This article provides a complete, practical walkthrough of the Code Schedule Create Scheduled n8n agent. It connects Schedule Trigger 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 Schedule Trigger, 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

• Schedule Trigger

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 News Extraction and Summarization from Non-RSS Websites Using n8n and OpenAI  
   
   **Meta Description:**  
   Learn how to build a fully automated no-code news scraping pipeline using n8n, OpenAI GPT-4, and NocoDB. This workflow extracts, filters, summarizes, and stores weekly news posts from a website without an RSS feed.  
   
   **Keywords:**  
   n8n, web scraping, no-code automation, OpenAI GPT-4, NocoDB, news summarization, CSS selectors, news data pipeline, workflow automation, Colt news  
   
   ---
   
   # Automating News Extraction and Summarization from Non-RSS News Sites Using n8n & OpenAI
   
   When a website doesn’t offer an RSS feed, keeping up with its latest updates—particularly in a structured, automated way—can be a challenge. But with powerful no-code platforms like n8n, paired with AI technologies like OpenAI and a backend such as NocoDB, it’s possible to build a complete, end-to-end news-scraping and summarization pipeline.
   
   In this article, we walk you through a robust n8n workflow built to scrape, process, and store news content from Colt Technology Services’ news page (https://www.colt.net/resources/type/news/), which lacks an RSS feed. This workflow is scheduled to run weekly and performs web scraping, semantic summarization with ChatGPT, keyword extraction, and finally, structured data storage.
   
   ---
   
   ## Why Automate Web Scraping News?
   
   Colt’s news page lists press releases and articles, but doesn’t distribute updates via RSS. That leaves manual checking or automated scraping as the only viable strategies for consistent tracking. Automating this ensures you never miss out on recent posts, and allows you to keep a searchable and categorized internal database of content relevant to your business or technology monitoring needs.
   
   ---
   
   ## Overview of the Solution
   
   The n8n workflow accomplishes the following steps:
   
   1. ✅ Loads the news summary page HTML.
   2. 🕸 Extracts individual news post URLs and corresponding publication dates.
   3. 📅 Filters out only recent posts (past 7 days — customizable).
   4. 🔗 Fetches full web pages of filtered news links.
   5. 📄 Extracts the title and main content from each page.
   6. 🤖 Uses OpenAI’s GPT-4 API to generate:
       - A short summary (≤ 70 words)
       - The top 3 technical keywords
   7. 🧾 Compiles all data into single items.
   8. 🗃 Saves into a NocoDB SQL table—ideal for further use, dashboarding, or notifications.
   
   Let’s break down the most crucial steps in detail.
   
   ---
   
   ## Step-by-Step Explanation
   
   ### 1. Scheduled Weekly Trigger
   
   The workflow starts with an `n8n-nodes-base.scheduleTrigger` node that runs the scenario automatically every Wednesday at 4:32 AM. This ensures consistent updates without manual intervention.
   
   ### 2. Scraping Links and Dates
   
   A HTTP Request node retrieves the HTML of the Colt news page. Using two separate HTML extraction nodes, the workflow parses:
   - The anchor (`<a>`) tags that link to individual posts.
   - The `<span>` elements holding publication dates, by targeting specific CSS selectors.
   
   These extracted arrays are then broken down into single JSON objects using `itemLists` nodes for easier downstream handling.
   
   ### 3. Filtering by Date
   
   A `code` node compares each extracted date to today’s date minus 7 days—keeping only timely articles for processing.
   
   ### 4. Fetching and Parsing News Content
   
   Each filtered news link is visited again using HTTP Request nodes. A dedicated HTML extraction node then pulls out:
   - The `<h1>` headline (Title)
   - The main content from a targeted `<div>` block (Content)
   
   This separates metadata (link + date) from raw content.
   
   ### 5. Enriching Data via GPT-4
   
   OpenAI's GPT-4 API is harnessed for natural language processing:
   
   - A call to ChatGPT creates a concise summary in less than 70 words.
   - A second call extracts three of the most important technical keywords mentioned in the content—no filler, just key terms.
   
   Both API responses are then cleaned and renamed using `Set` nodes for field consistency.
   
   ### 6. Merging All Data
   
   The workflow uses `Merge` nodes (in “mergeByPosition” mode) to combine:
   - Title
   - Date
   - URL
   - Summary from ChatGPT
   - Extracted keywords
   
   ### 7. Saving to the Database
   
   The final node pushes the merged dataset into a NocoDB table. Fields in the database include:
   - Title
   - Link
   - Date
   - Summary
   - Keywords
   - News Source (hardcoded as "Colt")
   
   NocoDB (an open-source alternative to Airtable) allows SQL-like querying and easy integrations into dashboards or analyses.
   
   ---
   
   ## Why This Approach is Valuable
   
   - 🧠 **AI Enhancement:** Instead of raw scraped text, you get structured insights powered by GPT-4.
   - 💾 **Structured Archiving:** Using NocoDB, you build a historical internal database for trend analysis.
   - 🔄 **Hands-off Automation:** Runs weekly via cron job—no human effort needed after setup.
   - 🧩 **No-Code Flexibility:** Every aspect can be modified and extended without needing to write complex scripts.
   
   ---
   
   ## Third-Party APIs and Tools Used
   
   1. **OpenAI GPT-4 API**  
      - Used for summarization and keyword extraction.
   
   2. **NocoDB API**  
      - Serves as the target SQL-like backend to store news data.
   
   ---
   
   ## Final Thoughts
   
   This n8n workflow showcases how you can turn a content source without an RSS feed into a structured, AI-powered news stream for your database or dashboard. It marries web scraping, natural language processing, and low-code automation into a single, scalable solution with real-world utility for any researcher, tech analyst, or business team.
   
   If you're managing news sources manually today, consider trying a no-code automation approach like this—it may save you hours and deliver better, AI-curated insights.
   
   ---
   
   Let us know your suggestions or share your own automations on Mastodon:  
   📬 [@askans@bonn.social](https://bonn.social/@askans)  
   
   Happy automating!

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.