AI-powered News-to-Email Agent

Tanush J — Sun, 31 Aug 2025 18:23:29 +0000

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

What I Built

I built an AI-powered News-to-Email Agent that automatically:

Fetches the latest news articles of specific categories you provide.
Uses an LLM to generate a professional, table-based HTML email newsletter with multiple articles formatted for email clients like Gmail, Outlook, and Apple Mail.
Sends the final email to subscribers with proper preheader text, article summaries, and "Read more" links.

This solves the problem of turning raw RSS/news data into a polished daily newsletter—without manual formatting.

Demo

Demo Video

n8n Workflow

Workflow Link

Technical Implementation

System Instructions: The system prompt was carefully crafted to enforce compatibility with email clients (table-based layout, inline CSS, no external assets). It also ensures each article is formatted consistently with title, source, summary, and link.

Model Choice: "meta-llama/Llama-3.1-8B-Instruct" from HuggingFace Inference API, good for text-generation and is helpful for template generation

Memory: Stateless, each workflow execution processes the latest batch of news articles.

Tools Used:

Bright Data Node (Google News Scraper) → get latest articles.
HTTP Request Node → send articles + system prompt to Hugging Face API.
Function Node → structure JSON payloads (e.g. { instruction, articles: [...] }).
SMTP Email Node → deliver the final HTML newsletter.

Bright Data Verified Node

I used the Bright Data Verified Node to fetch clean, reliable, and structured news data from various web sources. This ensured the agent always received up-to-date, accurate articles to include in the newsletter without scraping issues or inconsistent data.

Journey

This was my first time exploring n8n and building a workflow that integrates with LLMs. At the start, I had to learn how n8n handles data, especially:

Data cleaning and transformations: writing Function nodes to reshape and sanitize incoming JSON so that the model could consume it properly.
Understanding data flow between nodes: getting used to how n8n passes input/output made me rethink how to structure each step in the pipeline.

One of the biggest challenges came when I tried to connect the Hugging Face Inference API using n8n’s basic LLM Chain node. The node always defaulted to a conversational mode, which wasn’t suitable for structured HTML generation. After multiple attempts, I switched to using the HTTP Request node to directly access Hugging Face’s text-generation models. While this approach worked, it also raised concerns about security (exposing API keys when sharing workflows).

Final Thoughts

This workflow works end-to-end, but there’s still room for improvement:

The AI model doesn’t always generate a perfect email template.
Optimizations could reduce the running cost of the workflow.
Exploring safer and more scalable ways to integrate Hugging Face APIs in n8n would make this even more robust.

Forem: Tanush J