This article provides a complete, practical walkthrough of the Code Http 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:
   How This n8n Workflow Finds What’s Trending on YouTube in Your Niche
   
   Meta Description:
   Discover how an AI-powered n8n workflow helps YouTube creators uncover trending video content in any niche, providing valuable insights based on recent data analytics.
   
   Keywords:
   n8n, YouTube trends, YouTube content ideas, automation, AI content assistant, LangChain, trending videos, creator tools, YouTube API, video analysis, niche content strategy, n8n workflow, YouTube automation
   
   Third-Party APIs Used:
   1. YouTube Data API v3 (via HTTP Request and YouTube Node)
   2. OpenAI (via LangChain Integration)
   3. LangChain (agent, tools, memory, and reasoning utilities)
   
   —
   
   Article:
   
   Uncovering Trending YouTube Content with AI-Powered Workflows in n8n
   
   Staying relevant in the fast-paced world of YouTube content creation can feel like chasing a moving target. Trends shift rapidly, and manually scanning videos for trending content is a time sink most creators can’t afford. But what if there was a way to delegate that research to an intelligent digital assistant?
   
   Meet “Complete YouTube,” a powerful AI-driven workflow built with n8n—an open-source workflow automation tool. This workflow empowers YouTube creators by automatically identifying trending videos in a desired niche, summarizing viewer engagement stats, and highlighting emerging patterns—all within minutes. By combining AI agents, conversation memory, API calls, and structured data processing, “Complete YouTube” becomes a virtual YouTube strategist.
   
   Let’s break down how it works—and why it’s such a game-changer for creators everywhere.
   
   ⏱️ Starting with a Simple Input: The Chat Trigger
   
   This workflow kicks off whenever a user sends a chat message. This interaction is managed by the n8n LangChain Chat Trigger node, enabling dynamic two-way communication. From the get-go, the AI verifies whether the creator has specified a niche. If not, it prompts for one, offering genre suggestions like health, tech, entertainment, finance, or gaming.
   
   🧠 The AI Agent at the Helm
   
   The core of this workflow lies in the AI Agent node powered by LangChain and OpenAI. It doesn’t just regurgitate information—it acts strategically. Once a niche is confirmed, the AI plans up to three different searches using the YouTube Search tool. These search terms are intelligently generated based on recent trends, keywords, and the chosen niche.
   
   This agent has a purpose beyond trendspotting: its mission is to process recent data and uncover trends in narrative, tags, and video metadata. For example, if a user targets the "digital marketing" niche, the AI might find that videos about “mental triggers” and “psychological hacks” are dominating views over the past 48 hours.
   
   🔍 Deep Dive with YouTube Search
   
   Auxiliary to the main agent is the "youtube_search" tool—a separate reusable workflow designed to interface directly with the YouTube API.
   
   Here's the multi-step breakdown:
   
   1. Fetch recent videos from the YouTube Data API based on dynamic search terms.
   2. Sort them by relevance and limit results to videos published within the last 2 days.
   3. Extract video statistics such as view count, like count, comment count, duration, tags, and channel info.
   4. Filter out videos shorter than 3 minutes and 30 seconds, under the assumption that longer videos offer more substance.
   
   📊 Pattern Recognition with a Human Touch
   
   Raw video data is processed and sanitized—emojis and excessive formatting are stripped, and URLs removed—to help the AI agent work with clean content. From this data, patterns emerge. Instead of highlighting specific videos, the AI identifies commonalities across all results, such as common keywords in titles or tags, popular themes in descriptions, or recurring channel types.
   
   The AI's final output is an insightful, human-readable summary like:
   
   “It seems like content related to ‘minimalist living’ is trending in the lifestyle niche. Multiple high-engagement videos, each with over 100K views and 10K likes, focused on decluttering hacks, capsule wardrobes, and simplified routines.”
   
   The creator also receives YouTube links in a structured and clickable format so they can immediately dive into the referenced content:
   
   - Video: https://www.youtube.com/watch?v={video_id}
   - Channel: https://www.youtube.com/channel/{channel_id}
   
   🗃️ Memory Buffer That Learns with You
   
   Another critical component is the memory node powered by LangChain’s Window Buffer Memory. This keeps the conversation context persistent, allowing the AI to build upon previous queries and recommendations, creating a seamless experience for repeat users or evolving content strategies.
   
   🧩 Modular, Scalable, and Smart by Design
   
   "Complete YouTube" showcases the effectiveness of modular design in n8n:
   
   - The core workflow manages user interaction, AI logic, and report generation.
   - The reusable youtube_search sub-workflow manages specific API calls and data preparation.
   - If desired, additional niche-specific modules (e.g., “Fitness Trends” or “Crypto Buzz”) could be plugged into the ecosystem to further enhance insights.
   
   🚀 Why This Workflow Matters
   
   For content creators, this workflow significantly reduces the time spent researching. Instead of scrolling through endless search results and guessing what will resonate, creators are delivered real-time, data-backed insights tailored to their chosen niche.
   
   Moreover, content strategy evolves from reactive to proactive. The AI not only responds with what's trending now but helps creators anticipate future content based on meta-trends—such as rising interest in mental wellness within the entrepreneurship niche or AI tools among digital artists.
   
   👨‍🔬 A Smarter Way to Create
   
   In the world of content, being early is everything. With workflows like "Complete YouTube," creators can transition from guesswork to guidance, from trend followers to trendsetters.
   
   Whether you’re a newcomer to YouTube or a seasoned creator looking to supercharge your content strategy, this workflow proves just how far AI and automation can take you—without ever needing to leave your dashboard.
   
   —
   
   Ready to build your own version? Start with n8n, connect LangChain and YouTube Data API, and let AI do the heavy lifting while you focus on what you do best: creating.

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.