LLM追踪器
高级
这是一个Engineering, Multimodal AI领域的自动化工作流，包含 18 个节点。主要使用 If, N8n, Set, Code, Merge 等节点。 全面的LLM使用追踪器和成本监控器，带节点级分析
前置要求
- •无特殊前置要求，导入即可使用
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "7ZHcsUelwmA24Vi4",
"meta": {
"instanceId": "d1075415eaea563b4627f99a074dc98c2d4803d1e3eb41bed92c2b5dacadcd41",
"templateCredsSetupCompleted": true
},
"name": "llm-tracker",
"tags": [],
"nodes": [
{
"id": "0537d0ba-2393-492c-b61a-16ce4569b501",
"name": "获取执行",
"type": "n8n-nodes-base.n8n",
"position": [
160,
64
],
"parameters": {
"options": {
"activeWorkflows": true
},
"resource": "execution",
"operation": "get",
"executionId": "={{ $json.execution_id }}",
"requestOptions": {}
},
"credentials": {
"n8nApi": {
"id": "AeQWsukZ3B45UMnM",
"name": "n8n account"
}
},
"typeVersion": 1
},
{
"id": "952af380-bdf3-4cbe-a700-d1091bbd67bc",
"name": "当异常时",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-32,
160
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "execution_id",
"type": "number"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "e282f76e-5f44-4d09-a007-79e4443a4f9b",
"name": "提取所有模型名称",
"type": "n8n-nodes-base.code",
"position": [
464,
-48
],
"parameters": {
"jsCode": "// NODE 1: Extract All Model Names\n// Finds all unique model names in workflow execution data\n\nconst findAllModels = (data) => {\n const models = new Set();\n const execs = Array.isArray(data) ? data : [data];\n \n execs.forEach(exec => {\n const runData = exec?.data?.resultData?.runData || {};\n Object.values(runData).forEach(runs => {\n if (!Array.isArray(runs)) return;\n runs.forEach(run => {\n // Check all possible locations for model names\n const locations = [\n run.inputOverride?.ai_languageModel?.[0]?.[0]?.json?.options?.model,\n run.data?.ai_languageModel?.[0]?.[0]?.json?.options?.model,\n run.parameters?.model?.value,\n run.parameters?.model,\n run.options?.model\n ];\n \n locations.forEach(model => {\n if (model && typeof model === 'string') {\n models.add(model);\n }\n });\n });\n });\n });\n \n return Array.from(models);\n};\n\n// Process input and collect all unique models\nconst allModels = new Set();\n\nfor (const item of $input.all()) {\n const models = findAllModels(item.json);\n models.forEach(model => allModels.add(model));\n}\n\n// Return single item with models_used list\nreturn [{\n json: {\n models_used: Array.from(allModels),\n model_count: allModels.size,\n original_data: $input.all()[0].json\n }\n}];"
},
"typeVersion": 2
},
{
"id": "cc779a62-e23d-4bb9-abf7-9ef1791c1edd",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
608,
-336
],
"parameters": {
"width": 352,
"height": 304,
"content": "### 由用户定义"
},
"typeVersion": 1
},
{
"id": "40946ad8-f191-4d44-bca4-06853337a380",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1008,
-128
],
"parameters": {
"color": 3,
"width": 416,
"height": 240,
"content": "### 检查所有模型名称是否正确定义"
},
"typeVersion": 1
},
{
"id": "d4f77e57-f872-47e9-a49f-307f0da3aa50",
"name": "模型价格",
"type": "n8n-nodes-base.set",
"position": [
816,
-176
],
"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": "8334bc61-e44f-4dc2-91c0-519b64a3984f",
"name": "标准化名称",
"type": "n8n-nodes-base.set",
"position": [
656,
-176
],
"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 }\n}\n",
"includeFields": "models_used",
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "695df344-56fb-4bdc-8822-18240b214d42",
"name": "检查正确定义",
"type": "n8n-nodes-base.code",
"position": [
1072,
-64
],
"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": "a871ea02-82f7-420f-960c-bd3fcefa2512",
"name": "停止并报错",
"type": "n8n-nodes-base.stopAndError",
"position": [
1456,
-112
],
"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": "a8ef1a53-5ac4-4e63-8dd5-f6c3d5d58eaf",
"name": "如果未通过",
"type": "n8n-nodes-base.if",
"position": [
1264,
-64
],
"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": "c5f981cd-9949-4f66-be79-49b0fd7c5997",
"name": "合并",
"type": "n8n-nodes-base.merge",
"position": [
1536,
112
],
"parameters": {},
"typeVersion": 3.2
},
{
"id": "1b891bd1-bbe3-4e05-bb1f-ac77ccc70a7b",
"name": "智能提取 LLM 数据",
"type": "n8n-nodes-base.code",
"position": [
448,
128
],
"parameters": {
"jsCode": "// NODE 3: Extract LLM Usage (without cost calculation and summary)\n\nconst extractLLMUsageData = (data) => {\n const execs = Array.isArray(data) ? data : [data];\n const usages = [];\n \n execs.forEach(exec => {\n const runData = exec?.data?.resultData?.runData || {};\n const workflowName = exec?.workflowData?.name || 'unknown';\n const workflowId = exec?.workflowId || 'unknown';\n const executionId = exec?.id || 'unknown';\n const createdAt = exec?.createdAt || null;\n const executionStatus = exec?.status || 'unknown';\n \n Object.entries(runData).forEach(([node, runs]) => {\n if (!Array.isArray(runs)) return;\n runs.forEach(run => {\n // Find token usage in multiple locations\n let tokens = run.data?.ai_languageModel?.[0]?.[0]?.json?.tokenUsage || \n run.tokenUsage || null;\n \n if (tokens?.promptTokens || tokens?.completionTokens) {\n // Extract comprehensive model info\n const inputOverride = run.inputOverride?.ai_languageModel?.[0]?.[0]?.json || {};\n const options = inputOverride.options || {};\n let model = options.model || 'unknown';\n \n // Extract the actual messages/prompts if available\n const messages = inputOverride.messages || [];\n const promptPreview = messages.length > 0 ? \n messages[0].substring(0, 100) + (messages[0].length > 100 ? '...' : '') : '';\n \n // Extract response info\n const response = run.data?.ai_languageModel?.[0]?.[0]?.json?.response?.generations?.[0]?.[0] || {};\n const finishReason = response.generationInfo?.finish_reason || 'unknown';\n const responsePreview = response.text ? \n response.text.substring(0, 100) + (response.text.length > 100 ? '...' : '') : '';\n \n // Extract execution metadata\n const startTime = run.startTime || null;\n const executionIndex = run.executionIndex || 0;\n const nodeExecutionStatus = run.executionStatus || 'unknown';\n \n // Extract previous nodes chain\n const previousNodes = [];\n if (run.source && Array.isArray(run.source)) {\n run.source.forEach(src => {\n if (src.previousNode) previousNodes.push(src.previousNode);\n });\n }\n \n // Extract temperature and other model parameters\n const temperature = options.temperature || options.model_kwargs?.temperature || null;\n const maxTokens = options.max_tokens || options.maxTokens || null;\n const timeout = options.timeout || null;\n const maxRetries = options.max_retries || 0;\n \n // Extract any session/memory IDs\n const sessionId = run.metadata?.sessionId || null;\n \n usages.push({\n // Execution context\n workflowName,\n workflowId,\n executionId,\n createdAt,\n executionStatus,\n \n // Node info\n node,\n nodeExecutionStatus,\n executionIndex,\n \n // Model & tokens\n model,\n promptTokens: tokens.promptTokens || 0,\n completionTokens: tokens.completionTokens || 0,\n totalTokens: tokens.totalTokens || 0,\n \n // Performance\n executionTime: run.executionTime || 0,\n startTime,\n \n // Model parameters\n temperature,\n maxTokens,\n timeout,\n maxRetries,\n finishReason,\n \n // Context\n previousNodes: previousNodes.join(' → ') || 'Start',\n sessionId,\n \n // Content previews (useful for debugging)\n promptPreview,\n responsePreview,\n \n // Estimated tokens if not provided\n estimatedTokens: inputOverride.estimatedTokens || null\n });\n }\n });\n });\n });\n return usages;\n};\n\n// Process each input item\nconst results = [];\nfor (const item of $input.all()) {\n const llmUsages = extractLLMUsageData(item.json);\n \n // Add each usage as a separate output item\n llmUsages.forEach(usage => {\n results.push({\n json: usage\n });\n });\n \n // If no LLM usage found, still return the execution info\n if (llmUsages.length === 0) {\n results.push({\n json: {\n executionId: item.json.id || 'unknown',\n workflowId: item.json.workflowId || 'unknown',\n message: 'No LLM usage detected in this execution',\n totalTokens: 0\n }\n });\n }\n}\n\nreturn results;"
},
"typeVersion": 2
},
{
"id": "e731a100-392b-4d50-bf9f-ed73863ec6d5",
"name": "计算成本",
"type": "n8n-nodes-base.code",
"position": [
1744,
112
],
"parameters": {
"jsCode": "// NODE 4: Calculate Costs and Add Summary\n\n// Get dictionaries from first item and LLM usages from rest\nconst configItem = $input.all().find(item => item.json.passed === true) || $input.first();\nconst standardize_names_dic = configItem.json.standardize_names_dic || {};\nconst model_price_dic = configItem.json.model_price_dic || {};\n\n// Filter out config item and get only LLM usage items\nconst llmUsages = $input.all().filter(item => \n item.json.promptTokens !== undefined && \n item.json.completionTokens !== undefined\n);\n\n// Process each usage with costs\nconst results = llmUsages.map(item => {\n const usage = item.json;\n \n // Standardize model name\n const standardModel = standardize_names_dic[usage.model] || usage.model;\n \n // Get pricing (prices are per 1M tokens with \"input\"/\"output\" keys)\n const pricing = model_price_dic[standardModel] || { input: 0, output: 0 };\n \n // Calculate costs (divide by 1,000,000 since prices are per million)\n const promptCost = (usage.promptTokens / 1000000) * pricing.input;\n const completionCost = (usage.completionTokens / 1000000) * pricing.output;\n const totalCost = promptCost + completionCost;\n \n return {\n json: {\n ...usage,\n standardModel,\n promptCostUSD: promptCost.toFixed(8),\n completionCostUSD: completionCost.toFixed(8),\n totalCostUSD: totalCost.toFixed(8),\n pricePerMPrompt: pricing.input,\n pricePerMCompletion: pricing.output\n }\n };\n});\n\n// Calculate summary statistics if we have results\nif (results.length > 0) {\n const summary = {\n isSummary: true,\n totalExecutions: results.length,\n totalPromptTokens: 0,\n totalCompletionTokens: 0,\n totalTokens: 0,\n totalPromptCostUSD: 0,\n totalCompletionCostUSD: 0,\n totalCostUSD: 0,\n totalExecutionTimeMs: 0,\n byModel: {},\n byNode: {}\n };\n \n // Calculate totals\n results.forEach(r => {\n summary.totalPromptTokens += r.json.promptTokens;\n summary.totalCompletionTokens += r.json.completionTokens;\n summary.totalTokens += r.json.totalTokens;\n summary.totalPromptCostUSD += parseFloat(r.json.promptCostUSD);\n summary.totalCompletionCostUSD += parseFloat(r.json.completionCostUSD);\n summary.totalCostUSD += parseFloat(r.json.totalCostUSD);\n summary.totalExecutionTimeMs += r.json.executionTime;\n \n // Group by model\n const model = r.json.standardModel;\n if (!summary.byModel[model]) {\n summary.byModel[model] = {\n count: 0,\n promptTokens: 0,\n completionTokens: 0,\n totalTokens: 0,\n totalCostUSD: 0\n };\n }\n summary.byModel[model].count++;\n summary.byModel[model].promptTokens += r.json.promptTokens;\n summary.byModel[model].completionTokens += r.json.completionTokens;\n summary.byModel[model].totalTokens += r.json.totalTokens;\n summary.byModel[model].totalCostUSD += parseFloat(r.json.totalCostUSD);\n \n // Group by node\n const node = r.json.node;\n if (!summary.byNode[node]) {\n summary.byNode[node] = {\n count: 0,\n totalTokens: 0,\n totalCostUSD: 0\n };\n }\n summary.byNode[node].count++;\n summary.byNode[node].totalTokens += r.json.totalTokens;\n summary.byNode[node].totalCostUSD += parseFloat(r.json.totalCostUSD);\n });\n \n // Format costs\n summary.totalPromptCostUSD = summary.totalPromptCostUSD.toFixed(8);\n summary.totalCompletionCostUSD = summary.totalCompletionCostUSD.toFixed(8);\n summary.totalCostUSD = summary.totalCostUSD.toFixed(8);\n summary.avgCostPerCall = (parseFloat(summary.totalCostUSD) / summary.totalExecutions).toFixed(8);\n summary.avgTokensPerCall = Math.round(summary.totalTokens / summary.totalExecutions);\n \n // Format byModel costs\n Object.keys(summary.byModel).forEach(model => {\n summary.byModel[model].totalCostUSD = summary.byModel[model].totalCostUSD.toFixed(8);\n });\n \n // Format byNode costs\n Object.keys(summary.byNode).forEach(node => {\n summary.byNode[node].totalCostUSD = summary.byNode[node].totalCostUSD.toFixed(8);\n });\n \n // Add summary as last item\n results.push({ json: summary });\n}\n\nreturn results.length > 0 ? results : [{ \n json: { \n message: \"No LLM usage data found to calculate costs\" \n } \n}];"
},
"typeVersion": 2
},
{
"id": "77863173-ae06-485d-97c1-640aebe882e2",
"name": "测试 ID",
"type": "n8n-nodes-base.manualTrigger",
"notes": "283\n353",
"position": [
-32,
-48
],
"parameters": {},
"typeVersion": 1
},
{
"id": "ae66032f-a95f-4d09-afd5-1411fc463aea",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-288,
-176
],
"parameters": {
"color": 7,
"width": 368,
"height": 240,
"content": "### 使用执行 ID 进行测试"
},
"typeVersion": 1
},
{
"id": "c1f59812-a50c-4ce3-97b2-43d94e5cfb01",
"name": "便签说明4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-272,
-112
],
"parameters": {
"color": 7,
"width": 214,
"content": "执行 ID 在哪里？"
},
"typeVersion": 1
},
{
"id": "aacdc959-13f5-4df1-a883-968829231408",
"name": "便签说明5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1632,
-128
],
"parameters": {
"color": 7,
"width": 214,
"height": 80,
"content": "如果您操作有误，您可以查看遗漏了哪些模型需要添加和定义"
},
"typeVersion": 1
},
{
"id": "c7e046e2-431b-46aa-a70a-4797b2abff06",
"name": "便签 6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1936,
96
],
"parameters": {
"color": 7,
"width": 214,
"height": 144,
"content": "您可以用这些信息做任何事情："
},
"typeVersion": 1
}
],
"active": false,
"pinData": {
"Test id": [
{
"json": {
"execution_id": 353
}
}
]
},
"settings": {
"executionOrder": "v1"
},
"versionId": "e2a12056-bcac-48b9-b43f-c76ed22c19e3",
"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": "Extract all model names",
"type": "main",
"index": 0
},
{
"node": "Smart Extract LLM data",
"type": "main",
"index": 0
}
]
]
},
"Standardize names": {
"main": [
[
{
"node": "model prices",
"type": "main",
"index": 0
}
]
]
},
"Smart Extract LLM data": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"Check correctly defined": {
"main": [
[
{
"node": "If not passed",
"type": "main",
"index": 0
}
]
]
},
"Extract all model names": {
"main": [
[
{
"node": "Standardize names",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
高级 - 工程, 多模态 AI
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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工作流信息
难度等级
高级
节点数量18
分类2
节点类型9
作者
Amir Safavi-Naini
@amirsafavi🔬Postdoc exploring AI to revolutionize patient care (without breaking the bank) 👨🦯Teaching docs AI and nerds medicine: the amnesiac's guide to healthy AI
外部链接
在 n8n.io 查看 →
分享此工作流