This article provides a complete, practical walkthrough of the Code Manual 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:**  
   Automating YouTube Video Analysis with AI: A Complete n8n Workflow Tutorial
   
   **Meta Description:**  
   Discover how to automate YouTube video content analysis using n8n, GPT-4, and transcript APIs. This step-by-step AI-powered workflow extracts transcripts, generates summaries, and delivers insights via email—perfect for creators, educators, marketers, and researchers.
   
   **Keywords:**  
   n8n workflow, YouTube transcript analyzer, AI video summarizer, GPT-4 summary, DeepSeek AI, OpenRouter, YouTube API automation, automate content research, video analysis workflow, LangChain n8n, content intelligence, youtube-transcript.io
   
   **Third-Party APIs Used in the Workflow:**
   
   1. youtube-transcript.io API (for transcript extraction)
   2. OpenAI API (for GPT-4-based summarization)
   3. DeepSeek API (alternative language model for deeper analysis)
   4. OpenRouter API (accessible LLM model used via OpenRouter)
   5. SMTP Email Service (used for automatic delivery of the report)
   
   ---
   
   ## Automating AI-Powered YouTube Video Analysis with n8n
   
   In today’s content-driven digital landscape, analyzing large volumes of YouTube content manually can be both time-consuming and inefficient. Whether you're a content creator, marketer, student, or researcher, you know how valuable a structured and concise summary of video content can be. Enter the power of workflow automation and AI-driven insights. With n8n—the powerful low-code automation platform—you can now automate the entire process of extracting, analyzing, and emailing YouTube video summaries.
   
   Let’s explore a powerful workflow named “YouTube Video Analyzer with AI,” designed to automate the steps of analyzing YouTube videos using transcript APIs and language models like GPT-4 and DeepSeek.
   
   ### Overview: What This Workflow Achieves
   
   This n8n workflow turns a raw YouTube URL into a structured summary created by AI. Here’s what it does:
   
   1. Accepts a YouTube URL.
   2. Extracts the Video ID.
   3. Retrieves the video transcript using youtube-transcript.io.
   4. Compiles the transcript into a full text.
   5. Runs the content through an AI summary engine (GPT-4o-mini, DeepSeek Reasoner, or OpenRouter models).
   6. Parses the results into a structured format with a title and rich summary.
   7. Automatically emails the final report.
   
   Let’s break this down step by step.
   
   ---
   
   ### Step-by-Step Breakdown of the Workflow
   
   #### 1. Input the YouTube URL
   The user manually enters a YouTube URL into the workflow using the "Set YouTube URL" node. This starts the automation manually through the "Manual Trigger" node in n8n.
   
   A Note: Make sure to input a valid URL from either youtube.com or youtu.be formats.
   
   #### 2. Extract the Video ID
   A custom JavaScript function extracts the video ID from the provided URL using regex logic. This ID is critical to request transcript data from the YouTube Transcript API.
   
   #### 3. Fetch Transcript using youtube-transcript.io
   This POST request is sent to youtube-transcript.io, an external API that offers transcripts for publicly available videos. An API key with HTTP Header Authentication is used here.
   
   Note: Not all YouTube videos have available transcripts, so the workflow includes a condition check (“Exist?” node) to verify if transcript data is retrievable.
   
   #### 4. Prepare the Transcript for AI Processing
   The transcript is structured into a full-text string with a custom scripting node called "Get Fulltext." This ensures all snippets of the video's speech are concatenated in one string, ready for natural language processing.
   
   #### 5. Analyze and Summarize via Language Models
   This is where the AI kicks in. The "Analyze LLM Chain" node runs the compiled text through a prompt-driven instruction setup using LangChain's large language model (LLM) integration. It gives specific directives to the models to:
   
   - Organize content under key headers like Background, Features, Implementation, and Pros/Cons.
   - Format the data in Markdown with bold highlights, bullet points, and technical structuring.
   - Return a JSON result containing both a detailed description and a suggested title.
   
   Three different LLM providers are integrated:
   
   - GPT-4o-mini via OpenAI
   - DeepSeek Reasoner via DeepSeek API
   - deepseek/deepseek-r1 model via OpenRouter
   
   Optionally, each can be plugged into the same LangChain adapter, making the AI summary layer modular and flexible.
   
   #### 6. Parse and Email the Result
   The “Structured Output Parser” ensures the AI output conforms to a valid schema with “title” and “text” fields.
   
   The final node, “Send Email,” picks up that clean result and sends the summary to a predefined email address via SMTP. Custom subjects and body content are dynamically populated from the AI’s results.
   
   ---
   
   ### Use Cases & Benefits
   
   Here’s why this workflow is powerful:
   
   - 🧠 Content creators can analyze competitors’ videos for inspiration or summaries.
   - 📈 Marketers can extract insights from trending videos for campaign research.
   - 📚 Educators and students get structured notes without watching long videos.
   - 📥 Researchers receive automated summaries directly in their inbox.
   
   All this happens with just one click.
   
   ---
   
   ### Final Thoughts
   
   This YouTube Video Analyzer workflow demonstrates the true potential of low-code automation mixed with modern artificial intelligence. The integration of GPT-based models and transcript APIs highlights how AI can add real value to digital content workflows. Most importantly, it requires zero manual summarization effort, ensures high accuracy via structured prompts, and scales infinitely through API-driven automation.
   
   With a bit of customization, this same approach can be extended to podcast analysis, meeting recording reviews, webinar insights, and more.
   
   If you're already using n8n or looking to streamline your workflows, this is an excellent blueprint for intelligent video analysis at scale.
   
   —
   
   Build smarter. Automate faster. Let AI do the heavy lifting.

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.