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:**  
   Leveraging AI to Discover Trending YouTube Content: Inside an n8n Automation Workflow
   
   **Meta Description:**  
   Explore how n8n’s powerful low-code automation tools, integrated with AI and YouTube APIs, can help creators identify trending content in their niche to boost engagement and growth.
   
   **Keywords:**  
   n8n YouTube workflow, AI content discovery, YouTube trending tools, LangChain YouTube search, YouTube API automation, creator tools n8n, n8n workflow for content creators, YouTube video analytics automation
   
   ---
   
   ### Unlocking YouTube Trends with AI: An Inside Look at the “Complete YouTube” n8n Workflow
   
   In today’s content-driven digital economy, knowing what’s trending can give YouTube creators a substantial edge. However, manually scanning trending videos, extracting data, and analyzing patterns consumes time and effort. Enter n8n and LangChain—a no-code/low-code automation platform and a powerful AI toolkit—which together form an intelligent system that empowers creators by removing the guesswork from trend hunting.
   
   In this article, we’ll break down the “Complete YouTube” workflow powered by n8n, showing how it harnesses AI agents, YouTube APIs, and process automation to surface valuable insights about the newest and most engaging YouTube content in any given niche.
   
   ---
   
   ### How the Workflow Works: Step-by-Step Breakdown
   
   The “Complete YouTube” workflow is a sophisticated, modular automation script designed to interact conversationally with users and deliver timely, data-driven summaries of trending YouTube content from the past two days. Here's how it works:
   
   #### 1. Conversational Chat Input
   
   The user's journey begins with a chat trigger node (`chat_message_received`). Once a message is received, it gets passed to the “AI Agent” node—which is powered by LangChain and connected to OpenAI’s GPT model.
   
   The AI first verifies whether the user has specified a niche. If not, it prompts the user with suggested niches to choose from. This ensures that all future steps are performed contextually, focusing on a relevant content category.
   
   #### 2. Smart YouTube Searches
   
   Once the niche is confirmed, the AI agent triggers the “youtube_search” tool—a separate n8n sub-workflow that performs up to three searches using varied keywords related to the niche.
   
   Each search fetches a set of relevant videos containing:
   - Video ID
   - Description
   - Tags
   - Channel ID and Title
   - Statistics like view count, likes, and comments
   
   These videos must be published within the last 48 hours to ensure freshness in relevance and virality potential.
   
   #### 3. Detailed Video Fetch & Filtering
   
   For each video returned:
   - The `get_videos1` node communicates with YouTube’s Data API to retrieve filtered video metadata.
   - Videos are passed through a duration check using the `if_longer_than_3_` node. Only videos longer than 3 minutes and 30 seconds will proceed, ensuring quality content.
   - Video statistics and descriptive metadata are collected and cleaned via a series of `httpRequest`, `set`, and `code` nodes, with special care taken to normalize and sanitize descriptions, strip emojis, and deduplicate URLs.
   
   #### 4. Memory Handling & Data Aggregation
   
   Processed data is saved into a global memory store (`save_data_to_memory1`) to allow batch tracking across the workflow's sessions. Later, it's retrieved by `retrieve_data_from_memory1`, prepared for the AI agent to analyze.
   
   Once multiple videos have been processed, a single structured response is generated. Instead of presenting random video links, the AI interprets the data, uncovers patterns in tags, topics, and engagement metrics, and produces narrative insights.
   
   #### 5. AI-Powered Insights and Recommendations
   
   The final output emphasizes pattern recognition. For instance:
   > "It appears that in the last two days, the most engaging videos in the tech niche focused on AI-generated music and ChatGPT plugins, supported by 3 out of 5 top videos amassing over 500k views each, high comment counts, and overlapping tags like #ChatGPT, #OpenAI, and #musicAI."
   
   The user also receives clickable links to each video and its corresponding channel, formatted as:
   - Video: `https://www.youtube.com/watch?v={video_id}`
   - Channel: `https://www.youtube.com/channel/{channel_id}`
   
   ---
   
   ### Why This Workflow Matters for Creators
   
   The beauty of this system lies in automation and intelligent interpretation. Instead of providing dry video links, the workflow gives creators:
   - An understanding of viewer interests within their niche
   - Emerging formats and themes (e.g., challenges, reactions, reviews)
   - Historical patterns that feed new content ideation
   
   This allows video creators to adapt more quickly to trends, stand out, and engage with audiences in a more resonant and timely fashion.
   
   ---
   
   ### Third-Party APIs Used
   
   Here’s a list of external APIs and tools integrated into this workflow:
   
   1. **YouTube Data API v3**
      - Used to fetch recent videos, channel metadata, and key video statistics.
   2. **OpenAI GPT Integration via LangChain**
      - Powers the AI agent’s ability to analyze trends, carry conversational context, and summarize insights.
   3. **Google OAuth2**
      - Used for secure access to YouTube data where necessary.
   4. **n8n's LangChain Extensions**
      - Offers seamless connection to AI functions, memory handling, and operational tooling.
   
   ---
   
   ### The Power of No-Code AI Integration
   
   This workflow is an excellent example of how no-code platforms like n8n can democratize access to advanced technologies. Whether you’re a solo creator, part of a content team, or just starting your YouTube journey, having an AI assistant that tells you what’s hot and why, within minutes, is a game changer.
   
   With easily editable logic, users can customize search parameters, change the summary format, or even connect to other platforms (like Twitter/X or Instagram) for broader trend tracking.
   
   ---
   
   In conclusion, the “Complete YouTube” n8n workflow is more than just an automation tool – it’s an intelligent assistant for digital creators looking to make data-backed decisions. As content trends grow increasingly dynamic, tools like this keep creators ahead of the curve with minimal effort and maximum relevance.

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.