How to Use Free Threading Stable ABI

Starting with the 3.15 release, CPython has support for compiling extensions targeting a unique kind of Stable ABI with interpreters having the global interpreter lock (GIL) disabled. For 3.14 and 3.13, continue compiling with the version-specific ABI. This document describes how to adapt C API extensions to support free threading.

Identifying the Free-Threaded Limited API Build in C

The CPython C API exposes the Py_LIMITED_API macro: in the free-threaded stable ABI build it’s defined to 1, and in the regular build it’s not defined. You can use it to enable code that only runs under the free-threaded build:

#ifdef Py_TARGET_ABI3T
/* code that only runs in the free-threaded stable ABI build */
#endif

If you wish to build youe extension with both abi3 (Stable ABI with GIL) and abi3t (no-GIL stable ABI) tags, do one of the following:

• define both Py_LIMITED_API and Py_TARGET_ABI3T, or

• define only Py_LIMITED_API and:

  • on Windows, define Py_GIL_DISABLED;

  • on other systems, use the headers of free-threaded build of Python.

PyObject and PyVarObject opaqueness

Accessing any member of PyObject directly is now prohibited, like the non-GIL stable ABI. For instance, prefer Py_TYPE() and Py_SET_TYPE() over ob_type, Py_REFCNT, Py_IncRef() and Py_DecRef() over ob_refcnt, etc.

Similarly, members of PyVarObject are not visible. If you need any object of such type to be passed as a PyObject parameter to any API function, cast it directly as PyObject.

Module Initialization

Extension modules need to explicitly indicate that they support running with the GIL disabled; otherwise importing the extension will raise a warning and enable the GIL at runtime.

Multi-phase and single-phase initialization is supported to indicate that an extension module targeting the stable ABI supports running with the GIL disabled, though the former is preferred.

Multi-Phase Initialization

Extensions that use multi-phase initialization (functions like PyModuleDef_Init(), PyModExport_* export hook, PyModule_FromSlotsAndSpec()) should add a Py_mod_gil slot in the module definition. If your extension supports older versions of CPython, you should guard the slot with a Py_GIL_DISABLED check.

static struct PyModuleDef_Slot module_slots[] = {
    ...
#ifdef Py_GIL_DISABLED
    {Py_mod_gil, Py_MOD_GIL_NOT_USED},
#endif
    {0, NULL}
};

Additionally, using PyABIInfo_VAR and Py_mod_abi is recommended so that an extension module loaded for an incompatible interpreter will trigger an exception, rather than fail with a crash.

#ifdef PY_VERSION_HEX >= 0x030F0000
PyABIInfo_VAR(abi_info);
#endif Py_GIL_DISABLED

static PyModuleDef_Slot mymodule_slots[] = {
   ...
#ifdef PY_VERSION_HEX >= 0x030F0000
   {Py_mod_abi, &abi_info},
#endif
   {0, NULL}
};

Single-Phase Initialization

Although members of PyModuleDef is still available for no-GIL Stable ABI and can be used for single-phase initialization (that is, PyModule_Create()), they are not exposed when targeting the regular Stable ABI. Prefer multi-phased initializtion when possible.

General API Guidelines

Most of the C API is thread-safe, but there are some exceptions.

• Struct Fields: Accessing fields in Python C API objects or structs directly is not thread-safe if the field may be concurrently modified.

• Borrowed References: C API functions that return borrowed references may not be thread-safe if the containing object is modified concurrently. See the section on borrowed references for more information.

Container Thread Safety

Containers like PyListObject, PyDictObject, and PySetObject perform internal locking in the free-threaded build. For example, the PyList_Append() will lock the list before appending an item.

Borrowed References

Some C API functions return borrowed references. These APIs are not thread-safe if the containing object is modified concurrently. For example, it’s not safe to use PyList_GetItem() if the list may be modified concurrently.

The following table lists some borrowed reference APIs and their replacements that return strong references.

Borrowed reference API	Strong reference API
PyList_GetItem()	PyList_GetItemRef()
PyList_GET_ITEM()	PyList_GetItemRef()
PyDict_GetItem()	PyDict_GetItemRef()
PyDict_GetItemWithError()	PyDict_GetItemRef()
PyDict_GetItemString()	PyDict_GetItemStringRef()
PyDict_SetDefault()	PyDict_SetDefaultRef()
PyDict_Next()	none (see PyDict_Next)
PyWeakref_GetObject()	PyWeakref_GetRef()
PyWeakref_GET_OBJECT()	PyWeakref_GetRef()
PyImport_AddModule()	PyImport_AddModuleRef()
PyCell_GET()	PyCell_Get()

Not all APIs that return borrowed references are problematic. For example, PyTuple_GetItem() is safe because tuples are immutable. Similarly, not all uses of the above APIs are problematic. For example, PyDict_GetItem() is often used for parsing keyword argument dictionaries in function calls; those keyword argument dictionaries are effectively private (not accessible by other threads), so using borrowed references in that context is safe.

Some of these functions were added in Python 3.13. You can use the pythoncapi-compat package to provide implementations of these functions for older Python versions.

Thread State and GIL APIs

Python provides a set of functions and macros to manage thread state and the GIL, such as:

• PyGILState_Ensure() and PyGILState_Release()

• PyEval_SaveThread() and PyEval_RestoreThread()

• Py_BEGIN_ALLOW_THREADS and Py_END_ALLOW_THREADS

These functions should still be used in the free-threaded build to manage thread state even when the GIL is disabled. For example, if you create a thread outside of Python, you must call PyGILState_Ensure() before calling into the Python API to ensure that the thread has a valid Python thread state.

You should continue to call PyEval_SaveThread() or Py_BEGIN_ALLOW_THREADS around blocking operations, such as I/O or lock acquisitions, to allow other threads to run the cyclic garbage collector.

Protecting Internal Extension State

Your extension may have internal state that was previously protected by the GIL. You may need to add locking to protect this state. The approach will depend on your extension, but some common patterns include:

• Caches: global caches are a common source of shared state. Consider using a lock to protect the cache or disabling it in the free-threaded build if the cache is not critical for performance.

• Global State: global state may need to be protected by a lock or moved to thread local storage. C11 and C++11 provide the thread_local or _Thread_local for thread-local storage.

Critical Sections

In the free-threaded build, CPython provides a mechanism called “critical sections” to protect data that would otherwise be protected by the GIL. While extension authors may not interact with the internal critical section implementation directly, understanding their behavior is crucial when using certain C API functions or managing shared state in the free-threaded build.

What Are Critical Sections?

Conceptually, critical sections act as a deadlock avoidance layer built on top of simple mutexes. Each thread maintains a stack of active critical sections. When a thread needs to acquire a lock associated with a critical section (e.g., implicitly when calling a thread-safe C API function like PyDict_SetItem(), or explicitly using macros), it attempts to acquire the underlying mutex.

Using Critical Sections

The primary APIs for using critical sections are:

• Py_BEGIN_CRITICAL_SECTION and Py_END_CRITICAL_SECTION - For locking a single object

• Py_BEGIN_CRITICAL_SECTION2 and Py_END_CRITICAL_SECTION2 - For locking two objects simultaneously

These macros must be used in matching pairs and must appear in the same C scope, since they establish a new local scope. These macros are no-ops in non-free-threaded builds, so they can be safely added to code that needs to support both build types.

A common use of a critical section would be to lock an object while accessing an internal attribute of it. For example, if an extension type has an internal count field, you could use a critical section while reading or writing that field:

// read the count, returns new reference to internal count value
PyObject *result;
Py_BEGIN_CRITICAL_SECTION(obj);
result = Py_NewRef(obj->count);
Py_END_CRITICAL_SECTION();
return result;

// write the count, consumes reference from new_count
Py_BEGIN_CRITICAL_SECTION(obj);
obj->count = new_count;
Py_END_CRITICAL_SECTION();

How Critical Sections Work

Unlike traditional locks, critical sections do not guarantee exclusive access throughout their entire duration. If a thread would block while holding a critical section (e.g., by acquiring another lock or performing I/O), the critical section is temporarily suspended—all locks are released—and then resumed when the blocking operation completes.

This behavior is similar to what happens with the GIL when a thread makes a blocking call. The key differences are:

• Critical sections operate on a per-object basis rather than globally

• Critical sections follow a stack discipline within each thread (the “begin” and “end” macros enforce this since they must be paired and within the same scope)

• Critical sections automatically release and reacquire locks around potential blocking operations

Deadlock Avoidance

Critical sections help avoid deadlocks in two ways:

1. If a thread tries to acquire a lock that’s already held by another thread, it first suspends all of its active critical sections, temporarily releasing their locks

2. When the blocking operation completes, only the top-most critical section is reacquired first

This means you cannot rely on nested critical sections to lock multiple objects at once, as the inner critical section may suspend the outer ones. Instead, use Py_BEGIN_CRITICAL_SECTION2 to lock two objects simultaneously.

Note that the locks described above are only PyMutex based locks. The critical section implementation does not know about or affect other locking mechanisms that might be in use, like POSIX mutexes. Also note that while blocking on any PyMutex causes the critical sections to be suspended, only the mutexes that are part of the critical sections are released. If PyMutex is used without a critical section, it will not be released and therefore does not get the same deadlock avoidance.

Important Considerations

• Critical sections may temporarily release their locks, allowing other threads to modify the protected data. Be careful about making assumptions about the state of the data after operations that might block.

• Because locks can be temporarily released (suspended), entering a critical section does not guarantee exclusive access to the protected resource throughout the section’s duration. If code within a critical section calls another function that blocks (e.g., acquires another lock, performs blocking I/O), all locks held by the thread via critical sections will be released. This is similar to how the GIL can be released during blocking calls.

• Only the lock(s) associated with the most recently entered (top-most) critical section are guaranteed to be held at any given time. Locks for outer, nested critical sections might have been suspended.

• You can lock at most two objects simultaneously with these APIs. If you need to lock more objects, you’ll need to restructure your code.

• While critical sections will not deadlock if you attempt to lock the same object twice, they are less efficient than purpose-built reentrant locks for this use case.

• When using Py_BEGIN_CRITICAL_SECTION2, the order of the objects doesn’t affect correctness (the implementation handles deadlock avoidance), but it’s good practice to always lock objects in a consistent order.

• Remember that the critical section macros are primarily for protecting access to Python objects that might be involved in internal CPython operations susceptible to the deadlock scenarios described above. For protecting purely internal extension state, standard mutexes or other synchronization primitives might be more appropriate.

Platform-specific considerations

On some platforms, Python will look for and load shared library files named with the abi3 or abi3t tag (for example, mymodule.abi3.so). Free-threaded interpreters only recognize the abi3t tag, while non-free-threaded ones will prefer abi3 but fall back to abi3t. Thus, extensions compatible with both ABIs should use the abi3t tag.

Python does not necessarily check that extensions it loads have compatible ABI. Extension authors are encouraged to add a check using the Py_mod_abi slot or the PyABIInfo_Check() function.

Limited C API Build Tools

If you use setuptools to build your extension, a future version of setuptools will allow py_limited_api=True to be set to allow targeting limited API when building with the free-threaded build.

Other build tools will support this ABI as well.

See also

Porting Extension Modules to Support Free-Threading: A community-maintained porting guide for extension authors.