Automate Gmail Labeling Using Gemini AI & n8n

Overview

This guide describes a production-ready n8n workflow template that automatically labels incoming Gmail messages using Google Gemini AI. The workflow connects Gmail, Gemini-2.5-Pro, and n8n to classify new emails and apply one or more existing Gmail labels based on the content of each message.

The goal is to replace static Gmail filters with a dynamic AI classification pipeline that:

• Monitors new unread emails in Gmail
• Retrieves all existing Gmail labels and their IDs
• Asks Gemini AI to select the most relevant label or labels
• Maps label names back to Gmail label IDs
• Applies the labels to the message automatically

The workflow is built as a reusable n8n template, so you can import it, connect your own credentials, and adapt the classification rules to your own labeling scheme.

Architecture & Data Flow

The automation is implemented as a linear n8n workflow with a Gmail trigger at the start and a Gmail labeling operation at the end. Between these two ends, the workflow orchestrates label retrieval, AI inference, and mapping logic.

High-level execution sequence

1. Gmail Trigger node polls for new unread messages.
2. Get Many Labels node fetches all Gmail labels and their IDs.
3. Aggregate Labels node consolidates label data into a single collection.
4. Edit Fields node builds a comma-separated list of label names for the AI.
5. Gemini AI node (Gemini-2.5-Pro) receives the email subject and snippet plus the label list and returns a comma-separated list of label names to apply.
6. Code node maps AI-selected label names to their corresponding Gmail label IDs.
7. Gmail node applies the resolved label IDs to the specific email message.

Only newly received unread emails are processed, so existing mailbox content is not modified unless you deliberately adjust the trigger configuration.

Prerequisites & Setup

Required services

• n8n instance (self-hosted or cloud) with access to the n8n workflow template.
• Gmail account with labels already created and organized.
• Google Gemini API access with credentials that can call the Gemini-2.5-Pro model.

Credentials configuration in n8n

• Gmail credentials (OAuth2)
  Configure OAuth2 credentials in n8n for Gmail with permissions that allow:
  • Reading messages (including subject and snippet)
  • Listing labels
  • Modifying message labels
• Gemini AI credentials
  Add your Gemini API key or service credentials in n8n and configure the Gemini node to use the Gemini-2.5-Pro model for text classification.

Gmail label preparation

Before using the template, clean up and standardize your Gmail labels. The AI will only choose from labels that already exist in your account, so clear naming is important. Examples:

• Use descriptive labels such as Clients, Invoices, Campaign Reports, Personal, Client Support.
• Remove obsolete or redundant labels that you no longer want to use.
• Avoid multiple labels with very similar meanings unless you actually need that granularity.

Node-by-Node Breakdown

1. Gmail Trigger – Watch new unread messages

The workflow starts with a Gmail Trigger node configured to watch for new, unread emails. This node is responsible for:

• Polling Gmail at a defined interval.
• Emitting a new item in n8n each time a new unread email is detected.
• Passing message metadata such as:
  • Message ID (used later to apply labels)
  • Subject line
  • Snippet (a short preview of the body)

By default, the trigger focuses on unread messages only. This keeps the automation from retroactively modifying older messages unless you explicitly change the trigger parameters.

2. Get Many Labels – Retrieve Gmail labels and IDs

Next, a Get Many Labels node queries the Gmail API to fetch all labels associated with the connected account. This includes:

• System labels (for example, INBOX, SENT, STARRED)
• Custom labels created by the user (for example, Clients, Invoices, Campaign Reports)

For each label, the node retrieves:

• Label name (human-readable)
• Label ID (API identifier used when applying labels)

This mapping is essential because Gmail operations that add labels to a message require label IDs, not just label names.

3. Aggregate Labels – Build a unified label collection

The Aggregate Labels node consolidates the label data into a single, structured collection that can be easily reused downstream. Instead of treating each label as a separate item, this node:

• Combines all label records into a single array or object field.
• Makes it straightforward for subsequent nodes to iterate or reference labels without repeatedly querying Gmail.

This collection is later used to both provide the AI with the list of possible label names and to map AI-selected names back to label IDs.

4. Edit Fields – Construct the AI label list

The Edit Fields node transforms the raw label data into a format that Gemini can consume. It typically:

• Extracts the name of each label from the aggregated collection.
• Builds a comma-separated string containing all label names, for example:
  Clients, Invoices, Campaign Reports, Personal, Client Support
• Stores this string in a field such as labelList that is passed into the Gemini node.

Effectively, you are instructing the AI: “These are the only valid labels. Choose from this list and do not invent new categories.”

5. Gemini AI Node – Classify emails using Gemini-2.5-Pro

The Gemini node forms the core AI classification step. For each email emitted by the Gmail Trigger, this node:

• Receives the email subject and snippet as input text.
• Receives the labelList string as the set of allowed labels.
• Uses the Gemini-2.5-Pro model to infer which label or labels best describe the email.

The node is configured so that Gemini returns a comma-separated list of label names. Examples:

• A client brief might yield: Clients, Campaign Reports
• An invoice email might yield: Invoices
• A personal newsletter might yield: Personal

Key behavior:

• Gemini is instructed to select only from the existing labels provided in labelList.
• The output uses label names, not IDs, so a mapping step is still required before calling the Gmail API.

6. Code Node – Map label names to Gmail label IDs

After Gemini returns label names, a Code node performs a deterministic mapping from those names to actual Gmail label IDs. This node typically:

• Parses the AI output string into an array of label names, splitting on commas and trimming whitespace.
• Iterates over the aggregated label collection retrieved earlier.
• For each AI-selected label name, finds the corresponding label object and reads its label ID.
• Builds an array of label IDs to be attached to the email.

This step is critical because the final Gmail operation cannot work with names alone. It must receive valid label IDs that exist in the connected Gmail account.

7. Gmail Node – Apply labels to the message

The final step uses a Gmail node configured with the appropriate operation to add labels to a message. It:

• Receives the target message ID from the Gmail Trigger node.
• Receives the array of label IDs from the Code node.
• Calls the Gmail API to apply these labels to the specified message.

As a result, within a short time after arrival, each email is automatically tagged with one or more labels such as “Clients,” “Invoices,” or “Campaign Reports” according to the AI’s classification.

Configuration Notes & Best Practices

Trigger configuration and performance

• Unread-only processing keeps the workflow focused on new messages and avoids reprocessing older content.
• Polling interval can be tuned to your needs:
  • Shorter intervals approximate near real-time labeling.
  • Longer intervals reduce API calls and resource usage.

Label quality and naming

• Use distinct and descriptive names so Gemini can clearly differentiate labels, for example:
  • Client Support instead of a generic Support
  • Billing – Invoices instead of just Billing
• Avoid overlapping labels where the difference is ambiguous unless you plan to fine tune the AI prompt for that nuance.

Prompt tuning in the Gemini node

To improve classification accuracy, you can refine the prompt within the Gemini node. Typical adjustments include:

• Clarifying which types of emails should map to each label.
• Specifying whether multiple labels are preferred when an email spans several topics.
• Reinforcing that the AI must only output labels from the provided labelList.

Reviewing misclassified examples and updating the prompt accordingly can significantly improve results over time.

Multiple labels per message

The workflow supports assigning multiple labels to a single email. This is useful when messages are relevant to several categories, such as a client email that includes an invoice attachment, which might receive both Clients and Invoices.

Edge cases and behavior

During initial testing, you may encounter situations where the AI’s chosen label does not match your expectation. Common responses include:

• Renaming labels to be more explicit or less ambiguous.
• Updating the Gemini node prompt to clarify how to treat borderline messages.
• Adding conditional logic or extra nodes to handle specific cases before or after labeling, for example archiving certain categories once labeled.

Advanced Customization

Additional workflow steps

Once the core Gmail labeling is in place, you can extend the workflow to automate more of your email management. Examples of additional steps include:

• Auto-archiving certain labels after they are applied, such as newsletters or low-priority notifications.
• Filtering messages by sender or domain before AI classification, if you want to exclude specific sources from processing.
• Routing labeled messages into other systems, such as:
  • Creating tasks in a project management tool for “Clients” or “Campaign Reports.”
  • Forwarding “Invoices” to an accounting system or shared mailbox.

Adjusting trigger strategy

You can modify how and when emails are captured:

• Change the Gmail Trigger settings if you want to process messages based on additional criteria, such as specific folders or labels.
• Run the workflow on a schedule that suits your volume and latency requirements.

Iterative improvement workflow

To systematically improve accuracy over time:

• Periodically review a sample of labeled emails.
• Note where the chosen label is incorrect or incomplete.
• Refine:
  • Label names in Gmail
  • Prompt instructions in the Gemini node
  • Any conditional logic or filters in the n8n workflow

Practical Outcomes

In a typical usage scenario, such as a marketing manager handling a high volume of mixed emails, this automation delivers several tangible benefits:

• Dynamic labeling aligned with your workflow
  Since Gemini only selects from labels that already exist in your Gmail account, the resulting structure reflects your actual processes rather than arbitrary AI-generated categories.
• Multi-dimensional retrieval
  Messages that touch several topics can be found via multiple labels later, for example by searching under both “Clients” and “Campaign Reports.”
• Reduced manual effort
  You no longer need to maintain large sets of static Gmail filters or manually drag messages into folders. Labeling becomes a background process.
• High customizability
  Changes in your role or priorities can be reflected quickly by adding or renaming Gmail labels, updating the Gemini prompt, or inserting new nodes into the workflow.

Implementation Checklist

To replicate this AI-powered Gmail labeling setup in your own environment, follow this checklist:

• Connect your Gmail account in n8n using OAuth2 credentials with read and modify label permissions.
• Add your Gemini AI credentials and configure the model to Gemini-2.5-Pro.
• Prepare and clean your Gmail labels in advance, for example:
  • Work
  • Personal
  • Invoices
  • Clients
  • Campaign Reports
• Import the n8n Gmail labeling template into your n8n instance.
• Configure the Gmail Trigger node to watch for unread emails at a suitable polling interval.
• Review and adjust the prompt in the Gemini node to match your labeling rules and preferences.
• Optionally add filters or follow-up actions, such as archiving or routing messages based on labels.

Next Steps

By combining n8n, Gmail, and Google Gemini AI, you can transform a disorganized inbox into a self-organizing system that classifies and labels emails in the background. This template provides a robust starting point that you can adapt to your own label structure and business workflows.

Experiment with your labels, refine the Gemini prompt as you observe results, and extend the workflow with additional automation steps as your needs evolve.