Amir Safavi-Naini This workflow corresponds to n8n.io template #7398 — we link there as the canonical source.
The workflow JSON
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{
"id": "39pmihe4FUZNNFnQ",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "LLM Cost Tracking",
"tags": [],
"nodes": [
{
"id": "7e87063c-72a7-4c33-a5d4-9f5ef550da21",
"name": "Get an execution",
"type": "n8n-nodes-base.n8n",
"position": [
-688,
320
],
"parameters": {
"options": {
"activeWorkflows": true
},
"resource": "execution",
"operation": "get",
"executionId": "={{ $json.execution_id }}",
"requestOptions": {}
},
"credentials": {
"n8nApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "fdc6c95b-361d-445b-8306-1712e7235eaf",
"name": "When Exc.",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-880,
416
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "execution_id",
"type": "number"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "bce95431-c1be-47bc-96f3-99b318e78af8",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-240,
-80
],
"parameters": {
"width": 352,
"height": 304,
"content": "### Defined by User\n- Define the model name and standard name, even if you want to use same name. (Why? because we will use the standard name to find the cost of this model)\n- Model prices are per million token\n\n"
},
"typeVersion": 1
},
{
"id": "a1f0416a-723c-4724-a8f5-7ff3dbb6b829",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
160,
128
],
"parameters": {
"color": 3,
"width": 416,
"height": 240,
"content": "### Check all model names are correctly defined"
},
"typeVersion": 1
},
{
"id": "e55084c1-e2b6-4788-9f6e-1cfaeef59165",
"name": "model prices",
"type": "n8n-nodes-base.set",
"position": [
-32,
96
],
"parameters": {
"mode": "raw",
"include": "selected",
"options": {},
"jsonOutput": "{\n \"model_price_dic\":{\n \"gpt-5\": { \"input\": 1.25, \"output\": 10.0 },\n \"gpt-5-mini\": { \"input\": 0.25, \"output\": 2.0 },\n \"gpt-5-nano\": { \"input\": 0.05, \"output\": 0.4 },\n \"gpt-4.1\": { \"input\": 2.0, \"output\": 8.0 },\n \"gpt-4.1-mini\": { \"input\": 0.4, \"output\": 1.6 },\n \"gpt-4.1-nano\": { \"input\": 0.1, \"output\": 0.4 },\n \"gpt-4o\": { \"input\": 2.5, \"output\": 10.0 },\n \"gpt-4o-mini\": { \"input\": 0.15, \"output\": 0.6 },\n \"o1\": { \"input\": 15.0, \"output\": 60.0 },\n \"o1-pro\": { \"input\": 150.0, \"output\": 600.0 },\n \"o3-pro\": { \"input\": 20.0, \"output\": 80.0 },\n \"o3\": { \"input\": 2.0, \"output\": 8.0 },\n \"o3-deep-research\": { \"input\": 10.0,\"output\": 40.0 },\n \"o4-mini\": { \"input\": 1.1, \"output\": 4.4 },\n \"o4-mini-deep-research\": { \"input\": 2.0,\"output\": 8.0 },\n \"o3-mini\": { \"input\": 1.1, \"output\": 4.4 },\n \"o1-mini\": { \"input\": 1.1, \"output\": 4.4 }\n }\n }",
"includeFields": "standardize_names_dic, models_used",
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "19971fb6-2456-4629-ba2b-8bcacbeda97f",
"name": "Standardize names",
"type": "n8n-nodes-base.set",
"position": [
-208,
96
],
"parameters": {
"mode": "raw",
"include": "selected",
"options": {},
"jsonOutput": "{\n \"standardize_names_dic\":\n {\n \"gpt-4.1-mini\": \"gpt-4.1-mini\",\n \"gpt-4\": \"gpt-4\",\n \"gpt-5\": \"gpt-5\",\n \"gpt-5-mini\": \"gpt-5-mini\",\n \"gpt-5-nano\": \"gpt-5-nano\"\n }\n}\n",
"includeFields": "models_used",
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "fe9bde19-e1d5-40a9-9219-d79668d7a8c1",
"name": "Check correctly defined",
"type": "n8n-nodes-base.code",
"position": [
640,
192
],
"parameters": {
"jsCode": "// NODE 2: Validate Models Against Dictionaries\n\nconst item = $input.first().json;\nconst models_used = item.models_used || [];\nconst standardize_names_dic = item.standardize_names_dic || {};\nconst model_price_dic = item.model_price_dic || {};\n\n// Check which models are missing from each dictionary\nconst missing_from_names = models_used.filter(model => !standardize_names_dic[model]);\nconst missing_from_prices = models_used.filter(model => !model_price_dic[model]);\n\n// Build result\nif (missing_from_names.length === 0 && missing_from_prices.length === 0) {\n return [{\n json: {\n passed: true,\n message: \"All models validated successfully\",\n models_used: models_used,\n standardize_names_dic: standardize_names_dic,\n model_price_dic: model_price_dic,\n original_data: item.original_data\n }\n }];\n} else {\n const errors = [];\n if (missing_from_names.length > 0) {\n errors.push(`Missing from standardize_names_dic: ${missing_from_names.join(', ')}`);\n }\n if (missing_from_prices.length > 0) {\n errors.push(`Missing from model_price_dic: ${missing_from_prices.join(', ')}`);\n }\n \n return [{\n json: {\n passed: false,\n message: errors.join(' | '),\n missing_from_names: missing_from_names,\n missing_from_prices: missing_from_prices,\n models_used: models_used\n }\n }];\n}"
},
"typeVersion": 2
},
{
"id": "57480286-a52e-4e57-a85f-b588b8b3c74e",
"name": "Stop and Error",
"type": "n8n-nodes-base.stopAndError",
"position": [
1024,
144
],
"parameters": {
"errorMessage": "={{ $json.message }}\n\nThings missed from \"standardized names\":\n{{ $json.missing_from_names?.join(', ') || \"none\" }}\n\nThings missed from \"model price\":\n{{ $json.missing_from_prices?.join(', ') || \"none\" }}"
},
"typeVersion": 1
},
{
"id": "fbfa0f9b-ae78-4051-a878-a4ef4861c057",
"name": "If not passed",
"type": "n8n-nodes-base.if",
"position": [
832,
192
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "f0b929f5-c865-4380-b0d8-ca23ab3b3674",
"operator": {
"type": "boolean",
"operation": "false",
"singleValue": true
},
"leftValue": "={{ $json.passed }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "e1692a9d-fcb3-431b-aa32-17fdb888e749",
"name": "Merge",
"type": "n8n-nodes-base.merge",
"position": [
1024,
368
],
"parameters": {},
"typeVersion": 3.2
},
{
"id": "884153b3-4920-45e3-9266-f6f151e96f6f",
"name": "Calculate cost",
"type": "n8n-nodes-base.code",
"position": [
1216,
368
],
"parameters": {
"jsCode": "const inputData = $input.all();\n\n// Get the two items from input\nconst item1 = inputData[0].json;\nconst item2 = inputData[1]?.json || {};\n\n// Merge the data from both items\nconst data = {...item1, ...item2};\n\n// Get the components\nconst llm_run_data = data.llm_run_data || {};\nconst tokensByModel = data.tokensByModel || {};\nconst model_price_dic = data.model_price_dic || {};\n\n// Deep copy and add costs to llm_run_data\nconst updated_llm_run_data = {};\nObject.keys(llm_run_data).forEach(key => {\n const item = JSON.parse(JSON.stringify(llm_run_data[key]));\n const model = item.options?.model || item.model || 'unknown';\n const prices = model_price_dic[model];\n \n if (prices && item.tokenUsage) {\n const promptTokens = item.tokenUsage.promptTokens || 0;\n const completionTokens = item.tokenUsage.completionTokens || 0;\n \n // Calculate costs (price is per million tokens)\n const input_cost = (promptTokens / 1000000) * prices.input;\n const output_cost = (completionTokens / 1000000) * prices.output;\n \n item.tokenUsage_cost = {\n input_cost: input_cost,\n output_cost: output_cost,\n total_cost: input_cost + output_cost\n };\n } else {\n item.tokenUsage_cost = {\n input_cost: 0,\n output_cost: 0,\n total_cost: 0\n };\n }\n updated_llm_run_data[key] = item;\n});\n\n// Deep copy and add costs to tokensByModel\nconst updated_tokensByModel = {};\nObject.keys(tokensByModel).forEach(model => {\n const modelData = JSON.parse(JSON.stringify(tokensByModel[model]));\n const prices = model_price_dic[model];\n \n if (prices) {\n const promptTokens = modelData.promptTokens || 0;\n const completionTokens = modelData.completionTokens || 0;\n \n // Calculate costs (price is per million tokens)\n const input_cost = (promptTokens / 1000000) * prices.input;\n const output_cost = (completionTokens / 1000000) * prices.output;\n \n modelData.input_cost = input_cost;\n modelData.output_cost = output_cost;\n modelData.total_cost = input_cost + output_cost;\n } else {\n modelData.input_cost = 0;\n modelData.output_cost = 0;\n modelData.total_cost = 0;\n }\n updated_tokensByModel[model] = modelData;\n});\n\n// Calculate total costs across all models\nlet total_input_cost = 0;\nlet total_output_cost = 0;\n\nObject.values(updated_tokensByModel).forEach(modelData => {\n total_input_cost += modelData.input_cost || 0;\n total_output_cost += modelData.output_cost || 0;\n});\n\nconst cost = {\n input_cost: total_input_cost,\n output_cost: total_output_cost,\n total_cost: total_input_cost + total_output_cost\n};\n\n// Return merged data with updates\nreturn [{\n json: {\n ...data,\n llm_run_data: updated_llm_run_data,\n tokensByModel: updated_tokensByModel,\n cost: cost\n }\n}];"
},
"typeVersion": 2
},
{
"id": "cfb7c844-4921-4d27-828d-e5792e457943",
"name": "Test id",
"type": "n8n-nodes-base.manualTrigger",
"notes": "283\n353",
"position": [
-880,
208
],
"parameters": {},
"typeVersion": 1
},
{
"id": "765452b4-d9d7-463d-b465-11296af1fc73",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1136,
80
],
"parameters": {
"color": 7,
"width": 368,
"height": 240,
"content": "### Test with execution id\n\n"
},
"typeVersion": 1
},
{
"id": "102aa26e-b208-44f2-a4a8-11a56046b546",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1120,
144
],
"parameters": {
"color": 7,
"width": 214,
"content": "Where is the execution ID?\nIn your workflow at the top center, you can see \"Executions,\" and you can select any execution to see the ID, which is a number like 323.\n"
},
"typeVersion": 1
},
{
"id": "eab0b1f1-3cb4-4b0b-913e-6c5639b17b26",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
784,
128
],
"parameters": {
"color": 7,
"width": 214,
"height": 80,
"content": "In case you did something incorrectly, you can see what models you missed to add and define"
},
"typeVersion": 1
},
{
"id": "e275b913-ea04-4c43-b799-d5fa4f31ee65",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1456,
352
],
"parameters": {
"color": 7,
"width": 246,
"height": 144,
"content": "You can do anything with this info:\n- Send a follow-up message with cost\n- Send another request to continue a process ..."
},
"typeVersion": 1
},
{
"id": "481d382a-cfc8-47cc-9ac2-89a9c3118419",
"name": "Find Nodes with LLM Use",
"type": "n8n-nodes-base.code",
"position": [
-400,
384
],
"parameters": {
"jsCode": "/**\n * n8n JavaScript Code - Simple Version\n * Extracts node execution data and LLM run data from agentic framework\n */\n\n// Get the input data - adjust this based on your n8n node configuration\nconst inputData = $input.first().json;\n\n// Navigate to runData - adjust path if needed\nconst runData = inputData.resultData?.runData || \n inputData.data?.resultData?.runData || \n inputData.runData;\n\nif (!runData) {\n return [{\n json: {\n error: \"Could not find runData in input\",\n hint: \"Check if path to runData is correct\"\n }\n }];\n}\n\n// Initialize output structures\nconst all_nodes_summary = [];\nconst llm_run_data = {};\n\n// Process each node\nObject.keys(runData).forEach(nodeName => {\n const nodeExecutions = runData[nodeName];\n \n // Process each execution in the node's array\n nodeExecutions.forEach((execution, idx) => {\n // Store node execution summary\n all_nodes_summary.push({\n nodeName: nodeName,\n arrayIndex: idx,\n startTime: execution.startTime,\n executionTime: execution.executionTime,\n executionIndex: execution.executionIndex,\n executionStatus: execution.executionStatus\n });\n \n // Helper function to find ai_languageModel at level 0 and 1\n function findAILanguageModel(obj, depth = 0) {\n const found = [];\n \n if (!obj || depth > 1) return found;\n \n // Check current level\n if (obj.ai_languageModel) {\n found.push({ path: 'level_' + depth, data: obj.ai_languageModel });\n }\n \n // Check next level if at depth 0\n if (depth === 0) {\n Object.keys(obj).forEach(key => {\n if (key !== 'ai_languageModel' && typeof obj[key] === 'object' && obj[key]) {\n if (obj[key].ai_languageModel) {\n found.push({ path: key, data: obj[key].ai_languageModel });\n }\n }\n });\n }\n \n return found;\n }\n \n // Look for ai_languageModel in data\n if (execution.data) {\n const aiModels = findAILanguageModel(execution.data);\n \n aiModels.forEach(model => {\n // ai_languageModel is list of lists\n model.data.forEach((outerList, i) => {\n if (Array.isArray(outerList)) {\n outerList.forEach((item, ii) => {\n if (item?.json) {\n const key = `llm_${nodeName}_${idx}_${i}_${ii}`;\n // Store the entire json object\n llm_run_data[key] = item.json;\n }\n });\n }\n });\n });\n }\n \n // Look for ai_languageModel in inputOverride\n if (execution.inputOverride) {\n const aiModels = findAILanguageModel(execution.inputOverride);\n \n aiModels.forEach(model => {\n // ai_languageModel is list of lists\n model.data.forEach((outerList, i) => {\n if (Array.isArray(outerList)) {\n outerList.forEach((item, ii) => {\n if (item?.json) {\n const key = `llm_${nodeName}_${idx}_${i}_${ii}_inputOverride`;\n \n // Check if we already have this from data\n const dataKey = `llm_${nodeName}_${idx}_${i}_${ii}`;\n if (llm_run_data[dataKey]) {\n // Merge options from inputOverride with data\n llm_run_data[dataKey] = {\n ...llm_run_data[dataKey],\n ...item.json\n };\n } else {\n // Add as new entry\n llm_run_data[key] = item.json;\n }\n }\n });\n }\n });\n });\n }\n });\n});\n\n// Calculate tokensByModel\nconst tokensByModel = {};\n\nObject.values(llm_run_data).forEach(item => {\n // item is the complete json object from ai_languageModel\n // Get model from options.model or directly from item\n const model = item.options?.model || item.model || 'unknown';\n \n if (!tokensByModel[model]) {\n tokensByModel[model] = {\n count: 0,\n estimatedTokens: 0,\n actualTokens: 0,\n completionTokens: 0,\n promptTokens: 0\n };\n }\n \n tokensByModel[model].count++;\n tokensByModel[model].estimatedTokens += item.estimatedTokens || 0;\n \n // Read tokenUsage correctly from json.tokenUsage dictionary\n // Structure: json.tokenUsage.totalTokens, json.tokenUsage.completionTokens, json.tokenUsage.promptTokens\n if (item.tokenUsage && typeof item.tokenUsage === 'object') {\n tokensByModel[model].actualTokens += item.tokenUsage.totalTokens || 0;\n tokensByModel[model].completionTokens += item.tokenUsage.completionTokens || 0;\n tokensByModel[model].promptTokens += item.tokenUsage.promptTokens || 0;\n }\n});\n\n// Return the extracted data\nreturn [{\n json: {\n all_nodes_summary: all_nodes_summary,\n llm_run_data: llm_run_data,\n tokensByModel: tokensByModel\n }\n}];"
},
"typeVersion": 2
},
{
"id": "86e71222-0e8c-4b07-9b10-63de24aa5335",
"name": "create models_used list",
"type": "n8n-nodes-base.code",
"position": [
-416,
144
],
"parameters": {
"jsCode": "const inputData = $input.first().json;\n\n// Handle array input\nconst data = Array.isArray(inputData) ? inputData[0] : inputData;\n\n// Get model names from tokensByModel\nconst models_used = data.tokensByModel ? Object.keys(data.tokensByModel) : [];\n\nreturn [{\n json: {\n models_used: models_used\n }\n}];"
},
"typeVersion": 2
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "f429a378-92e1-46e9-a140-5405f3c49888",
"connections": {
"Merge": {
"main": [
[
{
"node": "Calculate cost",
"type": "main",
"index": 0
}
]
]
},
"Test id": {
"main": [
[
{
"node": "Get an execution",
"type": "main",
"index": 0
}
]
]
},
"When Exc.": {
"main": [
[
{
"node": "Get an execution",
"type": "main",
"index": 0
}
]
]
},
"model prices": {
"main": [
[
{
"node": "Check correctly defined",
"type": "main",
"index": 0
}
]
]
},
"If not passed": {
"main": [
[
{
"node": "Stop and Error",
"type": "main",
"index": 0
}
],
[
{
"node": "Merge",
"type": "main",
"index": 0
}
]
]
},
"Get an execution": {
"main": [
[
{
"node": "Find Nodes with LLM Use",
"type": "main",
"index": 0
}
]
]
},
"Standardize names": {
"main": [
[
{
"node": "model prices",
"type": "main",
"index": 0
}
]
]
},
"Check correctly defined": {
"main": [
[
{
"node": "If not passed",
"type": "main",
"index": 0
}
]
]
},
"Find Nodes with LLM Use": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
},
{
"node": "create models_used list",
"type": "main",
"index": 0
}
]
]
},
"create models_used list": {
"main": [
[
{
"node": "Standardize names",
"type": "main",
"index": 0
}
]
]
}
}
}
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About this workflow
> v2: Now it can read multiple types of LLM usages. Better dynamic approach for reading model usage.
Source: https://n8n.io/workflows/7398/ — original creator credit. Request a take-down →
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