User guide

Setting configuration options

There are several configuration options that can be set that influence how type checking is done. To change the options, import typeguard.config (which is of type TypeCheckConfiguration) and set the attributes you want to change.

Checking types directly

The most straightfoward way to do type checking with Typeguard is with check_type(). It can be used as as a beefed-up version of isinstance() that also supports checking against annotations in the typing module:

from typeguard import check_type

# Raises TypeCheckError if there's a problem
check_type([1234], List[int])

It’s also useful for safely casting the types of objects dynamically constructed from external sources:

import json
from typing import List, TypedDict

from typeguard import check_type

# Example contents of "people.json":
# [
#   {"name": "John Smith", "phone": "111-123123", "address": "123 Main Street"},
#   {"name": "Jane Smith", "phone": "111-456456", "address": "123 Main Street"}
# ]

class Person(TypedDict):
    name: str
    phone: str
    address: str

 with open("people.json") as f:
    people = check_type(json.load(f), List[Person])

With this code, static type checkers will recognize the type of people to be List[Person].

Using the decorator

The @typechecked decorator is the simplest way to add type checking on a case-by-case basis. It can be used on functions directly, or on entire classes, in which case all the contained methods are instrumented:

from typeguard import typechecked

def some_function(a: int, b: float, c: str, *args: str) -> bool:
    return retval

class SomeClass:
    # All type annotated methods (including static and class methods and properties)
    # are type checked.
    # Does not apply to inner classes!
    def method(x: int) -> int:

The decorator instruments functions by fetching the source code, parsing it to an abstract syntax tree using ast.parse(), modifying it to add type checking, and finally compiling the modified AST into byte code. This code is then used to make a new function object that is used to replace the original one.


You should always place this decorator closest to the original function, as it will not work when there is another decorator wrapping the function. For the same reason, when you use it on a class that has wrapping decorators on its methods, such methods will not be instrumented. In contrast, the import hook has no such restrictions.

Using the import hook

The import hook, when active, automatically instruments all type annotated functions to type check arguments, return values and values yielded by or sent to generator functions. This allows for a non-invasive method of run time type checking. This method does not modify the source code on disk, but instead modifies its AST (Abstract Syntax Tree) when the module is loaded.

Using the import hook is as straightforward as installing it before you import any modules you wish to be type checked. Give it the name of your top level package (or a list of package names):

from typeguard import install_import_hook

from myapp import some_module  # import only AFTER installing the hook, or it won't take effect

If you wish, you can uninstall the import hook:

manager = install_import_hook('myapp')
from myapp import some_module

or using the context manager approach:

with install_import_hook('myapp'):
    from myapp import some_module

You can also customize the logic used to select which modules to instrument:

from typeguard import TypeguardFinder, install_import_hook

class CustomFinder(TypeguardFinder):
    def should_instrument(self, module_name: str):
        # disregard the module names list and instrument all loaded modules
        return True

install_import_hook('', cls=CustomFinder)

Using the pytest plugin

Typeguard comes with a pytest plugin that installs the import hook (explained in the previous section). To use it, run pytest with the appropriate --typeguard-packages option. For example, if you wanted to instrument the and xyz packages for type checking, you can do the following:


There is currently no support for specifying a customized module finder.

Suppressing type checks

Temporarily disabling type checks

If you need to temporarily suppress type checking, you can use the suppress_type_checks() context manager to skip the checks:

from typeguard import check_type, suppress_type_checks

with suppress_type_checks():
    check_type(1, str)  # would fail without the suppression

These context managers will stack, so type checking is only done once all such context managers have exited.

Permanently suppressing type checks for selected functions

To exclude specific functions from run time type checking, you can use one of the following decorators:

  • @typeguard_ignore: prevents the decorated function from being instrumentated by the import hook

  • @no_type_check: as above, but disables static type checking too

For example, calling the function defined below will not result in a type check error when the containing module is instrumented by the import hook:

from typeguard import typeguard_ignore

def f(x: int) -> int:
    return str(x)


The @no_type_check_decorator decorator is not currently recognized by Typeguard.

Debugging instrumented code

If you find that your code behaves in an unexpected fashion with the Typeguard instrumentation in place, you should set the typeguard.config.debug_instrumentation flag to True. This will print all the instrumented code after the modifications, which you can check to find the reason for the unexpected behavior.

If you’re using the pytest plugin, you can also pass the --typeguard-debug-instrumentation and -s flags together for the same effect.