AI Email Draft Generator for Gmail (IMAP) in n8n
AI Email Draft Generator for Gmail (IMAP) in n8n
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£7.99
Regular price
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AI Email Draft Generator for Gmail (IMAP) in n8n
Regular price
£7.99
Regular price
£7.99
Sale price
Unit price
/
per
Automatically generate Gmail email drafts from new incoming messages using AI inside n8n
This n8n automation workflow detects new emails via IMAP, uses a custom AI model to draft a relevant response, and saves the result directly as a Gmail draft—so you can review and send faster without manually writing from scratch.
What this workflow does
- Detects new emails 24/7 using the Check New Email (IMAP) node, capturing incoming message content for processing.
- Analyzes and drafts responses with AI by passing the email content into a language model (e.g., Llama) to generate structured, relevant email content.
- Prepares the response for Gmail so the output is ready for human review rather than sending immediately.
- Saves the generated response as a Gmail draft, enabling a review-and-send workflow with reduced delays.
Use cases
- Customer support triage: automatically draft replies to incoming support emails for quick approval.
- Sales follow-ups: turn new inbound messages into polished draft responses while you stay focused on strategy.
- Operations and SaaS communications: generate consistent drafts for recurring questions, updates, or requests.
- Founder/solo operator efficiency: reduce repetitive email drafting and respond faster without constant monitoring.
Technical details
- Trigger/Email intake: Email Read IMAP via Check New Email (IMAP)
- Gmail integration: gmail node to create drafts
- AI generation pipeline: n8n-nodes-langchainchain llm and n8n-nodes-langchainlm ollama using a custom model (e.g., Llama)
- Workflow structure: set and sticky note for configuration and clarity
If you’re an n8n user, automation engineer, or SaaS operator looking for an AI Email Draft Generator for Gmail (IMAP), this workflow provides an always-on, draft-first email automation system designed to cut time while reducing human error.
