fivccliche 0.1.32

A modern async Python framework for building scalable applications with FastAPI and SQLModel

Fivccliche

A production-ready, multi-user backend framework designed specifically for AI agents. Built with FastAPI and SQLModel for high-performance, type-safe async operations that handle concurrent AI agent requests at scale.

✨ Features

• AI Agent Backend - Purpose-built for multi-user AI agent interactions and orchestration
• FastAPI - Modern, fast web framework for building high-performance APIs with Python 3.10+
• SQLModel - SQL ORM combining SQLAlchemy and Pydantic for type-safe database operations
• Async/Await - Full async support for handling concurrent AI agent requests at scale
• Type Safety - Built-in type hints with Pydantic 2.0 validation for reliable data handling
• Multi-User Support - Designed for managing multiple AI agents with proper isolation and access control
• Testing - Pytest with async support for comprehensive test coverage
• Code Quality - Black, Ruff, and MyPy configured for professional code standards
• Package Management - uv for fast, reliable dependency management

🚀 Quick Start

Prerequisites

• Python 3.10 or higher
• uv package manager (install)

Installation

# Clone the repository
git clone https://github.com/MindFiv/FivcCliche.git
cd FivcCliche

# Install production dependencies
uv pip install -e .

# Or install with development tools
uv pip install -e ".[dev]"

Using the CLI

The easiest way to run FivcCliche is using the built-in CLI:

# Start the server
python -m fivccliche.cli run

# Show project information
python -m fivccliche.cli info

# Clean temporary files and cache
python -m fivccliche.cli clean

# Initialize configuration
python -m fivccliche.cli setup

Visit http://localhost:8000/docs for interactive API documentation.

CLI Options

# Custom host and port
python -m fivccliche.cli run --host 127.0.0.1 --port 9000

# Production mode (no auto-reload)
python -m fivccliche.cli run --no-reload

# Test configuration without running
python -m fivccliche.cli run --dry-run

# Verbose output
python -m fivccliche.cli run --verbose

📚 Documentation

For detailed information, see the documentation in the docs/ folder:

• Getting Started - Comprehensive tutorial with examples
• Setup Summary - Installation and project structure
• Migration Plan - Technical migration details
• Completion Summary - What was accomplished

🛠️ Development

CLI Commands

make format  # Format code with Black
make lint    # Lint with Ruff
make check   # Run all checks (format, lint, type check)

Run Tests

pytest
pytest -v --cov=src  # With coverage

Code Quality

black src/ tests/      # Format code
ruff check src/ tests/ # Lint code
mypy src/              # Type check

Project Structure

fivccliche/
├── pyproject.toml              # Project configuration
├── src/
│   └── fivccliche/
│       ├── __init__.py
│       ├── cli.py              # CLI implementation
│       ├── services/
│       ├── utils/
│       ├── settings/
│       └── modules/
├── tests/                      # Add your tests here
└── docs/                       # Documentation

📦 Dependencies

Production Core: FastAPI, SQLModel, Uvicorn, Pydantic, SQLAlchemy

CLI & Output: Typer, Rich, python-dotenv

Component System: fivcglue, fivcplayground

Development: Pytest, Black, Ruff, MyPy, Coverage

See pyproject.toml for complete dependency list and versions.

📄 License

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

👤 Author

Charlie Zhang ([e-mail])

🔗 Links

• Repository: https://github.com/MindFiv/FivcCliche
• FastAPI: https://fastapi.tiangolo.com/
• SQLModel: https://sqlmodel.tiangolo.com/
• Pydantic: https://docs.pydantic.dev/