Setup and building

These instructions cover how to get a working copy of the source code and a compiled version of the CPython interpreter (CPython is the version of Python available from It also gives an overview of the directory structure of the CPython source code.

Alternatively, if you have Docker installed you might want to use our official images. These contain the latest releases of several Python versions, along with Git head, and are provided for development and testing purposes only.

See also

The Quick reference gives brief summary of the process from installing Git to submitting a pull request.

Install Git

CPython is developed using Git for version control. The Git command line program is named git; this is also used to refer to Git itself. Git is easily available for all common operating systems.

Get the source code

The CPython repo is hosted on GitHub. To get a copy of the source code you should fork the Python repository on GitHub, create a local clone of your personal fork, and configure the remotes.

You will only need to execute these steps once per machine:

  1. Go to

  2. Press Fork on the top right.

  3. When asked where to fork the repository, choose to fork it to your username.

  4. Your fork will be created at<username>/cpython.

  5. Clone your GitHub fork (replace <username> with your username):

    $ git clone<username>/cpython.git

    (You can use both SSH-based or HTTPS-based URLs.)

  1. Add an upstream remote, then configure git to pull main from upstream and always push to origin:

    $ cd cpython
    $ git remote add upstream
    $ git config --local branch.main.remote upstream
    $ git remote set-url --push upstream<your-username>/cpython.git
  2. Verify that your setup is correct:

    $ git remote -v
    origin<your-username>/cpython.git (fetch)
    origin<your-username>/cpython.git (push)
    upstream (fetch)
    upstream<your-username>/cpython.git (push)
    $ git config branch.main.remote

For more information about these commands see Git Bootcamp and Cheat Sheet.

If you did everything correctly, you should now have a copy of the code in the cpython directory and two remotes that refer to your own GitHub fork (origin) and the official CPython repository (upstream).

If you want a working copy of an already-released version of Python, i.e., a version in maintenance mode, you can checkout a release branch. For instance, to checkout a working copy of Python 3.8, do git switch 3.8.

You will need to re-compile CPython when you do such an update.

Do note that CPython will notice that it is being run from a working copy. This means that if you edit CPython’s source code in your working copy, changes to Python code will be picked up by the interpreter for immediate use and testing. (If you change C code, you will need to recompile the affected files as described below.)

Patches for the documentation can be made from the same repository; see Getting started.

Install pre-commit as a Git hook

To make sure your code is linted correctly, we recommend setting up pre-commit as a Git hook:

$ pre-commit install --allow-missing-config
pre-commit installed at .git/hooks/pre-commit

Now pre-commit will run automatically on git commit.

Compile and build

CPython provides several compilation flags which help with debugging various things. While all of the known flags can be found in the Misc/SpecialBuilds.txt file, the most critical one is the Py_DEBUG flag which creates what is known as a “pydebug” build. This flag turns on various extra sanity checks which help catch common issues. The use of the flag is so common that turning on the flag is a basic compile option.

You should always develop under a pydebug build of CPython (the only instance of when you shouldn’t is if you are taking performance measurements). Even when working only on pure Python code the pydebug build provides several useful checks that one should not skip.

See also

The effects of various configure and build flags are documented in the Python configure docs.


The core CPython interpreter only needs a C compiler to be built, however, some of the extension modules will need development headers for additional libraries (such as the zlib library for compression). Depending on what you intend to work on, you might need to install these additional requirements so that the compiled interpreter supports the desired features.

If you want to install these optional dependencies, consult the Install dependencies section below.

If you don’t need to install them, the basic steps for building Python for development is to configure it and then compile it.

Configuration is typically:

$ ./configure --with-pydebug

More flags are available to configure, but this is the minimum you should do to get a pydebug build of CPython.


You might need to run make clean before or after re-running configure in a particular build directory.

Once configure is done, you can then compile CPython with:

$ make -s -j2

This will build CPython with only warnings and errors being printed to stderr and utilize up to 2 CPU cores. If you are using a multi-core machine with more than 2 cores (or a single-core machine), you can adjust the number passed into the -j flag to match the number of cores you have (or if your version of Make supports it, you can use -j without a number and Make will not limit the number of steps that can run simultaneously.).

At the end of the build you should see a success message, followed by a list of extension modules that haven’t been built because their dependencies were missing:

The necessary bits to build these optional modules were not found:
To find the necessary bits, look in and config.log.

Checked 106 modules (31 built-in, 74 shared, 0 n/a on macosx-13.4-arm64, 0 disabled, 1 missing, 0 failed on import)

If the build failed and you are using a C89 or C99-compliant compiler, please open a bug report on the issue tracker.

If you decide to Install dependencies, you will need to re-run both configure and make.

Once CPython is done building you will then have a working build that can be run in-place; ./python on most machines (and what is used in all examples), ./python.exe wherever a case-insensitive filesystem is used (e.g. on macOS by default), in order to avoid conflicts with the Python directory. There is normally no need to install your built copy of Python! The interpreter will realize where it is being run from and thus use the files found in the working copy. If you are worried you might accidentally install your working copy build, you can add --prefix=/tmp/python to the configuration step. When running from your working directory, it is best to avoid using the --enable-shared flag to configure; unless you are very careful, you may accidentally run with code from an older, installed shared Python library rather than from the interpreter you just built.


If you are using clang to build CPython, some flags you might want to set to quiet some standard warnings which are specifically superfluous to CPython are -Wno-unused-value -Wno-empty-body -Qunused-arguments. You can set your CFLAGS environment variable to these flags when running configure.

If you are using clang with ccache, turn off the noisy parentheses-equality warnings with the -Wno-parentheses-equality flag. These warnings are caused by clang not having enough information to detect that extraneous parentheses in expanded macros are valid, because the preprocessing is done separately by ccache.

If you are using LLVM 2.8, also use the -no-integrated-as flag in order to build the ctypes module (without the flag the rest of CPython will still build properly).


If you are trying to improve CPython’s performance, you will probably want to use an optimized build of CPython. It can take a lot longer to build CPython with optimizations enabled, and it’s usually not necessary to do so. However, it’s essential if you want accurate benchmark results for a proposed performance optimization.

For an optimized build of Python, use configure --enable-optimizations --with-lto. This sets the default make targets up to enable Profile Guided Optimization (PGO) and may be used to auto-enable Link Time Optimization (LTO) on some platforms. See --enable-optimizations and --with-lto to learn more about these options.

$ ./configure --enable-optimizations --with-lto



If you are using the Windows Subsystem for Linux (WSL), clone the repository from a native Windows shell program like PowerShell or the cmd.exe command prompt, and use a build of Git targeted for Windows, e.g. the Git for Windows download from the official Git website. Otherwise, Visual Studio will not be able to find all the project’s files and will fail the build.

For a concise step by step summary of building Python on Windows, you can read Victor Stinner’s guide.

All supported versions of Python can be built using Microsoft Visual Studio 2017 or later. You can download and use any of the free or paid versions of Visual Studio.

When installing it, select the Python development workload and the optional Python native development tools component to obtain all of the necessary build tools. You can find Git for Windows on the Individual components tab if you don’t already have it installed.


If you want to build MSI installers, be aware that the build toolchain for them has a dependency on the Microsoft .NET Framework Version 3.5 (which may not be included on recent versions of Windows, such as Windows 10). If you are building on a recent Windows version, use the Control Panel (Programs ‣ Programs and Features ‣ Turn Windows Features on or off) and ensure that the entry .NET Framework 3.5 (includes .NET 2.0 and 3.0) is enabled.

Your first build should use the command line to ensure any external dependencies are downloaded:

PCbuild\build.bat -c Debug

The above command line build uses the -c Debug argument to build in the Debug configuration, which enables checks and assertions helpful for developing Python. By default, it builds in the Release configuration and for the 64-bit x64 platform rather than 32-bit Win32; use -c and -p to control build config and platform, respectively.

After this build succeeds, you can open the PCbuild\pcbuild.sln solution in the Visual Studio IDE to continue development, if you prefer. When building in Visual Studio, make sure to select build settings that match what you used with the script (the Debug configuration and the x64 platform) from the dropdown menus in the toolbar.


If you need to change the build configuration or platform, build once with the build.bat script set to those options first before building with them in VS to ensure all files are rebuilt properly, or you may encounter errors when loading modules that were not rebuilt.

Avoid selecting the PGInstrument and PGUpdate configurations, as these are intended for PGO builds and not for normal development.

You can run the build of Python you’ve compiled with:


See the PCBuild readme for more details on what other software is necessary and how to build.


WASI is a system interface standard for WebAssembly. Through a combination of C compilers that can target WebAssembly and wasi-libc providing POSIX-compatible shims for WASI, it’s possible for CPython to run on a WASI host/runtime as a guest.


The instructions below assume a Unix-based OS due to cross-compilation for CPython being designed for ./configure / make.

To build for WASI, you will need to cross-compile CPython. This requires a C compiler just like building for Unix as well as:

  1. A C compiler that can target WebAssembly (e.g. WASI SDK)

  2. A WASI host/runtime (e.g. Wasmtime)

All of this is provided in the devcontainer. You can also use what’s installed in the container as a reference of what versions of these tools are known to work.


CPython has only been verified with the above tools for WASI. Using other compilers, hosts, or WASI versions should work, but the above tools and their versions specified in the container are tested via a buildbot.

Building for WASI requires doing a cross-build where you have a build Python to help produce a WASI build of CPython (technically it’s a “host x host” cross-build because the build Python is also the target Python while the host build is the WASI build). This means you effectively build CPython twice: once to have a version of Python for the build system to use and another that’s the build you ultimately care about (i.e. the build Python is not meant for use by you directly, only the build system).

The easiest way to get a debug build of CPython for WASI is to use the Tools/wasm/ build command (which should be run w/ a recent version of Python you have installed on your machine):

$ python3 Tools/wasm/ build --quiet -- --config-cache --with-pydebug

That single command will configure and build both the build Python and the WASI build in cross-build/build and cross-build/wasm32-wasi, respectively.

You can also do each configuration and build step separately; the command above is a convenience wrapper around the following commands:

$ python Tools/wasm/ configure-build-python --quiet -- --config-cache --with-pydebug
$ python Tools/wasm/ make-build-python --quiet
$ python Tools/wasm/ configure-host --quiet -- --config-cache
$ python Tools/wasm/ make-host --quiet


The configure-host command infers the use of --with-pydebug from the build Python.

Running the separate commands after build is useful if you, for example, only want to run the make-host step after making code changes.

Once everything is complete, there will be a cross-build/wasm32-wasi/ helper file which you can use to run the python.wasm file (see the output from the configure-host subcommand):

$ cross-build/wasm32-wasi/ --version

You can also use Makefile targets and they will work as expected thanks to the HOSTRUNNER environment variable having been set to a similar value as used in

$ make -C cross-build/wasm32-wasi test


WASI uses a capability-based security model. This means that the WASI host does not give full access to your machine unless you tell it to. This also means things like files can end up being mapped to a different path inside the WASI host. So, if you try passing a file path to python.wasm/, it needs to match the path inside the WASI host, not the path on your machine (much like using a container).


Compiling Python for iOS requires a macOS machine, on a recent version of macOS, running a recent version of Xcode. Apple expects developers to keep their operating systems and tools up-to-date; if your macOS version is more than one major release out of date, or your Xcode version is more than a couple of minor versions out of date, you’ll likely encounter difficulties. It is not possible to compile for iOS using Windows or Linux as a build machine.

A complete build for Python on iOS requires compiling CPython four times: once for macOS; then once for each of the three underlying platforms used by iOS:

  • An ARM64 device (an iPhone or iPad);

  • An ARM64 simulator running on a recent macOS machine; and

  • An x86_64 simulator running on older macOS machine.

The macOS build is required because building Python involves running some Python code. On a normal desktop build of Python, you can compile a Python interpreter and then use that interpreter to run Python code. However, the binaries produced for iOS won’t run on macOS, so you need to provide an external Python interpreter. From the root of a CPython code checkout, run the following:

$ ./configure --prefix=$(pwd)/cross-build/macOS
$ make -j4 all
$ make install

This will build and install Python for macOS into the cross-build/macOS directory.

The CPython build system can compile a single platform at a time. It is possible to test a single platform at a time; however, for distribution purposes, you must compile all three, and merge the results. See the iOS README for details on this merging process.

The following instructions will build CPython for iOS with all extensions enabled, provided you have installed the build dependencies XZ, BZip2, OpenSSL and libFFI in subfolders of the cross-build folder. See the iOS section on installing build dependencies for details on how to obtain these dependencies. These dependencies are all strictly optional, however, including libFFI is highly recommended, as it is required by the ctypes module which is used on iOS to support accessing native system APIs.

$ export PATH="$(pwd)/iOS/Resources/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin"
$ ./configure \
      LIBLZMA_CFLAGS="-I$(pwd)/cross-build/iphoneos.arm64/xz/include" \
      LIBLZMA_LIBS="-L$(pwd)/cross-build/iphoneos.arm64/xz/lib -llzma" \
      BZIP2_CFLAGS="-I$(pwd)/cross-build/iphoneos.arm64/bzip2/include" \
      BZIP2_LIBS="-L$(pwd)/cross-build/iphoneos.arm64/bzip2/lib -lbz2" \
      LIBFFI_CFLAGS="-I$(pwd)/cross-build/iphoneos.arm64/libffi/include" \
      LIBFFI_LIBS="-L$(pwd)/cross-build/iphoneos.arm64/libffi/lib -lffi" \
      --with-openssl="$(pwd)/cross-build/iphoneos.arm64/openssl" \
      --host=arm64-apple-ios12.0 \
      --build=arm64-apple-darwin \
      --with-build-python=$(pwd)/cross-build/macOS/bin/python3.13 \
$ make -j4 all
$ make install
$ export PATH="$(pwd)/iOS/Resources/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin"
$ ./configure \
      LIBLZMA_CFLAGS="-I$(pwd)/cross-build/iphonesimulator.arm64/xz/include" \
      LIBLZMA_LIBS="-L$(pwd)/cross-build/iphonesimulator.arm64/xz/lib -llzma" \
      BZIP2_CFLAGS="-I$(pwd)/cross-build/iphonesimulator.arm64/bzip2/include" \
      BZIP2_LIBS="-L$(pwd)/cross-build/iphonesimulator.arm64/bzip2/lib -lbz2" \
      LIBFFI_CFLAGS="-I$(pwd)/cross-build/iphonesimulator.arm64/libffi/include" \
      LIBFFI_LIBS="-L$(pwd)/cross-build/iphonesimulator.arm64/libffi/lib -lffi" \
      --with-openssl="$(pwd)/cross-build/iphonesimulator.arm64/openssl" \
      --host=arm64-apple-ios12.0-simulator \
      --build=arm64-apple-darwin \
      --with-build-python=$(pwd)/cross-build/macOS/bin/python3.13 \
$ make -j4 all
$ make install
$ export PATH="$(pwd)/iOS/Resources/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin"
$ ./configure \
      LIBLZMA_CFLAGS="-I$(pwd)/cross-build/iphonesimulator.x86_64/xz/include" \
      LIBLZMA_LIBS="-L$(pwd)/cross-build/iphonesimulator.x86_64/xz/lib -llzma" \
      BZIP2_CFLAGS="-I$(pwd)/cross-build/iphonesimulator.x86_64/bzip2/include" \
      BZIP2_LIBS="-L$(pwd)/cross-build/iphonesimulator.x86_64/bzip2/lib -lbz2" \
      LIBFFI_CFLAGS="-I$(pwd)/cross-build/iphonesimulator.x86_64/libffi/include" \
      LIBFFI_LIBS="-L$(pwd)/cross-build/iphonesimulator.x86_64/libffi/lib -lffi" \
      --with-openssl="$(pwd)/cross-build/iphonesimulator.x86_64/openssl" \
      --host=x86_64-apple-ios12.0-simulator \
      --build=arm64-apple-darwin \
      --with-build-python=$(pwd)/cross-build/macOS/bin/python3.13 \
$ make -j4 all
$ make install

These instructions modify your PATH before the build. As iOS and macOS share a hardware architecture (ARM64), it is easy for a macOS ARM64 binary to be accidentally linked into your iOS build. This is especially common when Homebrew is present on the build system. The most reliable way to avoid this problem is to remove any potential source of other libraries from your PATH.

However, the PATH is not completely bare — it includes the iOS/Resources/bin folder. This folder contains a collection of scripts that wrap the invocation of the Xcode xcrun tool, removing user- and version-specific paths from the values encoded in the sysconfig module. Copies of these scripts are included in the final build products.

Once this build completes, the iOS/Frameworks folder will contain a Python.framework that can be used for testing.

To run the test suite on iOS, complete a build for a simulator platform, ensure the path modifications from the build are still in effect, and run:

$ make testios

The full test suite takes approximately 12 minutes to run on a 2022 M1 MacBook Pro, plus a couple of extra minutes to build the testbed application and boot the simulator. There will be an initial burst of console output while the Xcode test project is compiled; however, while the test suite is running, there is no console output or progress. This is a side effect of how Xcode operates when executed at the command line. You should see an iOS simulator appear during the testing process; the simulator will booth to an iOS landing screen, the testbed app will be installed, and then started. The screen of the simulator will be black while the test suite is running. When the test suite completes, success or failure will be reported at the command line. In the case of failure, you will see the full log of CPython test suite output.

You can also run the test suite in Xcode itself. This is required if you want to run on a physical device; it is also the easiest approach if you need to run a single test, or a subset of tests. See the iOS README for details.

Install dependencies

This section explains how to install additional extensions (e.g. zlib) on Linux, macOS and iOS.

For Unix-based systems, we try to use system libraries whenever available. This means optional components will only build if the relevant system headers are available. The best way to obtain the appropriate headers will vary by distribution, but the appropriate commands for some popular distributions are below.

On Fedora, RHEL, CentOS and other dnf-based systems:

$ sudo dnf install dnf-plugins-core  # install this to use 'dnf builddep'
$ sudo dnf builddep python3

On Debian, Ubuntu, and other apt-based systems, try to get the dependencies for the Python you’re working on by using the apt command.

First, make sure you have enabled the source packages in the sources list. You can do this by adding the location of the source packages, including URL, distribution name and component name, to /etc/apt/sources.list. Take Ubuntu 22.04 LTS (Jammy Jellyfish) for example:

$ deb-src jammy main

Alternatively, uncomment lines with deb-src using an editor, e.g.:

$ sudo nano /etc/apt/sources.list

For other distributions, like Debian, change the URL and names to correspond with the specific distribution.

Then you should update the packages index:

$ sudo apt-get update

Now you can install the build dependencies via apt:

$ sudo apt-get build-dep python3
$ sudo apt-get install pkg-config

If you want to build all optional modules, install the following packages and their dependencies:

$ sudo apt-get install build-essential gdb lcov pkg-config \
      libbz2-dev libffi-dev libgdbm-dev libgdbm-compat-dev liblzma-dev \
      libncurses5-dev libreadline6-dev libsqlite3-dev libssl-dev \
      lzma lzma-dev tk-dev uuid-dev zlib1g-dev libmpdec-dev

For macOS systems (versions 10.9+), the Developer Tools can be downloaded and installed automatically; you do not need to download the complete Xcode application.

If necessary, run the following:

$ xcode-select --install

This will also ensure that the system header files are installed into /usr/include.

Also note that macOS does not include several libraries used by the Python standard library, including libzma, so expect to see some extension module build failures unless you install local copies of them. As of OS X 10.11, Apple no longer provides header files for the deprecated system version of OpenSSL which means that you will not be able to build the _ssl extension. One solution is to install these libraries from a third-party package manager, like Homebrew or MacPorts, and then add the appropriate paths for the header and library files to your configure command.

For Homebrew, install dependencies using brew:

$ brew install pkg-config openssl@3.0 xz gdbm tcl-tk mpdecimal

For Python 3.13 and newer:

$ GDBM_CFLAGS="-I$(brew --prefix gdbm)/include" \
   GDBM_LIBS="-L$(brew --prefix gdbm)/lib -lgdbm" \
   ./configure --with-pydebug \
               --with-system-libmpdec \
               --with-openssl="$(brew --prefix openssl@3.0)"

For Python 3.11 and 3.12:

$ GDBM_CFLAGS="-I$(brew --prefix gdbm)/include" \
   GDBM_LIBS="-L$(brew --prefix gdbm)/lib -lgdbm" \
   ./configure --with-pydebug \
               --with-openssl="$(brew --prefix openssl@3.0)"

For Python 3.8, 3.9, and 3.10:

$ CPPFLAGS="-I$(brew --prefix gdbm)/include -I$(brew --prefix xz)/include" \
   LDFLAGS="-L$(brew --prefix gdbm)/lib -L$(brew --prefix xz)/lib" \
   ./configure --with-pydebug \
               --with-openssl="$(brew --prefix openssl@3.0)" \
               --with-tcltk-libs="$(pkg-config --libs tcl tk)" \
               --with-tcltk-includes="$(pkg-config --cflags tcl tk)"

For MacPorts, install dependencies using port:

$ sudo port install pkgconfig openssl xz gdbm tcl tk +quartz mpdecimal

For Python 3.13 and newer:

$ GDBM_CFLAGS="-I$(dirname $(dirname $(which port)))/include" \
   GDBM_LIBS="-L$(dirname $(dirname $(which port)))/lib -lgdbm" \
   ./configure --with-pydebug \

For Python 3.11 and 3.12:

$ GDBM_CFLAGS="-I$(dirname $(dirname $(which port)))/include" \
   GDBM_LIBS="-L$(dirname $(dirname $(which port)))/lib -lgdbm" \
   ./configure --with-pydebug

And finally, run make:

$ make -s -j2

There will sometimes be optional modules added for a new release which won’t yet be identified in the OS-level build dependencies. In those cases, just ask for assistance in the Core Development category on Discourse.

Explaining how to build optional dependencies on a Unix-based system without root access is beyond the scope of this guide.

For more details on various options and considerations for building, refer to the macOS README.


While you need a C compiler to build CPython, you don’t need any knowledge of the C language to contribute! Vast areas of CPython are written completely in Python: as of this writing, CPython contains slightly more Python code than C.

On Windows, extensions are already included and built automatically.

As with CPython itself, the dependencies for CPython must be compiled for each of the hardware architectures that iOS supports. Consult the documentation for XZ, bzip2, OpenSSL and libffi for details on how to configure the project for cross-platform iOS builds.

Alternatively, the BeeWare Project maintains a project for building iOS dependencies, and distributes pre-compiled binaries for each of the dependencies. If you use this project to build the dependencies yourself, the subfolders of the install folder can be used to configure CPython. If you use the pre-compiled binaries, you should unpack each tarball into a separate folder, and use that folder as the configuration target.

Regenerate configure

If a change is made to Python which relies on some POSIX system-specific functionality (such as using a new system call), it is necessary to update the configure script to test for availability of the functionality. Python’s configure script is generated from using GNU Autoconf.

After editing, run make regen-configure to generate configure,, and aclocal.m4. When submitting a pull request with changes made to, make sure you also commit the changes in the generated files.

Python’s script requires a specific version of GNU Autoconf. For Python 3.12 and newer, GNU Autoconf v2.71 is required. For Python 3.11 and earlier, GNU Autoconf v2.69 is required.

The recommended and by far the easiest way to regenerate configure is:

$ make regen-configure

This will use Podman or Docker to do the regeneration with the proper version of GNU Autoconf.

If you cannot (or don’t want to) use make regen-configure, install the autoconf-archive and pkg-config utilities, and make sure the pkg.m4 macro file located in the appropriate aclocal location:

$ ls $(aclocal --print-ac-dir) | grep pkg.m4


Running autoreconf is not the same as running autoconf. For example, running autoconf by itself will not regenerate autoreconf runs autoconf and a number of other tools repeatedly as appropriate.

Regenerate the ABI dump

Maintenance branches (not main) have a special file located in Doc/data/pythonX.Y.abi that allows us to know if a given Pull Request affects the public ABI. This file is used by the GitHub CI in a check called Check if the ABI has changed that will fail if a given Pull Request has changes to the ABI and the ABI file is not updated.

This check acts as a fail-safe and doesn’t necessarily mean that the Pull Request cannot be merged. When this check fails you should add the relevant release manager to the PR so that they are aware of the change and they can validate if the change can be made or not.


ABI changes are allowed before the first release candidate. After the first release candidate, all further releases must have the same ABI for ensuring compatibility with native extensions and other tools that interact with the Python interpreter. See the documentation about the release candidate phase.

When the PR check fails, the associated run will have the updated ABI file attached as an artifact. After release manager approval, you can download and add this file into your PR to pass the check.

You can regenerate the ABI file by yourself by invoking the regen abidump Make target. Note that for doing this you need to regenerate the ABI file in the same environment that the GitHub CI uses to check for it. This is because different platforms may include some platform-specific details that make the check fail even if the Python ABI is the same. The easier way to regenerate the ABI file using the same platform as the CI uses is by using Docker:

# In the CPython root:
$ docker run -v$(pwd):/src:Z -w /src --rm -it ubuntu:22.04 \
    bash /src/.github/workflows/

Note that the ubuntu version used to execute the script matters and must match the version used by the CI to check the ABI. See the .github/workflows/build.yml file for more information.

Troubleshoot the build

This section lists some of the common problems that may arise during the compilation of Python, with proposed solutions.

Avoid recreating auto-generated files

Under some circumstances you may encounter Python errors in scripts like Parser/ or Python/ while running make. Python auto-generates some of its own code, and a full build from scratch needs to run the auto-generation scripts. However, this makes the Python build require an already installed Python interpreter; this can also cause version mismatches when trying to build an old (2.x) Python with a new (3.x) Python installed, or vice versa.

To overcome this problem, auto-generated files are also checked into the Git repository. So if you don’t touch the auto-generation scripts, there’s no real need to auto-generate anything.

Editors and tools

Python is used widely enough that practically all code editors have some form of support for writing Python code. Various coding tools also include Python support.

For editors and tools which the core developers have felt some special comment is needed for coding in Python, see Additional resources.

Directory structure

There are several top-level directories in the CPython source tree. Knowing what each one is meant to hold will help you find where a certain piece of functionality is implemented. Do realize, though, there are always exceptions to every rule.


The official documentation. This is what uses. See also Building the documentation.


Contains the EBNF grammar file for Python.


Contains all interpreter-wide header files.


The part of the standard library implemented in pure Python.


Mac-specific code (e.g., using IDLE as a macOS application).


Things that do not belong elsewhere. Typically this is varying kinds of developer-specific documentation.


The part of the standard library (plus some other code) that is implemented in C.


Code for all built-in types.


Windows-specific code.


Build files for the version of MSVC currently used for the Windows installers provided on


Code related to the parser. The definition of the AST nodes is also kept here.


Source code for C executables, including the main function for the CPython interpreter.


The code that makes up the core CPython runtime. This includes the compiler, eval loop and various built-in modules.


Various tools that are (or have been) used to maintain Python.

Contribute using GitHub Codespaces

What is GitHub Codespaces?

If you’d like to start contributing to CPython without needing to set up a local developer environment, you can use GitHub Codespaces. Codespaces is a cloud-based development environment offered by GitHub that allows developers to write, build, test, and debug code directly within their web browser or in Visual Studio Code (VS Code).

To help you get started, CPython contains a devcontainer folder with a JSON configuration file that provides consistent and versioned codespace configurations for all users of the project. It also contains a Dockerfile that allows you to set up the same environment but locally in a Docker container if you’d prefer to use that directly.

Create a CPython codespace

Here are the basic steps needed to contribute a patch using Codespaces. You first need to navigate to the CPython repo hosted on GitHub.

Then you will need to:

  1. Press the , key to launch the codespace setup screen for the current branch (alternatively, click the green Code button and choose the codespaces tab and then press the green Create codespace on main button).

  2. A screen should appear that lets you know your codespace is being set up. (Note: Since the CPython devcontainer is provided, codespaces will use the configuration it specifies.)

  3. A web version of VS Code will open inside your web browser, already linked up with your code and a terminal to the remote codespace where CPython and its documentation have already been built.

  4. Use the terminal with the usual Git commands to create a new branch, commit and push your changes once you’re ready!

If you close your repository and come back later you can always resume your codespace by navigating to the CPython repo, selecting the codespaces tab and selecting your most recent codespaces session. You should then be able to pick up from where you left off!

Use Codespaces locally

On the bottom left side of the codespace screen you will see a green or grey square that says Codespaces. You can click this for additional options. If you prefer working in a locally installed copy of VS Code you can select the option Open in VS Code. You will still be working on the remote codespace instance, thus using the remote instance’s compute power. The compute power may be a much higher spec than your local machine which can be helpful.