This article provides a complete, practical walkthrough of the Splitout Redis Automation 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:
   Build a No-Code AI-Powered Interview Agent Using n8n, Forms, and Redis
   
   Meta Description:
   Learn how to automate user research interviews using n8n workflows, AI language models, and Redis, with transcripts saved to Google Sheets. A no-code solution for researchers.
   
   Keywords:
   n8n, AI interviewer, LangChain, Groq, Google Sheets, Redis, Upstash, no-code automation, AI research agent, automated survey, user interviews, form automation, AI-powered form, LLM interviews, n8n workflows
   
   Third-Party APIs & Integrations Used:
   
   1. Groq LLM (Groq API) – for AI-powered question generation using the Llama 3.2-90B model.
   2. Redis (via Upstash) – for session management and transcript storage.
   3. Google Sheets – for saving completed interview sessions for further analysis.
   
   Article:
   
   —
   
   Create Automated, AI-Powered User Interviews with n8n and LLMs
   
   Conducting in-depth qualitative user interviews has always been a time-consuming and resource-heavy task. From preparing questions and scheduling participants to recording and analyzing responses, the traditional method demands significant manpower. But what if you could offload the entire process to an AI-enabled system that collects insights from users in real-time, stores interview data, and runs 24/7?
   
   With n8n, a powerful no-code/low-code workflow automation platform, you can build exactly that—a self-operating AI Interview Agent. In this article, we’ll explore a fully functional workflow template that uses n8n Forms, LangChain Agents, and Redis to interview users about their experience with the UK practical driving test. The entire session is handled automatically, with responses securely stored via Redis and eventually exported to Google Sheets.
   
   Let’s break it down.
   
   Step 1: Initiating the Interview
   
   The workflow starts with the "Start Interview" node, using a Form Trigger in n8n. When a user submits their name, the interview begins with a welcoming message. Behind the scenes, a unique session ID (UUID) is generated and stored in Redis via Upstash, allowing each user session to be uniquely identified and persisted for up to 24 hours.
   
   The setup includes:
   - n8n’s Form Trigger to collect participant name
   - UUID generation for session management
   - Redis key creation for persistent storage (session_{{UUID}})
   
   Step 2: Assign Interview Context
   
   Once the session is created, the workflow sets the interview topic: "Your experience preparing for and taking the UK Practical Driving Test". This context is essential for the AI agent to frame relevant and coherent questions.
   
   Step 3: AI-Driven Questions with Looping Continuity
   
   Here’s where the magic happens. Powered by LangChain’s integration in n8n, an AI "Researcher Agent" engages the user with meaningful, open-ended questions. The LLM backend is Groq’s Llama 3.2-90B model. The AI receives the previous answer, analyzes it, then responds with a follow-up or a new question for deeper insights.
   
   The loop components involve:
   - AI Researcher (LangChain Agent with Groq)
   - Memory Buffer Window to maintain context
   - Continuous question-answer cycling until a stop condition is met
   
   The LLM ensures:
   - Questions stay relevant to the interview topic
   - Only one question is asked at a time
   - Irrelevant or vague answers are either clarified or redirected
   - If the user types “stop interview,” the AI stops generating new questions
   
   Step 4: Capturing and Updating Responses
   
   Every time a question-answer pair is completed, it's logged as a structured JSON object and pushed into the Redis session. This forms a growing list of user responses that collectively create the full transcript.
   
   n8n uses the Set and Redis nodes to:
   - Structure the question/answer JSON object
   - Timestamp each record
   - Update the session list with new entries
   
   Step 5: Ending the Interview
   
   If the user indicates they want to stop (e.g., typing “stop interview”), the AI returns an object with `"stop_interview": true`. The workflow branches accordingly:
   - A final “stop_interview” session entry is added
   - Session memory is cleared using LangChain’s memory manager
   - User is redirected to a custom confirmation page
   
   This ending step is user-friendly, ensuring the AI doesn't leave the experience hanging.
   
   Step 6: Displaying the Interview Transcript
   
   Instead of a generic "Thank You" message, this project includes a dynamic transcript viewer powered by a Webhook node. The session ID is captured from the URL, queried from Redis, and parsed into an HTML interface using n8n’s HTML node.
   
   If the session ID is invalid or expired, a 404 message is returned, keeping the user informed.
   
   This optional step shows:
   - Custom HTML rendering of user transcript
   - Server-side validation for expired sessions
   - Opportunity for data review and sharing
   
   Step 7: Storing Transcripts in Google Sheets
   
   For analytics, sharing, or archival purposes, all question-answer pairs are exported from Redis into a Google Sheet. Using n8n’s built-in Google Sheets node, each interview is appended row-by-row with:
   - Timestamp
   - Interview type (start, ongoing, stop)
   - Specific question and user answer
   - User info and session ID
   
   This allows your research team to independently audit the interviews at any time without relying on logs buried in a database or email chains.
   
   Why It Matters
   
   This workflow transforms how user interviews are conducted:
   - Fully automated with no human intervention
   - Always-on availability for participants
   - Structured, searchable responses for easy analysis
   - Lower cost of conducting qualitative research
   - Scalable design capable of handling hundreds of users
   
   And the best part? You don’t have to write a single line of backend code.
   
   Ready to Get Started?
   
   All of this is made possible by connecting the following tools in n8n:
   - n8n Form and Set nodes for interface and logic
   - LangChain’s Agent and Memory tools
   - Redis as a fast and scalable session store (hosted via Upstash)
   - Groq LLM for intelligent question generation
   - Google Sheets for collaborative documentation
   
   If you're a UX researcher, product designer, or even a solo founder looking to increase user feedback at scale, this no-code solution is a game changer.
   
   You can try out the full demo or clone the workflow from the official n8n community post:
   
   🔗 https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312
   
   —
   
   Need help building your own version or extending it with new capabilities like sentiment analysis, Slack integration, or database pipelines? Join the vibrant n8n community on Discord or post your questions in the 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.