python-engineering

A production-grade Python project requires a recommended src layout, strict naming conventions, and a configured pyproject.toml. This ensures consistent structure, maintainability, and reliable packaging across CLI tools and general development workflows.

Python error handling and typing best practices involve strict type checking using mypy and standardized formatting rules. Applying these practices ensures consistent, maintainable code and improves overall reliability across general Python development workflows.

Configuring ruff, mypy, and pytest requires specifying tooling integration within your pyproject.toml. This setup codifies best practices for formatting, typing, and testing to ensure production-grade quality during code reviews.

Yes, these production-grade Python guidelines apply directly to building AI agents with LangGraph. They provide the necessary structure, error handling, and testing conventions required for reliable agent development workflows.

The recommended way to handle Python packaging configurations is by defining settings within pyproject.toml. This standardizes project structure, tooling integration like uv, and ensures consistent maintainable code across environments.