TatSu

I believe lark [1] is the common Python solution. Personally, I like tatsu [2], which generates a parser in Python (and there appear to be projects doing the same for other languages from the same grammar, check the README). Another choice, though not necessarily taking in an EBNF, could be Racket [3]. [1]: https://lark-parser.readthedocs.io/ [2]: https://github.com/neogeny/TatSu [3]: https://docs.racket-lang.org/guide/hash-languages.html

When I surveyed what was out there last year I wrote down these 3 as the most interesting: https://github.com/lark-parser/lark https://github.com/neogeny/TatSu https://github.com/dabeaz/sly Lark seems to be the one with traction.

Yep. "Something equivalent" is pretty easy. To be able to regenerate the original input (spacing and other foibles) is a bit harder but also possible. Commercial vendors developed tools that can do this in the 00's to support UML and UML-like tooling. Related to this is getting sensible output structures. For instance, many parsing libraries based on parsing combinators output a "lispy" combination of lists/dicts/scalars which is dependent on the details of the grammar, for ex. https://github.com/neogeny/tatsu it is particularly obnoxious when operator precedence is implemented by creating extra nodes in the parse tree, and even more obnoxious when you have to copy those already over-complicated structures in your grammar to deal with complex context. Even in 2018 people are still using some compiler compilers that use a "callback he