It uses https://github.com/sqlparser-rs/sqlparser-rs as the parser and lexer. The binder, planner, optimizer and executor are in Python. The optimizer stage only works on the logical plan and the rules are heuristic only.
I have a SQL Engine in Python too (https://github.com/mabel-dev/opteryx). I focused my initial effort on supporting SQL statements and making the usage feel like a database - that probably reflects the problem I had in front of me when I set out - only handling handfuls of gigabytes in a batch environment for ETLs with a group of new-to-data-engineering engineers. Have recently started looking more at real-time performance, such as distributing work. Am interesting in how you've approached.