6c9e65a4-9cb4-4e13-bbaa-7403c09cf370",
"name": "Sticky Note2"
}
],
"connections": {
"Webhook": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "Load Book",
"type": "main",
"index": 0
}
]
]
},
"OpenAI": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"Load Book": {
"main": [
[
{
"node": "Load Suggestions",
"type": "main",
"index": 0
}
]
]
},
"Load Suggestions": {
"main": [
[
{
"node": "If Suggestions Empty",
"type": "main",
"index": 0
}
]
]
},
"If Suggestions Empty": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
],
[
{
"node": "PreProcess Suggestions",
"type": "main",
"index": 0
}
]
]
},
"Respond to Webhook": {
"main": [
[
{
"node": "Parse Response",
"type": "main",
"index": 0
}
]
]
},
"Parse Response": {
"main": [
[
{
"node": "Store Suggestions",
"type": "main",
"index": 0
}
]
]
},
"PreProcess Suggestions": {
"main": [
[
{
"node": "Respond to Webhook1",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1",
"availableInMCP": false,
"binaryMode": "separate"
},
"staticData": null,
"meta": {
"templateCredsSetupCompleted": true
},
"versionId": "9cd67fcc-c8bf-4f63-83b8-683e90caf6ef",
"activeVersionId": "9cd67fcc-c8bf-4f63-83b8-683e90caf6ef",
"triggerCount": 1,
"shared": [
{
"updatedAt": "2026-01-09T23:20:53.328Z",
"createdAt": "2026-01-09T23:20:53.328Z",
"role": "workflow:owner",
"workflowId": "F9OWClEBtwYHjHTv",
"projectId": "B7QJE85HA2Vij1it"
}
],
"activeVersion": {
"updatedAt": "2026-04-04T20:52:29.000Z",
"createdAt": "2026-04-04T20:52:16.460Z",
"versionId": "9cd67fcc-c8bf-4f63-83b8-683e90caf6ef",
"workflowId": "F9OWClEBtwYHjHTv",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "chat_suggestions",
"responseMode": "responseNode",
"options": {}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
-144,
256
],
"id": "150560c9-b6bf-40e8-9d3b-cf9427e30c52",
"name": "Webhook",
"webhookId": "5d2431c9-171b-40d3-ab9a-f043258399d7"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "7bdf57f2-c6ca-43ca-a584-60495cf70e2d",
"name": "book_id",
"value": "={{ $json.body.book_id }}",
"type": "string"
},
{
"id": "316af802-b37f-4d88-89e5-69c167410869",
"name": "locale",
"value": "={{ $json.body.locale }}",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
80,
256
],
"id": "59a3061a-c67b-424a-b559-59f3cd5a533d",
"name": "Edit Fields"
},
{
"parameters": {
"promptType": "define",
"text": "=book title: {{ $('Load Book').item.json.title}}\n\nsummary: {{$('Load Book').item.json.summary }}\n\n",
"options": {
"systemMessage": "=You are an executive thinking partner.\n You respond in the following language: {{$('Edit Fields').item.json.locale == 'de'? 'German' : 'English' }}\nYour only input is a book title and summary.\n\nBased solely on the implications, themes, and promises suggested by the title and summary, generate 2\u20133 sharp questions that a high-performing individual would naturally ask. \n\nAssume the reader is:\n- Experienced\n- Time-constrained\n- Focused on leverage, clarity, and outcomes\n\nYour questions must:\n- Be short, direct, and high-signal\n\n\nDo NOT:\n- Explain the questions\n- Add preamble or commentary\n\nOutput format:\nReturn ONLY a JSON array of strings.\n\nExample output:\n[\n \"What is meant by [key phrase in book title]?\",\n \"What are the key takeaways from this book?\",\n \"Where could I apply the principles to my business?\"\n]\n"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
976,
0
],
"id": "3bbc1ed2-9bd0-49ee-9578-d02002be98f1",
"name": "AI Agent"
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"builtInTools": {},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
1056,
224
],
"id": "416b2a68-67d7-4e1b-bd47-907c669cfdeb",
"name": "OpenAI",
"credentials": {
"openAiApi": {
"id": "BRRf66J5aSwt4UDP",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.5,
"position": [
1328,
112
],
"id": "5c6debe8-fc0e-43e0-86bf-a17a4adfa74f",
"name": "Respond to Webhook"
},
{
"parameters": {
"operation": "get",
"tableId": "books",
"filters": {
"conditions": [
{
"keyName": "id",
"keyValue": "={{ $json.book_id }}"
}
]
}
},
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
304,
256
],
"id": "b9a79999-fe19-4aea-b6c3-3532b6575dbf",
"name": "Load Book",
"credentials": {
"supabaseApi": {
"id": "fdzgJDGuPA2JozKn",
"name": "Supabase account"
}
}
},
{
"parameters": {
"operation": "getAll",
"tableId": "chat_suggestions",
"matchType": "allFilters",
"filters": {
"conditions": [
{
"keyName": "book_id",
"condition": "eq",
"keyValue": "={{ $json.id }}"
},
{
"keyName": "locale",
"condition": "eq",
"keyValue": "={{ $('Edit Fields').item.json.locale }}"
}
]
}
},
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
528,
256
],
"id": "2878f446-a007-447e-b45e-15e5a9325256",
"name": "Load Suggestions",
"alwaysOutputData": true,
"credentials": {
"supabaseApi": {
"id": "fdzgJDGuPA2JozKn",
"name": "Supabase account"
}
}
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 3
},
"conditions": [
{
"id": "76d6027e-ff87-47c2-b251-65d340567f19",
"leftValue": "={{ $json.isEmpty() }}",
"rightValue": "",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.if",
"typeVersion": 2.3,
"position": [
752,
256
],
"id": "f7195611-3650-44f9-80e4-38e57d353a9a",
"name": "If Suggestions Empty"
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.5,
"position": [
1328,
400
],
"id": "32302a5e-8c37-4981-8d17-448f869e2b0f",
"name": "Respond to Webhook1"
},
{
"parameters": {
"tableId": "chat_suggestions",
"fieldsUi": {
"fieldValues": [
{
"fieldId": "suggestion",
"fieldValue": "={{ $json.question }}"
},
{
"fieldId": "book_id",
"fieldValue": "={{ $('Load Book').item.json.id }}"
},
{
"fieldId": "locale",
"fieldValue": "={{ $('Edit Fields').item.json.locale }}"
}
]
}
},
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
1776,
112
],
"id": "ec1fe71b-f138-4d67-aff5-173f09fa5c65",
"name": "Store Suggestions",
"credentials": {
"supabaseApi": {
"id": "fdzgJDGuPA2JozKn",
"name": "Supabase account"
}
}
},
{
"parameters": {
"jsCode": "const raw = items[0].json.output;\n\n// Parse the stringified JSON array\nlet parsed;\ntry {\n parsed = JSON.parse(raw);\n} catch (e) {\n throw new Error(\"Failed to parse output as JSON array\");\n}\n\n// Ensure it's an array\nif (!Array.isArray(parsed)) {\n throw new Error(\"Parsed output is not an array\");\n}\n\n// Return one item per entry\nreturn parsed.map(question => ({\n json: {\n question\n }\n}));\n"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1552,
112
],
"id": "b4914a54-4c60-4f82-a258-f4a39f6eb514",
"name": "Parse Response"
},
{
"parameters": {
"jsCode": "// Extract suggestion strings from all incoming items\nconst suggestions = items.map(item => item.json.suggestion);\n\n// Encode as a JSON string (array of strings)\nconst encoded = JSON.stringify(suggestions, null, 2);\n\n// Return a single item matching the required format\nreturn [\n {\n json: {\n output: encoded\n }\n }\n];\n"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1040,
400
],
"id": "20fcb9eb-e0a5-4e65-b07e-4131cce462a9",
"name": "PreProcess Suggestions"
},
{
"parameters": {
"content": "## \ud83d\uddc2\ufe0f Chat Question Suggestions (with persistence)\n\n### Purpose\nGenerate and cache 2\u20133 high-signal executive questions for a book, returning them instantly on repeat requests.\n\n### How it works\n- Accepts a `book_id` via webhook \n- Loads the book (title + summary) from Supabase \n- Checks for existing chat suggestions for that book \n - **If found** \u2192 returns them immediately \n - **If missing** \u2192 asks an AI agent to generate new questions \n- Parses the AI response into individual questions \n- Persists the questions to Supabase for future requests \n- Responds with a single `output` field containing a JSON-encoded array of strings (LLM-friendly format)\n\n### Why this exists\n- Keeps chat UIs fast and deterministic \n- Avoids regenerating suggestions on every page load \n- Ensures consistent, high-quality starter questions per book \n\n",
"height": 512,
"width": 768,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-208,
-336
],
"id": "0407f240-b923-49fb-9f61-19dfcbd2564b",
"name": "Sticky Note"
},
{
"parameters": {
"content": "## Output\n```json\n{\n \"output\": \"[\\\"Question 1\\\", \\\"Question 2\\\", \\\"Question 3\\\"]\"\n}\n",
"height": 352,
"width": 672,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
1296,
-80
],
"id": "e1503ce7-18cf-4465-821e-303d99257812",
"name": "Sticky Note1"
},
{
"parameters": {
"content": "## Input\n```json\n{ \n \"book_id\": \"uuid\" \n}\n```",
"height": 224,
"width": 752,
"color": 7
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-192,
496
],
"id": "6c9e65a4-9cb4-4e13-bbaa-7403c09cf370",
"name": "Sticky Note2"
}
],
"connections": {
"Webhook": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "Load Book",
"type": "main",
"index": 0
}
]
]
},
"OpenAI": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"Load Book": {
"main": [
[
{
"node": "Load Suggestions",
"type": "main",
"index": 0
}
]
]
},
"Load Suggestions": {
"main": [
[
{
"node": "If Suggestions Empty",
"type": "main",
"index": 0
}
]
]
},
"If Suggestions Empty": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
],
[
{
"node": "PreProcess Suggestions",
"type": "main",
"index": 0
}
]
]
},
"Respond to Webhook": {
"main": [
[
{
"node": "Parse Response",
"type": "main",
"index": 0
}
]
]
},
"Parse Response": {
"main": [
[
{
"node": "Store Suggestions",
"type": "main",
"index": 0
}
]
]
},
"PreProcess Suggestions": {
"main": [
[
{
"node": "Respond to Webhook1",
"type": "main",
"index": 0
}
]
]
}
},
"authors": "Gordan Kljajic",
"name": "Version 9cd67fcc",
"description": "",
"autosaved": true
},
"tags": []
}
Credentials you'll need
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openAiApisupabaseApi
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About this workflow
Chat Question Suggestions With Persistence. Uses agent, lmChatOpenAi, supabase. Webhook trigger; 15 nodes.
Source: https://github.com/adamhaley/megyk-automations/blob/main/workflows/Chat_Question_Suggestions_With_Persistence.json — original creator credit. Request a take-down →
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