End-to-end Ai blog research and writer with Gemini AI, Supabase and Nano-Banana
Blog Research and Writer n8n Workflow - Ai Blog Writer
Fully automated blog creation system using n8n + AI Agents + Image Generation
Overview
This workflow automates the entire blog creation pipeline—from topic research to final publication. Three specialized AI agents collaborate to produce publication-ready blog posts with custom images, all saved directly to your Supabase database.
How It Works
1. Research Agent (Topic Discovery)
- Triggers: Runs on schedule (default: daily at 4 AM)
- Process:
- Fetches existing blog titles from Supabase to avoid duplicates
- Uses Google Search + RSS feeds to identify trending topics in your niche
- Scrapes competitor content to find content gaps
- Generates detailed topic briefs with SEO keywords, search intent, and differentiation angles
- Output: Comprehensive research document with SERP analysis and content strategy
2. Writer Agent (Content Creation)
- Triggers: Receives research from Agent 1
- Process:
- Writes full blog article based on research brief
- Follows strict SEO and readability guidelines (no AI fluff, natural tone, actionable content)
- Structures content with proper HTML markup
- Includes key sections: hook, takeaways, frameworks, FAQs, CTAs
- Places image placeholders with mock URLs (
https://db.com/image_1, etc.)
- Output: Complete JSON object with title, slug, excerpt, tags, category, and full HTML content
3. Image Prompt Writer (Visual Generation)
- Triggers: Receives blog content from Agent 2
- Process:
- Analyzes blog content to determine number and type of images needed
- Generates detailed 150-word prompts for each image (feature image + content images)
- Creates prompts optimized for Nano-Banana image model
- Names each image descriptively for SEO
- Output: Structured prompts for 3-6 images per blog post
4. Image Generation Pipeline
- Process:
- Loops through each image prompt
- Generates images via Nano-Banana API (Wavespeed.ai)
- Downloads and converts images to PNG
- Uploads to Supabase storage bucket
- Generates permanent signed URLs
- Replaces mock URLs in HTML with real image URLs
- Output: Blog HTML with all images embedded
5. Publication
- Final blog post saved to Supabase
blogstable as draft - Ready for immediate publishing or review
Key Features
✅ Duplicate Prevention: Checks existing blogs before researching new topics
✅ SEO Optimized: Natural language, proper heading structure, keyword integration
✅ Human-Like Writing: No robotic phrases, varied sentence structure, actionable advice
✅ Custom Images: Generated specifically for each blog's content
✅ Fully Structured: JSON output with all metadata (tags, category, excerpt, etc.)
✅ Error Handling: Automatic retries with wait periods between agent calls
✅ Tool Integration: Google Search, URL scraping, RSS feeds for research
Setup Requirements
1. API Keys Needed
- Google Gemini API: For Gemini 2.5 Pro/Flash models (content generation/writing)
- Groq API (optional): For Kimi-K2-Instruct model (research/writing)
- Serper.dev API: For Google Search (2,500 free searches/month)
- Wavespeed.ai API: For Nano-Banana image generation
- Supabase Account: For database and image storage
2. Supabase Setup
- Create
blogstable with fields:title,slug,excerpt,category,tags,featured_image,status,featured,content
- Create storage bucket for blog images
- Configure bucket as public or use signed URLs
3. Workflow Configuration
Update these placeholders:
- RSS Feed URLs: Replace
[your website's rss.xml]with your site's RSS feed - Storage URLs: Update Supabase storage paths in "Upload object" and "Generate presigned URL" nodes
- API Keys: Add your credentials to all HTTP Request nodes
- Niche/Brand: Customize Research Agent system prompt with your industry keywords
- Writing Style: Adjust Writer Agent prompt for your brand voice
Customization Options
Change Image Provider
Replace the "nano banana" node with:
- Gemini Imagen 3/4
- DALL-E 3
- Midjourney API
- Any Wavespeed.ai model
Adjust Schedule
Modify "Schedule Trigger" to run:
- Multiple times daily
- Specific days of week
- On-demand via webhook
Alternative Research Tools
Replace Serper.dev with:
- Perplexity API (included as alternative node)
- Custom web scraping
- Different search providers
Output Format
{
"title": "Your SEO-Optimized Title",
"slug": "your-seo-optimized-title",
"excerpt": "Compelling 2-3 sentence summary with key benefits.",
"category": "Your Category",
"tags": ["tag1", "tag2", "tag3", "tag4"],
"author_name": "Your Team Name",
"featured": false,
"status": "draft",
"content": "<article>...complete HTML with embedded images...</article>"
}
Performance Notes
- Average runtime: 15-25 minutes per blog post
- Cost per post: ~$0.10-0.30 (depending on API usage)
- Image generation: 10-15 seconds per image with Nano-Banana
- Retry logic: Automatically handles API timeouts with 5-15 minute wait periods
Best Practices
- Review Before Publishing: Workflow saves as "draft" status for human review
- Monitor API Limits: Track Serper.dev searches and image generation quotas
- Test Custom Prompts: Adjust Research/Writer prompts to match your brand
- Image Quality: Review generated images; regenerate if needed
- SEO Validation: Check slugs and meta descriptions before going live
Workflow Architecture
3 Main Phases:
- Research → Writer → Image Prompts (Sequential AI Agent chain)
- Image Generation → Upload → URL Replacement (Loop-based processing)
- Final Assembly → Database Insert (Single save operation)
Error Handling:
- Wait nodes between agents prevent rate limiting
- Retry logic on agent failures (max 2 retries)
- Conditional checks ensure content quality before proceeding
Result: Hands-free blog publishing that maintains quality while saving 3-5 hours per post.
End-to-End AI Blog Research and Writer with Gemini AI, Supabase, and Nano Banana
This n8n workflow automates the entire process of researching, writing, and publishing blog posts using AI, a database, and image manipulation. It's designed to streamline content creation, making it easier to generate high-quality blog articles with minimal manual intervention.
What it does
This workflow orchestrates several steps to produce a blog post:
- Triggers on a Schedule: The workflow starts at predefined intervals, ensuring continuous content generation.
- Fetches Blog Post Ideas: It retrieves blog post ideas from a Supabase database.
- Filters for Pending Posts: It checks if there are any blog posts with a "pending" status.
- Generates Blog Post Content: For each pending post, it uses an AI Agent (likely powered by Google Gemini or Groq) to:
- Research the topic.
- Write the blog post content.
- Extract structured information from the generated content.
- Generates a Featured Image: It creates or edits an image, presumably for use as a featured image for the blog post.
- Updates Supabase: The generated content and image information are then updated back into the Supabase database.
- Introduces a Delay: A "Wait" node is included to manage API rate limits or simply space out operations.
- Loops for Multiple Posts: The workflow is designed to process multiple blog post ideas in batches.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running instance of n8n.
- Supabase Account: A Supabase project with a database configured to store blog post ideas and generated content. You'll need your Supabase URL and API key.
- Google Gemini API Key OR Groq API Key: Credentials for either the Google Gemini Chat Model or the Groq Chat Model to power the AI Agent and Language Models.
- Image Editing Capabilities: While an "Edit Image" node is present, the specific image service or local setup it interacts with is not detailed in the JSON. Further configuration might be needed depending on how it's intended to be used (e.g., a local image processing library, an external image API).
Setup/Usage
- Import the Workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- Set up your Supabase credentials within n8n.
- Configure your Google Gemini or Groq credentials for the respective Language Model nodes.
- Customize Supabase Node: Adjust the "Supabase" node to point to your specific table and columns for blog post ideas and content.
- Configure AI Agent: Review and customize the prompts and tools used by the "AI Agent" and "Information Extractor" nodes to align with your desired blog post style, length, and content requirements.
- Adjust Image Editing: If the "Edit Image" node requires external services or specific settings, configure those accordingly.
- Set Schedule: Configure the "Schedule Trigger" node to run at your preferred frequency (e.g., daily, weekly).
- Activate Workflow: Once configured, activate the workflow to start automating your blog content generation.
Related Templates
Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets
This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.
Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax
Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions
Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation
PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.