Track LLM Token Usage and Agent Observability on Google Sheets
描述
分类
🔧 Engineering🤖 AI & Machine Learning
使用的节点
n8n-nodes-base.ifn8n-nodes-base.setn8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.googleSheets@n8n/n8n-nodes-langchain.code@n8n/n8n-nodes-langchain.agentn8n-nodes-base.googleSheetsTool@n8n/n8n-nodes-langchain.chatTrigger
价格免费
浏览量0
最后更新11/28/2025
workflow.json
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"name": "Track LLM Token Usage and Agent Observability on Google Sheets",
"tags": [],
"nodes": [
{
"id": "74b8b8f2-8152-4a8f-991b-6eb8dea2ad4a",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
0,
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],
"webhookId": "5e11697c-dfc4-457e-b535-43f65ba8da81",
"parameters": {
"public": true,
"options": {},
"initialMessages": "Welcome to Troopers!\n\nSo glad you’re here! 😊 Are you already a Troopers client, or just getting started?"
},
"typeVersion": 1.1
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{
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"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
464,
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],
"parameters": {
"text": "={{ $('When chat message received').item.json.chatInput }}",
"options": {
"systemMessage": "=You are a helpful assistant",
"returnIntermediateSteps": true
},
"promptType": "define"
},
"typeVersion": 2.1
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{
"id": "482a8e1a-3d9e-419c-a671-932fc0724c1c",
"name": "Token Usage Log",
"type": "n8n-nodes-base.googleSheetsTool",
"position": [
528,
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],
"parameters": {
"columns": {
"value": {
"date": "={{ $fromAI(\"date\") }}",
"model": "={{ $fromAI(\"model\") }}",
"llm_node": "={{ $fromAI(\"llm_node\") }}",
"client_id": "={{ $fromAI(\"client_id\") }}",
"input_cost": "={{ $fromAI(\"input_cost\") }}",
"total_cost": "={{ $fromAI(\"total_cost\") }}",
"output_cost": "={{ $fromAI(\"output_cost\") }}",
"workflow_id": "={{ $fromAI(\"workflow_id\") }}",
"execution_id": "={{ $fromAI(\"execution_id\") }}",
"input_tokens": "={{ $fromAI(\"input_tokens\") }}",
"total_tokens": "={{ $fromAI(\"total_tokens\") }}",
"output_tokens": "={{ $fromAI(\"output_tokens\") }}",
"input_token_cost ($ / million)": "={{ $fromAI(\"input_token_cost\") }}",
"output_token_cost ($ / million)": "={{ $fromAI(\"output_token_cost\") }}"
},
"schema": [
{
"id": "date",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "llm_node",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "llm_node",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "model",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "model",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "workflow_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "workflow_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "execution_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "execution_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "client_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "client_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "input_tokens",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "input_tokens",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "input_token_cost ($ / million)",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "input_token_cost ($ / million)",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "output_tokens",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "output_tokens",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "output_token_cost ($ / million)",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "output_token_cost ($ / million)",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_tokens",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "total_tokens",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "input_cost",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "input_cost",
"defaultMatch": false,
"canBeUsedToMatch": true
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{
"id": "output_cost",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "output_cost",
"defaultMatch": false,
"canBeUsedToMatch": true
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{
"id": "total_cost",
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"display": true,
"removed": false,
"required": false,
"displayName": "total_cost",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "autoMapInputData",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1O4PVOD584KWLzzj8gEym95hs8z3-Y4UjImMwKfby98c/edit#gid=0",
"cachedResultName": "Token cost tracker"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": ""
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "wOT0EGRAXG51Ihtb",
"name": "Google Sheets account (Augra)"
}
},
"typeVersion": 4.5
},
{
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"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"width": 624,
"height": 560,
"content": "## Start here: Step-by Step Youtube Tutorial :star:\nFor model-specific tweaks to the LangChain Code node, follow your provider’s LangChain setup. For a walkthrough, see the attached video:\n\n[](https://youtu.be/JSulRS128MA)\n"
},
"typeVersion": 1
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"parameters": {
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"content": "**Start here (required settings)**\n\n* In **Set • Workflow + Client Metadata**, set `client_id`. Keep `workflow_id` and `execution_id` as is.\n* In **LangChain Chat Model + Token Callback**, set `model`, `input_token_cost`, `output_token_cost`.\n* Ensure `OPENAI_API_KEY` or your provider key is set as an environment variable.\n* In **Token Usage Log**, select your Spreadsheet and the Metrics sheet. **Replace with…** Sheet ID and Sheet name.\n* In **Log • Token Metrics to Sheets (Tool)**, select your Spreadsheet and the Observability sheet. **Replace with…** Sheet ID and Sheet name.\n* Add **Google Sheets OAuth2** credentials.\n\n\n**What this workflow does**\n\n* Receives a chat message and runs an AI Agent.\n* Tracks tokens and computes costs in a LangChain callback.\n* Logs token metrics to a Metrics sheet.\n* Logs input, output, and tool usage to an Observability sheet.\n* Associates each row with workflow, execution, and client IDs.\n\n\n**Customize**\n\n* Change `model` and token prices to match your provider’s pricing.\n* Add more metadata fields in **Set metadata** as needed.\n* Extend the callback to include latency or provider request IDs.\n* Add a Limit or Sample node for high-volume runs.\n* Swap the Chat Trigger for Webhook Trigger if you do not need the built-in chat."
},
"typeVersion": 1
},
{
"id": "ae40d18f-8bc8-4a4f-8ddb-f31099fcd24b",
"name": "Set • Workflow + Client Metadata",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "ca1744cf-69e7-4a85-81d3-2fb2b936e496",
"name": "workflow_id",
"type": "string",
"value": "={{ $workflow.id }}"
},
{
"id": "205c7c4d-2dfa-471f-9306-33d30e16722a",
"name": "execution_id",
"type": "string",
"value": "={{ $execution.id }}"
},
{
"id": "3fa4932f-c3cc-41a8-90a4-d1b3a2d32cdd",
"name": "client_id",
"type": "string",
"value": "123"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0b9d9c4b-7bc7-4057-a78f-bc5fc9b6f857",
"name": "LangChain Chat Model + Token Callback",
"type": "@n8n/n8n-nodes-langchain.code",
"position": [
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],
"parameters": {
"code": {
"supplyData": {
"code": "const { ChatOpenAI } = require(\"@langchain/openai\");\n\n// 1. Configure as required.\n// - costs are per million tokens and depends on the model.\nconst openAIApiKey = process.env.OPENAI_API_KEY;\nconst model = \"gpt-5-mini-2025-08-07\";\nconst input_token_cost = 0.25;\nconst output_token_cost = 2.00;\n\n// 2. Customize LLM token tracker\nconst tools = await this.getInputConnectionData('ai_tool', 0);\nconst googleSheetTool = tools[0];\n\nconst {\n workflow_id,\n execution_id,\n client_id } = $('Set metadata').first().json;\n\nconst llm = new ChatOpenAI({\n apiKey: openAIApiKey,\n model,\n callbacks: [\n {\n handleLLMEnd: async function(output,runId,parentId) {\n const generation = output.generations[0][0];\n const message = generation.message;\n const row = {\n date: (new Date()).toGMTString(),\n workflow_id,\n execution_id,\n client_id,\n model,\n input_tokens: message.usage_metadata.input_tokens,\n output_tokens: message.usage_metadata.output_tokens,\n total_tokens: message.usage_metadata.total_tokens,\n input_cost: (message.usage_metadata.input_tokens / 1_000_000) * input_token_cost,\n output_cost: (message.usage_metadata.output_tokens / 1_000_000) * output_token_cost,\n };\n row.total_cost = row.input_cost + row.output_cost;\n await googleSheetTool.func(row);\n }\n }\n ]\n});\n\nreturn llm;"
}
},
"inputs": {
"input": [
{
"type": "ai_tool",
"required": true
}
]
},
"outputs": {
"output": [
{
"type": "ai_languageModel"
}
]
}
},
"typeVersion": 1
},
{
"id": "383dc17c-d479-4f3d-965f-f708931c4865",
"name": "Branch • Tool Used?",
"type": "n8n-nodes-base.if",
"position": [
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],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
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"caseSensitive": true,
"typeValidation": "strict"
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"combinator": "and",
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"operator": {
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"operation": "empty",
"singleValue": true
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"leftValue": "={{ $json.intermediateSteps }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "e9f7bef2-c21e-45e1-ade5-33fdcee5fc0a",
"name": "Log • Token Metrics to Sheets (Tool)",
"type": "n8n-nodes-base.googleSheets",
"position": [
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],
"parameters": {
"columns": {
"value": {
"date": "={{ $now }}",
"input": "={{ $('When chat message received').item.json.chatInput }}",
"output": "={{ $json.output }}",
"Tool use": "={{ $json.intermediateSteps[0].action.tool }}",
"client_id": "={{ $('Set • Workflow + Client Metadata').item.json.client_id }}",
"workflow_id": "={{ $('Set • Workflow + Client Metadata').item.json.workflow_id }}",
"execution_id": "={{ $('Set • Workflow + Client Metadata').item.json.execution_id }}"
},
"schema": [
{
"id": "date",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "workflow_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "workflow_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "execution_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "execution_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "client_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "client_id",
"defaultMatch": false,
"canBeUsedToMatch": true
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{
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"type": "string",
"display": true,
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"displayName": "input",
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"canBeUsedToMatch": true
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{
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"displayName": "Tool use",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
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"display": true,
"removed": false,
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"displayName": "output",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 94625050,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1O4PVOD584KWLzzj8gEym95hs8z3-Y4UjImMwKfby98c/edit#gid=94625050",
"cachedResultName": "Observability"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1O4PVOD584KWLzzj8gEym95hs8z3-Y4UjImMwKfby98c",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1O4PVOD584KWLzzj8gEym95hs8z3-Y4UjImMwKfby98c/edit?usp=drivesdk",
"cachedResultName": "Track LLM Tokens"
}
},
"credentials": {
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"id": "wOT0EGRAXG51Ihtb",
"name": "Google Sheets account (Augra)"
}
},
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],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "67022965-a398-44c9-b241-bd09709760ec",
"connections": {
"AI Agent": {
"main": [
[
{
"node": "Branch • Tool Used?",
"type": "main",
"index": 0
}
]
]
},
"Token Usage Log": {
"ai_tool": [
[
{
"node": "LangChain Chat Model + Token Callback",
"type": "ai_tool",
"index": 0
}
]
]
},
"Branch • Tool Used?": {
"main": [
[],
[
{
"node": "Log • Token Metrics to Sheets (Tool)",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Set • Workflow + Client Metadata",
"type": "main",
"index": 0
}
]
]
},
"Set • Workflow + Client Metadata": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"LangChain Chat Model + Token Callback": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
}
}
}