Guys I had created a n8n workflow with all the tools and everything in place.
Important Note
There are coding agents and LLMs smart enough to understand what you mean when you say something like "commit, push, pr" means it should commit the changes with a commit message which conforms to prior commit messages (so if we are using conventional commit message it will use the same format), push your changes, and then uses
ghto open a PR for you.I am saying this to just clarify the importance of LLM you are interacting with.
tl;dr
💡 Pro Tip
You must use a prompt improver and try to optimize your prompt/context for the LLM to improve performance dramatically. Here are some examples:
- https://promptoptimizer.tools
- https://www.maxai.co/ai-tools/ai-writer/prompt-enhancement/
- Or learn how to write prompts here if you wanna write it for Anthropic models.
- Or you can learn something from this Claude Code hook.
Here is the workflow:
{
"name": "My workflow",
"nodes": [
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.4,
"position": [
0,
0
],
"id": "a28a630c-8f87-4881-bf5a-1c0d3b9ab597",
"name": "When chat message received",
"webhookId": "43333eec-230f-44ed-a007-a8513f0904b1",
"alwaysOutputData": true
},
{
"parameters": {
"options": {
"systemMessage": "You are the a helpful assistant"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
208,
0
],
"id": "ec551627-7dc3-4884-9f35-bbda0bf038a0",
"name": "AI Agent"
},
{
"parameters": {
"model": {
"__rl": true,
"value": "gpt-4o-mini",
"mode": "list",
"cachedResultName": "gpt-4o-mini"
},
"builtInTools": {},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
80,
208
],
"id": "e5c2a58c-63b2-4850-9ed5-15aff77ce32b",
"name": "OpenAI Chat Model",
"credentials": {
"openAiApi": {
"id": "0h4kMjpTValhfJZv",
"name": "OpenAI account"
}
}
},
{
"parameters": {},
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"typeVersion": 1.4,
"position": [
224,
208
],
"id": "3cee5ca7-d6b9-4d82-a0e0-5d17430bde1f",
"name": "Simple Memory"
},
{
"parameters": {
"documentId": {
"__rl": true,
"value": "1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc",
"mode": "list",
"cachedResultName": "stock-portfolio",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "Sheet1",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit#gid=0"
},
"options": {}
},
"type": "n8n-nodes-base.googleSheetsTool",
"typeVersion": 4.7,
"position": [
400,
208
],
"id": "518fb3c0-f6ee-430d-a8e1-5865f6ac8c77",
"name": "Get row(s) in sheet in Google Sheets",
"credentials": {
"googleSheetsOAuth2Api": {
"id": "b7aCwM5d7t0oTzxp",
"name": "Google Sheets account"
}
}
},
{
"parameters": {
"symbols": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Ticker', ``, 'string') }}",
"filters": {
"latest": true
}
},
"type": "n8n-nodes-base.marketstackTool",
"typeVersion": 1,
"position": [
560,
208
],
"id": "8393c58b-aa11-4b0d-9224-4bd53607c714",
"name": "Get many EoD data in Marketstack",
"credentials": {
"marketstackApi": {
"id": "uykspUJ9pFnekPFQ",
"name": "Marketstack account"
}
}
},
{
"parameters": {
"operation": "update",
"documentId": {
"__rl": true,
"value": "1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc",
"mode": "list",
"cachedResultName": "stock-portfolio",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "Sheet1",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"price": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('price', ``, 'string') }}",
"stock": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('stock__using_to_match_', ``, 'string') }}"
},
"matchingColumns": [
"stock"
],
"schema": [
{
"id": "stock",
"displayName": "stock",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "quantity",
"displayName": "quantity",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": true
},
{
"id": "price",
"displayName": "price",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "row_number",
"displayName": "row_number",
"required": false,
"defaultMatch": false,
"display": true,
"type": "number",
"canBeUsedToMatch": true,
"readOnly": true,
"removed": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheetsTool",
"typeVersion": 4.7,
"position": [
720,
208
],
"id": "349e23c1-e12e-419f-934b-667239a2d27b",
"name": "Update row in sheet in Google Sheets",
"credentials": {
"googleSheetsOAuth2Api": {
"id": "b7aCwM5d7t0oTzxp",
"name": "Google Sheets account"
}
}
}
],
"pinData": {},
"connections": {
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Get row(s) in sheet in Google Sheets": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Get many EoD data in Marketstack": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Update row in sheet in Google Sheets": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1",
"binaryMode": "separate",
"availableInMCP": false
},
"versionId": "9f029cb8-8c3b-4002-af31-f3eef8ed0fc4",
"meta": {
"templateCredsSetupCompleted": true,
"instanceId": "d7f40ac67c918c6b77c1db2f31baa37e504a493b4a22e372f2a513b7bf0ca5a9"
},
"nodeGroups": [],
"id": "6bsOs8ssUv4uxhna",
"tags": []
}
And when I was passing it this prompt:
Can you update the price column of those stocks in my google sheet?
gpt-4.0-mini replied:
I can help with that! Please provide the details of the stock tickers and their corresponding prices that you want to update in your Google Sheet.
So it was NOT able to know how to use the tools available to it. But then when I just tweaked the model a little it started to work. Here is the new prompt which worked:
Please update the price column of those stocks in that sheet based on latest stock prices?
And the same model this time again replied 🤯:
The prices have been successfully updated in the sheet:
- GOOGL: $373.51
- AAPL: $303.42
- TSLA: $322.08
If you need any further assistance, feel free to ask!
And for this workflow:
{
"name": "My workflow",
"nodes": [
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.4,
"position": [
0,
0
],
"id": "2366f927-9935-46aa-9b9d-bc5505f12834",
"name": "When chat message received",
"webhookId": "4d8258af-e64c-4235-9a24-6e90f5596775",
"alwaysOutputData": true
},
{
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant and speak like a cowboy."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
208,
0
],
"id": "8828d816-259f-4b65-a3b8-8c8ff25b73d7",
"name": "AI Agent"
},
{
"parameters": {
"model": {
"__rl": true,
"value": "gpt-4o-mini",
"mode": "list",
"cachedResultName": "gpt-4o-mini"
},
"builtInTools": {},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
32,
208
],
"id": "26891c88-aed5-444a-b545-a5ef9bdc3ad0",
"name": "OpenAI Chat Model",
"credentials": {
"openAiApi": {
"id": "0h4kMjpTValhfJZv",
"name": "OpenAI account"
}
}
},
{
"parameters": {},
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"typeVersion": 1.4,
"position": [
176,
208
],
"id": "cadce2fb-f307-4e26-9423-51304618c03b",
"name": "Simple Memory"
},
{
"parameters": {
"documentId": {
"__rl": true,
"value": "1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc",
"mode": "list",
"cachedResultName": "stock-portfolio",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "Sheet1",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit#gid=0"
},
"options": {}
},
"type": "n8n-nodes-base.googleSheetsTool",
"typeVersion": 4.7,
"position": [
320,
320
],
"id": "813a2455-535f-4d85-8460-fa65e119f72f",
"name": "Get row(s) in sheet in Google Sheets",
"credentials": {
"googleSheetsOAuth2Api": {
"id": "b7aCwM5d7t0oTzxp",
"name": "Google Sheets account"
}
}
},
{
"parameters": {
"operation": "update",
"documentId": {
"__rl": true,
"value": "1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc",
"mode": "list",
"cachedResultName": "stock-portfolio",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "Sheet1",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"price": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('price', ``, 'string') }}",
"stock": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('stock__using_to_match_', ``, 'string') }}"
},
"matchingColumns": [
"stock"
],
"schema": [
{
"id": "stock",
"displayName": "stock",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "quantity",
"displayName": "quantity",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": true
},
{
"id": "price",
"displayName": "price",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "row_number",
"displayName": "row_number",
"required": false,
"defaultMatch": false,
"display": true,
"type": "number",
"canBeUsedToMatch": true,
"readOnly": true,
"removed": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheetsTool",
"typeVersion": 4.7,
"position": [
768,
224
],
"id": "c69bceb2-b6bc-45f1-84ca-9eaede574fae",
"name": "Update row in sheet in Google Sheets",
"credentials": {
"googleSheetsOAuth2Api": {
"id": "b7aCwM5d7t0oTzxp",
"name": "Google Sheets account"
}
}
},
{
"parameters": {
"symbols": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Ticker', ``, 'string') }}",
"filters": {
"latest": true
}
},
"type": "n8n-nodes-base.marketstackTool",
"typeVersion": 1,
"position": [
496,
320
],
"id": "ca2c2a3b-6f8b-468f-b3cd-11cfe15ab295",
"name": "Get many EoD data in Marketstack",
"credentials": {
"marketstackApi": {
"id": "uykspUJ9pFnekPFQ",
"name": "Marketstack account"
}
}
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc",
"mode": "list",
"cachedResultName": "stock-portfolio",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "Sheet1",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1dYZQzF6VPY9X47VCywv4MT1Wo9OpT39vhSBvpNn_Mbc/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"stock": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('stock', ``, 'string') }}",
"price": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('price', ``, 'string') }}"
},
"matchingColumns": [],
"schema": [
{
"id": "stock",
"displayName": "stock",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "quantity",
"displayName": "quantity",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": true
},
{
"id": "price",
"displayName": "price",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheetsTool",
"typeVersion": 4.7,
"position": [
672,
352
],
"id": "8340b44b-fab8-41da-a5a6-9f81d18251fa",
"name": "Append row in sheet in Google Sheets",
"credentials": {
"googleSheetsOAuth2Api": {
"id": "b7aCwM5d7t0oTzxp",
"name": "Google Sheets account"
}
}
}
],
"pinData": {},
"connections": {
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Get row(s) in sheet in Google Sheets": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Update row in sheet in Google Sheets": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Get many EoD data in Marketstack": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Append row in sheet in Google Sheets": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1",
"binaryMode": "separate",
"availableInMCP": false
},
"versionId": "f5f3fcf5-c9e2-4cff-876f-a6619e88e49a",
"meta": {
"templateCredsSetupCompleted": true,
"instanceId": "d7f40ac67c918c6b77c1db2f31baa37e504a493b4a22e372f2a513b7bf0ca5a9"
},
"nodeGroups": [],
"id": "6bsOs8ssUv4uxhna",
"tags": []
}
If you give it a prompt like this:
Please get all the stocks and update the sheet, leave the quantity empty and do it at max for the first 10 distinct stocks. Also make sure to keep the GOOGL, APPL, and TSLA.
Is going to just add 10 new rows to the Google Sheet, while my intention was more like 10 in total. And also it did NOT use the Marketstack API to fetch their latest stock price!
Prompts <> Node Names -- Limitations
Imagine you have a prompt like this:
Please ensure the portfolio is 60% equity and 40% fixed income.
1. Go and download my portfolio from Google Sheet (the sheet name is "shares").
2. Get a direct link to the Google Sheet document.
3. Get the current date.
4. Check the market price for today.
5. Upsert a new row for today with the new price.
6. Make decisions on portfolio rebalancing.
7. Check the new sheet and make sure the rebalancing objective was achieved. If you find any issue with rebalancing, update today's sheet to meet the objective.
8. Get a direct link to the Google Sheet.
9. Formulate a nice, neat looking email (NO html, just simple text for now suffice) with a link to the new sheet and send it to some@gmail.com.
- Do NOT write "Best regards, [Your Name]".
- Add a direct link to the Google Sheet document.
Or at least in my experience when the node was called "Append or update row..." it was NOT calling the tool! So I had to change it to "Upsert" and also use the same wording in the prompt.
💡 Tip
- I was unable to create new sheets with the same columns using existing tools.
- I was unable to get the GPT-4.0-mini to add a link to Google Share in the email!
System Prompts & MCP
Imagine you have a system prompt like this:
You find sales customers based on a specific query from user. You use your tools to search the internet and scrape websites as necessary to find the best potential clients for the user and respond with 5 sales customers.
And then you are giving it tools to do the crawling using firecrawl and searching internet. For the output you are asking it to return a list of objects which conform to this schema:
{
"firstName": "The customer's first name",
"lastName": "The customer's last name",
"company": "The company in which they work in",
"role": "Their role at that company",
"rationale": "Why is this a qualified potential client?",
"email": "Predict customer's email address"
}
LLM might hallucinate or come up wit random email addresses. But then if we change the system prompt and add another MCP client tool to your n8n AI Agent node it might skip the firecrawl MCP client tool all together:
You find sales prospects based on a specific query from user. You use your tools to search the internet and scrape websites as necessary to find the best potential clients for the user and respond with 3 sales prospect.
Do NOT forget to use your tools to find their most likely email address.
Just for completeness sake, this is user prompt: Hi, I am looking to sell a LLM-powered trading software in USA to major companies/organizations.
So as you can see the prompt play a crucial role. To see this even better you can add a clause to the system prompt forcing LLM to use the Firecrawl for searching:
You find sales customers based on a specific query from user. You use your tools to search the internet and scrape websites as necessary to find the best potential clients for the user and respond with 3 sales customers.
After finding the customers, USE ANOTHER SOURCE to ensure you are targeting the correct audience.
Do NOT forget to use your tools to find their most likely email address.
You can see my n8n workflow here:
{
"name": "My workflow",
"nodes": [
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.4,
"position": [
0,
0
],
"id": "f2951e92-0964-43bd-94e3-476e8bf4084d",
"name": "When chat message received",
"webhookId": "3ae43b38-017c-4181-b4a1-a7d0b748a0c5"
},
{
"parameters": {
"hasOutputParser": true,
"options": {
"systemMessage": "You find sales prospects based on a specific query from user. You use your tools to search the internet and scrape websites as necessary to find the best potential clients for the user and respond with 3 sales prospect.\n\nAfter finding the prospects, USE ANOTHER SOURCE to ensure you are targeting the correct audience.\n\nDo NOT forget to use your tools to find their most likely email address."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
224,
0
],
"id": "9b2210a0-20fa-4f33-b0ad-f36ced3aae32",
"name": "AI Agent"
},
{
"parameters": {
"model": {
"__rl": true,
"value": "gpt-5.2",
"mode": "list",
"cachedResultName": "gpt-5.2"
},
"builtInTools": {},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
176,
192
],
"id": "ba83a54b-4b97-44a9-a9ea-b8f9e8dc97d9",
"name": "OpenAI Chat Model",
"credentials": {
"openAiApi": {
"id": "0h4kMjpTValhfJZv",
"name": "OpenAI account"
}
}
},
{
"parameters": {
"jsonSchemaExample": "{\n \"prospects\": [\n {\n \"firstName\": \"The prospects first name\",\n \"lastName\": \"The prospect's last name\",\n \"company\": \"The company in which they work in\",\n \"role\": \"Their role at the company\",\n \"rationale\": \"Why is this prospect qualified as a potential client\",\n \"email\": \"Predict prospects email address\"\n }\n ]\n}"
},
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"typeVersion": 1.3,
"position": [
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],
"id": "5a288bac-e095-40f8-9d81-33238bb142be",
"name": "Structured Output Parser"
},
{
"parameters": {
"endpointUrl": "https://mcp.firecrawl.dev/v2/mcp",
"authentication": "bearerAuth",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.mcpClientTool",
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"name": "Firecrawl MCP",
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}
}
},
{
"parameters": {
"endpointUrl": "https://mcp.hunter.io/mcp",
"authentication": "bearerAuth",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.mcpClientTool",
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}
}
}
],
"pinData": {},
"connections": {
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
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}
]
]
},
"OpenAI Chat Model": {
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}
},
"active": false,
"settings": {
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"binaryMode": "separate",
"availableInMCP": false
},
"versionId": "7cb48faa-a964-4459-b096-dd4f66c54d9b",
"meta": {
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},
"nodeGroups": [],
"id": "jqcICzVZI18I6JQb",
"tags": []
}
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