This article provides a complete, practical walkthrough of the Wait Splitout 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:  
   Creating AI-Powered Video Narration with n8n and GPT-4 Vision
   
   Meta Description:  
   Learn how to build an automated n8n workflow that extracts frames from a video, generates a voiceover script using OpenAI's vision-capable GPT-4 model, and converts it into audio using text-to-speech—all without writing a single line of backend code.
   
   Keywords:  
   n8n, GPT-4 Vision, OpenAI, Video Narration, Text-to-Speech, AI Voiceover, OpenCV, Automation, Frame Extraction, Google Drive Upload, LangChain, Python, Video Processing
   
   Third-Party APIs Used:
   
   1. OpenAI API (GPT-4 Vision + TTS)
   2. Google Drive API
   3. Pixabay (video hosting platform used for demonstration)
   
   —
   
   Article:
   
   How to Automate AI Video Narration Using n8n and GPT-4 Vision
   
   Imagine creating professional video narrations without manually scripting, recording, or editing audio. What once required a suite of video editing software and voiceover talent can now be achieved automatically using n8n—a powerful workflow automation platform—coupled with OpenAI’s multimodal GPT-4 and text-to-speech (TTS) capabilities.
   
   In this article, we’ll walk through an n8n workflow that downloads a video, extracts frames, feeds those frames into GPT-4 to generate narration scripts, renders the script as an audio clip, and finally uploads the result to Google Drive—completely automated.
   
   👁️ Step 1: Download the Source Video
   
   The journey begins with a simple HTTP request node in n8n that pulls a sample video from Pixabay:
   https://cdn.pixabay.com/video/2016/05/12/3175-166339863_small.mp4
   
   This serves as the input for our entire automation. While this example uses a publicly available video, you can adapt this to your own video sources as long as they are compatible with OpenCV.
   
   🖼️ Step 2: Extract Evenly Distributed Frames with Python and OpenCV
   
   Videos are essentially a sequence of rapidly shown still-images or frames. To let GPT-4 “see” the video, we'll extract frames using the Python Code node in n8n.
   
   This custom node:
   - Decodes the video into byte format
   - Uses OpenCV to process it as video
   - Captures a maximum of 90 evenly spaced frames
   - Converts each frame to base64 format for further use
   
   This step ensures that the visual essence of the video is preserved while keeping the data manageable for GPT-4's token constraints.
   
   🚨 Performance Tip:
   Working with high-resolution or large-size videos can consume significant memory. Consider optimizing your video upfront or reducing the number of frames extracted.
   
   🔄 Step 3: Batch Processing Frames for GPT-4 Vision
   
   With frames in hand, they are split into batches of 15 and resized to 768x768 pixels—ideal for OpenAI’s image input specs. These batches are looped into the “Generate Narration Script” node powered by the GPT-4 Vision model.
   
   Each batch is passed alongside a running script prompt. The key here is incremental generation. GPT-4 is instructed to continue the narration from previous iterations, making the final script feel coherent and flowing—even though it was generated in segments.
   
   Example prompt used:
   "These are frames of a video. Create a short voiceover script in the style of David Attenborough. Only include the narration."
   
   This not only makes the narration engaging but also mimics real documentary-style storytelling, entirely generated from images.
   
   🔊 Step 4: Convert the Script into Audio with TTS
   
   Once all narration segments are combined using an Aggregate node, the resulting script is piped into OpenAI’s Text-to-Speech (TTS) engine via n8n’s LangChain integration.
   
   The script is rendered into a high-quality MP3 file using OpenAI’s realistic voice models. The speech file captures the spirit of the generated narration, including pacing, tone, and linguistic nuances—all automatically.
   
   ☁️ Step 5: Upload the Finished Voice Clip
   
   The final output is uploaded into a specified Google Drive folder using the Google Drive node in n8n. This makes the narrated audio immediately accessible and shareable, either standalone or for video reassembly.
   
   Here’s a sample generated audio file from this exact workflow:  
   🎧 https://drive.google.com/file/d/1-XCoii0leGB2MffBMPpCZoxboVyeyeIX/view?usp=sharing
   
   🛠️ Technologies Under the Hood
   
   - OpenCV: Used for video frame extraction
   - Python: Drives low-level frame processing in the n8n Code node
   - OpenAI GPT-4 Vision: Consumes visual content and generates script text
   - OpenAI TTS: Converts text narration into audio
   - Google Drive: Stores final MP3 output for easy access
   
   🌐 Community-Inspired Design
   
   This n8n template takes inspiration from OpenAI’s Cookbook on leveraging GPT-4’s vision capabilities with TTS and embodies it into a well-orchestrated automation. Whether for use in educational videos, travel vlogs, or AI-generated documentaries, this approach is modular and open for customization.
   
   💬 Final Thoughts
   
   With tools like n8n and OpenAI, the gap between human creativity and machine capability continues to shrink. What used to be a full-scale production process is now scriptable and scalable, giving creators more freedom to focus on storytelling and innovation.
   
   Whether you're a no-code enthusiast or an AI experimenter, this workflow shows the exciting potential of combining multiple AI modalities into an effortless creative pipeline.
   
   Want to try it yourself? Join the n8n community and explore what you can build next!
   
   🔗 Resources:
   - n8n Docs: https://docs.n8n.io/
   - OpenAI Cookbook (Vision + TTS): https://cookbook.openai.com/
   - Pixabay Video Link (used in demo): https://pixabay.com/videos/india-street-busy-rickshaw-people-3175/
   
   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.