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 →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "ed5363cf-1fb6-4662-b12c-073b2b3a3576",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-240,
140
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "e47a166f-3e70-433e-ad0d-2100309cac92",
"name": "Google Gemini Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
-60,
500
],
"parameters": {
"options": {
"topP": 1
},
"modelName": "models/gemini-2.0-flash-lite"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "5474805f-8d18-4a09-a3ea-5602af97a5de",
"name": "Auto-fixing Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserAutofixing",
"position": [
500,
360
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "d9a0eadc-54c7-4980-b4f8-79fd77627c32",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
600,
520
],
"parameters": {
"jsonSchemaExample": "{\n\t\"name\": \"Name of the prompt\",\n \"category\" : \"the prompt category\"\n}"
},
"typeVersion": 1.2
},
{
"id": "898f64cd-2332-42ad-9bac-a817dd9bf3d7",
"name": "Edit Fields",
"type": "n8n-nodes-base.set",
"position": [
220,
140
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "9c5fec90-b7f0-45f3-81a3-22e0956fc3bf",
"name": "text",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "4bbd160a-98bd-4622-a54e-77b61ff91b46",
"name": "Google Gemini Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
380,
540
],
"parameters": {
"options": {
"topP": 1
},
"modelName": "models/gemini-2.0-flash-lite"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "f45cbed4-c2b8-4f1b-8026-4686324a714a",
"name": "Return results",
"type": "n8n-nodes-base.set",
"position": [
960,
140
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "40aba86b-57b7-4c74-8e9f-d09cd2f344c5",
"name": "text",
"type": "string",
"value": "={{ $('Generate a new prompt').item.json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "25650ec5-b559-4bfc-a95a-f81c674bc680",
"name": "Categorize and name Prompt",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
360,
140
],
"parameters": {
"text": "={{ $json.text }}",
"messages": {
"messageValues": [
{
"message": "=Categorize the above prompt into a category that it can fall into"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.5
},
{
"id": "c324d952-0722-40aa-981c-fcb2007b43b9",
"name": "set prompt fields",
"type": "n8n-nodes-base.set",
"position": [
660,
140
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "cbf3b587-67fd-4f08-b50f-53561e869827",
"name": "name",
"type": "string",
"value": "={{ $json.output.name }}"
},
{
"id": "7fda5833-9a3b-4c8a-b18d-4c31b35dae94",
"name": "category",
"type": "string",
"value": "={{ $json.output.category }}"
},
{
"id": "50f06ab3-97d5-43cb-83ff-1a6aac45251b",
"name": "Prompt",
"type": "string",
"value": "={{ $('Edit Fields').item.json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "97ad8d84-141e-4c21-8ce4-930dbe921f76",
"name": "add to airtable",
"type": "n8n-nodes-base.airtable",
"position": [
800,
140
],
"parameters": {
"base": {
"__rl": true,
"mode": "list",
"value": "app994hU3fOw0ssrx",
"cachedResultUrl": "https://airtable.com/app994hU3fOw0ssrx",
"cachedResultName": "Prompt Library"
},
"table": {
"__rl": true,
"mode": "list",
"value": "tbldwJrCK2HmAeknA",
"cachedResultUrl": "https://airtable.com/app994hU3fOw0ssrx/tbldwJrCK2HmAeknA",
"cachedResultName": "Prompt Library"
},
"columns": {
"value": {
"Name": "={{ $json.name }}",
"Prompt": "={{ $json.Prompt }}",
"Category": "={{ $json.category }}"
},
"schema": [
{
"id": "Name",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Prompt",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Prompt",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Created ON",
"type": "string",
"display": true,
"removed": true,
"readOnly": true,
"required": false,
"displayName": "Created ON",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Updated",
"type": "string",
"display": true,
"removed": true,
"readOnly": true,
"required": false,
"displayName": "Updated",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Category",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Category",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "create"
},
"credentials": {
"airtableTokenApi": {
"name": "<your credential>"
}
},
"typeVersion": 2.1
},
{
"id": "516dc434-25d9-4011-9453-bb28521823ca",
"name": "Generate a new prompt",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-80,
140
],
"parameters": {
"messages": {
"messageValues": [
{
"message": "=You are an **expert n8n prompt engineer**, specializing in creating highly optimized, context-aware prompts for AI agents in n8n workflows. Your primary goal is to ensure AI agents execute well-defined tasks **accurately, autonomously, and efficiently**. \n\n### Instructions \n1. **Define the AI Agent's Role and Rules** \n - Use a structured role definition format: \n `\"You are a [SPECIFIC ROLE] working for [SPECIFIC BUSINESS CONTEXT].\"` \n - Clearly specify the agent's responsibilities and scope. \n\n2. **Provide Task Instructions** \n - Use a **step-by-step** numbered list to outline the process. \n - Ensure the instructions allow for flexibility but prevent errors. \n\n3. **Set Rules to Guide AI Behavior** \n - Enumerate key constraints such as: \n - Timezone requirements \n - Prohibitions on making assumptions \n - Required formatting for responses \n\n4. **Use Few-Shot Prompting** \n - Provide clear examples of desired outputs inside `<example>` tags. \n\n5. **Include Additional Context** \n - Define relevant business details, the current date/time, and any required environmental context. \n\n---\n\n## Input Layer \n### Structuring User Inputs \n1. **Define Input Type** \n - Specify whether inputs come from a human user (chat-based) or an external system (API calls). \n\n2. **Handle Dynamic Inputs** \n - Use placeholders (e.g., `{customer_name}`, `{appointment_date}`) for adaptable prompts. \n\n3. **Ensure Personalization** \n - Format prompts naturally while maintaining clarity and specificity. \n\n4. **Merge Static & Dynamic Data** \n - Concatenate fixed prompt structures with real-time system data from n8n. \n\n---\n## Action Layer \n### Tool and Function Calling \n1. **Standardized Tool Naming** \n - Use `snake_case` names for tools (e.g., `check_calendar_availability`). \n\n2. **Provide Clear Tool Descriptions** \n - Example: \n `\"Use the `fetch_customer_data` tool to retrieve details about a specific user based on their email address.\"` \n\n3. **Specify Tool Parameters & Expected Responses** \n - Define required inputs, expected formats, and error handling strategies. \n\n4. **Avoid Hallucinations** \n - AI should **only** use tools for their defined purposes. If information is missing, request clarification instead of guessing. \n\n---\n## Example Prompt for an AI Agent in n8n \n\n```yaml\n# System Layer\n## Role\nYou are a **Scheduling Assistant** working for a **beauty salon**. Your role is to help customers book appointments. \n\n## Instructions\n1. Ask the user for their preferred appointment date. \n2. Use `check_calendar_availability` to find open slots. \n3. If no slots are available, ask the user to select another day. \n4. Capture the user\u2019s **full name** and **email**. \n5. Use `create_calendar_appointment` to confirm the booking. \n6. Notify the user with appointment details. \n\n## Rules\n- Always use **UTC+1 timezone**. \n- Do not assume details\u2014ask if unsure. \n- If asked about non-scheduling topics, respond: `\"I can only assist with booking appointments.\"` \n\n## Few-shot Example \n<example>\n\"I have successfully booked your appointment:\n- Date & Time: **Wednesday, 15 March 2025, 14:00 (UTC+1)**\n- Booking Email: **jane.doe@example.com**\nIf you need to cancel, please call +49 123 456 789.\"\n</example>\n```\n---\n## Key Considerations \n\u2705 **Avoid vague roles** (e.g., \"You are an assistant\"). Always specify **business context**. \n\u2705 **Keep task steps structured** but flexible. \n\u2705 **Provide explicit tool instructions** in a separate section. \n\u2705 **Enable AI to ask clarifying questions** instead of making assumptions. \n\u2705 **Use examples to guide expected outputs.** \n\n\n"
}
]
}
},
"typeVersion": 1.5
}
],
"connections": {
"Edit Fields": {
"main": [
[
{
"node": "Categorize and name Prompt",
"type": "main",
"index": 0
}
]
]
},
"add to airtable": {
"main": [
[
{
"node": "Return results",
"type": "main",
"index": 0
}
]
]
},
"set prompt fields": {
"main": [
[
{
"node": "add to airtable",
"type": "main",
"index": 0
}
]
]
},
"Generate a new prompt": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Generate a new prompt",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Auto-fixing Output Parser",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Auto-fixing Output Parser": {
"ai_outputParser": [
[
{
"node": "Categorize and name Prompt",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Google Gemini Chat Model1": {
"ai_languageModel": [
[
{
"node": "Categorize and name Prompt",
"type": "ai_languageModel",
"index": 0
},
{
"node": "Auto-fixing Output Parser",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Categorize and name Prompt": {
"main": [
[
{
"node": "set prompt fields",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Generate a new prompt",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
airtableTokenApigooglePalmApi
About this workflow
Airtable. Uses chatTrigger, lmChatGoogleGemini, outputParserAutofixing, outputParserStructured. Chat trigger; 11 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →