Track LLM Costs and Usage Across Openai, Anthropic, Google and More

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 →

{
  "id": "4M68K9KGz1RzEj94",
  "meta": {
    "templateCredsSetupCompleted": true
  },
  "name": "LLM Cost Monitor - AI Usage Tracker",
  "tags": [],
  "nodes": [
    {
      "id": "trigger-1",
      "name": "When Called By Another Workflow",
      "type": "n8n-nodes-base.executeWorkflowTrigger",
      "position": [
        64,
        368
      ],
      "parameters": {
        "inputSource": "passthrough"
      },
      "typeVersion": 1.1
    },
    {
      "id": "trigger-2",
      "name": "Test with Execution ID",
      "type": "n8n-nodes-base.code",
      "disabled": true,
      "position": [
        80,
        160
      ],
      "parameters": {
        "jsCode": "// Manual test trigger - paste an execution ID here to test\nconst testExecutionId = 'PASTE_EXECUTION_ID_HERE';\n\nreturn [{ json: { executionId: testExecutionId } }];"
      },
      "typeVersion": 2
    },
    {
      "id": "extract-id",
      "name": "Extract Execution ID",
      "type": "n8n-nodes-base.set",
      "position": [
        224,
        304
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "exec-id",
              "name": "executionId",
              "type": "string",
              "value": "={{ $json.executionId || $json.body?.executionId || $json.query?.executionId || $execution.id }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "get-exec",
      "name": "Get Execution Data",
      "type": "n8n-nodes-base.n8n",
      "position": [
        448,
        304
      ],
      "parameters": {
        "data": "eyJ_YOUR_JWT_TOKEN_HERE",
        "name": "n8n",
        "resource": "credential",
        "requestOptions": {},
        "credentialTypeName": "n8nApi"
      },
      "credentials": {
        "n8nApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "extract-usage",
      "name": "Extract Token Usage",
      "type": "n8n-nodes-base.code",
      "position": [
        672,
        208
      ],
      "parameters": {
        "jsCode": "// ============================================================\n// EXTRACT TOKEN USAGE - Deep recursive extraction\n// Finds ALL LLM token usage from any nesting level\n// ============================================================\n\nconst executionData = $input.first().json;\nconst workflowName = executionData.workflowData?.name || 'Unknown Workflow';\nconst workflowId = executionData.workflowData?.id || 'unknown';\nconst executionId = executionData.id || 'unknown';\nconst executionStatus = executionData.status || executionData.finished ? 'success' : 'error';\n\nconst llmCalls = [];\n\nfunction extractTokenUsage(obj, nodeName, nodeType, path, depth) {\n  if (!obj || typeof obj !== 'object' || depth > 20) return;\n  \n  // Check for token usage patterns\n  if (obj.tokenUsage || obj.usage) {\n    const usage = obj.tokenUsage || obj.usage;\n    const promptTokens = usage.promptTokens || usage.prompt_tokens || usage.input_tokens || 0;\n    const completionTokens = usage.completionTokens || usage.completion_tokens || usage.output_tokens || 0;\n    const totalTokens = usage.totalTokens || usage.total_tokens || (promptTokens + completionTokens);\n    \n    if (totalTokens > 0) {\n      const model = obj.model || obj.response?.model || obj.modelName || '';\n      const finishReason = obj.finish_reason || obj.response?.choices?.[0]?.finish_reason || obj.finishReason || '';\n      const executionTime = obj.executionTime || obj.execution_time || 0;\n      const startTime = obj.startTime || obj.start_time || '';\n      \n      // Get content preview\n      let outputPreview = '';\n      try {\n        const content = obj.response?.choices?.[0]?.message?.content || obj.text || obj.output || obj.content || '';\n        outputPreview = String(content).substring(0, 100);\n      } catch(e) {}\n      \n      let inputPreview = '';\n      try {\n        const input = obj.prompt || obj.input || obj.messages?.[0]?.content || '';\n        inputPreview = String(input).substring(0, 100);\n      } catch(e) {}\n      \n      // Get model parameters\n      const temperature = obj.temperature ?? obj.options?.temperature ?? null;\n      const maxTokens = obj.maxTokens || obj.max_tokens || obj.options?.maxTokens || null;\n      const timeout = obj.timeout || obj.options?.timeout || null;\n      const retryCount = obj.retryCount || obj.options?.retryCount || null;\n      \n      llmCalls.push({\n        model: model,\n        nodeName: nodeName,\n        nodeType: nodeType,\n        promptTokens: promptTokens,\n        completionTokens: completionTokens,\n        totalTokens: totalTokens,\n        finishReason: finishReason,\n        executionTime: executionTime,\n        startTime: startTime,\n        outputPreview: outputPreview,\n        inputPreview: inputPreview,\n        temperature: temperature,\n        maxTokens: maxTokens,\n        timeout: timeout,\n        retryCount: retryCount,\n        path: path,\n        workflowName: workflowName,\n        workflowId: workflowId,\n        executionId: executionId,\n        executionStatus: executionStatus\n      });\n      return; // Don't recurse further into this branch\n    }\n  }\n  \n  // Recurse into arrays and objects\n  if (Array.isArray(obj)) {\n    obj.forEach((item, i) => extractTokenUsage(item, nodeName, nodeType, `${path}[${i}]`, depth + 1));\n  } else {\n    for (const key of Object.keys(obj)) {\n      if (key === 'binary' || key === 'pairedItem') continue;\n      extractTokenUsage(obj[key], nodeName, nodeType, `${path}.${key}`, depth + 1);\n    }\n  }\n}\n\n// Process all nodes in the execution\nconst runData = executionData.data?.resultData?.runData || {};\n\nfor (const [nodeName, nodeRuns] of Object.entries(runData)) {\n  if (!Array.isArray(nodeRuns)) continue;\n  \n  for (const run of nodeRuns) {\n    const nodeType = run.source?.[0]?.previousNode || '';\n    const actualNodeType = executionData.workflowData?.nodes?.find(n => n.name === nodeName)?.type || '';\n    \n    // Search in main data\n    if (run.data?.main) {\n      for (const outputSet of run.data.main) {\n        if (!outputSet) continue;\n        for (const item of outputSet) {\n          extractTokenUsage(item.json || item, nodeName, actualNodeType, nodeName, 0);\n        }\n      }\n    }\n    \n    // Search in inputOverride (for sub-nodes like LLM chains)\n    if (run.inputOverride) {\n      extractTokenUsage(run.inputOverride, nodeName, actualNodeType, `${nodeName}.inputOverride`, 0);\n    }\n  }\n}\n\nif (llmCalls.length === 0) {\n  return [{ json: { error: 'No LLM calls detected in this execution', workflowName, executionId, executionStatus } }];\n}\n\nreturn llmCalls.map(call => ({ json: call }));"
      },
      "typeVersion": 2
    },
    {
      "id": "find-nodes",
      "name": "Find Nodes with LLM Data",
      "type": "n8n-nodes-base.code",
      "position": [
        672,
        400
      ],
      "parameters": {
        "jsCode": "// ============================================================\n// FIND NODES WITH LLM DATA - Identifies all LLM-related nodes\n// Provides node-level metadata for analytics\n// ============================================================\n\nconst executionData = $input.first().json;\nconst workflowNodes = executionData.workflowData?.nodes || [];\nconst runData = executionData.data?.resultData?.runData || {};\nconst connections = executionData.workflowData?.connections || {};\n\nconst llmNodeTypes = [\n  '@n8n/n8n-nodes-langchain.lmChatOpenAi',\n  '@n8n/n8n-nodes-langchain.lmChatAnthropic',\n  '@n8n/n8n-nodes-langchain.lmChatGoogleGemini',\n  '@n8n/n8n-nodes-langchain.lmChatOllama',\n  '@n8n/n8n-nodes-langchain.lmChatAzureOpenAi',\n  '@n8n/n8n-nodes-langchain.lmChatMistralCloud',\n  '@n8n/n8n-nodes-langchain.lmChatGroq',\n  '@n8n/n8n-nodes-langchain.lmChatDeepSeek',\n  '@n8n/n8n-nodes-langchain.lmChatHuggingFace',\n  '@n8n/n8n-nodes-langchain.lmChatAwsBedrock',\n  '@n8n/n8n-nodes-langchain.agent',\n  '@n8n/n8n-nodes-langchain.chainLlm',\n  '@n8n/n8n-nodes-langchain.chainSummarization',\n  '@n8n/n8n-nodes-langchain.chainRetrievalQa',\n  '@n8n/n8n-nodes-langchain.openAi',\n  'n8n-nodes-base.openAi'\n];\n\nconst nodeDetails = [];\n\nfor (const node of workflowNodes) {\n  const isLLMNode = llmNodeTypes.some(t => node.type?.includes(t)) || \n                    node.type?.toLowerCase().includes('llm') ||\n                    node.type?.toLowerCase().includes('openai') ||\n                    node.type?.toLowerCase().includes('anthropic') ||\n                    node.type?.toLowerCase().includes('gemini') ||\n                    node.type?.toLowerCase().includes('agent');\n  \n  if (isLLMNode || runData[node.name]) {\n    // Find previous nodes chain\n    const prevNodes = [];\n    function findPrevious(nodeName, depth) {\n      if (depth > 10) return;\n      for (const [connName, connData] of Object.entries(connections)) {\n        if (!connData.main) continue;\n        for (const outputs of connData.main) {\n          if (!outputs) continue;\n          for (const conn of outputs) {\n            if (conn.node === nodeName) {\n              prevNodes.push(connName);\n              findPrevious(connName, depth + 1);\n            }\n          }\n        }\n      }\n    }\n    findPrevious(node.name, 0);\n    \n    const nodeRun = runData[node.name];\n    const executionTime = nodeRun?.[0]?.executionTime || 0;\n    const startTime = nodeRun?.[0]?.startTime || '';\n    \n    nodeDetails.push({\n      nodeName: node.name,\n      nodeType: node.type,\n      isLLMNode: isLLMNode,\n      executionTime: executionTime,\n      startTime: startTime,\n      previousNodes: prevNodes.reverse().join(' \u2192 '),\n      position: node.position\n    });\n  }\n}\n\nreturn nodeDetails.map(d => ({ json: d }));"
      },
      "typeVersion": 2
    },
    {
      "id": "standardize",
      "name": "Standardize Names",
      "type": "n8n-nodes-base.code",
      "position": [
        880,
        208
      ],
      "parameters": {
        "jsCode": "// ============================================================\n// STANDARDIZE MODEL NAMES\n// Maps raw API model names to canonical names\n// Covers 120+ model variations across 10 providers\n// ============================================================\n\nconst items = $input.all();\n\n// If this is an error item (no LLM calls found), pass through\nif (items.length === 1 && items[0].json.error) {\n  return items;\n}\n\nconst standardize_names_dic = {\n  // ===== OpenAI GPT-5.x Family =====\n  'gpt-5.4': 'gpt-5.4',\n  'gpt-5.4-mini': 'gpt-5.4-mini',\n  'gpt-5.4-nano': 'gpt-5.4-nano',\n  'gpt-5.4-pro': 'gpt-5.4-pro',\n  'gpt-5.3-chat-latest': 'gpt-5.3',\n  'gpt-5.3-codex': 'gpt-5.3-codex',\n  'gpt-5.3': 'gpt-5.3',\n  'gpt-5.2': 'gpt-5.2',\n  'gpt-5.2-0301': 'gpt-5.2',\n  'gpt-5': 'gpt-5',\n  'gpt-5-0125': 'gpt-5',\n  'gpt-5-mini': 'gpt-5-mini',\n  'gpt-5-mini-0125': 'gpt-5-mini',\n  'gpt-5-nano': 'gpt-5-nano',\n\n  // ===== OpenAI GPT-4.1 Family =====\n  'gpt-4.1': 'gpt-4.1',\n  'gpt-4.1-2025-04-14': 'gpt-4.1',\n  'gpt-4.1-mini': 'gpt-4.1-mini',\n  'gpt-4.1-mini-2025-04-14': 'gpt-4.1-mini',\n  'gpt-4.1-nano': 'gpt-4.1-nano',\n  'gpt-4.1-nano-2025-04-14': 'gpt-4.1-nano',\n\n  // ===== OpenAI GPT-4o Family =====\n  'gpt-4o': 'gpt-4o',\n  'gpt-4o-2024-05-13': 'gpt-4o',\n  'gpt-4o-2024-08-06': 'gpt-4o',\n  'gpt-4o-2024-11-20': 'gpt-4o',\n  'chatgpt-4o-latest': 'gpt-4o',\n  'gpt-4o-mini': 'gpt-4o-mini',\n  'gpt-4o-mini-2024-07-18': 'gpt-4o-mini',\n  'gpt-4o-audio-preview': 'gpt-4o',\n  'gpt-4o-realtime-preview': 'gpt-4o',\n  'gpt-4o-transcribe': 'gpt-4o-transcribe',\n  'gpt-4o-mini-transcribe': 'gpt-4o-mini-transcribe',\n\n  // ===== OpenAI GPT-4 Family =====\n  'gpt-4': 'gpt-4',\n  'gpt-4-0613': 'gpt-4',\n  'gpt-4-0314': 'gpt-4',\n  'gpt-4-32k': 'gpt-4-32k',\n  'gpt-4-32k-0613': 'gpt-4-32k',\n  'gpt-4-turbo': 'gpt-4-turbo',\n  'gpt-4-turbo-2024-04-09': 'gpt-4-turbo',\n  'gpt-4-turbo-preview': 'gpt-4-turbo',\n  'gpt-4-1106-preview': 'gpt-4-turbo',\n  'gpt-4-0125-preview': 'gpt-4-turbo',\n  'gpt-4-vision-preview': 'gpt-4-turbo',\n\n  // ===== OpenAI GPT-3.5 Family =====\n  'gpt-3.5-turbo': 'gpt-3.5-turbo',\n  'gpt-3.5-turbo-0125': 'gpt-3.5-turbo',\n  'gpt-3.5-turbo-1106': 'gpt-3.5-turbo',\n  'gpt-3.5-turbo-0613': 'gpt-3.5-turbo',\n  'gpt-3.5-turbo-16k': 'gpt-3.5-turbo',\n  'gpt-3.5-turbo-16k-0613': 'gpt-3.5-turbo',\n  'gpt-3.5-turbo-instruct': 'gpt-3.5-turbo',\n\n  // ===== OpenAI o-series (Reasoning) =====\n  'o1': 'o1',\n  'o1-2024-12-17': 'o1',\n  'o1-preview': 'o1',\n  'o1-preview-2024-09-12': 'o1',\n  'o1-mini': 'o1-mini',\n  'o1-mini-2024-09-12': 'o1-mini',\n  'o1-pro': 'o1-pro',\n  'o3': 'o3',\n  'o3-2025-04-16': 'o3',\n  'o3-mini': 'o3-mini',\n  'o3-mini-2025-01-31': 'o3-mini',\n  'o3-pro': 'o3-pro',\n  'o3-deep-research': 'o3-deep-research',\n  'o4-mini': 'o4-mini',\n  'o4-mini-2025-04-16': 'o4-mini',\n  'o4-mini-deep-research': 'o4-mini-deep-research',\n\n  // ===== OpenAI Specialized =====\n  'computer-use-preview': 'computer-use-preview',\n  'gpt-oss-120b': 'gpt-oss-120b',\n  'gpt-oss-20b': 'gpt-oss-20b',\n  'gpt-realtime-1.5': 'gpt-realtime-1.5',\n  'gpt-realtime-mini': 'gpt-realtime-mini',\n  'gpt-image-1.5': 'gpt-image-1.5',\n  'gpt-image-1-mini': 'gpt-image-1-mini',\n\n  // ===== Anthropic Claude 4.x =====\n  'claude-sonnet-4-6': 'claude-sonnet-4-6',\n  'claude-sonnet-4-6-20260201': 'claude-sonnet-4-6',\n  'claude-opus-4-6': 'claude-opus-4-6',\n  'claude-opus-4-6-20260201': 'claude-opus-4-6',\n  'claude-opus-4-5': 'claude-opus-4-5',\n  'claude-opus-4-5-20250520': 'claude-opus-4-5',\n  'claude-sonnet-4-5': 'claude-sonnet-4-5',\n  'claude-sonnet-4-5-20250514': 'claude-sonnet-4-5',\n  'claude-opus-4': 'claude-opus-4',\n  'claude-opus-4-20250514': 'claude-opus-4',\n  'claude-sonnet-4': 'claude-sonnet-4',\n  'claude-sonnet-4-20250514': 'claude-sonnet-4',\n  'claude-haiku-4.5': 'claude-haiku-4.5',\n  'claude-haiku-4-5-20250514': 'claude-haiku-4.5',\n\n  // ===== Anthropic Claude 3.x =====\n  'claude-3-7-sonnet-latest': 'claude-sonnet-3.7',\n  'claude-3-7-sonnet-20250219': 'claude-sonnet-3.7',\n  'claude-sonnet-3.7': 'claude-sonnet-3.7',\n  'claude-3-5-sonnet-latest': 'claude-sonnet-3.5',\n  'claude-3-5-sonnet-20241022': 'claude-sonnet-3.5',\n  'claude-3-5-sonnet-20240620': 'claude-sonnet-3.5',\n  'claude-sonnet-3.5': 'claude-sonnet-3.5',\n  'claude-3-5-haiku-latest': 'claude-haiku-3.5',\n  'claude-3-5-haiku-20241022': 'claude-haiku-3.5',\n  'claude-haiku-3.5': 'claude-haiku-3.5',\n  'claude-3-opus-latest': 'claude-opus-3',\n  'claude-3-opus-20240229': 'claude-opus-3',\n  'claude-opus-3': 'claude-opus-3',\n  'claude-3-sonnet-20240229': 'claude-sonnet-3',\n  'claude-3-haiku-20240307': 'claude-haiku-3',\n  'claude-haiku-3': 'claude-haiku-3',\n\n  // ===== Google Gemini 3.x =====\n  'gemini-3.1-pro-preview': 'gemini-3.1-pro',\n  'gemini-3.1-flash-lite-preview': 'gemini-3.1-flash-lite',\n  'gemini-3-pro-preview': 'gemini-3-pro',\n  'gemini-3-flash-preview': 'gemini-3-flash',\n\n  // ===== Google Gemini 2.x =====\n  'gemini-2.5-pro': 'gemini-2.5-pro',\n  'gemini-2.5-pro-latest': 'gemini-2.5-pro',\n  'gemini-2.5-pro-preview-0325': 'gemini-2.5-pro',\n  'gemini-2.5-flash': 'gemini-2.5-flash',\n  'gemini-2.5-flash-latest': 'gemini-2.5-flash',\n  'gemini-2.5-flash-preview-04-17': 'gemini-2.5-flash',\n  'gemini-2.5-flash-lite': 'gemini-2.5-flash-lite',\n  'gemini-2.0-flash': 'gemini-2.0-flash',\n  'gemini-2.0-flash-exp': 'gemini-2.0-flash',\n  'gemini-2.0-flash-lite': 'gemini-2.0-flash-lite',\n  'gemini-2.0-flash-thinking-exp': 'gemini-2.0-flash',\n\n  // ===== Google Gemini 1.x =====\n  'gemini-1.5-pro': 'gemini-1.5-pro',\n  'gemini-1.5-pro-latest': 'gemini-1.5-pro',\n  'gemini-1.5-pro-002': 'gemini-1.5-pro',\n  'gemini-1.5-flash': 'gemini-1.5-flash',\n  'gemini-1.5-flash-latest': 'gemini-1.5-flash',\n  'gemini-1.5-flash-002': 'gemini-1.5-flash',\n  'gemini-1.0-pro': 'gemini-1.0-pro',\n  'gemini-pro': 'gemini-1.0-pro',\n\n  // ===== DeepSeek =====\n  'deepseek-v3.2': 'deepseek-v3.2',\n  'deepseek-v3.1': 'deepseek-v3.1',\n  'deepseek-v3.1-terminus': 'deepseek-v3.1-terminus',\n  'deepseek-v3': 'deepseek-v3',\n  'deepseek-v3-turbo': 'deepseek-v3-turbo',\n  'deepseek-chat': 'deepseek-v3',\n  'deepseek-r1': 'deepseek-r1',\n  'deepseek-r1-turbo': 'deepseek-r1-turbo',\n  'deepseek-r1-distill-llama-70b': 'deepseek-r1-distill-70b',\n  'deepseek-reasoner': 'deepseek-r1',\n  'deepseek-prover-v2': 'deepseek-prover-v2',\n  'deepseek-ocr-2': 'deepseek-ocr-2',\n  'deepseek-coder': 'deepseek-v3',\n\n  // ===== Meta Llama =====\n  'llama-4-scout': 'llama-4-scout',\n  'llama-4-maverick': 'llama-4-maverick',\n  'llama-3.3-70b': 'llama-3.3-70b',\n  'llama-3.3-70b-instruct': 'llama-3.3-70b',\n  'llama-3.2-90b-vision': 'llama-3.2-90b-vision',\n  'llama-3.2-90b-vision-instruct': 'llama-3.2-90b-vision',\n  'llama-3.2-11b-vision': 'llama-3.2-11b-vision',\n  'llama-3.2-11b-vision-instruct': 'llama-3.2-11b-vision',\n  'llama-3.1-405b-instruct': 'llama-3.1-405b',\n  'llama-3.1-70b-instruct': 'llama-3.1-70b',\n  'llama-3.1-8b-instruct': 'llama-3.1-8b',\n  'llama-3.1-8b': 'llama-3.1-8b',\n  'llama-3-70b': 'llama-3-70b',\n  'llama-3-70b-instruct': 'llama-3-70b',\n  'llama-3-8b': 'llama-3-8b',\n  'llama-3-8b-instruct': 'llama-3-8b',\n  'meta-llama/llama-3-70b-instruct': 'llama-3-70b',\n  'meta-llama/llama-3.1-8b-instruct': 'llama-3.1-8b',\n  'meta-llama/llama-3.3-70b-instruct': 'llama-3.3-70b',\n\n  // ===== Mistral =====\n  'magistral-medium': 'magistral-medium',\n  'magistral-medium-latest': 'magistral-medium',\n  'magistral-small': 'magistral-small',\n  'magistral-small-latest': 'magistral-small',\n  'mistral-medium-3': 'mistral-medium-3',\n  'mistral-medium-latest': 'mistral-medium-3',\n  'mistral-large': 'mistral-large',\n  'mistral-large-latest': 'mistral-large',\n  'mistral-large-2411': 'mistral-large',\n  'mistral-small-3.2': 'mistral-small-3.2',\n  'mistral-small-latest': 'mistral-small-3.2',\n  'mistral-nemo': 'mistral-nemo',\n  'open-mistral-nemo': 'mistral-nemo',\n  'codestral': 'codestral',\n  'codestral-latest': 'codestral',\n  'devstral-medium': 'devstral-medium',\n  'devstral-small': 'devstral-small',\n  'pixtral-large': 'pixtral-large',\n  'pixtral-large-latest': 'pixtral-large',\n  'pixtral-12b': 'pixtral-12b',\n  'pixtral-12b-2409': 'pixtral-12b',\n\n  // ===== xAI Grok =====\n  'grok-4-0709': 'grok-4',\n  'grok-4': 'grok-4',\n  'grok-4-1-fast-non-reasoning': 'grok-4.1-fast',\n  'grok-4-1-fast-reasoning': 'grok-4.1-fast',\n  'grok-4-fast-non-reasoning': 'grok-4-fast',\n  'grok-4-fast-reasoning': 'grok-4-fast',\n  'grok-3': 'grok-3',\n  'grok-3-latest': 'grok-3',\n  'grok-3-mini': 'grok-3-mini',\n  'grok-3-mini-latest': 'grok-3-mini',\n  'grok-code-fast-1': 'grok-code-fast-1',\n  'grok-2': 'grok-2',\n  'grok-2-latest': 'grok-2',\n  'grok-beta': 'grok-2',\n\n  // ===== Cohere =====\n  'command-a-03-2025': 'command-a',\n  'command-r-08-2024': 'command-r',\n  'command-r-plus-08-2024': 'command-r-plus',\n  'command-r7b-12-2024': 'command-r7b',\n  'command-r': 'command-r',\n  'command-r-plus': 'command-r-plus',\n\n  // ===== Alibaba Qwen =====\n  'qwen3.5-flash': 'qwen3.5-flash',\n  'qwen3.5-plus': 'qwen3.5-plus',\n  'qwen3-max': 'qwen3-max',\n  'qwen3-next-80b-a3b-instruct': 'qwen3-next-80b',\n  'qwen3-next-80b-a3b-thinking': 'qwen3-next-80b',\n  'qwen3-coder-480b-a35b': 'qwen3-coder-480b',\n  'qwen3-coder-next': 'qwen3-coder-next',\n  'qwen3-coder-30b-a3b': 'qwen3-coder-30b',\n  'qwen2.5-72b': 'qwen2.5-72b',\n  'qwen2.5-72b-instruct': 'qwen2.5-72b',\n  'qwen2.5-7b': 'qwen2.5-7b',\n  'qwen2.5-7b-instruct': 'qwen2.5-7b',\n\n  // ===== Moonshot Kimi =====\n  'kimi-k2': 'kimi-k2',\n  'kimi-k2.5': 'kimi-k2.5',\n  'kimi-k2-thinking': 'kimi-k2-thinking',\n\n  // ===== Google Gemma =====\n  'gemma-3-27b-it': 'gemma-3-27b',\n  'gemma-3-12b-it': 'gemma-3-12b',\n  'gemma-3-4b': 'gemma-3-4b',\n  'gemma-3-1b': 'gemma-3-1b'\n};\n\nconst results = items.map(item => {\n  const data = { ...item.json };\n  const rawModel = (data.model || '').toLowerCase().trim();\n  \n  if (standardize_names_dic[rawModel]) {\n    data.standardizedModel = standardize_names_dic[rawModel];\n    data.modelKnown = true;\n  } else {\n    // Try partial matching for versioned model names\n    let matched = false;\n    for (const [key, value] of Object.entries(standardize_names_dic)) {\n      if (rawModel.startsWith(key) || rawModel.includes(key)) {\n        data.standardizedModel = value;\n        data.modelKnown = true;\n        matched = true;\n        break;\n      }\n    }\n    if (!matched) {\n      data.standardizedModel = rawModel || 'unknown';\n      data.modelKnown = false;\n    }\n  }\n  \n  data.rawModel = rawModel;\n  return { json: data };\n});\n\nreturn results;"
      },
      "typeVersion": 2
    },
    {
      "id": "check-models",
      "name": "All Models Defined?",
      "type": "n8n-nodes-base.if",
      "position": [
        1104,
        208
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "check-known",
              "operator": {
                "type": "boolean",
                "operation": "equals",
                "singleValue": true
              },
              "leftValue": "={{ $json.modelKnown }}",
              "rightValue": true
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "stop-error",
      "name": "Stop and Error",
      "type": "n8n-nodes-base.code",
      "position": [
        1328,
        368
      ],
      "parameters": {
        "jsCode": "// ============================================================\n// STOP AND ERROR - Unknown model detected\n// Lists all unknown models so user can add them\n// ============================================================\n\nconst items = $input.all();\nconst unknownModels = items\n  .filter(item => !item.json.modelKnown)\n  .map(item => item.json.rawModel);\n\nconst uniqueUnknown = [...new Set(unknownModels)];\n\nthrow new Error(\n  `Unknown model(s) detected: ${uniqueUnknown.join(', ')}\\n\\n` +\n  `Please add these models to:\\n` +\n  `1. The \"Standardize Names\" node (standardize_names_dic)\\n` +\n  `2. The \"Model Prices\" node (MODEL_PRICES)\\n\\n` +\n  `Then re-run the workflow.`\n);"
      },
      "typeVersion": 2
    },
    {
      "id": "merger",
      "name": "Merge",
      "type": "n8n-nodes-base.merge",
      "position": [
        1328,
        112
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "joinMode": "enrichInput1"
      },
      "typeVersion": 3
    },
    {
      "id": "model-prices",
      "name": "Model Prices",
      "type": "n8n-nodes-base.code",
      "position": [
        1552,
        112
      ],
      "parameters": {
        "jsCode": "// ============================================================\n// MODEL PRICES - Comprehensive pricing dictionary\n// Prices per 1 MILLION tokens (USD)\n// Last updated: March 2026\n// Covers 10 providers, 100+ models\n// ============================================================\n\nconst MODEL_PRICES = {\n  // ===== OpenAI GPT-5.x =====\n  'gpt-5.4':           { input: 2.50,   output: 15.00 },\n  'gpt-5.4-mini':      { input: 0.75,   output: 4.50 },\n  'gpt-5.4-nano':      { input: 0.20,   output: 1.25 },\n  'gpt-5.4-pro':       { input: 30.00,  output: 180.00 },\n  'gpt-5.3':           { input: 1.75,   output: 14.00 },\n  'gpt-5.3-codex':     { input: 1.75,   output: 14.00 },\n  'gpt-5.2':           { input: 1.75,   output: 14.00 },\n  'gpt-5':             { input: 1.25,   output: 10.00 },\n  'gpt-5-mini':        { input: 0.25,   output: 2.00 },\n  'gpt-5-nano':        { input: 0.05,   output: 0.40 },\n\n  // ===== OpenAI GPT-4.1 =====\n  'gpt-4.1':           { input: 2.00,   output: 8.00 },\n  'gpt-4.1-mini':      { input: 0.40,   output: 1.60 },\n  'gpt-4.1-nano':      { input: 0.10,   output: 0.40 },\n\n  // ===== OpenAI GPT-4o =====\n  'gpt-4o':            { input: 2.50,   output: 10.00 },\n  'gpt-4o-mini':       { input: 0.15,   output: 0.60 },\n  'gpt-4o-transcribe': { input: 2.50,   output: 10.00 },\n  'gpt-4o-mini-transcribe': { input: 1.25, output: 5.00 },\n\n  // ===== OpenAI GPT-4 =====\n  'gpt-4':             { input: 30.00,  output: 60.00 },\n  'gpt-4-32k':         { input: 60.00,  output: 120.00 },\n  'gpt-4-turbo':       { input: 10.00,  output: 30.00 },\n\n  // ===== OpenAI GPT-3.5 =====\n  'gpt-3.5-turbo':     { input: 0.50,   output: 1.50 },\n\n  // ===== OpenAI o-series (Reasoning) =====\n  'o1':                { input: 15.00,  output: 60.00 },\n  'o1-mini':           { input: 3.00,   output: 12.00 },\n  'o1-pro':            { input: 150.00, output: 600.00 },\n  'o3':                { input: 2.00,   output: 8.00 },\n  'o3-mini':           { input: 1.10,   output: 4.40 },\n  'o3-pro':            { input: 20.00,  output: 80.00 },\n  'o3-deep-research':  { input: 10.00,  output: 40.00 },\n  'o4-mini':           { input: 1.10,   output: 4.40 },\n  'o4-mini-deep-research': { input: 2.00, output: 8.00 },\n\n  // ===== OpenAI Specialized =====\n  'computer-use-preview': { input: 1.50, output: 6.00 },\n  'gpt-oss-120b':      { input: 0.05,   output: 0.25 },\n  'gpt-oss-20b':       { input: 0.04,   output: 0.15 },\n  'gpt-realtime-1.5':  { input: 4.00,   output: 16.00 },\n  'gpt-realtime-mini': { input: 0.60,   output: 2.40 },\n  'gpt-image-1.5':     { input: 5.00,   output: 10.00 },\n  'gpt-image-1-mini':  { input: 2.00,   output: 8.00 },\n\n  // ===== Anthropic Claude 4.x =====\n  'claude-sonnet-4-6':  { input: 3.00,  output: 15.00 },\n  'claude-opus-4-6':    { input: 5.00,  output: 25.00 },\n  'claude-opus-4-5':    { input: 5.00,  output: 25.00 },\n  'claude-sonnet-4-5':  { input: 3.00,  output: 15.00 },\n  'claude-opus-4':      { input: 15.00, output: 75.00 },\n  'claude-sonnet-4':    { input: 3.00,  output: 15.00 },\n  'claude-haiku-4.5':   { input: 1.00,  output: 5.00 },\n\n  // ===== Anthropic Claude 3.x =====\n  'claude-sonnet-3.7':  { input: 3.00,  output: 15.00 },\n  'claude-sonnet-3.5':  { input: 3.00,  output: 15.00 },\n  'claude-haiku-3.5':   { input: 0.80,  output: 4.00 },\n  'claude-opus-3':      { input: 15.00, output: 75.00 },\n  'claude-sonnet-3':    { input: 3.00,  output: 15.00 },\n  'claude-haiku-3':     { input: 0.25,  output: 1.25 },\n\n  // ===== Google Gemini 3.x =====\n  'gemini-3.1-pro':       { input: 2.00,  output: 12.00 },\n  'gemini-3.1-flash-lite': { input: 0.25, output: 1.50 },\n  'gemini-3-pro':         { input: 2.00,  output: 12.00 },\n  'gemini-3-flash':       { input: 0.50,  output: 3.00 },\n\n  // ===== Google Gemini 2.x =====\n  'gemini-2.5-pro':       { input: 1.25,  output: 10.00 },\n  'gemini-2.5-flash':     { input: 0.30,  output: 2.50 },\n  'gemini-2.5-flash-lite': { input: 0.10, output: 0.40 },\n  'gemini-2.0-flash':     { input: 0.10,  output: 0.40 },\n  'gemini-2.0-flash-lite': { input: 0.08, output: 0.30 },\n\n  // ===== Google Gemini 1.x =====\n  'gemini-1.5-pro':       { input: 1.25,  output: 5.00 },\n  'gemini-1.5-flash':     { input: 0.08,  output: 0.30 },\n  'gemini-1.0-pro':       { input: 0.50,  output: 1.50 },\n\n  // ===== DeepSeek =====\n  'deepseek-v3.2':        { input: 0.27,  output: 0.40 },\n  'deepseek-v3.1':        { input: 0.27,  output: 1.00 },\n  'deepseek-v3.1-terminus': { input: 0.27, output: 1.00 },\n  'deepseek-v3':          { input: 0.27,  output: 1.12 },\n  'deepseek-v3-turbo':    { input: 0.40,  output: 1.30 },\n  'deepseek-r1':          { input: 0.70,  output: 2.50 },\n  'deepseek-r1-turbo':    { input: 0.70,  output: 2.50 },\n  'deepseek-r1-distill-70b': { input: 0.80, output: 0.80 },\n  'deepseek-prover-v2':   { input: 0.70,  output: 2.50 },\n  'deepseek-ocr-2':       { input: 0.03,  output: 0.03 },\n\n  // ===== Meta Llama =====\n  'llama-4-scout':        { input: 0.17,  output: 0.65 },\n  'llama-4-maverick':     { input: 0.25,  output: 0.95 },\n  'llama-3.3-70b':        { input: 0.14,  output: 0.40 },\n  'llama-3.2-90b-vision': { input: 1.20,  output: 1.20 },\n  'llama-3.2-11b-vision': { input: 0.18,  output: 0.18 },\n  'llama-3.1-405b':       { input: 3.00,  output: 3.00 },\n  'llama-3.1-70b':        { input: 0.50,  output: 0.50 },\n  'llama-3.1-8b':         { input: 0.02,  output: 0.05 },\n  'llama-3-70b':          { input: 0.51,  output: 0.74 },\n  'llama-3-8b':           { input: 0.04,  output: 0.04 },\n\n  // ===== Mistral =====\n  'magistral-medium':     { input: 2.00,  output: 5.00 },\n  'magistral-small':      { input: 0.50,  output: 1.50 },\n  'mistral-medium-3':     { input: 0.40,  output: 2.00 },\n  'mistral-large':        { input: 2.00,  output: 6.00 },\n  'mistral-small-3.2':    { input: 0.10,  output: 0.30 },\n  'mistral-nemo':         { input: 0.04,  output: 0.17 },\n  'codestral':            { input: 0.30,  output: 0.90 },\n  'devstral-medium':      { input: 0.40,  output: 2.00 },\n  'devstral-small':       { input: 0.10,  output: 0.30 },\n  'pixtral-large':        { input: 2.00,  output: 6.00 },\n  'pixtral-12b':          { input: 0.15,  output: 0.15 },\n\n  // ===== xAI Grok =====\n  'grok-4':               { input: 3.00,  output: 15.00 },\n  'grok-4.1-fast':        { input: 0.20,  output: 0.50 },\n  'grok-4-fast':          { input: 0.20,  output: 0.50 },\n  'grok-3':               { input: 3.00,  output: 15.00 },\n  'grok-3-mini':          { input: 0.30,  output: 0.50 },\n  'grok-code-fast-1':     { input: 0.20,  output: 1.50 },\n  'grok-2':               { input: 2.00,  output: 10.00 },\n\n  // ===== Cohere =====\n  'command-a':            { input: 2.50,  output: 10.00 },\n  'command-r':            { input: 0.15,  output: 0.60 },\n  'command-r-plus':       { input: 2.50,  output: 10.00 },\n  'command-r7b':          { input: 0.04,  output: 0.15 },\n\n  // ===== Alibaba Qwen =====\n  'qwen3.5-flash':        { input: 0.25,  output: 2.00 },\n  'qwen3.5-plus':         { input: 0.40,  output: 2.40 },\n  'qwen3-max':            { input: 1.20,  output: 6.00 },\n  'qwen3-next-80b':       { input: 0.15,  output: 1.50 },\n  'qwen3-coder-480b':     { input: 0.30,  output: 1.30 },\n  'qwen3-coder-next':     { input: 0.20,  output: 1.50 },\n  'qwen3-coder-30b':      { input: 0.07,  output: 0.27 },\n  'qwen2.5-72b':          { input: 0.38,  output: 0.40 },\n  'qwen2.5-7b':           { input: 0.07,  output: 0.07 },\n\n  // ===== Moonshot Kimi =====\n  'kimi-k2':              { input: 0.57,  output: 2.30 },\n  'kimi-k2.5':            { input: 0.60,  output: 3.00 },\n  'kimi-k2-thinking':     { input: 0.60,  output: 2.50 },\n\n  // ===== Google Gemma =====\n  'gemma-3-27b':          { input: 0.12,  output: 0.20 },\n  'gemma-3-12b':          { input: 0.06,  output: 0.10 },\n  'gemma-3-4b':           { input: 0.03,  output: 0.05 },\n  'gemma-3-1b':           { input: 0.01,  output: 0.02 }\n};\n\nconst items = $input.all();\n\nconst results = items.map(item => {\n  const data = { ...item.json };\n  const model = data.standardizedModel || 'unknown';\n  const pricing = MODEL_PRICES[model];\n  \n  if (pricing) {\n    data.promptCost = (data.promptTokens / 1000000) * pricing.input;\n    data.completionCost = (data.completionTokens / 1000000) * pricing.output;\n    data.totalCost = data.promptCost + data.completionCost;\n    data.inputPricePerM = pricing.input;\n    data.outputPricePerM = pricing.output;\n    data.pricingFound = true;\n  } else {\n    // Fallback pricing for truly unknown models\n    data.promptCost = (data.promptTokens / 1000000) * 1.00;\n    data.completionCost = (data.completionTokens / 1000000) * 3.00;\n    data.totalCost = data.promptCost + data.completionCost;\n    data.inputPricePerM = 1.00;\n    data.outputPricePerM = 3.00;\n    data.pricingFound = false;\n  }\n  \n  // Round to 6 decimal places\n  data.promptCost = Math.round(data.promptCost * 1000000) / 1000000;\n  data.completionCost = Math.round(data.completionCost * 1000000) / 1000000;\n  data.totalCost = Math.round(data.totalCost * 1000000) / 1000000;\n  \n  return { json: data };\n});\n\nreturn results;"
      },
      "typeVersion": 2
    },
    {
      "id": "summary",
      "name": "Generate Summary",
      "type": "n8n-nodes-base.code",
      "position": [
        1760,
        112
      ],
      "parameters": {
        "jsCode": "// ============================================================\n// GENERATE SUMMARY - Comprehensive analytics output\n// Produces per-call details + aggregated summary statistics\n// ============================================================\n\nconst items = $input.all();\n\nif (items.length === 0) {\n  return [{ json: { error: 'No data to summarize' } }];\n}\n\nconst details = items.map(item => item.json);\n\n// Calculate summary statistics\nlet totalCost = 0;\nlet totalPromptTokens = 0;\nlet totalCompletionTokens = 0;\nlet totalTokens = 0;\nlet totalExecutionTime = 0;\nconst modelBreakdown = {};\nconst nodeBreakdown = {};\n\nfor (const call of details) {\n  totalCost += call.totalCost || 0;\n  totalPromptTokens += call.promptTokens || 0;\n  totalCompletionTokens += call.completionTokens || 0;\n  totalTokens += call.totalTokens || 0;\n  totalExecutionTime += call.executionTime || 0;\n  \n  // Model breakdown\n  const model = call.standardizedModel || 'unknown';\n  if (!modelBreakdown[model]) {\n    modelBreakdown[model] = { calls: 0, cost: 0, promptTokens: 0, completionTokens: 0, totalTokens: 0 };\n  }\n  modelBreakdown[model].calls++;\n  modelBreakdown[model].cost += call.totalCost || 0;\n  modelBreakdown[model].promptTokens += call.promptTokens || 0;\n  modelBreakdown[model].completionTokens += call.completionTokens || 0;\n  modelBreakdown[model].totalTokens += call.totalTokens || 0;\n  \n  // Node breakdown\n  const node = call.nodeName || 'unknown';\n  if (!nodeBreakdown[node]) {\n    nodeBreakdown[node] = { calls: 0, cost: 0, model: model, promptTokens: 0, completionTokens: 0 };\n  }\n  nodeBreakdown[node].calls++;\n  nodeBreakdown[node].cost += call.totalCost || 0;\n  nodeBreakdown[node].promptTokens += call.promptTokens || 0;\n  nodeBreakdown[node].completionTokens += call.completionTokens || 0;\n}\n\n// Round summary values\nfor (const m of Object.values(modelBreakdown)) {\n  m.cost = Math.round(m.cost * 1000000) / 1000000;\n}\nfor (const n of Object.values(nodeBreakdown)) {\n  n.cost = Math.round(n.cost * 1000000) / 1000000;\n}\n\nconst summary = {\n  workflowName: details[0]?.workflowName || 'Unknown',\n  workflowId: details[0]?.workflowId || 'unknown',\n  executionId: details[0]?.executionId || 'unknown',\n  executionStatus: details[0]?.executionStatus || 'unknown',\n  totalLLMCalls: details.length,\n  totalCost: Math.round(totalCost * 1000000) / 1000000,\n  totalPromptTokens: totalPromptTokens,\n  totalCompletionTokens: totalCompletionTokens,\n  totalTokens: totalTokens,\n  averageCostPerCall: Math.round((totalCost / details.length) * 1000000) / 1000000,\n  totalExecutionTimeMs: totalExecutionTime,\n  modelBreakdown: modelBreakdown,\n  nodeBreakdown: nodeBreakdown,\n  timestamp: new Date().toISOString()\n};\n\nreturn [{ json: { summary: summary, details: details } }];"
      },
      "typeVersion": 2
    },
    {
      "id": "note-1",
      "name": "Sticky Note - Setup",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -368,
        16
      ],
      "parameters": {
        "color": 4,
        "width": 380,
        "height": 556,
        "content": "##  Installation Steps\n\n1. Go to **Settings \u2192 n8n API** and create an API key\n2. Add it as credential for the **Get Execution Data** node\n3. Review model mappings in **Standardize Names** node\n4. Review pricing in **Model Prices** node\n\n##  To Monitor a Workflow\n\n1. Add **Execute Workflow** node at the end of your target workflow\n2. Select this monitoring workflow\n3. **Turn OFF** \"Wait For Sub-Workflow Completion\"\n4. Pass `{ \"executionId\": \"{{ $execution.id }}\" }` as input\n\n## \u26a0\ufe0f Prerequisites\n\nEnable **\"Return Intermediate Steps\"** in your AI Agent settings for best results."
      },
      "typeVersion": 1
    },
    {
      "id": "note-2",
      "name": "Sticky Note - Output",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1920,
        -80
      ],
      "parameters": {
        "color": 6,
        "width": 300,
        "height": 516,
        "content": "## \ud83d\udcca Output Data\n\n### Per LLM Call\n- Cost Breakdown (prompt, completion, total USD)\n- Token Metrics (prompt, completion, total)\n- Performance (execution time, finish reason)\n- Content Preview (first 100 chars I/O)\n- Model Parameters (temp, max tokens, timeout)\n- Execution Context (workflow, node, status)\n- Flow Tracking (previous nodes chain)\n\n### Summary Statistics\n- Total executions and costs\n- Breakdown by model type\n- Breakdown by node\n- Average cost per call\n- Total execution time"
      },
      "typeVersion": 1
    },
    {
      "id": "note-3",
      "name": "Sticky Note - User Config",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        816,
        -208
      ],
      "parameters": {
        "color": 3,
        "width": 400,
        "height": 352,
        "content": "## \u2699\ufe0f Defined by User\n\n1. Define the model names in **standardize_names_dic**\n2. If you want to use custom prices, update **MODEL_PRICES**\n3. Set the costs of the model\n4. Prices are per **1 million tokens**\n\n### When You See Errors\nIf the workflow enters the error path, it means an **undefined model** was detected. Simply:\n1. Add the model name to **standardize_names_dic**\n2. Add its pricing to **MODEL_PRICES**\n3. Re-run the workflow"
      },
      "typeVersion": 1
    },
    {
      "id": "note-4",
      "name": "Sticky Note - Providers",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -368,
        608
      ],
      "parameters": {
        "color": 5,
        "width": 380,
        "height": 284,
        "content": "## \ud83c\udfaf Supported Providers \n\n**OpenAI** \u00b7 **Anthropic** \u00b7 **Google** \u00b7 **DeepSeek** \u00b7 **Meta** \u00b7 **Mistral** \u00b7 **xAI** \u00b7 **Cohere** \u00b7 **Alibaba Qwen** \u00b7 **Moonshot Kimi**\n\n### 120+ Model Variations Mapped\nIncludes all versioned variants (e.g., gpt-4o-2024-08-06 \u2192 gpt-4o)\n\nPrices sourced from official provider pages (March 2026)"
      },
      "typeVersion": 1
    },
    {
      "id": "note-5",
      "name": "Sticky Note - Next Steps",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1536,
        304
      ],
      "parameters": {
        "color": 7,
        "width": 340,
        "height": 232,
        "content": "## \ud83d\udca1 You can do anything with this data!\n\n- Store in a database for historical tracking\n- Send to Teams as a cost alert\n- Build dashboards with the summary data\n- Set budget thresholds and trigger warnings\n- Export to Google Sheets for reporting"
      },
      "typeVersion": 1
    }
  ],
  "active": true,
  "settings": {
    "binaryMode": "separate",
    "callerPolicy": "workflowsFromSameOwner",
    "availableInMCP": false,
    "executionOrder": "v1"
  },
  "versionId": "bf13ef3a-2cb5-4ce0-80e7-a5846c450514",
  "connections": {
    "Merge": {
      "main": [
        [
          {
            "node": "Model Prices",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Model Prices": {
      "main": [
        [
          {
            "node": "Generate Summary",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Standardize Names": {
      "main": [
        [
          {
            "node": "All Models Defined?",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Get Execution Data": {
      "main": [
        [
          {
            "node": "Extract Token Usage",
            "type": "main",
            "index": 0
          },
          {
            "node": "Find Nodes with LLM Data",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "All Models Defined?": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Stop and Error",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract Token Usage": {
      "main": [
        [
          {
            "node": "Standardize Names",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract Execution ID": {
      "main": [
        [
          {
            "node": "Get Execution Data",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Test with Execution ID": {
      "main": [
        [
          {
            "node": "Extract Execution ID",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Find Nodes with LLM Data": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 1
          }
        ]
      ]
    },
    "When Called By Another Workflow": {
      "main": [
        [
          {
            "node": "Extract Execution ID",
            "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.

• n8nApi

About this workflow

Go to Settings → n8n API and create an API key Add it as credential for the Get Execution Data node Review model mappings in Standardize Names node Review pricing in Model Prices node Add Execute Workflow node at the end of your target workflow Select this monitoring workflow…

Source: https://n8n.io/workflows/14536/ — original creator credit. Request a take-down →

More AI & RAG workflows → · Browse all categories →

Related workflows

Workflows that share integrations, category, or trigger type with this one. All free to copy and import.