AgenticFlow is an advanced AI-powered automation platform designed to streamline digital communication and content management. The system intelligently processes emails, generates context-aware responses, and transforms newsletters into engaging social media content across multiple platforms.
- Automated Email Processing: Fetch and analyze incoming emails in real-time
- AI-Powered Responses: Generate contextually relevant email replies
- Newsletter Intelligence: Extract and categorize content from newsletters
- Smart Prioritization: Automatically categorize and prioritize incoming messages
- Multi-Platform Publishing: Seamless content distribution to Twitter, LinkedIn, and more
- Platform-Optimized Content: Automatic formatting for each social network's requirements
- Scheduled Posting: Plan and schedule content for optimal engagement
- Content Enrichment: Auto-generate hashtags, mentions, and media attachments
- CrewAI Orchestration: Coordinated team of specialized AI agents
- Modular Architecture: Easily extensible with new capabilities
- Self-Improving: Learns from user feedback and interactions
- Frontend: React.js with Tailwind CSS for a responsive UI
- Backend: FastAPI for high-performance async operations
- Database: PostgreSQL with SQLAlchemy ORM
- Authentication: JWT-based secure access
- Task Queue: Asynchronous task processing
- Comprehensive Logging: Built-in monitoring and debugging
- Python 3.10+
- PostgreSQL 13+
- Redis (for task queue)
- Gmail API credentials
- Social media API credentials (Twitter, LinkedIn)
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Clone the repository:
git clone https://github.com/yourusername/AgenticFlow.git cd AgenticFlow -
Set up the backend:
cd backend python -m venv venv source venv/bin/activate # On Windows: .\venv\Scripts\activate pip install -r requirements.txt
-
Configure environment variables: Copy
.env.exampleto.envand update with your credentials. -
Initialize the database:
alembic upgrade head
-
Start the development server:
uvicorn main:app --reload
AgenticFlow/
βββ backend/ # Backend application
β βββ agents/ # AI agent implementations
β βββ api/ # API endpoints
β βββ database/ # Database models and migrations
β βββ models/ # Pydantic models
β βββ services/ # Business logic
β βββ utils/ # Utility functions
β βββ main.py # FastAPI application
β βββ requirements.txt # Python dependencies
βββ frontend/ # React frontend (coming soon)
βββ tests/ # Test suite
βββ .env.example # Example environment variables
βββ .gitignore
βββ README.md # This file
The system is built around specialized AI agents that work together:
- Email Fetcher: Retrieves and processes incoming emails
- Email Analyzer: Analyzes email content and intent
- Reply Generator: Crafts context-aware email responses
- Newsletter Processor: Extracts valuable content from newsletters
- Post Formatter: Optimizes content for different social platforms
- Social Poster: Manages content distribution to social networks
- API Documentation (available when running locally)
- Architecture Decision Records
- Development Guide
pytest
This project uses:
- Black for code formatting
- isort for import sorting
- Flake8 for linting
Run formatting and linting:
black . isort . flake8
This project is licensed under the MIT License - see the LICENSE file for details.