This article provides a complete, practical walkthrough of the Emailreadimap Manual Send Webhook 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 Customer Email Responses Using AI with n8n: An Intelligent Email Auto-Responder Workflow
   
   Meta Description:  
   Discover how to create an AI-powered email auto-responder using n8n, OpenAI, Qdrant, and Google Drive to automatically summarize, classify, and reply to email inquiries with professional responses.
   
   Keywords:  
   n8n, AI email responder, automated email workflow, OpenAI, Qdrant, Google Drive, email classification, summarization, vector database, LangChain, DeepSeek, GPT-4o-mini, customer support automation
   
   Third-Party APIs and Services Used:
   
   1. OpenAI API (via OpenRouter and natively)
   2. Qdrant Vector Database API
   3. Google Drive API
   4. IMAP (for email retrieval)
   5. SMTP (for sending responses)
   
   Article:
   
   ---
   
   Email automation is no longer a luxury; it’s a necessity. Businesses flooded with inquiries need smart systems that can process, analyze, and respond to communications without constant human intervention. Enter n8n — the powerful open-source workflow automation platform — and this workflow: an AI-powered Email Auto-Responder that summarizes and professionally replies to incoming inquiries using technologies like OpenAI, Qdrant, and Google Drive.
   
   Let’s walk through how this intelligent system works, what technologies power it, and how it revolutionizes email handling.
   
   💡 Introducing the AI Email Auto-Responder in n8n
   
   Named “Email AI Auto-responder: Summarize and Send Email,” this workflow transforms tedious email triaging into a seamless AI-driven process. It utilizes several key integrations and AI models to classify, understand, and respond to emails appropriately.
   
   Here’s how it works:
   
   🟢 Step 1: Trigger and Prepare the Email Content
   
   The process kicks off with the “Email Trigger (IMAP)” node fetching new emails received by a business-facing address (info@n3witalia.com). The raw email—formatted in HTML—is converted into Markdown via the “Markdown” node to make it AI-readable.
   
   🔍 Step 2: AI Summarization
   
   Once the email is formatted, it’s passed to a summarization chain powered by DeepSeek R1 and LangChain modules. The system uses prompts to create a short, human-readable summary of the customer inquiry. This ensures subsequent AI components don’t need to parse the entire raw email but can quickly understand the request.
   
   🤖 Step 3: Classification with LLMs
   
   The summarized content is evaluated by the “Email Classifier” which uses OpenAI’s GPT-4o-mini model to determine the email category. If the category matches “Company info request,” the workflow proceeds to construct an answer autonomously. Otherwise, the workflow terminates at the "Do nothing" node to avoid incorrect or undesired automation.
   
   📚 Step 4: Retrieve Context from a Vector Database
   
   If classified for auto-response, the email passes through a context injection process. This taps into a Qdrant-powered vector store named company_knowladge_base, populated earlier via vectorized documents from Google Drive.
   
   It intelligently retrieves information relevant to the inquiry (such as store hours, services, promotions, etc.) based on the user query’s embedding produced by OpenAI.
   
   ✍️ Step 5: Draft a Relevant Business Reply
   
   Using the retrieved contextual info, the “Write email” agent is instructed to compose a professional reply in under 100 words. A system prompt ensures the response tone aligns with business standards, offering concise and accurate information to the customer.
   
   🔍 Step 6: Final Review and Formatting
   
   Before sending, the draft email is passed to a “Review email” node. This uses the DeepSeek model to structure and polish the response into proper HTML format. It allows necessary styling (like line breaks and paragraphs) ensuring readability and digital polish.
   
   📨 Step 7: Send the Email
   
   Finally, the system responds to the customer using the same email thread. The "Send Email" node (authenticated via SMTP) ensures the reply preserves legitimacy and professionalism.
   
   🛠 Backend Setup: Qdrant and Google Drive for Knowledge Management
   
   Before the main flow is triggered, the workspace populates its knowledge base. A two-step vectorization pipeline uploads company documents from a specific Google Drive folder, uses OpenAI’s embeddings to process them, and stores them in a Qdrant collection via HTTP API.
   
   This backend makes the workflow’s autonomous responses contextually rich and always up to date.
   
   🔗 Tools and APIs Involved
   
   This intelligent auto-responder is made possible through the integration of various APIs and platforms:
   
   1. OpenAI API: For summarizing, classifying, embedding, and generating replies via powerful models like GPT-4o-mini.
   2. DeepSeek R1 via OpenRouter: A fast and free LLM used specifically for email summarization and HTML formatting.
   3. Qdrant Vector Database: To store and retrieve domain-specific knowledge, useful in contextualizing email responses.
   4. Google Drive API: For maintaining up-to-date company documents that enhance the vector store's relevance.
   5. IMAP and SMTP: Standard protocols for reading and sending emails.
   
   🧠 Why This Workflow Matters
   
   This n8n-powered workflow eliminates repetitive tasks that often bottleneck customer support. By leveraging vector embeddings and LLMs, it guarantees fast and coherent responses to frequently asked questions — without human intervention. Moreover, categorization ensures only relevant requests are answered automatically, maintaining quality assurance.
   
   📈 Use Cases
   
   - SMBs receiving frequent product or business inquiries
   - Online stores offering detailed service information
   - Educational institutions automating course or event queries
   - Tech shops providing repair service info
   
   🌐 Final Thoughts
   
   This workflow demonstrates the power of combining AI with automation. It breaks down language, retrieves context, and responds — all within seconds — using an intuitive no-code environment. Whether you're a tech-savvy entrepreneur or enterprise developer, a workflow like this can drastically improve efficiency and customer satisfaction.
   
   Ready to modernize your support inbox? With n8n and a few API keys, you’re just a few flows away.
   
   ---
   
   Let me know if you’d like help deploying this workflow or customizing it further to fit your business!

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.