This article provides a complete, practical walkthrough of the Code Http 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:
   Automating YouTube Video Publishing with n8n, OpenAI, and Google APIs
   
   Meta Description:
   Discover how to fully automate your YouTube video upload process with n8n. This no-code workflow fetches transcripts, generates SEO-optimized titles, descriptions, and tags using AI tools like GPT-4 and Google Gemini, and uploads videos directly to your channel.
   
   Keywords:
   - n8n workflow automation
   - YouTube automation
   - GPT-4 video description
   - AI YouTube metadata
   - Google Drive automation
   - Apify YouTube transcript
   - SEO for YouTube
   - OpenAI GPT n8n
   - n8n YouTube integration
   - Google Gemini n8n
   
   Article:
   
   Automating YouTube Video Publishing with n8n, GPT-4, and Google APIs
   
   Content creators and digital marketers know that producing a high-quality YouTube video is only half the battle. Equally important is the back-end — uploading the video, writing compelling metadata, optimizing for SEO, and keeping everything consistent. Thanks to n8n, a powerful open-source workflow automation tool, all of this can now be handled with a single automated pipeline.
   
   In this article, we will explore an advanced n8n workflow that automates the entire process of uploading a new video to YouTube, generating a robust transcript, and producing an SEO-optimized title, description, and tags — all with the help of AI models like OpenAI’s GPT-4 and Google’s Gemini. Let’s take a detailed look at how this workflow functions and the third-party tools that make it possible.
   
   📥 Step 1: Detecting and Downloading New Videos
   
   It all starts with a Google Drive folder. The node “New Video?” uses the Google Drive Trigger to monitor a specific folder for newly added files. Whenever a new video is uploaded to this folder, n8n jumps into action.
   
   The “Download New Video” node downloads the video file by its file ID using the Google Drive API. At this point, the raw asset is ready for processing.
   
   📤 Step 2: Upload to YouTube
   
   Once the video is downloaded, it is passed to the “Upload Video to YouTube” node. The video is initially uploaded with a placeholder title (e.g., “adadada”) and is set to private to allow time for the metadata to be generated and added.
   
   🔍 Step 3: Pulling the Transcript
   
   Using the Apify platform’s YouTube Transcript Scraper, n8n sends a POST request to fetch the transcript based on the video’s YouTube URL. This request includes a token (which must be configured in the workflow), and the endpoint returns time-stamped transcript segments.
   
   A custom code node called “Adjust Transcript Format” then flattens the transcript data into a clean, readable string — ready for large language model (LLM) processing.
   
   ✍️ Step 4: Creating the YouTube Description
   
   The real magic happens when the transcript is sent to an OpenAI GPT-4.1 instance using the LangChain-based “Create Description” node. The model prompts the AI as a first-person economic content creator, instructing it to provide a detailed yet concise paragraph-style summary of the video, embellished with a few emojis and ending with relevant hashtags. The resulting description is informative, confident, and personalized for greater viewer engagement and SEO performance.
   
   🏷 Step 5: Generating Tags with Google Gemini
   
   In parallel, the updated transcript flows into the “YT Tags” node powered by Google’s Gemini 2.5 Flash Preview model. The prompt is straightforward — generate the most relevant YouTube tags for the content. The AI outputs a rich list of tags such as “#economics,” “#gold,” “#investmentreturns,” and more — tailored to improve search discoverability.
   
   📝 Step 6: Title Generation
   
   A final request is sent to OpenAI's GPT-4.1 for crafting a click-worthy, SEO-optimized video title. Limiting the character count to under 100 ensures the title fits YouTube’s constraints and appears fully in search previews.
   
   🧠 Step 7: Update Video Metadata
   
   Once title, description, and tags are ready, the “Update Video’s Metadata” node finalizes the video’s public-facing information through the YouTube API. The video can then be made public manually or automatically by updating its privacy settings.
   
   🧹 Step 8: Optional Cleanup
   
   To maintain a clean cloud storage environment, the “Delete File from Upload Folder” node optionally removes the original video file from Google Drive after it’s fully processed and uploaded.
   
   Third-Party APIs and Services Used in This Workflow
   
   This comprehensive automation leverages multiple third-party tools and APIs:
   
   1. Google Drive API
      - Used for detecting new video files and downloading/deleting them.
   
   2. YouTube Data API v3
      - Used to upload videos and update their metadata (title, description, tags).
   
   3. Apify YouTube Transcript Scraper
      - Fetches detailed transcripts from any public YouTube video URL.
   
   4. OpenAI API (GPT-4.1-nano)
      - Generates natural language YouTube descriptions and titles.
   
   5. Google Gemini 2.5 Flash Preview via Google PaLM API
      - Powered the AI-driven tag generation based on video transcript.
   
   Seamless Integration with AI for Content Optimization
   
   This n8n workflow stands as a game-changer for content creators, businesses, and social media teams who want to scale their content operations. By combining cloud storage, data scraping, video publishing, and multiple AI models into a single automated pipeline, it drastically reduces the time and effort needed to go live with professional-quality videos.
   
   Whether you're publishing economic commentaries, travel vlogs, or tutorials, using this n8n workflow ensures your YouTube uploads are optimized, informative, and discoverable — all without lifting a finger after recording.
   
   Conclusion
   
   In a world powered by automation and AI, workflows like this one highlight the future of content creation: frictionless, intelligent, and relentlessly efficient. If you're looking to streamline your YouTube publishing process, it's time to explore what n8n and AI-powered APIs can do for you.
   
   —
   
   Ready to automate your uploads with AI-enhanced workflows? Dive into n8n and start building your own powerhouse pipeline today. 💡💻🚀
   
   Hashtags:
   #n8n #YouTubeAutomation #AIContentCreation #videoSEO #GPT4 #GoogleAPI

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.