Facebook Gemini AI Agents - Complete Implementation

This is a comprehensive multi-agent system for Facebook page management using CrewAI and Google Gemini AI.

🚀 Features

AI Agents

• Content Creation Agent: Auto-generates engaging posts, captions, and hashtags
• Customer Service Agent: Automated Messenger responses and lead generation
• Analytics Agent: Performance monitoring and content optimization

Capabilities

• ✅ Intelligent content generation with Gemini AI
• ✅ Automated customer service with intent recognition
• ✅ Performance analytics and insights
• ✅ Facebook API integration (Pages, Messenger, Marketing)
• ✅ Web dashboard for agent management
• ✅ RESTful API with comprehensive endpoints
• ✅ Docker containerization support
• ✅ Comprehensive testing suite

📋 Prerequisites

Before running the application, ensure you have:

1. Facebook Developer Account with:

   • Facebook App with Page Management permissions
   • Page Access Token
   • App ID and App Secret

2. Google Cloud Account with:

   • Gemini API key
   • Enabled Generative AI API

3. Python 3.11+ installed

🔧 Installation

1. Clone and Setup

git clone <repository-url>
cd facebook-gemini-agents
pip install -r requirements.txt

2. Configure Environment

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

3. Configure API Keys

Edit .env file with your credentials:

# Facebook API
FACEBOOK_APP_ID=your_app_id
FACEBOOK_APP_SECRET=your_app_secret
FACEBOOK_ACCESS_TOKEN=your_access_token
FACEBOOK_PAGE_ID=your_page_id

# Google Gemini
GOOGLE_API_KEY=your_gemini_api_key

🚀 Quick Start

Run the Application

# Start the API server
python src/api/main.py

# Or use the CLI
python main.py content-generate --topic "summer vacation ideas"

Access the Web Interface

Open your browser to: http://localhost:8000/web/

📖 Usage Examples

Content Generation

from src.agents.content_agent import content_agent

# Generate a Facebook post
post = content_agent.create_post(
    topic="summer vacation",
    tone="friendly",
    length="medium"
)
print(post['content'])
print(post['hashtags'])

Customer Service

from src.agents.customer_service_agent import customer_service_agent

# Handle customer message
result = customer_service_agent.handle_message(
    message="I need help with my order",
    sender_info={'name': 'John'}
)
print(result['response'])
print(f"Intent: {result['intent']}")

Analytics

from src.agents.analytics_agent import analytics_agent

# Analyze page performance
insights = analytics_agent.analyze_page_performance(
    page_id="your_page_id",
    date_range="last_30d"
)
print(insights['insights_summary'])

🌐 API Endpoints

Content Agent

• POST /api/agents/content/generate - Generate content
• POST /api/agents/content/optimize - Optimize existing content
• POST /api/agents/content/calendar - Generate content calendar

Customer Service Agent

• POST /api/agents/service/respond - Handle customer message
• POST /api/agents/service/quick-reply - Handle quick reply
• GET /api/agents/service/metrics - Get service metrics

Analytics Agent

• POST /api/agents/analytics/insights - Get page insights
• POST /api/agents/analytics/trending - Get trending content
• POST /api/agents/analytics/schedule - Get posting schedule

Facebook Integration

• GET /api/facebook/pages - List Facebook pages
• POST /api/facebook/post - Create Facebook post
• GET /api/facebook/posts/{page_id} - Get page posts

🧪 Testing

Run Tests

# Run all tests
pytest

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

# Run with coverage
pytest --cov=src tests/

Test Coverage

The test suite includes:

• ✅ Unit tests for all agents
• ✅ API endpoint testing
• ✅ Integration tests
• ✅ Mock external services
• ✅ Error handling tests

🐳 Docker Deployment

Build and Run

# Build and start services
docker-compose up -d

# Check logs
docker-compose logs -f

# Stop services
docker-compose down

Services

• app: Main application (port 8000)
• redis: Redis cache (port 6379)
• nginx: Reverse proxy (port 80)

📁 Project Structure

facebook-gemini-agents/
├── src/
│   ├── agents/
│   │   ├── base_agent.py          # Base agent class
│   │   ├── content_agent.py     # Content creation agent
│   │   ├── customer_service_agent.py  # Customer service agent
│   │   └── analytics_agent.py     # Analytics agent
│   ├── config/
│   │   └── settings.py          # Configuration settings
│   ├── tools/
│   │   ├── facebook_tools.py    # Facebook API client
│   │   ├── gemini_tools.py        # Gemini AI tools
│   │   └── utils.py               # Utility functions
│   ├── api/
│   │   └── main.py               # FastAPI application
│   └── web/
│       └── index.html            # Web dashboard
├── tests/
│   ├── test_agents.py            # Agent tests
│   └── test_api.py               # API tests
├── requirements.txt              # Python dependencies
├── docker-compose.yml           # Docker services
├── Dockerfile                  # App container
└── main.py                      # CLI entry point

🔧 Configuration

Agent Settings

Configure agent behavior in src/config/settings.py:

AGENT_CONFIGS = {
    'content_agent': {
        'model': 'gemini-pro',
        'temperature': 0.7,
        'max_tokens': 1000,
    },
    'customer_service_agent': {
        'model': 'gemini-pro',
        'temperature': 0.3,
        'max_tokens': 500,
    },
    'analytics_agent': {
        'model': 'gemini-pro',
        'temperature': 0.1,
        'max_tokens': 2000,
    }
}

Facebook API Settings

Configure Facebook API endpoints and permissions in the same file.

🚨 Error Handling

The system includes comprehensive error handling:

• API Errors: Graceful handling of Facebook API errors
• Network Issues: Retry mechanisms for failed requests
• Validation: Input validation for all endpoints
• Logging: Detailed logging for debugging
• Fallback Responses: Backup responses when AI fails

🔒 Security

Security measures include:

• Environment Variables: All secrets in environment variables
• Input Sanitization: Clean user inputs
• Rate Limiting: Prevent API abuse
• Webhook Verification: Secure Facebook webhooks
• HTTPS: SSL/TLS encryption

📈 Performance Optimization

• Caching: Redis caching for frequently accessed data
• Async Processing: Background tasks for heavy operations
• Connection Pooling: Efficient database connections
• Lazy Loading: Load resources only when needed

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

For support and questions:

• Create an issue in the GitHub repository
• Check the documentation in the docs/ folder
• Review the API documentation at http://localhost:8000/docs

🙏 Acknowledgments

• CrewAI - Multi-agent orchestration framework
• Google Gemini - AI model for content generation
• Facebook - Social media platform and APIs
• FastAPI - Modern web framework
• Tailwind CSS - Utility-first CSS framework

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Ready to automate your Facebook page management with AI? 🚀

Start by configuring your environment variables and running the application. The intelligent agents will handle content creation, customer service, and analytics automatically!