我早就不想再等 Gmail 自己判断哪些邮件对我真的重要了。让 AI 接手我的个人邮件分类三个月之后，我已经很难想象再回到手动整理收件箱的日子。
这套系统简单得有点离谱：GPT-5-nano 会把每封邮件分到三类之一：直接归档、稍后阅读或需要回复。就这么简单。
整套流程都跑在 n8n 上。因为我用的是 OpenAI 最便宜、同时支持结构化输出的模型，所以成本几乎可以忽略。没错，OpenAI 现在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。
这不是什么纸上谈兵的概念。我已经把它稳定跑了三个月，每周都能实打实省下几个小时。
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，全都挤在同一个收件箱里。Gmail 自带的分类总是漏掉一些明明很明显的规律，而我每天早上都得花 20 分钟，光是判断哪些邮件值得看。
真正让我决定动手的，是我发现自己有 80% 的邮件连看都不看，直接就归档了。如果大多数邮件根本不需要我关注，为什么还要由我来判断哪些该看？
更喜欢视频教程？ 我录了一段一步一步的演示，把这套邮件自动化工作流从头到尾跑了一遍。点击查看用 n8n 和 LLM 做邮件自动化的教程，可以直接看到完整配置过程。
真正管用的三分类法
我的 AI 会把每一封邮件严格归到下面三类之一：
- 直接归档 - 我订阅了但几乎不会看的新闻邮件、自动通知、促销邮件
- 稍后阅读 - 我想看，但不需要回复的内容
- 需要回复 - 需要我亲自回信的邮件
不需要复杂的文件夹结构，也不需要优先级体系。就这三类处理动作，已经能覆盖我全部的邮件处理场景。
搭建这个 n8n 工作流
下面就是把这一切自动跑起来的完整 n8n 工作流：
每当 Gmail 收件箱里有新邮件进来，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，把暂时不需要我处理的邮件直接归档。
需要的 n8n 节点
你的工作流里需要这些节点：
- Gmail Trigger（Gmail 触发器）- 监控新邮件
- OpenAI Chat Model - 负责判断邮件类别
- Gmail - 添加标签并归档邮件
- IF 条件节点 - 根据 AI 的判断把邮件分流到不同分支
真正关键的是 OpenAI 节点的配置。下面是我的设置方式：
让这套系统跑起来的 LLM 提示词
提示词是这套系统的核心。试了几十种版本之后，这一版给我的结果最稳定：
说明：下面这段提示词我保留英文原文，方便你直接复制到 n8n 或 OpenAI 节点里使用。它只包含邮件分类逻辑；如果你想看完整的 n8n 自动化工作流（包括所有节点和连接关系），请继续往下滚到“完整 n8n 工作流 JSON”那一节。
点击展开完整英文提示词
Act as an Email classifier. You will get email data and need to return the correct label from the list of available labels.
<email-data>
<from>{{ $json.From }}</from>
<to>{{ $json.To }}</to>
<email-subject>
{{ $json.Subject }}
</email-subject>
<email-snippet>
{{ $json.snippet }}
</email-snippet>
</email-data>
<possible-labels>
<to_read>
- `to_read` — for emails that need to be read by a human.
<to_read-examples>
- a secret code from the app/service
- notification about a message in another system, Kirill needs to log in and answer
- notification about something important Kirill needs to do
- notification from GitHub about a new message or issue in my project
</to_read-examples>
</to_read>
<to_hide>
- `to_hide` — for emails that are OK to be in the archive, but not needed to be read by a human right now.
<to_hide-examples>
- Invoices
- Some subscription for charity or news
- Product updates
- Advertising
- Proposals from companies without a history of our communications, but do not hide proposals from individuals
- Mentioning me in Discord or Slack (most likely spam and group mentions, or I will see it again in Slack, and no need to see here)
- Slack notification about new messages because Kirill will see them in Slack, no need to see them in email.
- Community summaries
- Some service incidents are mentioned, and an overview
- Notification about one more subscriber, because it does not need action from Kirill, and we can hide it.
- meeting notes from some AI software, because it is OK to have them in the archive, but Kirill doesn't need to reread them now.
</to_hide-examples>
</to_hide>
<to_answer>
- `to_answer` — for emails that need an answer.
<to_answer-examples>
- some emails with questions from a person or a company
</to_answer-examples>
<to_answer-bad-examples>
- message in external system, not in Gmail — bad example because Kirill can not answer via email. We need to mark these emails as to_read.
</to_answer-bad-examples>
</to_answer>
</possible-labels>
{{$node["exceptions"].json["additional-data-for-classify"]}}
Answer in JSON with two fields:
- `reasoning`
- `label`
我这里用结构化输出，是为了确保 AI 每次都返回合法类别。这样不会有解析错误，也能避免模型临时发挥，把返回格式搞乱。
三个月的真实效果
这套系统真正跑起来之后：
- 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% 的误差完全在可接受范围内。AI 分错的时候，我只要把邮件移到正确类别就行，然后继续往下处理。整体还是比手动一封封分拣快得多。
安全性上的取舍，以及为什么我能接受
是的，OpenAI 现在会处理我所有个人邮件。从隐私角度看，这当然不算理想，但我最后还是接受了，主要有几个原因。
第一，我早就用了密码管理器，2FA 验证码也不走邮箱，所以就算邮箱本身出问题，也不至于造成灾难。第二，OpenAI 通过我平时使用 ChatGPT，本来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，省下来的时间值得这点隐私上的取舍。
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 的整体搭法是一样的。
开始搭你自己的邮件自动化
你可以按下面的顺序自己搭这套系统：
- 搭好 n8n - 自托管也行，用 n8n Cloud 也行
- 连接 Gmail - 你需要完成 Gmail 账号授权
- 获取 OpenAI API 访问权限 - 注册账号并拿到 API 密钥
- 导入工作流 - 如果有人需要，我会分享 JSON 导出文件
- 定制提示词 - 按照你的邮件模式调整分类逻辑
- 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 分钟就够了。要是从零开始，可能需要一个小时左右。
为什么它比 Gmail 自带功能更好用
Gmail 的自动分类是为所有人设计的，也就意味着它很难真正贴合某一个具体的人。我的系统针对的是我自己的邮件模式和处理偏好。
而且我随时都能改逻辑。想加第四类？改提示词。需要对某些发件人做特殊处理？加一个条件节点。Gmail 的规则很僵硬，这套系统则可以跟着我的需求一起变。
最后总结
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离不开的一部分。它并不完美，但和每周手动整理几百封邮件相比，已经省心太多了。
配置不复杂，持续成本很低，节省下来的时间也是真实存在的。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：先从最简单的版本开始，充分测试，不要自动化任何你没法轻松撤销的东西。
完整 n8n 工作流 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
{
"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": "<additional-data-for-classify>\n- All messages from Deel with the main idea, like `New contractor submission`, please mark as `to_hide`.\n</additional-data-for-classify>",
"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<email-data>\n<from>{{ $json.From }}</from>\n<to>{{ $json.To }}</to>\n<email-subject>\n{{ $json.Subject }}\n</email-subject>\n<email-snippet>\n{{ $json.snippet }}\n</email-snippet>\n</email-data>\n\n<possible-lables>\n\n<to_read>\n- `to_read` — for emails that need to be read by a human. \n<to_read-examples>\n- a secret code from the app/service\n- notification about a message in another system, Kirill needs to log in and answer\n- notification about something important Kirill needs to do\n- notification from GitHub about a new message or issue in my project\n</to_read-examples>\n</to_read>\n\n<to_hide>\n- `to_hide` — for emails that are OK to be in the archive, but not needed to be read by a human right now. \n<to_hide-examples> \n- Invoices \n- Some subscription for charity or news\n- Product uddates\n- Advertising\n- Proposals from companies without a history of our communications, but do not hide proposals from individuals\n- Mentioning me in Discord or Slack (most likely spam and group mentions, or I will see it again in Slack, and no need to see here)\n- Slack notification about new messages because Kirill will see them in Slack, no need to see them in email.\n- Community summaries\n- Some service incidents are mentioned, and an overview\n- Notification about one more subscriber, because it does not need action from Kirill, and we can hide it.\n- meeting notes from some AI software, because it is OK to have them in the archive, but Kirill doesn't need to reread them now.\n</to_hide-examples>\n</to_hide>\n\n<to_answer>\n- `to_answer` — for emails that need an answer.\n<to_answer-examples>\n- some emails with questions from a person or a company\n</to_answer-examples>\n<to_answer-bad-examples>\n- message in external system, not in Gmail — bad example because Kirill can not answer via email. We need to mark these emails as to_read.\n</to_answer-bad-examples>\n</to_answer>\n\n</possible-lables>\n\n{{$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
- 复制上面的 JSON
- 把它保存为
email-ai-automation-personal.json - 在 n8n 里进入
Workflows → Import from JSON - 选择刚保存的文件，然后点击
Import - 配置 Gmail 和 OpenAI 凭据
- 把
__REPLACE_WITH_YOUR_PROCESSED_LABEL_ID__替换成你想用于已处理邮件的标签 ID - 配置在出错时发送的提醒邮件
- 在启用触发器之前，先用几封邮件测试
导入完成后，记得更新 OpenAI API 密钥和 Gmail 认证信息。
视频教程：完整邮件自动化配置
如果你更喜欢跟着视频学，我也录了一个完整教程，把这套邮件自动化系统从搭建到测试完整走了一遍：
视频里会展示如何创建 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。你会看到各个节点的具体配置、LLM 提示词的实际效果，以及系统如何实时分类真实邮件。
