A tiny and efficient C++/Python binding library with faster compilation, smaller binaries, and lower runtime overhead than pybind11.
nanobind is a small binding library that exposes C++ types in Python and vice versa, enabling seamless interoperability between the two languages. It solves the problem of inefficient bindings by offering faster compilation, smaller binaries, and lower runtime overhead compared to alternatives like pybind11 and Cython.
Developers and researchers working on projects that require high-performance C++/Python interoperability, such as machine learning frameworks, scientific computing libraries, and performance-critical applications.
Developers choose nanobind for its significant efficiency gains—up to 4× faster compilation, 5× smaller binaries, and 10× lower runtime overhead—alongside support for Python's Stable ABI, which simplifies deployment and reduces version-specific builds.
nanobind: tiny and efficient C++/Python bindings
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Benchmarks show up to ~4× faster compile times than pybind11, drastically reducing development iteration times.
Produces ~5× smaller binaries compared to pybind11, which is critical for deployment in resource-constrained environments.
Offers ~10× lower runtime overheads, making it ideal for performance-sensitive applications like machine learning frameworks.
Can target Python's Stable ABI from Python 3.12, eliminating the need for per-version builds and simplifying distribution.
Uses near-identical syntax to pybind11, allowing easy adoption for developers already experienced with similar binding libraries.
As a newer project, it lacks the extensive third-party plugins and community tools available for pybind11, which can complicate integration in complex setups.
Key features like Stable ABI targeting require Python 3.12 or later, making it less suitable for projects locked into older Python versions.
While documentation is available, it may not cover as many edge cases or advanced scenarios as the more mature pybind11 documentation, potentially increasing debugging time.
Being actively developed, users might encounter breaking changes more frequently, as indicated by the detailed changelog, compared to stable alternatives.