This article provides a complete, practical walkthrough of the Telegram Manual Automate Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   How This Automated AI-Powered n8n Workflow Organizes and Declutters Your Gmail Inbox  
   
   Meta Description:  
   Discover how this advanced n8n workflow leverages AI through Google Gemini, Gmail API, and Telegram to intelligently classify, delete, and notify you about unwanted emails — all without manual intervention.  
   
   Keywords:  
   n8n Gmail workflow, automate Gmail, Gmail spam cleanup, Google Gemini AI, email classification AI, delete spam automations, Telegram notifications, n8n AI workflow  
   
   Third-Party APIs Used:  
   
   1. Gmail API (via Gmail OAuth2)  
   2. Google Gemini (PaLM API)  
   3. Telegram API  
   
   ---
   
   Automating Email Cleanup with AI: A Smarter Gmail Inbox Using n8n and Google Gemini
   
   Most of us deal with an overload of marketing pitches, spam, and irrelevant system alerts landing in our inbox daily. Cleaning it up manually is tedious — and occasionally risky if you accidentally delete something important. Fortunately, automation tools like n8n now empower users to tackle this problem intelligently and securely.
   
   In this article, we dive into a powerful n8n workflow that automatically classifies your incoming emails using Google Gemini AI, scraps the unwanted ones, and notifies you in real-time via Telegram. Ideal for professionals and power users alike, this workflow offers an elegant solution to inbox overload.
   
   Let’s break down how this workflow works.
   
   🚀 Overview: Smart Email Processing Workflow
   
   The n8n workflow is engineered to load emails from a Gmail account in batches, assess their contents using Google Gemini's large language model (LLM), classify them based on context, and determine their worthiness to keep. Based on the LLM scores, it either deletes spammy or irrelevant emails or labels them for later review. Notifications for each decision are sent via Telegram.
   
   ⏱ Triggering the Workflow
   
   The flow starts with a Manual Trigger node which allows for testing and iterative validation. Upon triggering, it initializes and increments pagination variables to loop through emails in 14-day segments using Gmail's search filters. This makes the system scalable over large inbox histories.
   
   📨 Gmail: Fetch Emails in Segments
   
   The Gmail node retrieves all emails matching a dynamically generated query. Specifically, it fetches emails from a set two-week window (offset by pages) and avoids those marked with a "n8n-skipped" label to prevent re-processing.
   
   Example Gmail filter:
   
   ```
   before:2024/05/14 after:2024/04/30 -label:n8n-skipped
   ```
   
   This approach supports continuous cleanup even for old inboxes by navigating backward through time.
   
   🧠 AI Email Classification via Google Gemini Model
   
   This is where magic happens.
   
   Each email's metadata is passed into a large language model via the "AI Check Email" node powered by the Google Gemini (PaLM) API. The model is prompted to classify the message using decimal values (0 to 1) for:
   
   - isUnwantedConfidence  
   - isMarketingConfidence  
   - isSpamConfidence  
   
   Based on these metrics, the AI provides a briefReason and decides whether the message should be marked for deletion. A typical AI response might look like:
   
   ```json
   {
     "emailId": "abc123",
     "isUnwantedConfidence": 0.87,
     "isMarketingConfidence": 0.91,
     "isSpamConfidence": 0.08,
     "briefReason": "Marketing email about an expired offer.",
     "emailFrom": "newsletter@promo.com"
   }
   ```
   
   📊 If Node: Decide What Happens
   
   The decision-making logic lies in a conditional "If" node that reviews the confidence scores. If any of the three fields exceed the 0.5 threshold, the email is marked as irrelevant or spam and sent for deletion. Otherwise, it is kept and labeled.
   
   📤 Output Parsing for Structured Data
   
   To ensure the AI output is usable by subsequent nodes, the "Unwanted Email Output Parser" structures the JSON data to match a defined schema. This guarantees that conditions and deletion steps act on reliable data formats.
   
   🗑 Deleting or Labeling Emails Intelligently
   
   For unwanted emails:
   
   - The GmailDeleteEmail node deletes the message identified by the model.
   - A Telegram notification is sent: “Email Deleted | Sender | Reason”
   
   For emails that don’t exceed the 0.5 thresholds:
   
   - They are labeled as retained using Gmail Labels.
   - Telegram sends a status: “Skipping Email | Sender | Reason”
   
   ⚠️ Handling Errors and Fail-Safes
   
   Smart automation isn't just about intelligence — it's about resilience.
   
   - If the Gemini model fails to return results, an error Telegram message is sent: “AI Error | Can’t Check Email”
   - If an email fails to delete, another alert goes out: “Can’t Delete Email”
   
   This ensures the user is always in the loop regarding the system's decisions and any hiccups that may need attention.
   
   🔁 Looping with Pagination
   
   Once a batch of emails has been processed, the workflow aggregates the results and loops to the next page using the custom Set nodes (Increment Loop Var, Forward Prev Page Num). This way, the workflow continues seamlessly until all pages are processed.
   
   📬 Telegram Notifications: Staying in the Know
   
   Thanks to Telegram integration, this workflow provides real-time transparency. The three possible Telegram messages are:
   
   1. Email Deleted  
   2. Email Skipped  
   3. AI or Deletion Error  
   
   Having instant feedback on what the automation is doing ensures reliability and trustworthiness, especially when actions like deletion are involved.
   
   🎯 Final Thoughts
   
   This n8n workflow exemplifies how AI and automation, when combined, can transform daily digital chores like inbox management into a fully autonomous, intelligent system. By leveraging powerful APIs like Gmail, Google Gemini, and Telegram, users can reclaim control of their inbox without endless scrolling and decision fatigue.
   
   Whether you’re a professional receiving hundreds of emails daily or just someone tired of marketing clutter, this workflow might just be the inbox management upgrade you’ve been waiting for.
   
   —
   
   Want your inbox to manage itself? Try setting up a similar workflow with n8n and enjoy a smarter, clutter-free email experience.
   
   

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.