This article provides a complete, practical walkthrough of the Webhook Respondtowebhook 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:
   Creating a Dynamic Podcast RSS Feed for ARD Audiothek Using n8n
   
   Meta Description:
   Learn how to build a fully automated podcast RSS feed for the "Kalk und Welk" show on ARD Audiothek using n8n. This workflow extracts episode metadata, formats it into RSS, and serves it via a custom webhook.
   
   Keywords:
   n8n, ARD Audiothek, podcast RSS feed, automation, web scraping, workflow automation, JavaScript, HTTP Request, HTML Extract, JSON parsing, content syndication, low-code integration
   
   Third-Party APIs Used:
   - ARD Audiothek (https://www.ardaudiothek.de): Used as the data source for the podcast show and episodes (no official API is called directly, but their public website serves as data input via HTTP requests)
   
   Article:
   
   Automating Podcast RSS Feeds with n8n: A No-Code Solution for ARD Audiothek Content
   
   In the ever-growing world of podcasting, automation is key to managing and distributing content efficiently. Whether you're a podcast producer or a developer looking for creative ways to repurpose media content, this article walks you through a powerful no-code (well, low-code) solution created with n8n—a popular workflow automation platform. In this example, we build a real-time RSS feed for episodes from the German comedy podcast "Kalk und Welk" found on ARD Audiothek, a public broadcasting platform.
   
   ⏩ Objective
   
   The goal is to scrape the ARD Audiothek overview page for all episodes of “Kalk und Welk,” extract relevant metadata (title, audio URL, publish date, description, etc.), format the data into a compliant RSS feed, and serve it via a public endpoint using n8n's built-in webhook features—without requiring direct access to an API.
   
   📦 Tools Used
   
   We use the open-source workflow tool n8n, which supports a wide range of nodes for HTTP requests, HTML parsing, data manipulation, and output formatting. Only unofficial access to ARD Audiothek is used in this example (via public HTML scraping). No login or API key is required.
   
   🛠️ Workflow Overview
   
   Below is an explanation of each step in the n8n workflow.
   
   1. Manual Trigger (or Webhook Trigger)
   The workflow can be triggered manually via a button ("On clicking 'execute'") or through an HTTP webhook endpoint prepared using n8n’s Webhook node. This gives you the flexibility to choose between testing on click or calling it on-demand through a URL.
   
   2. Get Overview Page
   We start by making an HTTP GET request to the ARD Audiothek's “Kalk und Welk” overview page:
   https://www.ardaudiothek.de/sendung/kalk-und-welk/10777871/
   
   This page contains links to all individual podcast episodes.
   
   3. Extract Episode Links
   Using n8n’s HTML Extract node, we parse the overview page’s HTML to find all anchor tags (<a>) that include "/episode/" in their href attribute. This gives us a list of relative URLs to each episode detail page.
   
   4. Parse and Clean URLs
   We split these hrefs into individual items using the "Split out lists" node. Then, "Remove duplicate links" ensures we don't process the same episode twice.
   
   5. Visit Each Episode Page
   For each clean link, we dynamically construct a new HTTP request to the full URL (https://www.ardaudiothek.de + relative path to episode). We fetch each episode page's HTML content for further parsing.
   
   6. Extract Metadata from Script Tag
   Interestingly, the episode metadata is embedded in a <script> tag on the episode page (usually as JSON-LD structured data). We extract the second <script> element, which includes a rich JSON representation of the episode with fields like:
   - name (title)
   - datePublished
   - description
   - contentUrl (audio file)
   - encodingFormat (usually audio/mpeg)
   - identifier
   - timeRequired
   - partOfSeries (parent show metadata)
   
   7. Parse JSON Content
   Next, we parse the extracted JSON data using a JavaScript Set node. This gives us access to fields we’ll need to construct the RSS feed.
   
   8. Build RSS Feed
   The core of this workflow is a JavaScript Function node that takes the parsed metadata and formats it into an RSS 2.0 feed structure. It programmatically constructs XML <item> entries for each episode and wraps them into an <rss> feed with all the necessary metadata tags including:
   - <title>
   - <description>
   - <pubDate>
   - <enclosure> (with audio URL and content type)
   - <itunes:image>, <itunes:author>, and other useful tags
   
   The function also escapes special characters to ensure valid XML formatting.
   
   9. Serve the Feed
   Finally, the built XML is served via a Webhook Respond node. It sets the Content-Type to application/rss+xml and returns the feed content when someone accesses the unique webhook URL (e.g., /3fbd94de-2fb3-4b32-a46e-c237865479b9.rss).
   
   ⛓️ Automation Flow Summary:
   
   Trigger → Get Overview Page → Extract Episode Links → Clean/De-duplicate → Fetch Episode Pages → Extract JSON Metadata → Parse JSON → Build RSS Feed → Serve XML Feed
   
   ✅ Why This Workflow Matters
   
   - You can extend or customize this flow to any other podcast on ARD Audiothek or any other site with similar DOM structure.
   - Keeps content dynamically updated—refresh the workflow and always get the latest.
   - No official API? No problem. HTML extraction gives access to structured data buried in script tags.
   - Enables content syndication through standard RSS readers or podcast apps.
   
   💡 Extra Tips
   
   - Schedule the workflow to run periodically using a Cron node.
   - Add error handling or data caching logic to make it production-ready.
   - Use authentication and rate limiting if your solution goes public.
   
   Conclusion
   
   With just a few nodes and a bit of clever scripting, n8n empowers anyone to turn publicly available podcast content into a fully functional RSS feed. The "Kalk und Welk" workflow demonstrates how modern scraping techniques and automation platforms can democratize access to content and simplify distribution for creators, curators, and listeners alike.
   
   By leveraging n8n’s flexibility and ARD Audiothek’s structured data, you can now build dynamic podcast feeds tailored to your own needs—all without writing a full application.
   
   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.