Classify Gmail emails with OpenAI and Telegram feedback

Description

An AI-powered Gmail assistant built with n8n that automatically labels emails, learns from your decisions, and safely improves over time using human-in-the-loop training.

This workflow combines:

Gmail
OpenAI
Telegram
n8n Data Tables

to create a trainable AI inbox workflow that behaves more like an executive assistant than a traditional spam filter.

Features

✅ AI-powered Gmail classification
✅ Dynamic Gmail label discovery
✅ Human-in-the-loop review system
✅ Trainable via Telegram
✅ Historical learning from previous decisions
✅ Gmail-safe architecture (labels first, no auto-delete)
✅ Backfill support for older emails
✅ Configurable confidence thresholds

How It Works

The system uses a simple but powerful workflow:

New Email
↓
AI analyzes email
↓
Apply labels if confident
↓
If uncertain → send for review
↓
User teaches AI through Telegram
↓
Future emails become easier to classify


The workflow dynamically loads all your labels but it filters for sub-labels under the parent label AI.

IMPORTANT Gmail Setup

Create a parent Gmail label named:

AI

Then create sub-labels underneath it.

Becuase the system dynamically fetches your labels:
you can customize your own labels
the AI only uses valid existing labels
labels stay synchronized with Gmail automatically

What's happening underneath the hood?

Every processed email receives the AI parent label so emails are not repeatedly reprocessed.

The workflow uses a configurable confidence threshold.

// Example
item
.json.confidenceThreshold = .9;

| Threshold | Result |
|-----------|----------|
| 0.95 | Very conservative|
| 0.90 | Recommended|
| 0.80 | More automation|
| 0.70 | Aggressive automation|

If confidence is:

ABOVE threshold → labels are applied automatically
BELOW threshold → email enters review queue

Human-in-the-Loop Training

When the AI is uncertain the email is tagged and a rule is added to the data table for review. Once you trigger the training flow, Telegram asks the user how future emails should be handled.

For each email rule, the user can:
choose which label should apply
reject the classification
add additional instructions for more nuanced behavior

Historical Learning

Before asking the user for help, the AI searches previous decisions from the same sender.

If previous reviewed decisions exist:
confidence may increase
historical labels may be reused
automation becomes more accurate over time

Pending/unreviewed decisions are treated as weak references only.

Data Table Setup

Create an n8n Data Table named:
Email Rules


Recommended columns:

|Column| Type|
|------|------------|
|emailId| string|
|threadId| string|
|fromEmail| string|
|fromName| string|
|subject| string|
|snippet| string|
|confidence| number|
|labelsApplied| string|
|reason| string|
|userQuestion| string|
|ruleSuggestion| string|
|isPending| boolean|
|recommendedAction| string|

Suggested Improvements

Ideas for future upgrades:

broad email provider support
Notion-based rule management
AI-generated email drafts
Daily review digests
Multi-user support
Chat-based rule revision
Web dashboard
Safe-deletion

Requirements
Gmail (with sub-labels under AI)
OpenAI API Key
Telegram Chatbot