{ "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": "" } }, "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 } ] ] } } }