AI Gmail Auto-Labeler Workflow Explained

A Marketer, an Inbox, and One Too Many Labels

By 9:15 a.m., Lina’s day was already off track.

As the marketing lead at a fast-growing SaaS startup, she lived in her Gmail inbox. Customer feedback, partner proposals, invoices, support escalations, internal approvals, product updates – everything passed through the same crowded place. She had done what every productivity blog suggested. She created labels. Lots of them.

Work, Personal, Finance, Support, Paid Campaigns, Product Feedback, High Priority and a dozen more. In theory, her inbox should have been a beautifully organized command center. In reality, it was a maze.

Some mornings she would spend 30 minutes just dragging emails into the right labels. Other times she would give up and let everything pile up in the Inbox label, promising herself she would “sort it later.” She never did.

One afternoon, after missing an important partner email that had been buried between newsletters, Lina decided something had to change. Manual sorting was no longer an option. She needed automation, not another color-coded system that depended on her willpower.

Discovering an AI-Powered Shortcut in n8n

Lina already used n8n for a few marketing automations, like syncing leads and sending notifications. While browsing templates, a title caught her eye:

AI Gmail Auto-Labeler

The description sounded exactly like what she wanted. An n8n workflow template that would:

• Watch her Gmail for new unread emails
• Send the content to Gemini AI for classification
• Automatically apply the right Gmail labels

No more manual dragging. No more inbox guilt. Just automatic, AI-powered labeling that respected the labels she already had.

What convinced her was the architecture. The workflow did not hardcode any labels. Instead, it dynamically pulled her existing Gmail labels, passed them to Gemini AI, and only allowed the AI to choose from that list. That meant the automation would adapt if she added, renamed, or removed labels.

It sounded promising, but Lina wanted to know how it actually worked. So she opened the template and started following the data trail.

Inside the Workflow: How the Story Flows

As Lina explored the workflow, she realized it followed a simple, linear path. Each n8n node took the output of the previous one, transformed it, and passed it on as structured JSON. The sequence looked like this:

1. Gmail Trigger – Listens for new unread emails.
2. Get Many Labels – Pulls all Gmail labels with names and IDs.
3. Aggregate – Combines all label records into one array.
4. Edit Fields – Turns label names into a comma-separated list.
5. Message a Model – Sends email content and label list to Gemini AI.
6. Code – Maps AI-chosen label names back to Gmail label IDs.
7. Add Label to Message – Applies the chosen labels to the email.

It was technical, but it also felt surprisingly intuitive. The workflow was basically acting like a smart assistant: watch, understand, decide, then label.

Rising Tension: The Pain of Manual Labeling

Before setting it up, Lina thought about all the edge cases that had burned her in the past.

• What if she added a new label later? Would she have to edit the workflow?
• What if the AI invented labels that did not exist?
• What if an important email was misclassified?

The template documentation addressed each of these concerns. Labels were fetched dynamically, so the workflow always used her current Gmail label set. The AI was explicitly restricted to the labels provided to it. And if anything went wrong, the email would simply stay unlabeled, not broken or deleted.

That was enough to convince her to try. She started from the beginning of the workflow and walked through each node as if she were the email traveling through it.

Step Into the Story: An Email Enters the Workflow

The Moment the Email Arrives: Gmail Trigger

Lina imagined a new customer email landing in her inbox at 10:02 a.m.

The Gmail Trigger node was the first gatekeeper. It was configured to:

• Trigger type: New email
• Filter: Unread messages only
• Polling interval: 1 minute

Every minute, n8n checked for new unread emails. When it found one, it captured key details:

• Message ID
• Subject
• Snippet or preview text
• From and To fields
• Thread information

If no new unread emails appeared, nothing else ran. That meant no wasted API calls and no risk of reprocessing old messages she had already handled.

Learning the Language of Labels: Get Many Labels and Aggregate

Next, the workflow needed to understand Lina’s labeling system.

The Get Many Labels node connected to the Gmail API using her Gmail credentials and pulled the full list of labels from her account.

• Operation: Get Many Labels
• Output: An array of label objects, each with:
  • id – the Gmail label identifier used by the API
  • name – the human-readable label name in Gmail

Each label came out as its own item. To make life easier for the next nodes, the workflow used an Aggregate node.

• Input: Multiple items, one per label
• Operation: Combine items into a single array
• Output: One item containing an array of all label objects

Now the workflow had a single, tidy list of every label Lina had created. No hardcoding, no manual updates. If she ever added a new label like “Partnerships” or “VIP”, the workflow would automatically see it.

Preparing the AI’s Menu: Edit Fields

To make sure Gemini AI did not go off script, Lina needed to give it a clear menu of options.

The Edit Fields node took the aggregated label array and transformed it into a format the AI could easily consume.

• Input: Aggregated label array
• Transformation:
  • Map each label object to its name
  • Join those names into one comma-separated string
• Output: A string like "Work,Personal,Finance,Support"

This string was added to the item as a field. It would be passed along to the AI as part of the prompt. By limiting Gemini AI to that explicit list, Lina ensured it could not invent labels that did not exist in her Gmail account.

The Turning Point: AI Reads the Email

Gemini AI Enters the Scene: Message a Model

Now came the brain of the operation.

The Message a Model node connected n8n to the Gemini AI endpoint using her configured Gemini API credentials. It fed the AI two main inputs:

• The email subject and snippet from the Gmail Trigger node
• The comma-separated label list from the Edit Fields node

The system prompt was carefully crafted. It instructed Gemini AI to:

• Read the email subject and snippet
• Select the most appropriate Gmail label or labels
• Choose only from the provided label list

The node sent a structured request and expected a simple response: a text string containing one or more label names, separated by commas. For example:

Finance,Important

Lina liked that the workflow supported multi-label classification. A single email could be both “Finance” and “Important” or “Support” and “Product Feedback”. That matched the messy reality of her inbox better than strict one-label rules.

Bridging AI and Gmail: Code Node

There was one problem. Gmail did not care about label names. It only worked with label id values.

The Code node acted as the translator between AI output and Gmail requirements.

• Input:
  • The AI response string with label names separated by commas
  • The full Gmail label metadata (names and IDs) from earlier nodes
• Responsibilities:
  • Split the AI response into individual label names
  • Trim whitespace and normalize formatting
  • Match each AI label name to the corresponding Gmail label object
  • Extract the id for each matched label
  • Build the data structure expected by the Gmail API for label assignment

The result was a clean array of Gmail label IDs that exactly matched the names chosen by the AI.

If Gemini AI returned a label name that did not match any existing label, the Code node simply skipped it. No errors, no broken workflow. Only valid labels were passed onward. Lina appreciated that safety net, especially while she was still building trust in the system.

The Final Move: Add Label to Message

Everything came together in the last node.

The Add Label to Message node called the Gmail API and applied the selected labels to the actual email.

• Input:
  • The Gmail message ID from the Gmail Trigger node
  • The list of label IDs generated by the Code node
• Operation: Add Label to Message
• Effect: The email in Gmail was updated with the new labels

Once this node executed successfully, the message appeared in Lina’s Gmail with all AI-selected labels applied. The workflow then went quiet again, waiting for the next unread email to arrive.

Behind the Scenes: Configuring the Workflow Safely

Credentials and Security

Before Lina hit “Activate,” she checked the security details. The template required two sets of credentials:

• Gmail credentials for:
  • The Gmail Trigger node
  • All Gmail operation nodes, including Get Many Labels and Add Label to Message
• Gemini API credentials for:
  • The Message a Model node

She made sure her Gmail OAuth scope allowed reading messages and modifying labels. In n8n, she stored both Gmail and Gemini credentials securely and avoided putting any secrets directly into Code nodes or environment variables unless properly managed.

Designing Labels That AI Can Understand

Lina realized that good automation depended on good label design.

• She defined her Gmail labels in advance, knowing the workflow would automatically read the full set.
• She kept in mind that the AI was restricted to her existing labels and would not invent new ones.
• She adjusted label names to be unique and descriptive, avoiding multiple labels with nearly identical meanings.
• She reviewed system labels like Inbox, Sent, and Drafts and decided how comfortable she was with the AI using or ignoring them.

With a clearer label taxonomy, she gave both herself and the AI a better chance at consistent classification.

Tuning the Trigger: What Gets Classified

Next, Lina refined which emails should go through AI classification.

• The default behavior was to monitor unread emails only, which she liked. It prevented reprocessing and kept things clean.
• She noted that she could change the Gmail Trigger if needed to:
  • Include read messages
  • Filter by sender, subject, or other conditions
  • Target a specific label or folder
• She kept the polling interval at 1 minute to balance responsiveness and API usage.

For now, she stayed with the default configuration and planned to tighten filters later if needed.

What Happens When Things Go Wrong

Lina knew that no automation is perfect. She wanted to understand failure modes before trusting it with her whole inbox.

The data flow was mostly linear. Each node depended on the output of the previous one. That meant:

• If the Gemini API call failed, no labels would be applied. The email would remain unmodified in Gmail.
• If the AI returned an empty or malformed label list, the Code node would not find any matching label IDs, so the Add Label to Message node would have nothing to do.
• If she removed a label from Gmail, the workflow would simply stop using it because labels were fetched dynamically each time.

There were no destructive operations, no deletions, no risky moves. The worst-case scenario was that an email stayed unlabeled, which Lina could live with.

Leveling Up: How Lina Customized the Workflow

Pre-Classification Filters

Once the basic template was running, Lina started thinking about optimization. She did not want to send every single email to Gemini AI.

She experimented with adding nodes before the Message a Model step:

• A Filter node to skip emails from internal senders she already handled with other rules.
• A Function or Code node to build a richer prompt using more of the email body when available.
• Simple rule-based checks, such as:
  • If the subject contained “invoice” or “receipt,” assign a “Finance” label directly without calling AI.

This hybrid approach let her reserve AI calls for the emails that were genuinely ambiguous or nuanced.

Post-Classification Actions

Once she trusted the AI Gmail Auto-Labeler, Lina started to see it as more than just an inbox organizer.

After the Add Label to Message node, she added optional branches to:

• Send a summary notification to Slack whenever high-priority labels were applied.
• Log classification results to a spreadsheet for later review and tuning.
• Trigger follow-up automations, such as:
  • Creating tasks when certain labels appeared
  • Forwarding specific categories to other teams

What started as a way to avoid manual labeling turned into a small, intelligent routing system for her entire communication flow.

The Payoff: Benefits Lina Actually Felt

After a few days, the impact was obvious.

• Dynamic labeling – When Lina added or renamed labels in Gmail, the workflow automatically adapted. She never had to touch the template.
• AI-driven accuracy – Gemini AI handled contextual classification surprisingly well and stayed consistent because it was limited to her existing labels.
• Multi-label support – Complex emails could carry multiple labels like “Finance” and “Important” or “Support” and “Product Feedback,” reflecting real-world nuance.
• Time-saving automation – Manual sorting dropped to nearly zero. Her inbox felt lighter and more structured, and she could finally focus on the messages that mattered.

Instead