This article provides a complete, practical walkthrough of the Splitout Redis Create 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:
   Automated AI Interviews with n8n: How to Build a Scalable AI-Powered Interviewer
   
   Meta Description:
   Discover how to build a fully automated, AI-powered interview system using n8n workflows, LangChain, Redis, and Google Sheets. Capture user insights effortlessly with endless AI-generated questions and real-time session storage.
   
   Keywords:
   n8n, AI interviewer, workflow automation, LangChain, Redis, Upstash, Google Sheets, AI research, user interviews, AI agent, AI automation, no-code, form automation, n8n tutorial, digital surveys, practical driving test survey
   
   Third-Party APIs and Services Used:
   
   1. LangChain (Langchain AI Agent & Memory Nodes)
      - Used for generating context-aware AI interviewer questions and managing conversational memory.
   
   2. Groq API (LLM Provider)
      - Powered by the LLaMA-3.2-90B model for ultra-fast and scalable AI-generated content.
   
   3. Upstash Redis
      - Redis cloud storage for session-based data persistence and retrieval, ensuring conversational integrity over time.
   
   4. Google Sheets API
      - Stores interview transcripts for analysis and easy collaboration among team members.
   
   5. n8n Cloud / Webhook Services
      - Supports dynamic routing and response handling including redirect URLs, custom form triggers, and webhook endpoints.
   
   —
   
   Article:
   
   Build Your Own AI Interview Assistant with n8n and LangChain
   
   Traditionally, conducting user interviews demands a considerable investment in planning, coordination, and execution. But with today’s automation tools and AI-driven technologies, this lengthy process can be reimagined. What if user research interviews could be conducted by an always-on AI interviewer, tailored specifically to the user, operating on their schedule, and storing insights in real time for analysis?
   
   This guide walks through how to build exactly that—an endlessly looping AI-powered interviewer using n8n, LangChain, Redis (via Upstash), and Google Sheets. In just a few steps, you'll create a fully automated, scalable AI that can interview users on any topic, record their responses, and provide valuable data for your research team.
   
   Step 1: Launch the Interview with a Form Trigger
   
   The process begins with n8n’s Form Trigger node. When a user visits the form (e.g., for a survey on their UK practical driving test experience), they're prompted with a simple question: “What is your name?” This kicks off the interview session and generates a unique identifier (UUID) for the user.
   
   Step 2: Set Up the Session in Redis
   
   Using Upstash Redis, a new session is stored with the user’s details. This acts as a memory buffer throughout the interview, capturing question-answer pairs so the AI agent can refer back to past responses and maintain context.
   
   Each interaction is stored as a JSON object with a timestamp, question, and answer, helping ensure chronological tracking of the conversation.
   
   Step 3: Let the AI Take Over
   
   Enter LangChain and Groq’s LLaMA-3.2 model. Armed with a system prompt tailored to the chosen interview topic, the AI agent generates open-ended questions in JSON format and understands when the user chooses to exit the interview.
   
   After each user response, n8n validates whether the user wants to continue. If not, the interview gracefully stops. Otherwise, the AI processes the answer using LangChain memory and returns another tailored question.
   
   Step 4: Record Responses Between Each Loop
   
   Each question and answer cycle is captured with n8n’s Set and Redis nodes, persistently updating the session memory. Intermediary steps optionally send the user's answer back to the AI, ensuring that their response guides the next question. Every exchange is echoed into a Redis list and optionally saved to Google Sheets for reporting.
   
   Step 5: Save Data to Google Sheets
   
   For easier analysis, the full transcript of the interview session can be retrieved from Redis and written into a connected Google Sheet. Each row includes the session ID, timestamp, question, answer, and token type (start_interview, next_question, stop_interview).
   
   Step 6: End the Interview Gracefully
   
   When the user indicates they’re done (e.g., by typing “stop interview”), the conversation loop stops. An HTML-based completion page is shown via a custom webhook. This page optionally displays a transcript summary of the interview session fetched from Redis, providing transparency and a satisfying user experience.
   
   A secondary webhook checks for valid sessions and either displays the transcript or returns a graceful 404 message if the session is invalid or expired.
   
   Why This Workflow Stands Out
   
   - 🧠 AI-Driven: Uses a powerful LLM (LLaMA-3.2 via Groq API) optimized for conversational interaction.
   - 💾 Real-Time Storage: Upstash Redis provides fast and reliable session management.
   - 📊 Share-Ready Outcomes: Data is prepared for immediate analysis in Google Sheets.
   - 🔁 Infinite Looping: The conversation continues until the user explicitly opts out.
   - 🛠️ No-Code Logic: Built entirely within n8n’s drag-and-drop interface.
   
   Use Cases Beyond Driving Tests
   
   This template isn’t exclusive to driving test feedback. It can be adapted for:
   
   - Customer satisfaction surveys
   - Candidate pre-screening
   - Post-event feedback
   - Employee onboarding interviews
   - Product research discussions
   
   Conclusion
   
   With just a few integrations and n8n’s intuitive workflow logic, you can build an enterprise-grade AI interviewer that operates on autopilot, records key data, and enhances your ability to make data-informed decisions.
   
   Whether you're a researcher, product manager, or UX enthusiast, this approach offers an automated, scalable solution to gain deeper user insights—without sacrificing time or accuracy.
   
   Start building your own today and follow the conversation on the n8n Forum or Discord!
   
   —
   
   Looking for the template or live demo?
   
   👀 Explore the Workflow Showcase:  
   https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312
   
   🧪 Demo Transcript Sheet:  
   https://docs.google.com/spreadsheets/d/1wKjVdm7HeufJkHrUJn_bW9bFI_blm0laoI_jgXKDe0Q/edit?usp=sharing

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.