Content
# AI Agent & Workflow Management Platform
A comprehensive platform for creating, managing, and executing AI agent workflows using crewAI.
## Features
- **Agent Management**: Create and configure AI agents with different roles, goals, and capabilities
- **Workflow Designer**: Visual workflow builder with drag-and-drop interface
- **Knowledge Base**: Upload and manage documents for RAG (Retrieval Augmented Generation)
- **Model Management**: Support for multiple LLM providers (OpenAI, Anthropic, Ollama, etc.)
- **Execution Monitoring**: Real-time tracking and logging of workflow executions
- **Analytics Dashboard**: Performance metrics and usage analytics
## Architecture
The platform follows a microservices architecture with the following services:
- **API Gateway**: Routes requests and handles cross-cutting concerns
- **Auth Service**: User authentication and authorization
- **Agent Service**: Agent CRUD operations and management
- **Workflow Service**: Workflow and task management
- **Model Service**: LLM model configuration management
- **Knowledge Base Service**: Document processing and RAG functionality
- **Orchestration Engine**: crewAI workflow execution
- **Execution Monitoring**: Logging and tracking of executions
- **Analytics Service**: Data aggregation and reporting
## Technology Stack
### Backend
- **FastAPI**: Web framework
- **PostgreSQL**: Primary database
- **ChromaDB**: Vector database for embeddings
- **Redis**: Caching and message broker
- **Celery**: Background task processing
- **SQLAlchemy**: ORM
- **crewAI**: AI agent orchestration
### Frontend
- **React 18**: Frontend framework
- **TypeScript**: Type safety
- **Tailwind CSS**: Styling
- **React Flow**: Workflow canvas
- **Zustand**: State management
- **React Query**: Data fetching
## Getting Started
### Prerequisites
- Python 3.11+
- Node.js 18+
- Docker & Docker Compose
- Poetry (Python dependency management)
### Installation
1. **Clone the repository**
```bash
git clone <repository-url>
cd ai-agent-platform
```
2. **Set up the backend**
```bash
# Install Poetry if not already installed
curl -sSL https://install.python-poetry.org | python3 -
# Install dependencies
poetry install
# Activate virtual environment
poetry shell
# Set up pre-commit hooks
pre-commit install
```
3. **Environment Setup**
```bash
cp .env.example .env
# Edit .env with your configuration
```
4. **Start services with Docker**
```bash
docker-compose up -d
```
5. **Run database migrations**
```bash
alembic upgrade head
```
6. **Start the development server**
```bash
# Start API Gateway
uvicorn src.gateway.main:app --reload --port 8000
# Start individual services (in separate terminals)
uvicorn src.services.auth.main:app --reload --port 8001
uvicorn src.services.agents.main:app --reload --port 8002
# ... etc for other services
```
### Development
1. **Run tests**
```bash
pytest
```
2. **Code formatting**
```bash
black src/
flake8 src/
mypy src/
```
3. **Database migrations**
```bash
# Create new migration
alembic revision --autogenerate -m "description"
# Apply migrations
alembic upgrade head
```
## API Documentation
Once the services are running, API documentation is available at:
- API Gateway: http://localhost:8000/docs
- Individual services: http://localhost:800X/docs (where X is the service port)
## Project Structure
```
ai-agent-platform/
├── src/
│ ├── services/ # Microservices
│ │ ├── auth/ # Authentication service
│ │ ├── agents/ # Agent management service
│ │ ├── workflows/ # Workflow management service
│ │ ├── models/ # LLM model management
│ │ ├── knowledge_base/ # Knowledge base service
│ │ ├── orchestration/ # crewAI orchestration engine
│ │ ├── execution_monitoring/ # Execution tracking
│ │ └── analytics/ # Analytics service
│ ├── shared/ # Shared utilities
│ │ ├── database/ # Database models and utilities
│ │ ├── auth/ # Authentication utilities
│ │ ├── models/ # Shared Pydantic models
│ │ └── utils/ # Common utilities
│ └── gateway/ # API Gateway
├── frontend/ # React frontend application
├── tests/ # Test files
├── alembic/ # Database migrations
├── docker-compose.yml # Docker services
├── pyproject.toml # Python dependencies and config
└── README.md # This file
```
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some 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, please open an issue on GitHub or contact the development team.
