The first section presents a simple working example of using CFFI to call a C function in a compiled shared object (DLL) from Python. CFFI is flexible and covers several other use cases presented in the second section. The third section shows how to export Python functions to a Python interpreter embedded in a C or C++ application. The last two sections delve deeper in the CFFI library.

Make sure you have cffi installed.

You can find these and some other complete demos in the demo directory of the repository.

Main mode of usage

The main way to use CFFI is as an interface to some already-compiled shared object which is provided by other means. Imagine that you have a system-installed shared object called piapprox.dll (Windows) or libpiapprox.so (Linux and others) or libpiapprox.dylib (OS X), exporting a function float pi_approx(int n); that computes some approximation of pi given a number of iterations. You want to call this function from Python. Note this method works equally well with a static library piapprox.lib (Windows) or libpiapprox.a.

Create the file piapprox_build.py:

from cffi import FFI
ffibuilder = FFI()

# cdef() expects a single string declaring the C types, functions and
# globals needed to use the shared object. It must be in valid C syntax.
    float pi_approx(int n);

# set_source() gives the name of the python extension module to
# produce, and some C source code as a string.  This C code needs
# to make the declarated functions, types and globals available,
# so it is often just the "#include".
     #include "pi.h"   // the C header of the library
     libraries=['piapprox'])   # library name, for the linker

if __name__ == "__main__":

Execute this script. If everything is OK, it should produce _pi_cffi.c, and then invoke the compiler on it. The produced _pi_cffi.c contains a copy of the string given in set_source(), in this example the #include "pi.h". Afterwards, it contains glue code for all the functions, types and globals declared in the cdef() above.

At runtime, you use the extension module like this:

from _pi_cffi import ffi, lib

That’s all! In the rest of this page, we describe some more advanced examples and other CFFI modes. In particular, there is a complete example if you don’t have an already-installed C library to call.

For more information about the cdef() and set_source() methods of the FFI class, see Preparing and Distributing modules.

When your example works, a common alternative to running the build script manually is to have it run as part of a setup.py. Here is an example using the Setuptools distribution:

from setuptools import setup

    cffi_modules=["piapprox_build:ffibuilder"], # "filename:global"

Other CFFI modes

CFFI can be used in one of four modes: “ABI” versus “API” level, each with “in-line” or “out-of-line” preparation (or compilation).

The ABI mode accesses libraries at the binary level, whereas the faster API mode accesses them with a C compiler. We explain the difference in more details below.

In the in-line mode, everything is set up every time you import your Python code. In the out-of-line mode, you have a separate step of preparation (and possibly C compilation) that produces a module which your main program can then import.

Simple example (ABI level, in-line)

May look familiar to those who have used ctypes.

>>> from cffi import FFI
>>> ffi = FFI()
>>> ffi.cdef("""
...     int printf(const char *format, ...);   // copy-pasted from the man page
... """)
>>> C = ffi.dlopen(None)                     # loads the entire C namespace
>>> arg = ffi.new("char[]", b"world")        # equivalent to C code: char arg[] = "world";
>>> C.printf(b"hi there, %s.\n", arg)        # call printf
hi there, world.
17                                           # this is the return value

Note that char * arguments expect a bytes object. If you have a str (or a unicode on Python 2) you need to encode it explicitly with somestring.encode(myencoding).

Python 3 on Windows: ffi.dlopen(None) does not work. This problem is messy and not really fixable. The problem does not occur if you try to call a function from a specific DLL that exists on your system: then you use ffi.dlopen("path.dll").

This example does not call any C compiler. It works in the so-called ABI mode, which means that it will crash if you call some function or access some fields of a structure that was slightly misdeclared in the cdef().

If using a C compiler to install your module is an option, it is highly recommended to use the API mode instead. (It is also faster.)

Struct/Array Example (minimal, in-line)

from cffi import FFI
ffi = FFI()
    typedef struct {
        unsigned char r, g, b;
    } pixel_t;
image = ffi.new("pixel_t[]", 800*600)

f = open('data', 'rb')     # binary mode -- important

image[100].r = 255
image[100].g = 192
image[100].b = 128

f = open('data', 'wb')

This can be used as a more flexible replacement of the struct and array modules, and replaces ctypes. You could also call ffi.new(“pixel_t[600][800]”) and get a two-dimensional array.

This example does not call any C compiler.

This example also admits an out-of-line equivalent. It is similar to the first example Main mode of usage above, but passing None as the second argument to ffibuilder.set_source(). Then in the main program you write from _simple_example import ffi and then the same content as the in-line example above starting from the line image = ffi.new("pixel_t[]", 800*600).

API Mode, calling the C standard library

# file "example_build.py"

# Note: we instantiate the same 'cffi.FFI' class as in the previous
# example, but call the result 'ffibuilder' now instead of 'ffi';
# this is to avoid confusion with the other 'ffi' object you get below

from cffi import FFI
ffibuilder = FFI()

   r""" // passed to the real C compiler,
        // contains implementation of things declared in cdef()
        #include <sys/types.h>
        #include <pwd.h>

        // We can also define custom wrappers or other functions
        // here (this is an example only):
        static struct passwd *get_pw_for_root(void) {
            return getpwuid(0);
    libraries=[])   # or a list of libraries to link with
    # (more arguments like setup.py's Extension class:
    # include_dirs=[..], extra_objects=[..], and so on)

    // declarations that are shared between Python and C
    struct passwd {
        char *pw_name;
        ...;     // literally dot-dot-dot
    struct passwd *getpwuid(int uid);     // defined in <pwd.h>
    struct passwd *get_pw_for_root(void); // defined in set_source()

if __name__ == "__main__":

You need to run the example_build.py script once to generate “source code” into the file _example.c and compile this to a regular C extension module. (CFFI selects either Python or C for the module to generate based on whether the second argument to set_source() is None or not.)

You need a C compiler for this single step. It produces a file called e.g. _example.so or _example.pyd. If needed, it can be distributed in precompiled form like any other extension module.

Then, in your main program, you use:

from _example import ffi, lib

p = lib.getpwuid(0)
assert ffi.string(p.pw_name) == b'root'
p = lib.get_pw_for_root()
assert ffi.string(p.pw_name) == b'root'

Note that this works independently of the exact C layout of struct passwd (it is “API level”, as opposed to “ABI level”). It requires a C compiler in order to run example_build.py, but it is much more portable than trying to get the details of the fields of struct passwd exactly right. Similarly, in the cdef() we declared getpwuid() as taking an int argument; on some platforms this might be slightly incorrect—but it does not matter.

Note also that at runtime, the API mode is faster than the ABI mode.

To integrate it inside a setup.py distribution with Setuptools:

from setuptools import setup


API Mode, calling C sources instead of a compiled library

If you want to call some library that is not precompiled, but for which you have C sources, then the easiest solution is to make a single extension module that is compiled from both the C sources of this library, and the additional CFFI wrappers. For example, say you start with the files pi.c and pi.h:

/* filename: pi.c*/
# include <stdlib.h>
# include <math.h>

/* Returns a very crude approximation of Pi
   given a int: a number of iteration */
float pi_approx(int n){

  double i,x,y,sum=0;



    if (sqrt(x*x+y*y) < sqrt((double)RAND_MAX*RAND_MAX))
      sum++; }

  return 4*(float)sum/(float)n; }
/* filename: pi.h*/
float pi_approx(int n);

Create a script named pi_extension_build.py, building the C extension:

from cffi import FFI
ffibuilder = FFI()

ffibuilder.cdef("float pi_approx(int n);")

ffibuilder.set_source("_pi",  # name of the output C extension
    #include "pi.h"
    sources=['pi.c'],   # includes pi.c as additional sources
    libraries=['m'])    # on Unix, link with the math library

if __name__ == "__main__":

Build the extension:

python pi_extension_build.py

Observe, in the working directory, the generated output files: _pi.c, _pi.o and the compiled C extension (called _pi.so on Linux for example). It can be called from Python:

from _pi.lib import pi_approx

approx = pi_approx(10)
assert str(approx).startswith("3.")

approx = pi_approx(10000)
assert str(approx).startswith("3.1")

Purely for performance (API level, out-of-line)

A variant of the section above where the goal is not to call an existing C library, but to compile and call some C function written directly in the build script:

# file "example_build.py"

from cffi import FFI
ffibuilder = FFI()

ffibuilder.cdef("int foo(int *, int *, int);")

    static int foo(int *buffer_in, int *buffer_out, int x)
        /* some algorithm that is seriously faster in C than in Python */

if __name__ == "__main__":
# file "example.py"

from _example import ffi, lib

buffer_in = ffi.new("int[]", 1000)
# initialize buffer_in here...

# easier to do all buffer allocations in Python and pass them to C,
# even for output-only arguments
buffer_out = ffi.new("int[]", 1000)

result = lib.foo(buffer_in, buffer_out, 1000)

You need a C compiler to run example_build.py, once. It produces a file called e.g. _example.so or _example.pyd. If needed, it can be distributed in precompiled form like any other extension module.

Out-of-line, ABI level

The out-of-line ABI mode is a mixture of the regular (API) out-of-line mode and the in-line ABI mode. It lets you use the ABI mode, with its advantages (not requiring a C compiler) and problems (crashes more easily).

This mixture mode lets you massively reduces the import times, because it is slow to parse a large C header. It also allows you to do more detailed checkings during build-time without worrying about performance (e.g. calling cdef() many times with small pieces of declarations, based on the version of libraries detected on the system).

# file "simple_example_build.py"

from cffi import FFI

ffibuilder = FFI()
# Note that the actual source is None
ffibuilder.set_source("_simple_example", None)
    int printf(const char *format, ...);

if __name__ == "__main__":

Running it once produces _simple_example.py. Your main program only imports this generated module, not simple_example_build.py any more:

from _simple_example import ffi

lib = ffi.dlopen(None)      # Unix: open the standard C library
#import ctypes.util         # or, try this on Windows:
#lib = ffi.dlopen(ctypes.util.find_library("c"))

lib.printf(b"hi there, number %d\n", ffi.cast("int", 2))

Note that this ffi.dlopen(), unlike the one from in-line mode, does not invoke any additional magic to locate the library: it must be a path name (with or without a directory), as required by the C dlopen() or LoadLibrary() functions. This means that ffi.dlopen("libfoo.so") is ok, but ffi.dlopen("foo") is not. In the latter case, you could replace it with ffi.dlopen(ctypes.util.find_library("foo")). Also, None is only recognized on Unix to open the standard C library.

For distribution purposes, remember that there is a new _simple_example.py file generated. You can either include it statically within your project’s source files, or, with Setuptools, you can say in the setup.py:

from setuptools import setup


In summary, this mode is useful when you wish to declare many C structures but do not need fast interaction with a shared object. It is useful for parsing binary files, for instance.

In-line, API level

The “API level + in-line” mode combination exists but is long deprecated. It used to be done with lib = ffi.verify("C header"). The out-of-line variant with set_source(“modname”, “C header”) is preferred and avoids a number of problems when the project grows in size.


New in version 1.5.

CFFI can be used for embedding: creating a standard dynamically-linked library (.dll under Windows, .so elsewhere) which can be used from a C application.

import cffi
ffibuilder = cffi.FFI()

    int do_stuff(int, int);

ffibuilder.set_source("my_plugin", "")

    from my_plugin import ffi

    def do_stuff(x, y):
        print("adding %d and %d" % (x, y))
        return x + y

ffibuilder.compile(target="plugin-1.5.*", verbose=True)

This simple example creates plugin-1.5.dll or plugin-1.5.so as a DLL with a single exported function, do_stuff(). You execute the script above once, with the interpreter you want to have internally used; it can be CPython 2.x or 3.x or PyPy. This DLL can then be used “as usual” from an application; the application doesn’t need to know that it is talking with a library made with Python and CFFI. At runtime, when the application calls int do_stuff(int, int), the Python interpreter is automatically initialized and def do_stuff(x, y): gets called. See the details in the documentation about embedding.

What actually happened?

The CFFI interface operates on the same level as C - you declare types and functions using the same syntax as you would define them in C. This means that most of the documentation or examples can be copied straight from the man pages.

The declarations can contain types, functions, constants and global variables. What you pass to the cdef() must not contain more than that; in particular, #ifdef or #include directives are not supported. The cdef in the above examples are just that - they declared “there is a function in the C level with this given signature”, or “there is a struct type with this shape”.

In the ABI examples, the dlopen() calls load libraries manually. At the binary level, a program is split into multiple namespaces—a global one (on some platforms), plus one namespace per library. So dlopen() returns a <FFILibrary> object, and this object has got as attributes all function, constant and variable symbols that are coming from this library and that have been declared in the cdef(). If you have several interdependent libraries to load, you would call cdef() only once but dlopen() several times.

By opposition, the API mode works more closely like a C program: the C linker (static or dynamic) is responsible for finding any symbol used. You name the libraries in the libraries keyword argument to set_source(), but never need to say which symbol comes from which library. Other common arguments to set_source() include library_dirs and include_dirs; all these arguments are passed to the standard distutils/setuptools.

The ffi.new() lines allocate C objects. They are filled with zeroes initially, unless the optional second argument is used. If specified, this argument gives an “initializer”, like you can use with C code to initialize global variables.

The actual lib.*() function calls should be obvious: it’s like C.

ABI versus API

Accessing the C library at the binary level (“ABI”) is fraught with problems, particularly on non-Windows platforms.

The most immediate drawback of the ABI level is that calling functions needs to go through the very general libffi library, which is slow (and not always perfectly tested on non-standard platforms). The API mode instead compiles a CPython C wrapper that directly invokes the target function. It can be massively faster (and works better than libffi ever will).

The more fundamental reason to prefer the API mode is that the C libraries are typically meant to be used with a C compiler. You are not supposed to do things like guess where fields are in the structures. The “real example” above shows how CFFI uses a C compiler under the hood: this example uses set_source(…, “C source…”) and never dlopen(). When using this approach, we have the advantage that we can use literally “...” at various places in the cdef(), and the missing information will be completed with the help of the C compiler. CFFI will turn this into a single C source file, which contains the “C source” part unmodified, followed by some “magic” C code and declarations derived from the cdef(). When this C file is compiled, the resulting C extension module will contain all the information we need—or the C compiler will give warnings or errors, as usual e.g. if we misdeclare some function’s signature.

Note that the “C source” part from set_source() can contain arbitrary C code. You can use this to declare some more helper functions written in C. To export these helpers to Python, put their signature in the cdef() too. (You can use the static C keyword in the “C source” part, as in static int myhelper(int x) { return x * 42; }, because these helpers are only referenced from the “magic” C code that is generated afterwards in the same C file.)

This can be used for example to wrap “crazy” macros into more standard C functions. The extra layer of C can be useful for other reasons too, like calling functions that expect some complicated argument structures that you prefer to build in C rather than in Python. (On the other hand, if all you need is to call “function-like” macros, then you can directly declare them in the cdef() as if they were functions.)

The generated piece of C code should be the same independently on the platform on which you run it (or the Python version), so in simple cases you can directly distribute the pre-generated C code and treat it as a regular C extension module (which depends on the _cffi_backend module, on CPython). The special Setuptools lines in the example above are meant for the more complicated cases where we need to regenerate the C sources as well—e.g. because the Python script that regenerates this file will itself look around the system to know what it should include or not.