This project was created as a learning exercise to understand how multiple AI agents work together through orchestration. It demonstrates the coordination between different specialized agents (Research and Writer) and how they can be orchestrated to create a cohesive content creation pipeline, providing insights into multi-agent system architecture and workflow management.

Content Creation Pipeline MVP

A simple but robust MVP implementation of an AI-powered content creation pipeline using multi-agent architecture.

Features

• Multi-Agent System: Research and Writer agents working in coordination
• RESTful API: FastAPI-based API for content creation
• Database Integration: Supabase integration for data persistence
• Quality Assessment: Basic quality scoring for generated content
• Async Processing: Background task processing for content generation
• Clean Architecture: Modular, testable, and extensible codebase

Architecture

┌─────────────────────────────────────────┐
│              FastAPI App                │
├─────────────────────────────────────────┤
│           Content Orchestrator          │
├─────────────────────────────────────────┤
│  Research Agent    │    Writer Agent    │
├─────────────────────────────────────────┤
│            Supabase Database            │
└─────────────────────────────────────────┘

Quick Start

Prerequisites

• Python 3.11+
• OpenAI API key

Installation

1. Clone and setup environment:

   git clone <repository>
   cd content-pipeline
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

2. Configure environment:

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

3. Set up database: The database schema is already created via Supabase MCP. The tables include:

   • organizations
   • users
   • content_requests
   • content_pieces
   • agent_tasks
   • quality_assessments

4. Run the application:

   uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

5. Access the API:

   • API Documentation: http://localhost:8000/docs
   • Health Check: http://localhost:8000/health

API Usage

Create Content

curl -X POST "http://localhost:8000/api/v1/content/create" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "test-user-123",
    "topic": "artificial intelligence in healthcare",
    "content_type": "blog_post",
    "target_audience": "healthcare professionals",
    "word_count": 800,
    "style_requirements": {
      "tone": "professional",
      "include_statistics": true
    }
  }'

Get Content Status

curl "http://localhost:8000/api/v1/content/{content_id}/status"

Retrieve Generated Content

curl "http://localhost:8000/api/v1/content/{content_id}"

List All Content

curl "http://localhost:8000/api/v1/content?limit=10&offset=0"

Project Structure

content-pipeline/
├── app/
│   ├── agents/
│   │   ├── __init__.py
│   │   ├── base_agent.py          # Abstract base class for agents
│   │   ├── research_agent.py      # Research and information gathering
│   │   └── writer_agent.py        # Content creation and writing
│   ├── core/
│   │   ├── __init__.py
│   │   └── orchestrator.py        # Main pipeline coordinator
│   ├── models/
│   │   ├── __init__.py
│   │   └── content.py             # Pydantic models and enums
│   ├── config.py                  # Configuration management
│   └── main.py                    # FastAPI application
├── tests/
│   ├── __init__.py
│   ├── conftest.py               # Test configuration and fixtures
│   ├── test_agents.py            # Agent tests
│   └── test_orchestrator.py      # Orchestrator tests
├── requirements.txt
├── .env.example
└── README.md

Agent Details

Research Agent

• Purpose: Gathers information and validates sources
• Capabilities:
  • Wikipedia API integration
  • Search query generation
  • Source credibility scoring
  • Information structuring using AI

Writer Agent

• Purpose: Creates structured, engaging content
• Capabilities:
  • Content outline generation
  • Multi-section writing
  • Style adaptation
  • Metadata generation
  • Reading time calculation

Testing

Run the test suite:

# Install test dependencies
pip install pytest pytest-asyncio pytest-cov

# Run all tests
pytest

# Run with coverage
pytest --cov=app --cov-report=html

# Run specific test file
pytest tests/test_agents.py -v

Configuration

Environment Variables

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key for AI agents	Required
DATABASE_URL	Supabase database connection string	Required
REDIS_URL	Redis connection for caching	redis://localhost:6379
MAX_CONTENT_LENGTH	Maximum content word count	5000
DEFAULT_QUALITY_THRESHOLD	Minimum quality score	0.7

Content Types

• blog_post: Conversational blog articles
• article: Professional informative articles
• product_description: Marketing-focused descriptions
• social_media: Short, engaging posts
• email: Personal, action-oriented content

Quality Metrics

The system calculates several quality scores:

• Overall Quality: Based on content length, research depth, and structure
• SEO Score: Title length, meta description, headings, keyword usage
• Fact-Check Score: Based on research confidence and source credibility
• Research Depth: Number and quality of sources used

Error Handling

The system includes comprehensive error handling:

• Input validation
• API timeout handling
• Graceful degradation
• Detailed error messages
• Background task error recovery

Future Enhancements

This MVP provides a solid foundation for adding:

• Additional agents (SEO, Editor, Fact-Checker, Visual)
• Advanced quality gates
• User authentication and authorization
• Rate limiting and usage tracking
• Content templates and personalization
• Multi-language support
• Advanced analytics and reporting

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For questions or issues:

1. Check the API documentation at /docs
2. Review the test files for usage examples
3. Check logs for detailed error information