使用AI全自动分类Outlook邮件
高级
这是一个Miscellaneous, AI Summarization, Multimodal AI领域的自动化工作流,包含 23 个节点。主要使用 If, Set, Code, Merge, Filter 等节点。 使用Outlook和GPT-4o自动分类和组织邮件
前置要求
- •无特殊前置要求,导入即可使用
使用的节点 (23)
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": "ZrzN9UGogerbca4m",
"meta": {
"instanceId": "eebd3ce207847e87041097897f06c5f83f85f285defd8c5b4ec159692e0a5eed",
"templateId": "2454",
"templateCredsSetupCompleted": true
},
"name": "使用 AI 全自动分类 Outlook 邮件",
"tags": [],
"nodes": [
{
"id": "d16f59dd-f54e-487b-9aac-67f109ba9869",
"name": "便签8",
"type": "n8n-nodes-base.stickyNote",
"position": [
1424,
-192
],
"parameters": {
"color": 7,
"width": 872,
"height": 111,
"content": "# 使用 AI 全自动分类 Outlook 邮件"
},
"typeVersion": 1
},
{
"id": "d4969259-a3ae-473d-82ef-0c9f7933c899",
"name": "遍历项目1",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1440,
64
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "ebf606f9-099c-4218-b23b-66e2487262d0",
"name": "Markdown1",
"type": "n8n-nodes-base.markdown",
"notes": "Converts the body of the email to markdown",
"position": [
1680,
64
],
"parameters": {
"html": "={{ $('Loop Over Items1').item.json.body.content }}",
"options": {}
},
"notesInFlow": true,
"typeVersion": 1
},
{
"id": "ff447dd5-3ef6-4a02-8453-3489af8bf6b5",
"name": "变量邮件1",
"type": "n8n-nodes-base.set",
"notes": "Set email fields",
"position": [
1856,
64
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "edb304e1-3e9f-4a77-918c-25646addbc53",
"name": "subject",
"type": "string",
"value": "={{ $json.subject }}"
},
{
"id": "57a3ef3a-2701-40d9-882f-f43a7219f148",
"name": "importance",
"type": "string",
"value": "={{ $json.importance }}"
},
{
"id": "d8317f4f-aa0e-4196-89af-cb016765490a",
"name": "sender",
"type": "object",
"value": "={{ $json.sender.emailAddress }}"
},
{
"id": "908716c8-9ff7-4bdc-a1a3-64227559635e",
"name": "from",
"type": "object",
"value": "={{ $json.from.emailAddress }}"
},
{
"id": "ce007329-e221-4c5a-8130-2f8e9130160f",
"name": "body",
"type": "string",
"value": "={{ $json.data\n .replace(/<[^>]*>/g, '') // Remove HTML tags\n .replace(/\\[(.*?)\\]\\((.*?)\\)/g, '') // Remove Markdown links like [text](link)\n .replace(/!\\[.*?\\]\\(.*?\\)/g, '') // Remove Markdown images like \n .replace(/\\|/g, '') // Remove table separators \"|\"\n .replace(/-{3,}/g, '') // Remove horizontal rule \"---\"\n .replace(/\\n+/g, ' ') // Remove multiple newlines\n .replace(/([^\\w\\s.,!?@])/g, '') // Remove special characters except essential ones\n .replace(/\\s{2,}/g, ' ') // Replace multiple spaces with a single space\n .trim() // Trim leading/trailing whitespace\n}}\n"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0a19d15c-0cd3-4f26-9be2-4914522751fb",
"name": "过滤器1",
"type": "n8n-nodes-base.filter",
"position": [
1248,
64
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "c8cd6917-f94e-4fb7-8601-b8ed8f1aa8bf",
"operator": {
"type": "array",
"operation": "empty",
"singleValue": true
},
"leftValue": "={{ $json.categories }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "96e6e31c-6306-44a8-a57a-2b5216636b00",
"name": "条件判断1",
"type": "n8n-nodes-base.if",
"notes": "Checks if the email has been read",
"position": [
2592,
288
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "f8cf2a56-cea8-4150-b7a0-048dbda20f2f",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json.isRead }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "e2d8e7b5-4447-4327-9f4e-b8d52765667e",
"name": "捕获错误1",
"type": "n8n-nodes-base.set",
"position": [
1184,
528
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "0dc6d439-60fb-49f6-b4d5-f5cce6f030ad",
"name": "error",
"type": "string",
"value": "={{ $json }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "17f6ac43-51e4-4bee-b0d8-13deb3bf3cc9",
"name": "变量JSON1",
"type": "n8n-nodes-base.set",
"onError": "continueErrorOutput",
"position": [
1168,
288
],
"parameters": {
"options": {
"ignoreConversionErrors": true
},
"assignments": {
"assignments": [
{
"id": "0c52f57f-74eb-4385-ac6b-f3e5f4f50e73",
"name": "output",
"type": "object",
"value": "={{ $json.output.replace(/^.*?({.*}).*$/s, '$1') }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "85ff0348-16dc-46e6-bf70-48a10fe0ded8",
"name": "AI Agent1",
"type": "@n8n/n8n-nodes-langchain.agent",
"onError": "continueErrorOutput",
"position": [
848,
288
],
"parameters": {
"text": "=Categorise the following email\n<email>\n{{ $('varEmal1').first().json.toJsonString() }}\n</email>\n\nEnsure your final output is valid JSON with no additional text or token in the following format:\n\n{\n \"subject\": \"SUBJECT_LINE\",1\n \"category\": \"CATEGORY\",\n \"subCategory\": \"SUBCATEGORY\", //use sparingly\n \"analysis\": \"ANALYSIS_REASONING\"\n}\n\nRemember you can only use ONE of the following categories {{ $json.category }}. No other categories can be used. Use the subcategory for additional context, for example, if a SaaS email requires action, or if a business email requires action. Do not create any additional subcategories, you can only use ONE of the following {{ $json.category }}",
"options": {
"systemMessage": "=You are an advanced AI email categorization system for a freelance developer. Your mission is to intelligently analyze and categorize emails with maximum accuracy and context awareness.\n\n**INTELLIGENT CATEGORIZATION ENGINE:**\n\n**Phase 1 - Dynamic Category Processing:**\n- Parse and interpret all available categories from {{ $json.category }}\n- Automatically detect category hierarchies and relationships\n- Build intelligent mapping between email characteristics and categories\n\n**Phase 2 - Multi-Layer Email Analysis:**\n- **Sender Intelligence**: Domain reputation, email patterns, historical behavior\n- **Subject Line Analysis**: Keywords, urgency indicators, spam patterns, language detection\n- **Content Deep Scan**: Body text analysis, link analysis, attachment detection, HTML structure\n- **Contextual Understanding**: Business relevance, timing, frequency patterns\n- **Spam/Threat Detection**: Advanced phishing detection, scam patterns, malicious content identification\n\n**Phase 3 - Advanced Decision Logic:**\n\n**PRIORITY CATEGORIZATION RULES:**\n1. **SECURITY THREATS** → Immediate junk classification for phishing, scams, suspicious links\n2. **ACTION REQUIRED** → Critical business emails needing immediate response get \"Action\" category\n3. **BUSINESS CONTEXT** → Legitimate business communications vs promotional content distinction\n4. **SENDER REPUTATION** → Known vendors/partners vs unknown senders treatment\n5. **CONTENT ANALYSIS** → Marketing language detection vs technical communication\n6. **URGENCY DETECTION** → Time-sensitive content identification\n\n**SPECIALIZED CATEGORIZATION INTELLIGENCE:**\n- **Domain/Hosting/Server**: Automatically detect hosting providers, domain registrars, server notifications\n- **SaaS Action Items**: Distinguish between promotional SaaS emails vs critical system notifications\n- **E-commerce Detection**: Advanced marketplace email recognition (Amazon, local platforms)\n- **Financial Intelligence**: Invoice detection, payment confirmations, banking communications\n- **Support Ticket Analysis**: Technical support vs general customer service distinction\n- **Corporate Communication**: Contract emails, official business correspondence detection\n\n**ANTI-SPAM INTELLIGENCE:**\n- **Marketing Pattern Detection**: Newsletter signatures, unsubscribe links, promotional language\n- **Emoji Spam Detection**: Excessive emoji usage indicating promotional content\n- **Subject Line Red Flags**: Discount offers, limited time offers, \"act now\" language\n- **Sender Spoofing Detection**: Fake sender analysis, domain verification\n- **Content Authenticity**: Legitimate business communication vs mass marketing\n\n**DUAL CATEGORY LOGIC:**\n- Primary category assignment based on email nature\n- Secondary category for action requirements or business context\n- Smart combination logic (e.g., \"Technology & SaaS\" + \"Action\" for critical system alerts)\n\n**CONTEXTUAL LEARNING PATTERNS:**\n- Developer-specific email patterns recognition\n- Client communication identification\n- Project-related correspondence detection\n- Vendor relationship understanding\n- Support ticket escalation recognition\n\n**OUTPUT PRECISION:**\nReturn only valid JSON with enhanced analysis:\n{\n \"subject\": \"EXACT_EMAIL_SUBJECT\",\n \"category\": \"PRIMARY_CATEGORY_FROM_AVAILABLE_LIST\",\n \"subCategory\": \"SECONDARY_CATEGORY_IF_APPLICABLE\",\n \"analysis\": \"Detailed reasoning explaining categorization decision with specific indicators found\",\n \"confidence\": \"HIGH/MEDIUM/LOW based on analysis certainty\"\n}\n\n**AVAILABLE CATEGORIES:** {{ $json.category }}\n\n**CRITICAL INSTRUCTIONS:**\n- Use ONLY categories from the provided list\n- Never create new categories\n- Prioritize accuracy over speed\n- When uncertain, err on the side of caution (prefer Action category for potentially important emails)\n- Always provide detailed analysis reasoning\n- Consider the freelance developer context in all decisions\n\nAnalyze the email in <email> tags and provide intelligent categorization."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.6
},
{
"id": "93e7be79-9035-4b58-9a83-b9182a0515f8",
"name": "合并1",
"type": "n8n-nodes-base.merge",
"position": [
2400,
288
],
"parameters": {},
"typeVersion": 3
},
{
"id": "b0972a88-e3c8-450c-a66f-eb4af5d8f868",
"name": "代码",
"type": "n8n-nodes-base.code",
"position": [
2416,
64
],
"parameters": {
"jsCode": "// Gelen değer array olabilir\nconst raw = $json.appended_displayName;\n\n// Array olup olmadığını kontrol et, stringe çevir\nlet output;\nif (Array.isArray(raw)) {\n output = raw.join(\", \");\n} else {\n output = String(raw);\n}\n\nreturn {\n cleanedString: output\n};\n"
},
"typeVersion": 2
},
{
"id": "314869ec-fc75-4789-981b-5ada18b5b27c",
"name": "获取多个文件夹",
"type": "n8n-nodes-base.microsoftOutlook",
"position": [
2064,
64
],
"webhookId": "a26b9ab2-cc4c-4960-b864-767defad3f27",
"parameters": {
"filters": {
"filter": ""
},
"options": {
"fields": [
"displayName",
"childFolderCount"
],
"includeChildFolders": true
},
"resource": "folder",
"operation": "getAll",
"returnAll": true
},
"credentials": {
"microsoftOutlookOAuth2Api": {
"id": "3Rl3v9O6pZH09LqE",
"name": "Microsoft Outlook account"
}
},
"typeVersion": 2
},
{
"id": "40313f17-5ede-4d85-9b29-410c14eeee57",
"name": "摘要",
"type": "n8n-nodes-base.summarize",
"position": [
2240,
64
],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "displayName",
"aggregation": "append",
"includeEmpty": true
}
]
}
},
"typeVersion": 1.1
},
{
"id": "fab488d0-033f-4d10-92e1-c3ae83c8fe3c",
"name": "变量ID分类",
"type": "n8n-nodes-base.set",
"position": [
2608,
64
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "de2ad4f2-7381-4715-a3f4-59611e161b74",
"name": "id",
"type": "string",
"value": "={{ $('RecievedEmail').item.json.id }}"
},
{
"id": "ea74a279-6763-4b67-9eb4-5e347e1d32f4",
"name": "category",
"type": "string",
"value": "={{ $json.cleanedString }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "46f626c0-fccb-4117-a744-7bbae8f63be7",
"name": "获取多个文件夹1",
"type": "n8n-nodes-base.microsoftOutlook",
"position": [
1440,
288
],
"webhookId": "a26b9ab2-cc4c-4960-b864-767defad3f27",
"parameters": {
"filters": {},
"options": {
"fields": [
"displayName",
"childFolderCount",
"parentFolderId"
],
"includeChildFolders": true
},
"resource": "folder",
"operation": "getAll",
"returnAll": true
},
"credentials": {
"microsoftOutlookOAuth2Api": {
"id": "3Rl3v9O6pZH09LqE",
"name": "Microsoft Outlook account"
}
},
"typeVersion": 2
},
{
"id": "9a98c939-d5b9-4a35-ae55-aa273d899168",
"name": "如果",
"type": "n8n-nodes-base.if",
"position": [
1664,
288
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "cccc6725-7053-4064-bef6-1c6b07dee680",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('varJSON1').item.json.output.category }}",
"rightValue": "={{ $json.displayName }}"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "e885cfdf-e498-493b-b4f3-a26c651ff239",
"name": "定时触发器",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
848,
64
],
"parameters": {
"rule": {
"interval": [
{
"field": "minutes",
"minutesInterval": 1
}
]
}
},
"typeVersion": 1.2
},
{
"id": "0583e196-37a5-43db-8c0a-aa624029c926",
"name": "已接收邮件",
"type": "n8n-nodes-base.microsoftOutlook",
"position": [
1040,
64
],
"webhookId": "e05342b2-15e9-4df8-a32b-9366ad0630c9",
"parameters": {
"limit": 1,
"fields": [
"flag",
"from",
"importance",
"replyTo",
"sender",
"subject",
"toRecipients",
"body",
"categories",
"isRead"
],
"output": "fields",
"options": {},
"filtersUI": {
"values": {
"filters": {
"custom": "flag/flagStatus eq 'notFlagged' and not categories/any()",
"foldersToInclude": [
"AQMkAGExN2Q4YTQ0AC01NDI4LTRhNmYtODYwZS05YzU0YzE5MzAyZjQALgAAA9jkZp65v9BIjLPAC68z-1MBAKhQNfuxpX5CpnvHa8QQO84AAAIBDAAAAA=="
]
}
}
},
"operation": "getAll"
},
"credentials": {
"microsoftOutlookOAuth2Api": {
"id": "3Rl3v9O6pZH09LqE",
"name": "Microsoft Outlook account"
}
},
"typeVersion": 2
},
{
"id": "a70910ff-2716-427a-8e47-7a5e4b22384f",
"name": "更新分类",
"type": "n8n-nodes-base.microsoftOutlook",
"position": [
1936,
272
],
"webhookId": "51f99d33-6a5a-4465-973a-0843b9e00ad0",
"parameters": {
"messageId": {
"__rl": true,
"mode": "id",
"value": "={{ $('VarID Category').item.json.id }}"
},
"operation": "update",
"updateFields": {
"categories": "={{ \n [$('varJSON1').first().json.output.category, $('varJSON1').first().json.output.subCategory]\n .filter(item => item && item.trim() !== \"\")\n .map(item => item.charAt(0).toUpperCase() + item.slice(1))\n}}"
}
},
"credentials": {
"microsoftOutlookOAuth2Api": {
"id": "3Rl3v9O6pZH09LqE",
"name": "Microsoft Outlook account"
}
},
"typeVersion": 2
},
{
"id": "88b8d031-249c-445d-9c74-0ac928a847ff",
"name": "移动文件夹",
"type": "n8n-nodes-base.microsoftOutlook",
"position": [
2160,
272
],
"webhookId": "8eb4cbc8-a0e8-49ad-ab20-9a7112abce35",
"parameters": {
"folderId": {
"__rl": true,
"mode": "id",
"value": "={{ $('If').item.json.id }}"
},
"messageId": {
"__rl": true,
"mode": "id",
"value": "={{ $('VarID Category').item.json.id }}"
},
"operation": "move"
},
"credentials": {
"microsoftOutlookOAuth2Api": {
"id": "3Rl3v9O6pZH09LqE",
"name": "Microsoft Outlook account"
}
},
"typeVersion": 2
},
{
"id": "6360c01a-f285-4653-97df-1a0e23c5e806",
"name": "OpenRouter聊天模型",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
784,
528
],
"parameters": {
"model": "deepseek/deepseek-chat-v3.1:free",
"options": {}
},
"credentials": {
"openRouterApi": {
"id": "ZLtYK0MmEJgJn2mU",
"name": "OpenRouter account"
}
},
"typeVersion": 1
},
{
"id": "553437d9-6a18-4748-a4e3-10c35740c379",
"name": "AI 代理",
"type": "@n8n/n8n-nodes-langchain.agent",
"onError": "continueErrorOutput",
"position": [
944,
832
],
"parameters": {
"text": "=Categorise the following email\n<email>\n{{ $('varEmal1').first().json.toJsonString() }}\n</email>\n\nEnsure your final output is valid JSON with no additional text or token in the following format:\n\n{\n \"subject\": \"SUBJECT_LINE\",1\n \"category\": \"CATEGORY\",\n \"subCategory\": \"SUBCATEGORY\", //use sparingly\n \"analysis\": \"ANALYSIS_REASONING\"\n}\n\nRemember you can only use ONE of the following categories {{ $json.category }}. No other categories can be used. Use the subcategory for additional context, for example, if a SaaS email requires action, or if a business email requires action. Do not create any additional subcategories, you can only use ONE of the following {{ $json.category }}",
"options": {
"systemMessage": "=You are an advanced AI email categorization system for a freelance developer. Your mission is to intelligently analyze and categorize emails with maximum accuracy and context awareness.\n\n**INTELLIGENT CATEGORIZATION ENGINE:**\n\n**Phase 1 - Dynamic Category Processing:**\n- Parse and interpret all available categories from {{ $json.category }}\n- Automatically detect category hierarchies and relationships\n- Build intelligent mapping between email characteristics and categories\n\n**Phase 2 - Multi-Layer Email Analysis:**\n- **Sender Intelligence**: Domain reputation, email patterns, historical behavior\n- **Subject Line Analysis**: Keywords, urgency indicators, spam patterns, language detection\n- **Content Deep Scan**: Body text analysis, link analysis, attachment detection, HTML structure\n- **Contextual Understanding**: Business relevance, timing, frequency patterns\n- **Spam/Threat Detection**: Advanced phishing detection, scam patterns, malicious content identification\n\n**Phase 3 - Advanced Decision Logic:**\n\n**PRIORITY CATEGORIZATION RULES:**\n1. **SECURITY THREATS** → Immediate junk classification for phishing, scams, suspicious links\n2. **ACTION REQUIRED** → Critical business emails needing immediate response get \"Action\" category\n3. **BUSINESS CONTEXT** → Legitimate business communications vs promotional content distinction\n4. **SENDER REPUTATION** → Known vendors/partners vs unknown senders treatment\n5. **CONTENT ANALYSIS** → Marketing language detection vs technical communication\n6. **URGENCY DETECTION** → Time-sensitive content identification\n\n**SPECIALIZED CATEGORIZATION INTELLIGENCE:**\n- **Domain/Hosting/Server**: Automatically detect hosting providers, domain registrars, server notifications\n- **SaaS Action Items**: Distinguish between promotional SaaS emails vs critical system notifications\n- **E-commerce Detection**: Advanced marketplace email recognition (Amazon, local platforms)\n- **Financial Intelligence**: Invoice detection, payment confirmations, banking communications\n- **Support Ticket Analysis**: Technical support vs general customer service distinction\n- **Corporate Communication**: Contract emails, official business correspondence detection\n\n**ANTI-SPAM INTELLIGENCE:**\n- **Marketing Pattern Detection**: Newsletter signatures, unsubscribe links, promotional language\n- **Emoji Spam Detection**: Excessive emoji usage indicating promotional content\n- **Subject Line Red Flags**: Discount offers, limited time offers, \"act now\" language\n- **Sender Spoofing Detection**: Fake sender analysis, domain verification\n- **Content Authenticity**: Legitimate business communication vs mass marketing\n\n**DUAL CATEGORY LOGIC:**\n- Primary category assignment based on email nature\n- Secondary category for action requirements or business context\n- Smart combination logic (e.g., \"Technology & SaaS\" + \"Action\" for critical system alerts)\n\n**CONTEXTUAL LEARNING PATTERNS:**\n- Developer-specific email patterns recognition\n- Client communication identification\n- Project-related correspondence detection\n- Vendor relationship understanding\n- Support ticket escalation recognition\n\n**OUTPUT PRECISION:**\nReturn only valid JSON with enhanced analysis:\n{\n \"subject\": \"EXACT_EMAIL_SUBJECT\",\n \"category\": \"PRIMARY_CATEGORY_FROM_AVAILABLE_LIST\",\n \"subCategory\": \"SECONDARY_CATEGORY_IF_APPLICABLE\",\n \"analysis\": \"Detailed reasoning explaining categorization decision with specific indicators found\",\n \"confidence\": \"HIGH/MEDIUM/LOW based on analysis certainty\"\n}\n\n**AVAILABLE CATEGORIES:** {{ $json.category }}\n\n**CRITICAL INSTRUCTIONS:**\n- Use ONLY categories from the provided list\n- Never create new categories\n- Prioritize accuracy over speed\n- When uncertain, err on the side of caution (prefer Action category for potentially important emails)\n- Always provide detailed analysis reasoning\n- Consider the freelance developer context in all decisions\n\nAnalyze the email in <email> tags and provide intelligent categorization."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.6
},
{
"id": "e8ce531c-28ad-4ce9-bbd2-a215e60c00ed",
"name": "OpenRouter Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
976,
1024
],
"parameters": {
"model": "deepseek/deepseek-chat-v3.1:free",
"options": {}
},
"credentials": {
"openRouterApi": {
"id": "mPpc0Uce9lIl5UXc",
"name": "OpenRouter account backup"
}
},
"typeVersion": 1
}
],
"active": true,
"pinData": {},
"settings": {},
"versionId": "97d331c2-d67e-475e-a2a8-f617060d3cc1",
"connections": {
"If": {
"main": [
[
{
"node": "Update Category",
"type": "main",
"index": 0
}
],
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"If1": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 1
}
],
[]
]
},
"Code": {
"main": [
[
{
"node": "VarID Category",
"type": "main",
"index": 0
}
]
]
},
"Merge1": {
"main": [
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"Filter1": {
"main": [
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "varJSON1",
"type": "main",
"index": 0
}
]
]
},
"varEmal1": {
"main": [
[
{
"node": "Get many folders",
"type": "main",
"index": 0
}
]
]
},
"varJSON1": {
"main": [
[
{
"node": "Get many folders1",
"type": "main",
"index": 0
}
],
[
{
"node": "Catch Errors1",
"type": "main",
"index": 0
}
]
]
},
"AI Agent1": {
"main": [
[
{
"node": "varJSON1",
"type": "main",
"index": 0
}
],
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Markdown1": {
"main": [
[
{
"node": "varEmal1",
"type": "main",
"index": 0
}
]
]
},
"Summarize": {
"main": [
[
{
"node": "Code",
"type": "main",
"index": 0
}
]
]
},
"Move Folder": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 0
}
]
]
},
"Catch Errors1": {
"main": [
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"RecievedEmail": {
"main": [
[
{
"node": "Filter1",
"type": "main",
"index": 0
}
]
]
},
"VarID Category": {
"main": [
[
{
"node": "AI Agent1",
"type": "main",
"index": 0
}
]
]
},
"Update Category": {
"main": [
[
{
"node": "Move Folder",
"type": "main",
"index": 0
}
]
]
},
"Get many folders": {
"main": [
[
{
"node": "Summarize",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items1": {
"main": [
null,
[
{
"node": "Markdown1",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "RecievedEmail",
"type": "main",
"index": 0
}
]
]
},
"Get many folders1": {
"main": [
[
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"OpenRouter Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent1",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"OpenRouter Chat Model1": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"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 - 杂项, AI 摘要总结, 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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