  E-graphs have emerged as a versatile data structure with applications in
synthesis, optimization, and verification through techniques such as equality
saturation. This paper introduces Python bindings for the experimental egglog
library (previously called egg-smol), which aims to bring the benefits of
e-graphs to the Python ecosystem. The bindings offer a high-level, Pythonic API
providing an accessible and familiar interface for Python users. By integrating
e-graph techniques with Python, we hope to enable collaboration and innovation
across various domains in the scientific computing and machine learning
communities. We discuss the advantages of using Python bindings for both Python
and existing egg-smol users, as well as possible future directions for
development.
