patchy631--ai-engineering-hub/motia-content-creation

Social Media Automation workflow using Motia

A streamlined content generation agent built with Motia that transforms articles into engaging Twitter threads and LinkedIn posts using AI.

We use the following tech stack:

• Motia as the unified backend framework
• Firecrawl to scrape web content
• Ollama for serving Deepseek-R1 locally

🎯Overview

Workflow

Our workflow consists of 4 main steps:-

API → Scrape → Generate → Schedule

1. API: Receives article URL via POST request
2. Scrape: Extracts content using Firecrawl in markdown format
3. Generate: Creates Twitter & LinkedIn content using Deepseek-R1
4. Schedule: Saves content as drafts in Typefully for review

🛠️ Setup

Prerequisites

• Node.js 18+
• Python 3.x
• API keys for:
  • Firecrawl
  • Typefully

Installation

1. Install Ollama:

   # Setting up Ollama on linux
   curl -fsSL https://ollama.com/install.sh | sh
   
   # Pull the Deepseek-R1 model
   ollama pull deepseek-r1
   

2. Install project dependencies:

   npm install or pnpm install
   

3. Configure environment:

   cp .env.example .env
   # Edit .env with your API keys
   

   or Create a .env file in the root directory with the following variables:

    FIRECRAWL_API_KEY=your_firecrawl_api_key
    TYPEFULLY_API_KEY=your_typefully_api_key
   

4. Start the development server:

   npm run dev
   

🚀 Usage

Generate Content

Send a POST request to trigger content generation:

curl -X POST http://localhost:3000/generate-content \\
  -H "Content-Type: application/json" \\
  -d '{"url": "https://example.com/article"}'

Response:

{
  "message": "Content generation started",
  "requestId": "req_123456",
  "url": "https://example.com/article",
  "status": "processing"
}

View Results

After processing completes:

1. Visit Typefully
2. Review your generated Twitter thread and LinkedIn post
3. Edit if needed and publish!

📁 Project Structure

social-media-automation/
├── steps/
│   ├── api.step.py                   # API endpoint handler
│   ├── scrape.step.py                # Firecrawl integration
│   ├── generate-linkedin.step.py     # Ollama Linkedin generation
│   ├── generate-twitter.step.py      # Ollama Twitter generation
│   ├── schedule-twitter.step.ts      # Twitter Typefully scheduling
│   └── schedule-linkedin.step.ts     # LinkedIn Typefully scheduling
├── prompts/
│   ├── twitter-prompt.txt   # Twitter generation prompt
│   └── linkedin-prompt.txt  # LinkedIn generation prompt
├── config/
│   └── index.js             # Configuration management
├── package.json
├── motia-workbench.json
├── requirements.txt
└── README.md

🔍 Monitoring

The Motia workbench provides an interactive UI where you can easily deb ug and monitor your flows as interactive diagrams. It runs automatically with the development server.

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Contribution

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.