Intercom对话评论
高级
这是一个Ticket Management, AI Summarization领域的自动化工作流，包含 22 个节点。主要使用 If, Code, Merge, Webhook, SplitOut 等节点。 使用Intercom、GPT和Google Sheets自动化支持质量评估
前置要求
- •HTTP Webhook 端点（n8n 会自动生成）
- •可能需要目标 API 的认证凭证
- •Google Sheets API 凭证
- •OpenAI API Key
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": "U8L7ojJGYnPXUZZm",
"meta": {
"instanceId": "5f48daf3a0baf541941ac99dac87a58469ec38ada0698ec3f47e71ef7e50b0ff",
"templateCredsSetupCompleted": true
},
"name": "Intercom 对话评论",
"tags": [],
"nodes": [
{
"id": "3a663159-b082-440d-b15f-81a4f246e8c2",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
720,
420
],
"webhookId": "a716761a-b062-4f93-9734-04fdc83e9a9a",
"parameters": {
"path": "a716761a-b062-4f93-9734-04fdc83e9a9a",
"options": {},
"httpMethod": "POST"
},
"typeVersion": 2
},
{
"id": "82fb7f3c-137f-4d28-8764-4b71ecca18d4",
"name": "HTTP 请求",
"type": "n8n-nodes-base.httpRequest",
"position": [
940,
420
],
"parameters": {
"url": "=https://api.intercom.io/conversations/{{ $json.body.data.item.id }}?display_as=plaintext' ",
"options": {},
"authentication": "predefinedCredentialType",
"nodeCredentialType": "intercomApi"
},
"credentials": {
"intercomApi": {
"id": "Tx85thcOrnYamj7m",
"name": "Intercom account"
}
},
"typeVersion": 4.2
},
{
"id": "42d3660f-ef89-4b05-92b7-8efbe1d5a9d4",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
2480,
120
],
"parameters": {
"text": "={{ $json }}",
"options": {
"systemMessage": "I manage a customer support team based in India. I want to evaluate how our agents handle live chat conversations. \n\nThe goal is to improve response speed, clarity, urgency handling, tone, ownership, and problem resolution.\n\nYou are to act like a human QA reviewer — not an AI. \n\nProvide straight, coaching-style feedback in simple, easy-to-understand language that agents can learn and act on.\n\nConversation mostly have involvement of AI replies in the name of \"Alisha ( AI Bot ) from Saleshandy\" initially and then if not solved real human team member participate. \n\n🧠 Tone Reminder\nUse a friendly, coaching tone — never sound like a complaint. The goal is to help the agent grow, not to shame them.\n\n🔧 GPT Feedback Instructions\n\n✅ Be factual, crisp, and direct\n\n✅ Avoid technical or fancy words\n\n✅ Use exact sentences from the chat to highlight mistakes\n\n✅ Suggest a clear and improved reply\n\n✅ Explain the impact and what the agent should do instead\n\n✅ Use the agent’s first name (from the transcript) — never say “agent” or mention AI\n\n✅ Adjust tone and expectations if the customer was angry, confused, or seemed in a hurry\n\n✅ Our support hours are from 10:00 AM to 2:30 AM IST\n\n✅ If team member has not participated in the conversation, mention the rating is for AI bot i.e Alisa. \n\n✅ Ensure the feedback is easy to read:\n- Use proper line breaks and spacing\n- Keep sentences short and clear (ideally under 20 words)\n- Use bullets or emojis where helpful\n- Avoid large blocks of text\n\n\nFedback formate\n\nResponse Time: (Fast / Okay / Slow)\n\nWas the reply quick or delayed? Include the customer's wait time if available.\n\nClarity of Answers: (Clear / Confusing / Incomplete)\n\nWas the explanation simple and understandable?\n\nTone & Behavior: (Polite / Robotic / Rude)\n\nDid the agent sound human, helpful, and respectful?\n\nUrgency Handling: (Handled Well / Ignored / Unaware)\n\nDid the agent notice urgency and respond accordingly?\n\nOwnership & Accountability: (Strong / Weak / Avoiding)\n\nDid the agent take responsibility or deflect the issue?\n\nDid They Solve the Problem? (Yes / Partially / No)\n\nWas the issue resolved or clearly guided?\n\nGive conversation start date & end date in seperate lines (follow yyyy-mm-dd formate)\n\nOverall Score: (1 to 5)\n\nScoring Guide\n5 – Excellent (All aspects handled well with ownership)\n4 – Good (Minor gaps, customer likely satisfied)\n3 – Average (Some confusion, but no major damage)\n2 – Poor (Multiple issues, customer likely frustrated)\n1 – Unacceptable (Neglected, robotic, or disrespectful)\n\n\nOutput Format Requirements:\n\n⚠️ Return output as a pure JSON array, no extra keys, no string wrapping, no markdown, no triple backticks.\n\nDo NOT wrap the JSON in any extra string or code block.\n\n🔁 Only return the array.\n✅ No markdown formatting\n✅ No output: key\n✅ No escaping of quotes\n✅ No explanation\n✅ No wrapping in a string\n\nSample below:\n\n[\n {\n \"Response Time\": \"Fast\",\n \"Clarity of Answers\": \"Confusing\",\n \"Tone & Behavior\": \"Polite\",\n \"Urgency Handling\": \"Ignored\",\n \"Ownership & Accountability\": \"Weak\",\n \"Did They Solve the Problem?\": \"No\",\n \"Start Date\": \"2025-07-07\",\n \"End Date\": \"2025-07-07\",\n \"Overall Score\": 2\n }\n]"
},
"promptType": "define"
},
"typeVersion": 1.8
},
{
"id": "b71a3cfd-e33a-4bbe-8e14-d6b25aed76aa",
"name": "OpenAI 聊天模型",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2568,
340
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "CAQP7fWZtZcilByu",
"name": "Dhruv's OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "0dec2602-4e2d-43d4-b494-094f9a3c5e29",
"name": "分离输出",
"type": "n8n-nodes-base.splitOut",
"position": [
1380,
570
],
"parameters": {
"options": {},
"fieldToSplitOut": "conversation_parts"
},
"typeVersion": 1
},
{
"id": "b5a0fd7a-c73c-44b6-bbda-86daf62dc1aa",
"name": "代码",
"type": "n8n-nodes-base.code",
"position": [
1600,
570
],
"parameters": {
"jsCode": "// Start from items[1], since items[0] only has metadata\nconst conversationParts = items[1].json;\n\nconst output = [];\n\nfor (const key in conversationParts) {\n const part = conversationParts[key];\n\n if (part && part.part_type === 'comment') {\n output.push({\n json: part,\n pairedItem: {\n item: 1, // link to items[1], where the data came from\n },\n });\n }\n}\n\nreturn output;\n"
},
"typeVersion": 2,
"alwaysOutputData": true
},
{
"id": "0ba2ff75-4ee6-4db8-8aef-8d7bbfc56ccd",
"name": "拆分输出1",
"type": "n8n-nodes-base.splitOut",
"position": [
1820,
570
],
"parameters": {
"options": {},
"fieldToSplitOut": "body, created_at, author.type, author.name, author.email"
},
"typeVersion": 1
},
{
"id": "963b2239-32af-4cb0-a130-e3b067b6fe7e",
"name": "代码1",
"type": "n8n-nodes-base.code",
"position": [
2040,
570
],
"parameters": {
"jsCode": "function formatTimestamp(unixTime) {\n const date = new Date(unixTime * 1000);\n return date.toISOString().replace('T', ' ').split('.')[0]; // \"YYYY-MM-DD HH:mm:ss\"\n}\n\nlet combinedText = items.map(item => {\n const body = item.json.body || '';\n const author = item.json[\"author.name\"] || 'Unknown';\n const role = item.json[\"author.type\"] || 'unknown';\n const timestamp = item.json.created_at ? formatTimestamp(item.json.created_at) : 'Unknown time';\n\n const cleanBody = body.replace(/<[^>]*>/g, '').trim();\n\n let speaker = 'Unknown';\n if (role === 'user') {\n speaker = 'User';\n } else if (role === 'bot') {\n speaker = 'Bot';\n } else if (role === 'admin') {\n speaker = 'Admin';\n }\n\n return `[${timestamp}] ${speaker} (${author}): ${cleanBody}`;\n}).join('\\n\\n');\n\nreturn [\n {\n json: {\n conversation: combinedText\n }\n }\n];\n"
},
"typeVersion": 2
},
{
"id": "e51d2ee7-fd29-4535-88be-94bbaf5083dd",
"name": "代码2",
"type": "n8n-nodes-base.code",
"position": [
2856,
220
],
"parameters": {
"jsCode": "const raw = $json[\"output\"];\nreturn JSON.parse(raw);\n"
},
"typeVersion": 2
},
{
"id": "d15b437b-e2a3-4fcc-a167-7bcc1c1ac8ff",
"name": "拆分输出2",
"type": "n8n-nodes-base.splitOut",
"position": [
1820,
170
],
"parameters": {
"options": {},
"fieldToSplitOut": "source.body, source.delivered_as, source.author.name, source.author.email, "
},
"typeVersion": 1
},
{
"id": "a4d0c944-d7d3-4fa2-8549-24e0895f9ecd",
"name": "代码3",
"type": "n8n-nodes-base.code",
"position": [
2040,
170
],
"parameters": {
"jsCode": "return items.map(item => {\n const data = item.json;\n\n // Remove HTML tags from body if needed\n const plainBody = data[\"source.body\"]\n ? data[\"source.body\"].replace(/<[^>]*>/g, '').trim()\n : '';\n\n const name = data[\"source.author.name\"] || 'Unknown';\n const email = data[\"source.author.email\"] || 'unknown@example.com';\n\n return {\n json: {\n conversationinitiatied: `${name} (${email}): ${plainBody}`\n }\n };\n});\n"
},
"typeVersion": 2
},
{
"id": "f8e6b7dc-9a3d-4ffa-ace0-dc2da6dd5534",
"name": "合并",
"type": "n8n-nodes-base.merge",
"position": [
2260,
220
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineByPosition"
},
"typeVersion": 3.2
},
{
"id": "9afa1edb-2693-4705-b1af-ac5b56beea2c",
"name": "AI Agent1",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
2480,
620
],
"parameters": {
"text": "={{ $json.conversation }}",
"options": {
"systemMessage": "Analyze the following conversation transcript.\n\nYour tasks:\n\nCount the number of messages sent by each role: Bot, User, and Admin.\n\nIdentify who was the last admin person to send a message (include their name, avoid considering bot or user for the last messsage).\n\n✅ Output the result in the following JSON format:\n\njson\n{\n {\n \"Bot\": 10,\n \"User\": 8,\n \"Admin\": 5\n \"last_message_by\":\n \"name\": \"Kinjal\",\n}\n"
},
"promptType": "define"
},
"typeVersion": 2
},
{
"id": "e41e5ed9-069b-4a6b-b0d1-e07a628b4be8",
"name": "OpenAI 聊天模型1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2568,
840
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "W1K5DlnXfMcbyFKe",
"name": "Raj Bheda's OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "7e77f48d-2201-4e87-9426-9e9f0a84460d",
"name": "合并1",
"type": "n8n-nodes-base.merge",
"position": [
3076,
420
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineByPosition"
},
"typeVersion": 3.2
},
{
"id": "d1390d6c-d5f7-4789-8751-89bfdf2c11d7",
"name": "代码5",
"type": "n8n-nodes-base.code",
"position": [
2856,
620
],
"parameters": {
"jsCode": "let raw = $json[\"output\"];\n\n// Remove Markdown-style code block if present\nraw = raw.replace(/^```json\\n/, '').replace(/\\n```$/, '');\n\nreturn JSON.parse(raw);\n"
},
"typeVersion": 2
},
{
"id": "69277b71-7eb0-444d-9b9b-0f4bc3795811",
"name": "在表格中追加行",
"type": "n8n-nodes-base.googleSheets",
"position": [
3296,
320
],
"parameters": {
"columns": {
"value": {
"Tone": "={{ $json[\"Tone & Behavior\"] }}",
"Solved": "={{ $json[\"Did They Solve the Problem?\"] }}",
"Clarity": "={{ $json[\"Clarity of Answers\"] }}",
"Urgency": "={{ $json[\"Urgency Handling\"] }}",
"Ownership": "={{ $json[\"Ownership & Accountability\"] }}",
"#Bot Replies ": "={{ $json.Bot }}",
"Overall Score": "={{ $json[\"Overall Score\"] }}",
"Response time": "={{ $json[\"Response Time\"] }}",
"Conversation ID": "={{ $item(0).$node[\"HTTP Request\"].json[\"id\"] }}",
"Last Message by": "={{ $json.last_message_by.name }}",
"Our Replies count": "={{ $json.Admin }}",
"#User Replies Count": "={{ $json.User }}",
"Conversation end date": "={{ $json[\"End Date\"] }}",
"Conversation Start Date": "={{ $json[\"Start Date\"] }}"
},
"schema": [
{
"id": "Conversation end date",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Conversation end date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Conversation Start Date",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Conversation Start Date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Conversation ID",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Conversation ID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Intercom URL",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "Intercom URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Response time",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Response time",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Clarity",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Clarity",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Tone",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Tone",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Urgency",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Urgency",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Ownership",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Ownership",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Solved",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Solved",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Overall Score",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Overall Score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Time to close (Days)",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "Time to close (Days)",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "#Bot Replies ",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "#Bot Replies ",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "#User Replies Count",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "#User Replies Count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Our Replies count",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Our Replies count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Last Message by",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Last Message by",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"Conversation Start Date"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 1593039064,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1f9VmrRKolmn3PKIyLWy0yVSiTlQ1QbCAz-8E93XzTJM/edit#gid=1593039064",
"cachedResultName": "AI.Coach"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1f9VmrRKolmn3PKIyLWy0yVSiTlQ1QbCAz-8E93XzTJM",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1f9VmrRKolmn3PKIyLWy0yVSiTlQ1QbCAz-8E93XzTJM/edit?usp=drivesdk",
"cachedResultName": "Daily Input Update (DIU) - Customer Support"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "WqAbIK1oyEv92RRM",
"name": "Dhruv's Google Sheets"
}
},
"typeVersion": 4.6
},
{
"id": "cc12f47b-8421-44da-887d-d096635a8be4",
"name": "条件判断",
"type": "n8n-nodes-base.if",
"position": [
1160,
420
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "or",
"conditions": [
{
"id": "b7f80639-c3cd-49ae-acbb-0f54d399844a",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.custom_attributes.Type }}",
"rightValue": "Spam/Promotional"
},
{
"id": "344ac3eb-6308-4973-882b-274cee353dac",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.custom_attributes.Type }}",
"rightValue": "Other"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "a86ada21-36c9-46cd-81d4-b3c038311f34",
"name": "条件判断1",
"type": "n8n-nodes-base.if",
"position": [
3296,
520
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "ef9a24d9-2cc2-4199-b9e4-2d8d85771b4f",
"operator": {
"type": "number",
"operation": "lte"
},
"leftValue": "={{ $json['Overall Score'] }}",
"rightValue": 3
}
]
}
},
"typeVersion": 2.2
},
{
"id": "86e0be5c-4328-4468-a567-7ba66039ae5a",
"name": "AI 智能体2",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
3516,
520
],
"parameters": {
"text": "={{ $('Merge').item.json.conversationinitiatied }}{{ $('Merge').item.json.conversation }}",
"options": {
"systemMessage": "I manage a customer support team based in India. I want to evaluate how our agents handle live chat conversations. \n\nThe goal is to improve response speed, clarity, urgency handling, tone, ownership, and problem resolution.\n\nYou are to act like a human QA reviewer — not an AI. \n\nProvide straight, coaching-style feedback in simple, easy-to-understand language that agents can learn and act on.\n\nConversation mostly have involvement of AI replies in the name of \"Alisha ( AI Bot ) from Saleshandy\" initially and then if not solved real human team member participate. \n\n🧠 Tone Reminder\nUse a friendly, coaching tone — never sound like a complaint. The goal is to help the agent grow, not to shame them.\n\n🔧 GPT Feedback Instructions\n\n✅ Be factual, crisp, and direct\n\n✅ Avoid technical or fancy words\n\n✅ Use exact sentences from the chat to highlight mistakes\n\n✅ Suggest a clear and improved reply\n\n✅ Explain the impact and what the agent should do instead\n\n✅ Use the agent’s first name (from the transcript) — never say “agent” or mention AI\n\n✅ Adjust tone and expectations if the customer was angry, confused, or seemed in a hurry\n\n✅ Our support hours are from 10:00 AM to 2:30 AM IST\n\n✅ If team member has not participated in the conversation, mention the rating is for AI bot i.e Alisa. \n\n✅ Ensure the feedback is easy to read:\n- Use proper line breaks and spacing\n- Keep sentences short and clear (ideally under 20 words)\n- Use bullets or emojis where helpful\n- Avoid large blocks of text\n\n\n📣 Feedback for Team member \n\nWhat you did well\n\nMention 1–2 positive points (e.g., polite tone, clear guidance, fast reply)\n\nWhere you messed up\n❌ Quote the exact problematic sentence\n✅ Show a better version\n➕ Explain the impact and the expected behavior\n\nWhat the customer likely felt\n\nExplain in simple, emotional terms (e.g., “The customer probably felt ignored…”)\n\nHow to fix it next time\n\nGive practical advice the agent can follow in future chats\n\n🔄 Example Feedback\n❌ You said: “We will check and revert in 1 hour” — but the customer had already waited 12 hours.\n\nThis reply added no value and made the customer feel ignored.\n\n✅ Instead: “Apologies for the delay. I’ve checked internally — we need another hour and will update you by 4 PM IST today.”\n→ This shows urgency, ownership, and clear commitment.\n\n❌ You wrote: “We regret the inconvenience caused” without offering a solution.\n✅ Don’t paste sympathy lines. Understand the issue and respond with a real fix.\n"
},
"promptType": "define"
},
"typeVersion": 2
},
{
"id": "3970a104-1a7a-4df2-b6ab-7e4a4e1a5b87",
"name": "OpenAI 聊天模型2",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
3604,
740
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "chatgpt-4o-latest",
"cachedResultName": "chatgpt-4o-latest"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "W1K5DlnXfMcbyFKe",
"name": "Raj Bheda's OpenAi account"
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}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
高级 - 工单管理, AI 摘要总结
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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