This article provides a complete, practical walkthrough of the Schedule Removeduplicates Create Scheduled 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:
   Automating Support Ticket Triage and Issue Creation with n8n, OpenAI, Gmail, and Linear
   
   Meta Description:
   Discover how to automate the triage of support emails and seamlessly create actionable issues in Linear using n8n, OpenAI's GPT, Gmail, and structured output parsing. Save time, reduce clutter, and improve your support operations.
   
   Keywords:
   n8n automation, support ticket triage, Linear issue creation, OpenAI GPT automation, Gmail ticket automation, AI customer support, AI-powered triage, no-code automation, ticket parsing AI, streamline support workflow
   
   Third-Party APIs Used:
   
   - Gmail API: For retrieving support emails from a designated mailbox.
   - OpenAI API: For analyzing and triaging emails using GPT-4o (OpenAI's LLM).
   - Linear API: For creating issues automatically in Linear based on AI-generated output.
   
   Article:
   
   Streamline Customer Support with AI-Powered Triage Using n8n, Gmail, OpenAI, and Linear
   
   Efficient customer support is the backbone of any responsive organization, but managing incoming support tickets can be a resource-intensive task. Fortunately, with no-code/low-code automation tools like n8n and modern AI models like OpenAI’s GPT, you can transform your support operations by automating the triage and issue creation process. In this guide, we’ll explore how a powerful n8n workflow can be used to fetch incoming Gmail support messages, analyze them with GPT, and create well-categorized issues directly in Linear.
   
   The Problem ⛔
   Support teams often face a backlog of tickets that need manual classification, summarization, and transfer into issue tracking tools. Doing all of this manually can lead to slow response times, inconsistencies, and operational drag — especially for teams with limited resources.
   
   The Solution ✅
   Using this n8n-powered workflow, incoming support requests are automatically processed every hour. The AI component triages the ticket: assigning labels (like "Billing" or "Security"), assessing priority, and rewriting a clear, concise summary and description. Once that’s done, the data is used to create a properly categorized and prioritized issue in Linear with zero manual overhead.
   
   Workflow Breakdown 🔄
   
   1. Scheduled Trigger:
   A Schedule Trigger node executes the workflow hourly, making sure that any new support request in the Gmail inbox is promptly processed.
   
   2. Gmail Integration:
   The Gmail node fetches messages sent to a dedicated support email (e.g., support@example.com). By applying the filter to only include messages addressed to this account, we ensure only relevant tickets enter the funnel.
   
   3. Duplicate Handling:
   The Mark as Seen node ensures that previously processed emails aren’t reprocessed, avoiding spammy duplication and redundant issues in the system.
   
   4. Markdown Conversion:
   The email’s HTML body is converted into markdown format to improve the readability and consistency of the AI model's input.
   
   5. AI-Powered Triage via OpenAI:
   This is the heart of the workflow. By using the Langchain-integrated Chain LLM node powered by GPT-4o from OpenAI, the email content is:
   
   - Tagged with one or more relevant labels (e.g. Technical, Account, Billing, etc.),
   - Prioritized on a scale from 1 (highest) to 5 (lowest),
   - Rewritten into a factual, emotionless summary and a clearer problem description.
   
   Prompt engineering guides the AI to discard anecdotal sentiments and focus on clarity — essential for effective issue resolution.
   
   6. Structured Output Parsing:
   To ensure the LLM output is machine-consumable, the Structured Output Parser reformats the text into a JSON structure with “labels”, “priority”, “summary”, and “description” fields.
   
   7. Creating an Issue in Linear:
   Finally, the workflow uses the Linear API to generate a new issue with the AI-suggested summary, description, tags (as hashtags), and the appropriate priority level. The issue is assigned directly to a target team and workflow state in Linear.
   
   Benefits of This Workflow 🚀
   
   - Reduced Manual Effort: Eliminate the need to manually read, summarize, classify, and input ticket details into your issue tracker.
   - Faster Response Times: Let AI do the heavy lifting so your team can focus on solving problems — not sorting through emails.
   - Scalable Support Operations: Whether you’re handling tens or thousands of incoming support requests, this workflow ensures consistency and accuracy.
   - Easy Customization: Extend the workflow with additional filtering (e.g., classify internal vs. external queries), auto-responses, or even capacity planning.
   
   What You’ll Need to Get Started 🛠️
   
   - A Gmail account configured to receive support tickets.
   - An OpenAI account (with access to GPT-4o).
   - A Linear workspace where issues can be created.
   - An n8n instance (cloud or self-hosted) to run the workflow.
   
   Customizations You Can Explore 🔧
   
   - Change or expand labeling categories in the AI’s system message.
   - Add nodes to ping team channels when a new Linear issue is created.
   - Create auto-responses to customers based on the priority or type of issue.
   - Analyze resolution times based on label or complexity for reporting.
   
   In Summary 🧩
   This integration brings AI-enhanced support automation within reach of any modern support or engineering team. With minimal setup, you can convert a flood of unstructured support queries into structured, actionable issues in a matter of seconds. Team velocity improves, customer satisfaction rises, and your support operation becomes leaner and more efficient.
   
   Need Help Getting Started?
   Join the vibrant n8n community on Discord or seek assistance through the community forum!
   
   Happy automating! 🤖✨

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.