pybind/pybind11

pybind11 (v3) — Seamless interoperability between C++ and Python

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pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Its goals and syntax are similar to the excellent Boost.Python library by David Abrahams: to minimize boilerplate code in traditional extension modules by inferring type information using compile-time introspection.

The main issue with Boost.Python—and the reason for creating such a similar project—is Boost. Boost is an enormously large and complex suite of utility libraries that works with almost every C++ compiler in existence. This compatibility has its cost: arcane template tricks and workarounds are necessary to support the oldest and buggiest of compiler specimens. Now that C++11-compatible compilers are widely available, this heavy machinery has become an excessively large and unnecessary dependency.

Think of this library as a tiny self-contained version of Boost.Python with everything stripped away that isn't relevant for binding generation. Without comments, the core header files only require ~4K lines of code and depend on Python (CPython 3.9+, PyPy, or GraalPy) and the C++ standard library. This compact implementation was possible thanks to some C++11 language features (specifically: tuples, lambda functions and variadic templates). Since its creation, this library has grown beyond Boost.Python in many ways, leading to dramatically simpler binding code in many common situations.

Tutorial and reference documentation is provided at pybind11.readthedocs.io. A PDF version of the manual is available here. And the source code is always available at github.com/pybind/pybind11.

pybind11 can map the following core C++ features to Python:

• Functions accepting and returning custom data structures per value, reference, or pointer
• Instance methods and static methods
• Overloaded functions
• Instance attributes and static attributes
• Arbitrary exception types
• Enumerations
• Callbacks
• Iterators and ranges
• Custom operators
• Single and multiple inheritance
• STL data structures
• Smart pointers with reference counting like std::shared_ptr
• Internal references with correct reference counting
• C++ classes with virtual (and pure virtual) methods can be extended in Python
• Integrated NumPy support (NumPy 2 requires pybind11 2.12+)

In addition to the core functionality, pybind11 provides some extra goodies:

• CPython 3.9+, PyPy3 7.3.17+, and GraalPy 24.1+ are supported with an implementation-agnostic interface (see older versions for older CPython and PyPy versions).
• It is possible to bind C++11 lambda functions with captured variables. The lambda capture data is stored inside the resulting Python function object.
• pybind11 uses C++11 move constructors and move assignment operators whenever possible to efficiently transfer custom data types.
• It's easy to expose the internal storage of custom data types through Pythons' buffer protocols. This is handy e.g. for fast conversion between C++ matrix classes like Eigen and NumPy without expensive copy operations.
• pybind11 can automatically vectorize functions so that they are transparently applied to all entries of one or more NumPy array arguments.
• Python's slice-based access and assignment operations can be supported with just a few lines of code.
• Everything is contained in just a few header files; there is no need to link against any additional libraries.
• Binaries are generally smaller by a factor of at least 2 compared to equivalent bindings generated by Boost.Python. A recent pybind11 conversion of PyRosetta, an enormous Boost.Python binding project, reported a binary size reduction of 5.4x and compile time reduction by 5.8x.
• Function signatures are precomputed at compile time (using constexpr), leading to smaller binaries.
• With little extra effort, C++ types can be pickled and unpickled similar to regular Python objects.

pybind11 is exercised in continuous integration across a range of operating systems, Python versions, C++ standards, and toolchains. For an up-to-date view of the combinations we currently test, please see the pybind11 GitHub Actions and AppVeyor logs.

The test matrix naturally evolves over time as older platforms and compilers fall out of use and new ones are added by the community. Closely related versions of a tested compiler or platform will often work as well in practice, but we cannot promise to validate every possible combination. If a configuration you rely on is missing from the matrix or regresses, issues and pull requests to extend coverage are very welcome. At the same time, we need to balance the size of the test matrix with the available CI resources, such as GitHub's limits on concurrent jobs under the free tier.

This project was created by Wenzel Jakob. Significant features and/or improvements to the code were contributed by Jonas Adler, Lori A. Burns, Sylvain Corlay, Eric Cousineau, Aaron Gokaslan, Ralf Grosse-Kunstleve, Trent Houliston, Axel Huebl, @hulucc, Yannick Jadoul, Sergey Lyskov, Johan Mabille, Tomasz Miąsko, Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim Schellart, Henry Schreiner, Ivan Smirnov, Dustin Spicuzza, Boris Staletic, Ethan Steinberg, Patrick Stewart, Ivor Wanders, and Xiaofei Wang.

We thank Google for a generous financial contribution to the continuous integration infrastructure used by this project.

See the contributing guide for information on building and contributing to pybind11.

pybind11 is provided under a BSD-style license that can be found in the LICENSE file. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.