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n8n Newsletter Automation

Multi-Agent AI Content Generation

Multi-agent workflow that autonomously researches, plans, writes, and formats professional newsletters. Uses Tavily for trending news research, Gemini 2.5 Pro for three specialized agents (Planning, Writing, Editing), and n8n for workflow orchestration. Generates weekly newsletters with proper citations, HTML formatting, and Gmail draft integration.

GitHub Live
Case study snapshot
Role
AI systems builder
Audience
AI product users and recruiters
Timeline
2025-01
Role
AI systems builder
Audience
AI product users and recruiters
Date
2025-01
Problem

Why it mattered

Saves 3-4 hours per newsletter edition; Cost ~$0.05 per run; Structured JSON output

Solution

What shipped

Multi-agent workflow that autonomously researches, plans, writes, and formats professional newsletters. Uses Tavily for trending news research, Gemini 2.5 Pro for three specialized agents (Planning, Writing, Editing), and n8n for workflow orchestration. Generates weekly newsletters with proper citations, HTML formatting, and Gmail draft integration.

Impact

Proof signal

Saves 3-4 hours per newsletter edition; Cost ~$0.05 per run; Structured JSON output

< architecture />

System flow

Schedule Trigger -> Research Trends -> Plan Topics -> Research Topics -> Write Sections -> Edit & Format -> Gmail Draft.

Schedule Trigger
Research Trends
Plan Topics
Research Topics
Write Sections
Edit & Format
Gmail Draft
Visual architecture
01
Schedule Trigger
02
Research Trends
03
Plan Topics
04
Research Topics
05
Write Sections
06
Edit & Format
07
Gmail Draft
quality score

Honest maturity snapshot

Code Quality9/10
UI / UX6/10
Scalability7/10
Production Ready9/10
Before

Manual questions, scattered context, and slow follow-up.

After

An agent workflow that routes intent, calls tools, and returns useful next actions.

capabilities

Features

  • n8n integrated into the workflow.
  • Google Gemini integrated into the workflow.
  • Tavily API integrated into the workflow.
  • Gmail API integrated into the workflow.
  • Schedule Trigger stage documented in the delivery flow.
  • Research Trends stage documented in the delivery flow.
tradeoffs

Engineering challenges

  • Keeping the implementation clear enough to explain while still solving the core technical problem.
  • Choosing a scope that could be shipped, tested, and documented.
  • Turning technical work into proof a visitor can evaluate quickly.
proof

Results

  • Saves 3-4 hours per newsletter edition; Cost ~$0.05 per run; Structured JSON output
  • Documented the engineering path and important learnings.
  • Made source code or technical proof available for review.
reflection

What I learned

  • Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
  • Proof learned: Saves 3-4 hours per newsletter edition; Cost ~$0.05 per run; Structured JSON output
  • Next iteration: Add richer screenshots or a narrated demo.
< stack />
n8nGoogle GeminiTavily APIGmail APIJSON Schema
< next iteration />

Future improvements

Add richer screenshots or a narrated demo.
Add before/after metrics and usage notes.
Package the project as a reusable template or deployable demo.
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