attrs

Coming from manually writing `__init__`, `__repr__`, and `__eq__` methods, attrs felt immediately intuitive. The basic `@define` decorator handles 90% of use cases with zero configuration - just declare your attributes and you're done. The progression from simple classes to more advanced features like validators, converters, and frozen instances is extremely gradual, which made the learning curve almost non-existent.

Error messages are outstanding. When you mess up a validator or converter, attrs tells you exactly what went wrong and where. Stack traces are clean and point directly to your code, not buried in framework internals. The documentation strikes a perfect balance - the overview gets you productive in minutes, while the comprehensive API reference and examples cover edge cases thoroughly.

Debugging is straightforward because attrs generates readable code that behaves predictably. The ecosystem integration is seamless - it plays nicely with type checkers, serialization libraries, and testing frameworks. Community support on GitHub is responsive, though you'll rarely need it since the docs answer most questions upfront.

attrs is a pure Python library for generating class boilerplate like __init__, __repr__, and __eq__. From a security perspective, it's refreshingly low-risk: zero external dependencies means no transitive supply chain exposure, and it doesn't touch networking, crypto, or authentication. The library simply generates Python code at class definition time.

The validator system is where you need to pay attention. While attrs provides decorators for input validation (@validators.instance_of, etc.), it's easy to forget validators don't run on programmatic attribute assignment after initialization unless you explicitly use setters or frozen classes. I've seen this bite teams who assume validation happens automatically. The frozen=True flag is your friend for immutability and preventing accidental state mutation.

Error messages are clean and don't leak sensitive data - validation failures show type mismatches without dumping entire object contents. The library follows secure-by-default principles reasonably well, though you must consciously choose frozen classes and explicit converters/validators rather than getting them automatically.

From a security perspective, attrs is remarkably safe to use. It's a pure Python library with zero dependencies, which dramatically reduces supply chain risk. The codebase is mature, well-audited, and doesn't touch network boundaries, cryptography, or system resources—it just generates Python classes. This narrow scope means there's minimal attack surface.

In practice, attrs excels at input validation through its validator and converter mechanisms. You can enforce type safety, value constraints, and normalization at attribute definition time, which prevents many classes of injection and data integrity issues. The frozen=True option creates immutable objects, which is excellent for security-sensitive data structures. Error messages are informative without leaking sensitive implementation details.

The library follows secure-by-default principles well: validators run automatically, there's no eval/exec usage, and the generated code is predictable. I've used it extensively in authentication systems and API handlers where input validation is critical, and it's never been a source of vulnerabilities. The main caveat is that you still need to write good validators—attrs provides the plumbing, not the security logic itself.