Build systems#

Building with setuptools#

For projects on PyPI, building with setuptools is the way to go. Sylvain Corlay has kindly provided an example project which shows how to set up everything, including automatic generation of documentation using Sphinx. Please refer to the [python_example] repository.

A helper file is provided with pybind11 that can simplify usage with setuptools.

To use pybind11 inside your, you have to have some system to ensure that pybind11 is installed when you build your package. There are four possible ways to do this, and pybind11 supports all four: You can ask all users to install pybind11 beforehand (bad), you can use PEP 518 requirements (Pip 10+ required) (good, but very new and requires Pip 10), Classic setup_requires (discouraged by Python packagers now that PEP 518 is available, but it still works everywhere), or you can Copy manually (always works but you have to manually sync your copy to get updates).

An example of a using pybind11’s helpers:

from glob import glob
from setuptools import setup
from pybind11.setup_helpers import Pybind11Extension

ext_modules = [
        sorted(glob("src/*.cpp")),  # Sort source files for reproducibility

setup(..., ext_modules=ext_modules)

If you want to do an automatic search for the highest supported C++ standard, that is supported via a build_ext command override; it will only affect Pybind11Extensions:

from glob import glob
from setuptools import setup
from pybind11.setup_helpers import Pybind11Extension, build_ext

ext_modules = [

setup(..., cmdclass={"build_ext": build_ext}, ext_modules=ext_modules)

If you have single-file extension modules that are directly stored in the Python source tree (foo.cpp in the same directory as where a would be located), you can also generate Pybind11Extensions using setup_helpers.intree_extensions: intree_extensions(["path/to/foo.cpp", ...]) returns a list of Pybind11Extensions which can be passed to ext_modules, possibly after further customizing their attributes (libraries, include_dirs, etc.). By doing so, a foo.*.so extension module will be generated and made available upon installation.

intree_extension will automatically detect if you are using a src-style layout (as long as no namespace packages are involved), but you can also explicitly pass package_dir to it (as in setuptools.setup).

Since pybind11 does not require NumPy when building, a light-weight replacement for NumPy’s parallel compilation distutils tool is included. Use it like this:

from pybind11.setup_helpers import ParallelCompile

# Optional multithreaded build


The argument is the name of an environment variable to control the number of threads, such as NPY_NUM_BUILD_JOBS (as used by NumPy), though you can set something different if you want; CMAKE_BUILD_PARALLEL_LEVEL is another choice a user might expect. You can also pass default=N to set the default number of threads (0 will take the number of threads available) and max=N, the maximum number of threads; if you have a large extension you may want set this to a memory dependent number.

If you are developing rapidly and have a lot of C++ files, you may want to avoid rebuilding files that have not changed. For simple cases were you are using pip install -e . and do not have local headers, you can skip the rebuild if an object file is newer than its source (headers are not checked!) with the following:

from pybind11.setup_helpers import ParallelCompile, naive_recompile

ParallelCompile("NPY_NUM_BUILD_JOBS", needs_recompile=naive_recompile).install()

If you have a more complex build, you can implement a smarter function and pass it to needs_recompile, or you can use [Ccache] instead. CXX="cache g++" pip install -e . would be the way to use it with GCC, for example. Unlike the simple solution, this even works even when not compiling in editable mode, but it does require Ccache to be installed.

Keep in mind that Pip will not even attempt to rebuild if it thinks it has already built a copy of your code, which it deduces from the version number. One way to avoid this is to use [setuptools_scm], which will generate a version number that includes the number of commits since your last tag and a hash for a dirty directory. Another way to force a rebuild is purge your cache or use Pip’s --no-cache-dir option.

PEP 518 requirements (Pip 10+ required)#

If you use PEP 518’s pyproject.toml file, you can ensure that pybind11 is available during the compilation of your project. When this file exists, Pip will make a new virtual environment, download just the packages listed here in requires=, and build a wheel (binary Python package). It will then throw away the environment, and install your wheel.

Your pyproject.toml file will likely look something like this:

requires = ["setuptools>=42", "pybind11>=2.6.1"]
build-backend = "setuptools.build_meta"


The main drawback to this method is that a PEP 517 compliant build tool, such as Pip 10+, is required for this approach to work; older versions of Pip completely ignore this file. If you distribute binaries (called wheels in Python) using something like cibuildwheel, remember that and pyproject.toml are not even contained in the wheel, so this high Pip requirement is only for source builds, and will not affect users of your binary wheels. If you are building SDists and wheels, then pypa-build is the recommended official tool.

Classic setup_requires#

If you want to support old versions of Pip with the classic setup_requires=["pybind11"] keyword argument to setup, which triggers a two-phase run, then you will need to use something like this to ensure the first pass works (which has not yet installed the setup_requires packages, since it can’t install something it does not know about):

    from pybind11.setup_helpers import Pybind11Extension
except ImportError:
    from setuptools import Extension as Pybind11Extension

It doesn’t matter that the Extension class is not the enhanced subclass for the first pass run; and the second pass will have the setup_requires requirements.

This is obviously more of a hack than the PEP 518 method, but it supports ancient versions of Pip.

Copy manually#

You can also copy directly to your project; it was designed to be usable standalone, like the old example You can set include_pybind11=False to skip including the pybind11 package headers, so you can use it with git submodules and a specific git version. If you use this, you will need to import from a local file in and ensure the helper file is part of your MANIFEST.

Closely related, if you include pybind11 as a subproject, you can run the inplace. If loaded correctly, this should even pick up the correct include for pybind11, though you can turn it off as shown above if you want to input it manually.

Suggested usage if you have pybind11 as a submodule in extern/pybind11:

DIR = os.path.abspath(os.path.dirname(__file__))

sys.path.append(os.path.join(DIR, "extern", "pybind11"))
from pybind11.setup_helpers import Pybind11Extension  # noqa: E402

del sys.path[-1]

Changed in version 2.6: Added setup_helpers file.

Building with cppimport#

[cppimport] is a small Python import hook that determines whether there is a C++ source file whose name matches the requested module. If there is, the file is compiled as a Python extension using pybind11 and placed in the same folder as the C++ source file. Python is then able to find the module and load it.

Building with CMake#

For C++ codebases that have an existing CMake-based build system, a Python extension module can be created with just a few lines of code:

cmake_minimum_required(VERSION 3.5...3.27)
project(example LANGUAGES CXX)

pybind11_add_module(example example.cpp)

This assumes that the pybind11 repository is located in a subdirectory named pybind11 and that the code is located in a file named example.cpp. The CMake command add_subdirectory will import the pybind11 project which provides the pybind11_add_module function. It will take care of all the details needed to build a Python extension module on any platform.

A working sample project, including a way to invoke CMake from for PyPI integration, can be found in the [cmake_example] repository.

Changed in version 2.6: CMake 3.4+ is required.

Changed in version 2.11: CMake 3.5+ is required.

Further information can be found at CMake helpers.


To ease the creation of Python extension modules, pybind11 provides a CMake function with the following signature:

pybind11_add_module(<name> [MODULE | SHARED] [EXCLUDE_FROM_ALL]
                    [NO_EXTRAS] [THIN_LTO] [OPT_SIZE] source1 [source2 ...])

This function behaves very much like CMake’s builtin add_library (in fact, it’s a wrapper function around that command). It will add a library target called <name> to be built from the listed source files. In addition, it will take care of all the Python-specific compiler and linker flags as well as the OS- and Python-version-specific file extension. The produced target <name> can be further manipulated with regular CMake commands.

MODULE or SHARED may be given to specify the type of library. If no type is given, MODULE is used by default which ensures the creation of a Python-exclusive module. Specifying SHARED will create a more traditional dynamic library which can also be linked from elsewhere. EXCLUDE_FROM_ALL removes this target from the default build (see CMake docs for details).

Since pybind11 is a template library, pybind11_add_module adds compiler flags to ensure high quality code generation without bloat arising from long symbol names and duplication of code in different translation units. It sets default visibility to hidden, which is required for some pybind11 features and functionality when attempting to load multiple pybind11 modules compiled under different pybind11 versions. It also adds additional flags enabling LTO (Link Time Optimization) and strip unneeded symbols. See the FAQ entry for a more detailed explanation. These latter optimizations are never applied in Debug mode. If NO_EXTRAS is given, they will always be disabled, even in Release mode. However, this will result in code bloat and is generally not recommended.

As stated above, LTO is enabled by default. Some newer compilers also support different flavors of LTO such as ThinLTO. Setting THIN_LTO will cause the function to prefer this flavor if available. The function falls back to regular LTO if -flto=thin is not available. If CMAKE_INTERPROCEDURAL_OPTIMIZATION is set (either ON or OFF), then that will be respected instead of the built-in flag search.


If you want to set the property form on targets or the CMAKE_INTERPROCEDURAL_OPTIMIZATION_<CONFIG> versions of this, you should still use set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF) (otherwise a no-op) to disable pybind11’s ipo flags.

The OPT_SIZE flag enables size-based optimization equivalent to the standard /Os or -Os compiler flags and the MinSizeRel build type, which avoid optimizations that that can substantially increase the size of the resulting binary. This flag is particularly useful in projects that are split into performance-critical parts and associated bindings. In this case, we can compile the project in release mode (and hence, optimize performance globally), and specify OPT_SIZE for the binding target, where size might be the main concern as performance is often less critical here. A ~25% size reduction has been observed in practice. This flag only changes the optimization behavior at a per-target level and takes precedence over the global CMake build type (Release, RelWithDebInfo) except for Debug builds, where optimizations remain disabled.

Configuration variables#

By default, pybind11 will compile modules with the compiler default or the minimum standard required by pybind11, whichever is higher. You can set the standard explicitly with CMAKE_CXX_STANDARD:

set(CMAKE_CXX_STANDARD 14 CACHE STRING "C++ version selection")  # or 11, 14, 17, 20
set(CMAKE_CXX_STANDARD_REQUIRED ON)  # optional, ensure standard is supported
set(CMAKE_CXX_EXTENSIONS OFF)  # optional, keep compiler extensions off

The variables can also be set when calling CMake from the command line using the -D<variable>=<value> flag. You can also manually set CXX_STANDARD on a target or use target_compile_features on your targets - anything that CMake supports.

Classic Python support: The target Python version can be selected by setting PYBIND11_PYTHON_VERSION or an exact Python installation can be specified with PYTHON_EXECUTABLE. For example:


# Another method:
cmake -DPYTHON_EXECUTABLE=/path/to/python ..

# This often is a good way to get the current Python, works in environments:
cmake -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") ..

find_package vs. add_subdirectory#

For CMake-based projects that don’t include the pybind11 repository internally, an external installation can be detected through find_package(pybind11). See the Config file docstring for details of relevant CMake variables.

cmake_minimum_required(VERSION 3.4...3.18)
project(example LANGUAGES CXX)

find_package(pybind11 REQUIRED)
pybind11_add_module(example example.cpp)

Note that find_package(pybind11) will only work correctly if pybind11 has been correctly installed on the system, e. g. after downloading or cloning the pybind11 repository :

# Classic CMake
cd pybind11
mkdir build
cd build
cmake ..
make install

# CMake 3.15+
cd pybind11
cmake -S . -B build
cmake --build build -j 2  # Build on 2 cores
cmake --install build

Once detected, the aforementioned pybind11_add_module can be employed as before. The function usage and configuration variables are identical no matter if pybind11 is added as a subdirectory or found as an installed package. You can refer to the same [cmake_example] repository for a full sample project – just swap out add_subdirectory for find_package.

FindPython mode#

CMake 3.12+ (3.15+ recommended, 3.18.2+ ideal) added a new module called FindPython that had a highly improved search algorithm and modern targets and tools. If you use FindPython, pybind11 will detect this and use the existing targets instead:

cmake_minimum_required(VERSION 3.15...3.22)
project(example LANGUAGES CXX)

find_package(Python 3.6 COMPONENTS Interpreter Development REQUIRED)
find_package(pybind11 CONFIG REQUIRED)
# or add_subdirectory(pybind11)

pybind11_add_module(example example.cpp)

You can also use the targets (as listed below) with FindPython. If you define PYBIND11_FINDPYTHON, pybind11 will perform the FindPython step for you (mostly useful when building pybind11’s own tests, or as a way to change search algorithms from the CMake invocation, with -DPYBIND11_FINDPYTHON=ON.


If you use FindPython to multi-target Python versions, use the individual targets listed below, and avoid targets that directly include Python parts.

There are many ways to hint or force a discovery of a specific Python installation), setting Python_ROOT_DIR may be the most common one (though with virtualenv/venv support, and Conda support, this tends to find the correct Python version more often than the old system did).


When the Python libraries (i.e. libpythonXX.a and on Unix) are not available, as is the case on a manylinux image, the Development component will not be resolved by FindPython. When not using the embedding functionality, CMake 3.18+ allows you to specify Development.Module instead of Development to resolve this issue.

New in version 2.6.

Advanced: interface library targets#

Pybind11 supports modern CMake usage patterns with a set of interface targets, available in all modes. The targets provided are:


Just the pybind11 headers and minimum compile requirements


Python headers + pybind11::headers


Just the “linking” part of pybind11:module


Everything for extension modules - pybind11::pybind11 + Python::Module (FindPython CMake 3.15+) or pybind11::python_link_helper


Everything for embedding the Python interpreter - pybind11::pybind11 + Python::Python (FindPython) or Python libs

pybind11::lto / pybind11::thin_lto

An alternative to INTERPROCEDURAL_OPTIMIZATION for adding link-time optimization.


/bigobj and /mp for MSVC.


/Os for MSVC, -Os for other compilers. Does nothing for debug builds.

Two helper functions are also provided:


Strips a target (uses CMAKE_STRIP after the target is built)


Sets the correct extension (with SOABI) for a target.

You can use these targets to build complex applications. For example, the add_python_module function is identical to:

cmake_minimum_required(VERSION 3.5...3.27)
project(example LANGUAGES CXX)

find_package(pybind11 REQUIRED)  # or add_subdirectory(pybind11)

add_library(example MODULE main.cpp)

target_link_libraries(example PRIVATE pybind11::module pybind11::lto pybind11::windows_extras)

    # Strip unnecessary sections of the binary on Linux/macOS

set_target_properties(example PROPERTIES CXX_VISIBILITY_PRESET "hidden"
                                         CUDA_VISIBILITY_PRESET "hidden")

Instead of setting properties, you can set CMAKE_* variables to initialize these correctly.


Since pybind11 is a metatemplate library, it is crucial that certain compiler flags are provided to ensure high quality code generation. In contrast to the pybind11_add_module() command, the CMake interface provides a composable set of targets to ensure that you retain flexibility. It can be especially important to provide or set these properties; the FAQ contains an explanation on why these are needed.

New in version 2.6.

Advanced: NOPYTHON mode#

If you want complete control, you can set PYBIND11_NOPYTHON to completely disable Python integration (this also happens if you run FindPython2 and FindPython3 without running FindPython). This gives you complete freedom to integrate into an existing system (like Scikit-Build’s PythonExtensions). pybind11_add_module and pybind11_extension will be unavailable, and the targets will be missing any Python specific behavior.

New in version 2.6.

Embedding the Python interpreter#

In addition to extension modules, pybind11 also supports embedding Python into a C++ executable or library. In CMake, simply link with the pybind11::embed target. It provides everything needed to get the interpreter running. The Python headers and libraries are attached to the target. Unlike pybind11::module, there is no need to manually set any additional properties here. For more information about usage in C++, see Embedding the interpreter.

cmake_minimum_required(VERSION 3.5...3.27)
project(example LANGUAGES CXX)

find_package(pybind11 REQUIRED)  # or add_subdirectory(pybind11)

add_executable(example main.cpp)
target_link_libraries(example PRIVATE pybind11::embed)

Building manually#

pybind11 is a header-only library, hence it is not necessary to link against any special libraries and there are no intermediate (magic) translation steps.

On Linux, you can compile an example such as the one given in Creating bindings for a simple function using the following command:

$ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)

The python3 -m pybind11 --includes command fetches the include paths for both pybind11 and Python headers. This assumes that pybind11 has been installed using pip or conda. If it hasn’t, you can also manually specify -I <path-to-pybind11>/include together with the Python includes path python3-config --includes.

On macOS: the build command is almost the same but it also requires passing the -undefined dynamic_lookup flag so as to ignore missing symbols when building the module:

$ c++ -O3 -Wall -shared -std=c++11 -undefined dynamic_lookup $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)

In general, it is advisable to include several additional build parameters that can considerably reduce the size of the created binary. Refer to section Building with CMake for a detailed example of a suitable cross-platform CMake-based build system that works on all platforms including Windows.


On Linux and macOS, it’s better to (intentionally) not link against libpython. The symbols will be resolved when the extension library is loaded into a Python binary. This is preferable because you might have several different installations of a given Python version (e.g. the system-provided Python, and one that ships with a piece of commercial software). In this way, the plugin will work with both versions, instead of possibly importing a second Python library into a process that already contains one (which will lead to a segfault).

Building with Bazel#

You can build with the Bazel build system using the pybind11_bazel repository.

Generating binding code automatically#

The Binder project is a tool for automatic generation of pybind11 binding code by introspecting existing C++ codebases using LLVM/Clang. See the [binder] documentation for details.

[AutoWIG] is a Python library that wraps automatically compiled libraries into high-level languages. It parses C++ code using LLVM/Clang technologies and generates the wrappers using the Mako templating engine. The approach is automatic, extensible, and applies to very complex C++ libraries, composed of thousands of classes or incorporating modern meta-programming constructs.

[robotpy-build] is a is a pure python, cross platform build tool that aims to simplify creation of python wheels for pybind11 projects, and provide cross-project dependency management. Additionally, it is able to autogenerate customizable pybind11-based wrappers by parsing C++ header files.

[litgen] is an automatic python bindings generator with a focus on generating documented and discoverable bindings: bindings will nicely reproduce the documentation found in headers. It is is based on srcML (, a highly scalable, multi-language parsing tool with a developer centric approach. The API that you want to expose to python must be C++14 compatible (but your implementation can use more modern constructs).