This article provides a complete, practical walkthrough of the Telegram 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:
   Automated YouTube Video Summarization and Analysis Using n8n and GPT-4
   
   Meta Description:
   Discover how an advanced n8n workflow integrates YouTube, GPT-4, and Telegram to automatically retrieve, transcribe, analyze, summarize, and share YouTube video content through a webhook-triggered automation pipeline.
   
   Keywords:
   n8n workflow, YouTube video analysis, GPT-4 summarization, video transcription automation, telegram bot, YouTube API, LangChain, AI content summary, OpenAI, webhook automation
   
   Third-party APIs Used:
   
   - YouTube Data API (via n8n YouTube node)
   - YouTube Transcript API (via custom "YouTube Transcript" node)
   - OpenAI GPT-4 API (via LangChain and gpt-4o-mini integration)
   - Telegram Bot API (via n8n Telegram node)
   
   Article:
   
   Automated YouTube Video Summarization and Analysis Using n8n and GPT-4
   
   In today’s fast-paced digital world, sifting through hours of video content can be a time-consuming task. Whether you're a researcher, content creator, or marketer, summarizing YouTube video content into digestible insights is increasingly valuable. This task becomes significantly more manageable with workflow automation tools like n8n, especially when integrated with AI language models like OpenAI’s GPT-4. In this article, we explore an n8n workflow that takes a YouTube video URL, transcribes the video content, summarizes and analyzes it using GPT-4, and delivers the results through Telegram.
   
   Overview of the Workflow
   
   This robust workflow is triggered via a webhook and walks through the following key steps:
   
   1. Accepts a YouTube video URL from an external source
   2. Extracts the video ID and metadata
   3. Transcribes the audio content from the video
   4. Analyzes and summarizes the transcript using GPT-4
   5. Returns a structured response
   6. Posts the summarized content directly to a Telegram chat
   
   Let’s break it down step-by-step.
   
   Step 1: Trigger the Workflow via Webhook
   
   The process begins with a Webhook node configured to listen for a POST request to a custom endpoint (e.g., /ytube). This allows third-party applications or users to send a YouTube video URL directly to n8n.
   
   Once the data arrives, a Set node extracts the video URL from the incoming request and stores it for further processing.
   
   Step 2: Extracting the YouTube Video ID
   
   A custom JavaScript code node parses the submitted YouTube URL (handling both youtube.com and youtu.be formats) to extract the unique 11-character video ID. This ID is crucial for making API calls to retrieve both metadata and transcript data.
   
   Step 3: Fetching Video Metadata
   
   Using the video ID, the YouTube node (leveraging the YouTube Data API) retrieves the video’s metadata, including title, description, and internal ID. This information is used later to construct the final summary output and provide enriched context to users.
   
   Step 4: Transcribing Video Content
   
   The workflow incorporates a custom "YouTube Transcript" node that extracts the entire transcript of the video. The transcript is then parsed and split using the Split Out node to handle the text in manageable segments.
   
   These smaller pieces are then concatenated using the Summarize node to form a single block of text, ready for AI processing.
   
   Step 5: GPT-4 Summary and Analysis
   
   Here’s where the power of AI comes in. The concatenated transcript is passed into the LangChain integration node, using OpenAI’s GPT-4o model. This node prompts the model with structured instructions:
   
   - Break the text into main topics using level-2 headers
   - Bullet-point key insights under each topic
   - Keep details technically accurate and concise
   - Use markdown formatting
   - Include tables for comparisons and highlight keywords in bold
   
   The output is a highly structured, digestible summary ideal for technical teams, educators, or busy professionals looking to distill video content at a glance.
   
   Step 6: Build Response and Share via Telegram
   
   The structured AI summary is routed into a response object that organizes and maps all previous outputs (title, summary, video ID, and Youtube URL).
   
   From here, two things happen:
   
   1. The original webhook sender receives a response including the YouTube title, summary, and metadata.
   2. A message containing the video title and link is simultaneously pushed to a Telegram channel or direct chat through the Telegram node—instantly notifying interested parties of new content.
   
   Use Cases and Benefits
   
   This workflow is an excellent fit for anyone who wants to automate YouTube content consumption and analysis, including:
   
   - Educators wanting summaries of educational content
   - Journalists monitoring industry-relevant updates
   - Researchers collecting multimedia insights
   - Marketing teams assessing competitor content
   - Developers automating learning from tech videos
   
   By removing the need for manual review and summarization, teams can act faster, reduce cognitive load, and focus on what really matters—strategy and execution.
   
   Conclusion
   
   This workflow demonstrates the true potential of combining n8n’s automation capabilities with AI and messaging platforms. By integrating YouTube, OpenAI GPT-4 via LangChain, and Telegram, the system offers a seamless way to transform lengthy video content into structured insights.
   
   If you're someone who regularly works with video resources, this n8n-based solution could drastically improve your productivity. Best of all, it's fully extensible—allowing you to connect it with Slack, Notion, or even email, depending on your use case.
   
   Whether you're summarizing videos for research, internal team briefings, or public distribution, this workflow sets the foundation for next-level content curation.
   
   Ready to automate? Head to n8n and give it a try!
   
   — End of Article —

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.