This article provides a complete, practical walkthrough of the Manual 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:  
   Automate Tech News Monitoring: Scraping the Latest 20 TechCrunch Articles with n8n
   
   Meta Description:  
   Learn how to use n8n's visual workflow automation to scrape, parse, and aggregate the latest 20 TechCrunch articles — complete with article metadata and content. Ideal for tech enthusiasts, journalists, and content curators.
   
   Keywords:  
   n8n workflow, web scraping, TechCrunch, automation, no-code tools, content aggregation, html parsing, HTTP request, extract article data, productivity tools
   
   Third-Party APIs Used:
   - TechCrunch (https://techcrunch.com) – Used as the source from which articles are scraped.
   
   Article:
   
   How to Automatically Scrape the Latest 20 TechCrunch Articles with n8n
   
   In today’s fast-paced digital landscape, staying updated with the latest tech news can be both necessary and time-consuming. Fortunately, workflow automation platforms like n8n allow users to build sophisticated scraping mechanisms without writing extensive code.
   
   This article walks you through an n8n workflow designed to scrape and parse the 20 latest TechCrunch articles—from fetching the content, extracting metadata, to formatting the results for future processing. Whether you’re a tech blogger, curator, content strategist, or developer monitoring digital trends, this hands-on example demonstrates how to harness n8n’s robust capabilities for smarter workflows.
   
   Overview of the Workflow
   
   The n8n workflow titled “Scrape Latest 20 TechCrunch Articles” consists of several connected nodes that perform distinct but sequentially dependent tasks. Here is a breakdown of the key components:
   
   1. Manual Trigger
   The workflow begins with a Manual Trigger node, giving the user the flexibility to run it on-demand by clicking “Test workflow.” This makes it perfect for testing or one-off data pulls.
   
   2. Fetch the Latest Articles Page
   Using the HTTP Request node, the workflow makes a GET request to TechCrunch’s latest articles page at https://techcrunch.com/latest/0. This pulls in the raw HTML from the site, which includes a structured list of recent posts.
   
   3. Extract the Posts Container
   The next node—a HTML node—targets and extracts the HTML content of the unordered list (`ul.wp-block-post-template`) that houses all the post previews. This effectively separates the section of the page we care about from the rest of the HTML.
   
   4. Parse Individual Posts
   Another HTML node extracts each list item (`li.wp-block-post`) as a separate entity, turning them into an array of individual post snippets. This sets the stage for further processing of each post.
   
   5. Split the Posts Array
   A Split Out node is employed to break this array into single units so that each post can be parsed and analyzed independently in the next steps.
   
   6. Extract Key Metadata from Each Post
   Each individual post snippet is passed through a detailed HTML parsing node. Using CSS selectors, this node pulls the following pieces of data:
   - Title (from `h3.loop-card__title`)
   - Article URL (`data-destinationlink` from `h3>a`)
   - Image source (`src` from `img`)
   - Publication datetime (`datetime` from `time`)
   
   7. Request Full Article Details
   After acquiring each post’s URL, the workflow launches a new HTTP Request for the full article page. This allows it to scrape the complete article content, rather than relying only on preview information.
   
   8. Final Parsing of Full Content
   This HTML node performs an in-depth extraction of the complete article. It pulls:
   - Full article content (from `div.entry-content`)
   - Main article headline (from `h1.wp-block-post-title`)
   - Featured thumbnail image (from `img.attachment-post-thumbnail`)
   - Accurate creation date (from `time` element with `datetime` attribute)
   
   9. Format and Save the Data
   A Set node consolidates all the extracted information—title, content, image, URL, and timestamp—into a structured format that can then be piped into another service or stored for future use.
   
   Use Cases and Applications
   
   This workflow can be plugged into broader systems or customized further to suit any of the following goals:
   - Automate newsletter generation based on the latest posts
   - Feed a database or CMS for republishing or analysis
   - Monitor breaking tech news as part of a media intelligence system
   - Build a custom RSS-style feed for internal tools or dashboards
   - Train AI/ML models using timely tech content
   
   Why n8n?
   
   n8n stands out due to its open-source nature and visual programming interface. Unlike more rigid systems, n8n offers unmatched flexibility by combining built-in nodes with external APIs, logical operations, and data transformation—all in one intuitive flow. This makes it a go-to tool for developers and non-developers alike.
   
   Limitations & Ethical Considerations
   
   While this workflow provides an efficient way to extract and work with TechCrunch articles, users must be aware of the limitations and ethical considerations:
   - Website structure changes can break the workflow.
   - Always ensure compliance with the site’s robots.txt and terms of use.
   - Rate limits and IP bans may affect large-scale automated scraping.
   
   Conclusion
   
   This n8n workflow illustrates how you can efficiently automate the collection of tech news content using free, no-code tools. By programmatically scraping and parsing TechCrunch’s latest articles, users gain access to near-real-time news feeds structured for use in apps, analytics, and much more.
   
   Whether for media aggregation, research, or content development, this workflow is a powerful example of how n8n can reduce redundancy, enhance productivity, and keep you connected to the ever-evolving tech ecosystem.

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.