# 如何用 n8n 和 LLM 自动分类邮件
我已经厌倦了继续等 Gmail 自己搞明白哪些邮件对我真的重要。让 AI 接管我的个人邮件分类三个月之后，我已经无法想象再回到手动整理的状态。
我的系统简单得有点离谱：GPT-5-nano 会把每封邮件丢进三个桶里的一个。归档、阅读或者回复。就这么简单。
整套东西都跑在 n8n 上，因为我用的是 OpenAI 最便宜、又支持结构化输出的模型，所以成本几乎可以忽略不计。没错，现在 OpenAI 能看到我所有个人邮件了，但说实话，他们大概早就从我在 ChatGPT 里的对话里知道更多了。
这不是什么我拿出来兜售的理论概念。我已经把它在线上环境跑了三个月，而且它每周都在给我省下好几个小时。
## 我为什么做这套邮件自动化系统
我收到的邮件实在太多了。新闻订阅、服务通知、真人发来的重要消息，全都落在同一个收件箱里。Gmail 自带的分类总是漏掉一些很明显的模式，而我每天早上都要花 20 分钟，仅仅是决定哪些邮件值得读。
真正让我受不了的时刻，是我意识到自己有 80% 的邮件其实都没看，直接就归档了。如果大多数邮件根本不需要我注意，那为什么还得由我来决定哪些需要？
> **更喜欢视频教程？** 我录了一个完整的分步骤演示，把这套邮件自动化工作流从头到尾走了一遍。[查看用 n8n 和 LLM 做邮件自动化的教程](https://www.youtube.com/watch?v=xn1RyMlZudE)，可以直接看到完整配置过程。
[](https://www.youtube.com/watch?v=xn1RyMlZudE)
## 真正有效的三分类系统
我的 AI 会把每一封邮件严格分成三类：
- **归档** - 我订阅了但基本不看的新闻邮件、自动通知、促销邮件
- **阅读** - 我想看，但不需要回复的内容
- **回复** - 需要我亲自回复的邮件
不需要复杂的文件夹结构，也不需要优先级体系。就三个简单动作，覆盖我 100% 的邮件。
## 搭建 n8n 工作流
下面就是能把这一切自动跑起来的完整 n8n 工作流：
每当 Gmail 收件箱里进来新邮件，这个工作流就会触发。它会抓取邮件内容，发给 OpenAI 做分类，然后在 Gmail 里打上对应标签，把那些不需要我关注的邮件直接归档。
### 需要的 n8n 节点
你的工作流里需要这些节点：
1. **Gmail Trigger** - 监控新邮件
2. **OpenAI Chat Model** - 分类邮件内容
3. **Gmail** - 添加标签并归档邮件
4. **IF conditions** - 根据 AI 的判断把邮件路由到不同分支
真正的关键在 OpenAI 节点的配置上。下面是我的设置方式：
## 真正让它跑起来的 LLM Prompt
Prompt 是这套系统的核心。试了几十种变体之后，这一版给我的结果最稳定：
> **说明**：下面只是邮件分类用的 LLM prompt。如果你想看完整的 n8n 自动化工作流（包括所有节点和连线），请直接往下滚到“完整 n8n Workflow JSON”那一节。
点击展开完整 LLM prompt
```
Act as an Email classifier. You will get email data and need to return the correct label from the list of available labels.
{{ $json.From }}
{{ $json.To }}
{{ $json.Subject }}
{{ $json.snippet }}
- `to_read` — for emails that need to be read by a human.
- a secret code from the app/service
- notification about a message in another system, Kirill needs to log in and answer
- notification about something important Kirill needs to do
- notification from GitHub about a new message or issue in my project
- `to_hide` — for emails that are OK to be in the archive, but not needed to be read by a human right now.
- Invoices
- Some subscription for charity or news
- Product updates
- Advertising
- Proposals from companies without a history of our communications, but do not hide proposals from individuals
- Mentioning me in Discord or Slack (most likely spam and group mentions, or I will see it again in Slack, and no need to see here)
- Slack notification about new messages because Kirill will see them in Slack, no need to see them in email.
- Community summaries
- Some service incidents are mentioned, and an overview
- Notification about one more subscriber, because it does not need action from Kirill, and we can hide it.
- meeting notes from some AI software, because it is OK to have them in the archive, but Kirill doesn't need to reread them now.
- `to_answer` — for emails that need an answer.
- some emails with questions from a person or a company
- message in external system, not in Gmail — bad example because Kirill can not answer via email. We need to mark these emails as to_read.
{{$node["exceptions"].json["additional-data-for-classify"]}}
Answer in JSON with two fields:
- `reasoning`
- `label`
```
我用结构化输出，确保 AI 始终返回合法类别。这样就没有解析错误，也不会出现模型突然用奇怪格式作答的边角情况。
## 三个月真实使用结果
自从我把这套系统跑起来之后：
- **节省时间**：每天大约省下 15 到 20 分钟的邮件分拣时间
- **准确率**：AI 对邮件的分类准确率大约在 95%
- **成本**：使用 GPT-5-nano 时每月不到 3 美元
- **误判**：大概每周只有 2 到 3 封邮件会被分错
5% 的误差完全可以接受。AI 分错的时候，我只要把邮件移到正确类别就行，整体还是比手动分拣快得多。
## 安全性考虑（以及我为什么能接受）
是的，OpenAI 现在会处理我所有个人邮件。从隐私角度看，这当然不完美，但我最终还是接受了，原因主要有几个。
第一，我早就用密码管理器和不依赖邮件的 2FA 验证码，所以即便邮箱本身出问题，也不至于灾难性。第二，OpenAI 通过我平时用 ChatGPT，原本就已经知道不少关于我的事。第三，对我这种个人工作流来说，节省下来的时间值得这笔隐私交换。
如果你处理的是敏感的商业邮件，那你可能更适合用本地 LLM，而不是 OpenAI API。n8n 这套搭法本身不变。
## 怎么开始搭你自己的邮件自动化
你可以这样自己搭这套系统：
1. **搭好 n8n** - 自托管也行，用 n8n Cloud 也行
2. **连接 Gmail** - 你需要完成 Gmail 账号授权
3. **获取 OpenAI API 访问权限** - 注册账号并拿到 API key
4. **导入工作流** - 如果有人需要，我会分享 JSON 导出文件
5. **定制 prompt** - 按照你的邮件模式调整分类逻辑
6. **先拿少量邮件测试** - 在全面自动化之前先小规模验证
如果你已经熟悉 n8n，整个配置大概 30 分钟就够。要是从零开始，可能需要一个小时左右。
## 为什么这比 Gmail 自带功能更好
Gmail 的自动分类是为所有人设计的，也就意味着它不会为任何一个具体的人做到真正优化。我的系统学的是我自己的邮件模式和偏好。
而且我随时都能改逻辑。想加第四个类别？改 prompt。需要对某些发件人特殊处理？加一个条件节点。Gmail 的规则很僵硬，而这套系统可以跟着我的需求一起变。
## 结论
跑了三个月之后，这套邮件自动化系统已经成了我日常工作流里不可缺少的一部分。它并不完美，但和每周手动整理几百封邮件相比，已经好太多了。
配置不复杂，持续成本很低，节省时间也是真实的。如果你也像我之前一样被邮件淹没，这个方法值得试试看。
只要记住一点：先从简单开始，充分测试，不要自动化任何你没法轻松撤销的东西。
## 完整 n8n Workflow JSON
如果你已经准备好亲手实现这套系统，下面就是可以直接导入的完整 n8n 工作流：
点击展开完整 JSON 工作流
```json
{
"name": "email-ai-automation-personal",
"nodes": [
{
"parameters": {
"rules": {
"values": [
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"leftValue": "={{ $json.message.content.label }}",
"rightValue": "to_read",
"operator": {
"type": "string",
"operation": "equals"
},
"id": "585b8b65-2369-4f4e-ba0d-4a7dfc7cdef9"
}
],
"combinator": "and"
},
"renameOutput": true,
"outputKey": "to_read"
},
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"id": "99e46836-ea2f-41d7-8ebc-24bd5cfadd41",
"leftValue": "={{ $json.message.content.label }}",
"rightValue": "to_hide",
"operator": {
"type": "string",
"operation": "equals",
"name": "filter.operator.equals"
}
}
],
"combinator": "and"
},
"renameOutput": true,
"outputKey": "to_hide"
},
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"id": "2a10fb26-99e6-43ab-854c-2d208f701ac3",
"leftValue": "={{ $json.message.content.label }}",
"rightValue": "to_answer",
"operator": {
"type": "string",
"operation": "equals",
"name": "filter.operator.equals"
}
}
],
"combinator": "and"
},
"renameOutput": true,
"outputKey": "to_answer"
}
]
},
"options": {
"fallbackOutput": "extra"
}
},
"type": "n8n-nodes-base.switch",
"typeVersion": 3.2,
"position": [
-1552,
432
],
"id": "ff266540-edd6-42ef-8e07-f1754c1aa394",
"name": "Switch"
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.splitInBatches",
"typeVersion": 3,
"position": [
-2256,
432
],
"id": "66a72457-b0e5-4ab0-bf49-ef00a06213aa",
"name": "Loop Over Items"
},
{
"parameters": {
"resource": "thread",
"operation": "addLabels",
"threadId": "={{ $json.threadId }}",
"labelIds": []
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.1,
"position": [
-1296,
112
],
"id": "47db305c-2f55-4797-b646-331903a798ad",
"name": "Add label to_hide",
"webhookId": "",
"credentials": {}
},
{
"parameters": {
"resource": "thread",
"operation": "addLabels",
"threadId": "={{ $json.threadId }}",
"labelIds": []
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.1,
"position": [
-1296,
480
],
"id": "bb1c5387-99a2-42ba-b615-ae65f39e9337",
"name": "Add label to_answer",
"webhookId": "",
"credentials": {}
},
{
"parameters": {
"resource": "thread",
"operation": "addLabels",
"threadId": "={{ $json.threadId }}",
"labelIds": []
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.1,
"position": [
-1296,
288
],
"id": "82f68387-c342-4222-87c3-f6bbb1a23f77",
"name": "Add label to_read",
"webhookId": "",
"credentials": {}
},
{
"parameters": {
"resource": "thread",
"operation": "addLabels",
"threadId": "={{ $('Merge2').item.json.threadId }}",
"labelIds": []
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.1,
"position": [
-880,
656
],
"id": "7de8f655-3c3d-49f9-9074-b39eaf4abaf2",
"name": "Add label processed_by_ai",
"webhookId": "",
"credentials": {}
},
{
"parameters": {
"resource": "thread",
"operation": "removeLabels",
"threadId": "={{ $('Merge2').item.json.threadId }}",
"labelIds": [
"INBOX"
]
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.1,
"position": [
-1040,
208
],
"id": "438c1618-edf3-41a1-b991-fef18b003edf",
"name": "Remove label from thread",
"webhookId": "",
"credentials": {}
},
{
"parameters": {
"pollTimes": {
"item": [
{
"mode": "everyX"
}
]
},
"filters": {}
},
"type": "n8n-nodes-base.gmailTrigger",
"typeVersion": 1.2,
"position": [
-2560,
160
],
"id": "676e065d-fc0b-4ae4-8c1b-4b663267023f",
"name": "Gmail Trigger",
"credentials": {}
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "be8e84b9-48af-4a49-b4f7-797382c13afe",
"name": "additional-data-for-classify",
"value": "\n- All messages from Deel with the main idea, like `New contractor submission`, please mark as `to_hide`.\n",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
-2240,
-32
],
"id": "9cb4e14e-cd43-4a7b-96c9-7acd93b8b947",
"name": "exceptions"
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-5-mini",
"mode": "list",
"cachedResultName": "GPT-5-MINI"
},
"messages": {
"values": [
{
"content": "=Act as an Email classifier. You will get email data and need to return the correct label from the list of available labels.\n\n\n{{ $json.From }}\n{{ $json.To }}\n\n{{ $json.Subject }}\n\n\n{{ $json.snippet }}\n\n\n\n\n\n\n- `to_read` — for emails that need to be read by a human. \n\n- a secret code from the app/service\n- notification about a message in another system, Kirill needs to log in and answer\n- notification about something important Kirill needs to do\n- notification from GitHub about a new message or issue in my project\n\n\n\n\n- `to_hide` — for emails that are OK to be in the archive, but not needed to be read by a human right now. \n \n- Invoices \n- Some subscription for charity or news\n- Product uddates\n- Advertising\n- Proposals from companies without a history of our communications, but do not hide proposals from individuals\n- Mentioning me in Discord or Slack (most likely spam and group mentions, or I will see it again in Slack, and no need to see here)\n- Slack notification about new messages because Kirill will see them in Slack, no need to see them in email.\n- Community summaries\n- Some service incidents are mentioned, and an overview\n- Notification about one more subscriber, because it does not need action from Kirill, and we can hide it.\n- meeting notes from some AI software, because it is OK to have them in the archive, but Kirill doesn't need to reread them now.\n\n\n\n\n- `to_answer` — for emails that need an answer.\n\n- some emails with questions from a person or a company\n\n\n- message in external system, not in Gmail — bad example because Kirill can not answer via email. We need to mark these emails as to_read.\n\n\n\n\n\n{{$node[\"exceptions\"].json[\"additional-data-for-classify\"]}}\n\nAnswer in JSON with two fields:\n- `reasoning`\n- `label`"
}
]
},
"jsonOutput": true,
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
-2032,
560
],
"id": "4f65ce04-67eb-4c28-9d56-6d2f03dedd41",
"name": "llm categorization",
"alwaysOutputData": false,
"credentials": {}
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"id": "95f8838a-df88-496c-a0ea-d19c612eb81b",
"leftValue": "={{ $json.labels.filter(item => item.id == \"__REPLACE_WITH_YOUR_PROCESSED_LABEL_ID__\") }}",
"rightValue": "={{ \"\" }}",
"operator": {
"type": "array",
"operation": "empty",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.filter",
"typeVersion": 2.2,
"position": [
-1936,
144
],
"id": "cc8dda5a-414c-4fb8-9774-6bdfe1224976",
"name": "Filter: not processed"
},
{
"parameters": {
"sendTo": "",
"subject": "There is a problem with AI email workflow — AI choose the wrong option",
"emailType": "text",
"message": "!",
"options": {}
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.1,
"position": [
-1296,
656
],
"id": "90cfc5e9-cddd-418e-bf95-5e9eb77b6778",
"name": "Send error message",
"webhookId": "",
"credentials": {}
},
{
"parameters": {
"operation": "getAll",
"returnAll": true,
"filters": {
"q": "=label:INBOX -label:__REPLACE_WITH_YOUR_PROCESSED_LABEL_ID__ after:2025/06/15"
}
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.1,
"position": [
-2400,
160
],
"id": "5d534d0c-0210-4d32-853d-d26a5b13c646",
"name": "Get emails with filter",
"webhookId": "",
"credentials": {}
},
{
"parameters": {
"mode": "combine",
"combineBy": "combineByPosition",
"options": {}
},
"type": "n8n-nodes-base.merge",
"typeVersion": 3.2,
"position": [
-2080,
144
],
"id": "c6e6b5bd-0196-4a4f-a36d-1f119df3ee5d",
"name": "Merge1"
},
{
"parameters": {
"mode": "combine",
"combineBy": "combineByPosition",
"options": {}
},
"type": "n8n-nodes-base.merge",
"typeVersion": 3.2,
"position": [
-1696,
464
],
"id": "d109e9f9-1089-4060-89e4-e70762d0aea5",
"name": "Merge2"
}
],
"pinData": {},
"connections": {
"Switch": {
"main": [
[
{
"node": "Add label to_read",
"type": "main",
"index": 0
}
],
[
{
"node": "Add label to_hide",
"type": "main",
"index": 0
}
],
[
{
"node": "Add label to_answer",
"type": "main",
"index": 0
}
],
[
{
"node": "Send error message",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "llm categorization",
"type": "main",
"index": 0
},
{
"node": "Merge2",
"type": "main",
"index": 0
}
]
]
},
"Add label to_hide": {
"main": [
[
{
"node": "Remove label from thread",
"type": "main",
"index": 0
}
]
]
},
"Add label to_answer": {
"main": [
[
{
"node": "Add label processed_by_ai",
"type": "main",
"index": 0
}
]
]
},
"Add label to_read": {
"main": [
[
{
"node": "Add label processed_by_ai",
"type": "main",
"index": 0
}
]
]
},
"Add label processed_by_ai": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Remove label from thread": {
"main": [
[
{
"node": "Add label processed_by_ai",
"type": "main",
"index": 0
}
]
]
},
"Gmail Trigger": {
"main": [
[
{
"node": "Get emails with filter",
"type": "main",
"index": 0
}
]
]
},
"exceptions": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 0
}
]
]
},
"llm categorization": {
"main": [
[
{
"node": "Merge2",
"type": "main",
"index": 1
}
]
]
},
"Filter: not processed": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Send error message": {
"main": [
[
{
"node": "Add label processed_by_ai",
"type": "main",
"index": 0
}
]
]
},
"Get emails with filter": {
"main": [
[
{
"node": "exceptions",
"type": "main",
"index": 0
},
{
"node": "Merge1",
"type": "main",
"index": 1
}
]
]
},
"Merge1": {
"main": [
[
{
"node": "Filter: not processed",
"type": "main",
"index": 0
}
]
]
},
"Merge2": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
}
},
"active": true,
"settings": {
"executionOrder": "v1",
"callerPolicy": "workflowsFromSameOwner"
},
"versionId": "",
"meta": {
"templateCredsSetupCompleted": true,
"instanceId": ""
},
"id": "",
"tags": []
}
```
---
### 导入说明
1. 复制上面的 JSON
2. 将它保存为名为 `email-ai-automation-personal.json` 的文件
3. 在 n8n 里进入 Workflows → Import from JSON
4. 选择你保存的文件，然后点击 Import
5. 配置 Gmail 和 OpenAI 的凭据
6. 把 `__REPLACE_WITH_YOUR_PROCESSED_LABEL_ID__` 替换成你要给已处理邮件使用的标签 ID
7. 配置出错时发送的邮件（fallback）
8. 在启用触发器之前，先用几封邮件测试
导入之后，记得更新 OpenAI API key 和 Gmail 认证信息。
## 视频教程：看完整配置过程
如果你更喜欢视频学习，我也录了一个完整教程，把这套邮件自动化系统的搭建过程全部走了一遍：
[](https://www.youtube.com/watch?v=xn1RyMlZudE)
视频会展示创建 n8n 工作流、配置 OpenAI 集成、连接 Gmail，以及测试整个自动化流程的每一步。你会看到节点的精确配置、LLM prompt 的实际效果，以及系统如何实时分类真实邮件。
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