This article provides a complete, practical walkthrough of the Splitout Limit Create 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:
   From Headlines to Headlines: Auto-Generating Podcasts from BBC News with n8n and AI Integrations
   
   Meta Description:
   Learn how a powerful no-code n8n workflow turns live BBC News headlines into dynamic, conversational podcasts using Google Gemini, Hugging Face, and Langchain AI integrations.
   
   Keywords:
   n8n workflow, podcast automation, AI podcast generator, Google Gemini, Hugging Face TTS, Langchain, text-to-speech, BBC News scraping, ElevenLabs script, automation, generative AI, LLM, news-to-podcast
   
   Third-Party APIs Used:
   
   - Google Gemini (PaLM) via Langchain: For text classification and script generation.
   - Hugging Face Inference API (Text-to-Speech Model): To convert narrative scripts into audio podcasts.
   - BBC News Website (scraped via HTTP request): Source for raw news content.
   
   Article:
   
   Transforming News into Podcasts: A Next-Gen n8n Automation Workflow
   
   In today's content-saturated era, transforming raw news into digestible, engaging formats is more valuable than ever. One innovative solution lies at the intersection of automation and AI—specifically, using n8n, a popular no-code workflow automation tool, to convert the latest BBC News headlines into rich, conversational podcasts with zero manual effort.
   
   This article explores a powerful n8n workflow that automates the journey from scraping headlines to generating a ready-to-read podcast script and even rendering it into speech using state-of-the-art language and voice models. Whether you're a media enthusiast, a podcaster looking to save time, or a developer curious about integrating AI tools, this workflow offers a glimpse into the future of automated content creation.
   
   The Workflow in a Nutshell
   
   At its core, the workflow follows a robust pipeline:
   
   1. Fetch real-time BBC News homepage content
   2. Extract and process individual news article snippets
   3. Classify article suitability for a podcast using AI
   4. Fetch full article content for suitable entries
   5. Transform content into a natural-sounding podcast script
   6. Convert the script into an audio file using a voice model
   
   It’s a seamless blend of scraping, transformation, classification, and generative output, orchestrated entirely inside n8n.
   
   Step 1: Fetch the Latest Headlines
   
   Triggered manually via the Manual Trigger node, the workflow starts by fetching the HTML content of the BBC News homepage. This involves parsing the page using the HTML Extract node with a CSS selector targeting article blocks (e.g., `.eGcloy`). The goal is to extract small blocks of HTML that each represent a news teaser with a title and link.
   
   Step 2: Parsing and Structuring the Articles
   
   The Split Out node dissects the previously extracted array into individual news items. Each item is parsed again to extract the key data points:
   
   - Title (via h2 tags)
   - Link (from anchor tags)
   - Short description (if available)
   
   This gives us a list of news entries with structured metadata ready for downstream analysis.
   
   Step 3: Classifying News Suitability Using Google Gemini
   
   Not every news article makes for an engaging podcast segment—this is where Google Gemini (PaLM) comes in. Via Langchain’s textClassifier node, the headlines and summaries are evaluated against custom-defined criteria like storytelling potential, audience interest, tone, and relevance.
   
   Articles are filtered based on whether they are "Suitable" (e.g., discoveries, science, human interest) or "Not Suitable" (e.g., violence, niche topics, routine politics).
   
   Step 4: Getting the Full Story
   
   For articles deemed suitable, the workflow sends HTTP requests to their full BBC News URLs to retrieve the detailed article HTML. Another HTML node parses the full content block, targeting key narrative elements through selectors (e.g., `.dlWCEZ .fYAfXe`).
   
   To ensure quality, a filter checks if the fetched detail content is non-empty, safeguarding against blank or irrelevant articles.
   
   Step 5: Aggregating and Scriptwriting with Langchain + Gemini
   
   Filtered article data is aggregated into one object and fed to a Langchain LLMChain node powered by Google Gemini. Here, the magic of generative AI happens. The workflow provides the LLM with a system message prompt that lays out an explicit instruction set:
   
   - Convert article content into a single podcast script
   - Use a warm and conversational tone
   - Include transitions between stories
   - Structure the script for direct audio synthesis
   
   The model returns a structured JSON output containing the podcast under the key: podcast_script.
   
   Step 6: Speak It Out with Hugging Face
   
   To bring the script to life, the workflow sends the final podcast script to a Hugging Face-hosted TTS (Text-to-Speech) model via an API call. The model used here is facebook/mms-tts-eng, a general-purpose English TTS engine from Hugging Face that converts text to spoken-word audio.
   
   With the final audio in hand, the user has a finished, auto-generated news podcast—all crafted in moments.
   
   Why This Workflow Matters
   
   This workflow exemplifies an emerging paradigm in content automation:
   
   - AI-powered content creation is now accessible to no-code users.
   - News content can be curated and filtered for positivity and engagement.
   - Podcasts can be generated without ever speaking into a microphone.
   - Real-time news can be converted to consumable formats automatically.
   
   It also introduces ethical curation through AI classification, ensuring that podcast content avoids negative or harmful stories and focuses on what audiences will find uplifting and fascinating.
   
   Key Advantages:
   
   - Modular and scalable
   - Easy to customize CSS selectors and AI prompts
   - Utilizes reliable LLMs and open-source TTS
   - No manual writing required
   - Easily integrates with podcast publishing platforms
   
   Third-Party Integrations That Power the Workflow
   
   - Google Gemini (via Langchain): For classifying headlines and performing LLM transformations into podcast scripts.
   - Hugging Face Inference API: For high-quality text-to-speech synthesis.
   - BBC News Website: The live, public content source for real-time news scraping.
   
   Conclusion: Robots With a Voice
   
   With just a few nodes and smart integrations, this n8n workflow demonstrates how AI and automation are revolutionizing the media landscape. By blending GPT-style language modeling with the instant availability of TTS technology, we now have the ability to not just report on the news—but to narrate it, humanize it, and distribute it faster than ever before.
   
   Whether you’re an indie podcaster, a digital storyteller, or a news outlet looking to diversify your media streams, this automated pipeline provides a scalable, ethical, and creative approach to podcast content generation—one headline at a time.

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.