class Logger[source]

An object to dispatch logging messages to configured handlers.

The Logger is the core object of loguru, every logging configuration and usage pass through a call to one of its methods. There is only one logger, so there is no need to retrieve one before usage.

Once the logger is imported, it can be used to write messages about events happening in your code. By reading the output logs of your application, you gain a better understanding of the flow of your program and you more easily track and debug unexpected behaviors.

Handlers to which the logger sends log messages are added using the add() method. Note that you can use the Logger right after import as it comes pre-configured (logs are emitted to sys.stderr by default). Messages can be logged with different severity levels and they can be formatted using curly braces (it uses str.format() under the hood).

When a message is logged, a “record” is associated with it. This record is a dict which contains information about the logging context: time, function, file, line, thread, level… It also contains the __name__ of the module, this is why you don’t need named loggers.

You should not instantiate a Logger by yourself, use from loguru import logger instead.

add(sink, *, level='DEBUG', format='<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>', filter=None, colorize=None, serialize=False, backtrace=True, diagnose=True, enqueue=False, context=None, catch=True, **kwargs)[source]

Add a handler sending log messages to a sink adequately configured.

  • sink (file-like object, str, pathlib.Path, callable, coroutine function or logging.Handler) – An object in charge of receiving formatted logging messages and propagating them to an appropriate endpoint.

  • level (int or str, optional) – The minimum severity level from which logged messages should be sent to the sink.

  • format (str or callable, optional) – The template used to format logged messages before being sent to the sink.

  • filter (callable, str or dict, optional) – A directive optionally used to decide for each logged message whether it should be sent to the sink or not.

  • colorize (bool, optional) – Whether the color markups contained in the formatted message should be converted to ansi codes for terminal coloration, or stripped otherwise. If None, the choice is automatically made based on the sink being a tty or not.

  • serialize (bool, optional) – Whether the logged message and its records should be first converted to a JSON string before being sent to the sink.

  • backtrace (bool, optional) – Whether the exception trace formatted should be extended upward, beyond the catching point, to show the full stacktrace which generated the error.

  • diagnose (bool, optional) – Whether the exception trace should display the variables values to eases the debugging. This should be set to False in production to avoid leaking sensitive data.

  • enqueue (bool, optional) – Whether the messages to be logged should first pass through a multiprocessing-safe queue before reaching the sink. This is useful while logging to a file through multiple processes. This also has the advantage of making logging calls non-blocking.

  • context (multiprocessing.Context or str, optional) – A context object or name that will be used for all tasks involving internally the multiprocessing module, in particular when enqueue=True. If None, the default context is used.

  • catch (bool, optional) – Whether errors occurring while sink handles logs messages should be automatically caught. If True, an exception message is displayed on sys.stderr but the exception is not propagated to the caller, preventing your app to crash.

  • **kwargs – Additional parameters that are only valid to configure a coroutine or file sink (see below).

If and only if the sink is a coroutine function, the following parameter applies:


loop (AbstractEventLoop, optional) – The event loop in which the asynchronous logging task will be scheduled and executed. If None, the loop used is the one returned by asyncio.get_running_loop() at the time of the logging call (task is discarded if there is no loop currently running).

If and only if the sink is a file path, the following parameters apply:

  • rotation (str, int, datetime.time, datetime.timedelta or callable, optional) – A condition indicating whenever the current logged file should be closed and a new one started.

  • retention (str, int, datetime.timedelta or callable, optional) – A directive filtering old files that should be removed during rotation or end of program.

  • compression (str or callable, optional) – A compression or archive format to which log files should be converted at closure.

  • delay (bool, optional) – Whether the file should be created as soon as the sink is configured, or delayed until first logged message. It defaults to False.

  • watch (bool, optional) – Whether or not the file should be watched and re-opened when deleted or changed (based on its device and inode properties) by an external program. It defaults to False.

  • mode (str, optional) – The opening mode as for built-in open() function. It defaults to "a" (open the file in appending mode).

  • buffering (int, optional) – The buffering policy as for built-in open() function. It defaults to 1 (line buffered file).

  • encoding (str, optional) – The file encoding as for built-in open() function. It defaults to "utf8".

  • **kwargs – Others parameters are passed to the built-in open() function.


int – An identifier associated with the added sink and which should be used to remove() it.


ValueError – If any of the arguments passed to configure the sink is invalid.


Extended summary follows.

The sink parameter

The sink handles incoming log messages and proceed to their writing somewhere and somehow. A sink can take many forms:

  • A file-like object like sys.stderr or open("file.log", "w"). Anything with a .write() method is considered as a file-like object. Custom handlers may also implement flush() (called after each logged message), stop() (called at sink termination) and complete() (awaited by the eponymous method).

  • A file path as str or pathlib.Path. It can be parametrized with some additional parameters, see below.

  • A callable (such as a simple function) like lambda msg: print(msg). This allows for logging procedure entirely defined by user preferences and needs.

  • A asynchronous coroutine function defined with the async def statement. The coroutine object returned by such function will be added to the event loop using loop.create_task(). The tasks should be awaited before ending the loop by using complete().

  • A built-in logging.Handler like logging.StreamHandler. In such a case, the Loguru records are automatically converted to the structure expected by the logging module.

Note that the logging functions are not reentrant. This means you should avoid using the logger inside any of your sinks or from within signal handlers. Otherwise, you may face deadlock if the module’s sink was not explicitly disabled.

The logged message

The logged message passed to all added sinks is nothing more than a string of the formatted log, to which a special attribute is associated: the .record which is a dict containing all contextual information possibly needed (see below).

Logged messages are formatted according to the format of the added sink. This format is usually a string containing braces fields to display attributes from the record dict.

If fine-grained control is needed, the format can also be a function which takes the record as parameter and return the format template string. However, note that in such a case, you should take care of appending the line ending and exception field to the returned format, while "\n{exception}" is automatically appended for convenience if format is a string.

The filter attribute can be used to control which messages are effectively passed to the sink and which one are ignored. A function can be used, accepting the record as an argument, and returning True if the message should be logged, False otherwise. If a string is used, only the records with the same name and its children will be allowed. One can also pass a dict mapping module names to minimum required level. In such case, each log record will search for it’s closest parent in the dict and use the associated level as the filter. The dict values can be int severity, str level name or True and False to respectively authorize and discard all module logs unconditionally. In order to set a default level, the "" module name should be used as it is the parent of all modules (it does not suppress global level threshold, though).

Note that while calling a logging method, the keyword arguments (if any) are automatically added to the extra dict for convenient contextualization (in addition to being used for formatting).

The severity levels

Each logged message is associated with a severity level. These levels make it possible to prioritize messages and to choose the verbosity of the logs according to usages. For example, it allows to display some debugging information to a developer, while hiding it to the end user running the application.

The level attribute of every added sink controls the minimum threshold from which log messages are allowed to be emitted. While using the logger, you are in charge of configuring the appropriate granularity of your logs. It is possible to add even more custom levels by using the level() method.

Here are the standard levels with their default severity value, each one is associated with a logging method of the same name:

Level name

Severity value

Logger method






















The record dict

The record is just a Python dict, accessible from sinks by message.record. It contains all contextual information of the logging call (time, function, file, line, level, etc.).

Each of the record keys can be used in the handler’s format so the corresponding value is properly displayed in the logged message (e.g. "{level}" will return "INFO"). Some records’ values are objects with two or more attributes. These can be formatted with "{key.attr}" ("{key}" would display one by default).

Note that you can use any formatting directives available in Python’s str.format() method (e.g. "{key: >3}" will right-align and pad to a width of 3 characters). This is particularly useful for time formatting (see below).





The time elapsed since the start of the program

See datetime.timedelta


The formatted exception if any, None otherwise

type, value, traceback


The dict of attributes bound by the user (see bind())



The file where the logging call was made

name (default), path


The function from which the logging call was made



The severity used to log the message

name (default), no, icon


The line number in the source code



The logged message (not yet formatted)



The module where the logging call was made



The __name__ where the logging call was made



The process in which the logging call was made

name, id (default)


The thread in which the logging call was made

name, id (default)


The aware local time when the logging call was made

See datetime.datetime

The time formatting

To use your favorite time representation, you can set it directly in the time formatter specifier of your handler format, like for example format="{time:HH:mm:ss} {message}". Note that this datetime represents your local time, and it is also made timezone-aware, so you can display the UTC offset to avoid ambiguities.

The time field can be formatted using more human-friendly tokens. These constitute a subset of the one used by the Pendulum library of @sdispater. To escape a token, just add square brackets around it, for example "[YY]" would display literally "YY".

If you prefer to display UTC rather than local time, you can add "!UTC" at the very end of the time format, like {time:HH:mm:ss!UTC}. Doing so will convert the datetime to UTC before formatting.

If no time formatter specifier is used, like for example if format="{time} {message}", the default one will use ISO 8601.





2000, 2001, 2002 … 2012, 2013


00, 01, 02 … 12, 13



1 2 3 4



January, February, March …


Jan, Feb, Mar …


01, 02, 03 … 11, 12


1, 2, 3 … 11, 12

Day of Year


001, 002, 003 … 364, 365


1, 2, 3 … 364, 365

Day of Month


01, 02, 03 … 30, 31


1, 2, 3 … 30, 31

Day of Week


Monday, Tuesday, Wednesday …


Mon, Tue, Wed …


0, 1, 2 … 6

Days of ISO Week


1, 2, 3 … 7



00, 01, 02 … 23, 24


0, 1, 2 … 23, 24


01, 02, 03 … 11, 12


1, 2, 3 … 11, 12



00, 01, 02 … 58, 59


0, 1, 2 … 58, 59



00, 01, 02 … 58, 59


0, 1, 2 … 58, 59

Fractional Second


0 1 … 8 9


00, 01, 02 … 98, 99


000 001 … 998 999


000[0..] 001[0..] … 998[0..] 999[0..]


000000 000001 … 999998 999999






-07:00, -06:00 … +06:00, +07:00


-0700, -0600 … +0600, +0700



Seconds timestamp


1381685817, 1234567890.123

Microseconds timestamp



The file sinks

If the sink is a str or a pathlib.Path, the corresponding file will be opened for writing logs. The path can also contain a special "{time}" field that will be formatted with the current date at file creation. The file is closed at sink stop, i.e. when the application ends or the handler is removed.

The rotation check is made before logging each message. If there is already an existing file with the same name that the file to be created, then the existing file is renamed by appending the date to its basename to prevent file overwriting. This parameter accepts:

  • an int which corresponds to the maximum file size in bytes before that the current logged file is closed and a new one started over.

  • a datetime.timedelta which indicates the frequency of each new rotation.

  • a datetime.time which specifies the hour when the daily rotation should occur.

  • a str for human-friendly parametrization of one of the previously enumerated types. Examples: "100 MB", "0.5 GB", "1 month 2 weeks", "4 days", "10h", "monthly", "18:00", "sunday", "w0", "monday at 12:00", …

  • a callable which will be invoked before logging. It should accept two arguments: the logged message and the file object, and it should return True if the rotation should happen now, False otherwise.

The retention occurs at rotation or at sink stop if rotation is None. Files resulting from previous sessions or rotations are automatically collected from disk. A file is selected if it matches the pattern "basename(.*).ext(.*)" (possible time fields are beforehand replaced with .*) based on the configured sink. Afterwards, the list is processed to determine files to be retained. This parameter accepts:

  • an int which indicates the number of log files to keep, while older files are deleted.

  • a datetime.timedelta which specifies the maximum age of files to keep.

  • a str for human-friendly parametrization of the maximum age of files to keep. Examples: "1 week, 3 days", "2 months", …

  • a callable which will be invoked before the retention process. It should accept the list of log files as argument and process to whatever it wants (moving files, removing them, etc.).

The compression happens at rotation or at sink stop if rotation is None. This parameter accepts:

  • a str which corresponds to the compressed or archived file extension. This can be one of: "gz", "bz2", "xz", "lzma", "tar", "tar.gz", "tar.bz2", "tar.xz", "zip".

  • a callable which will be invoked before file termination. It should accept the path of the log file as argument and process to whatever it wants (custom compression, network sending, removing it, etc.).

Either way, if you use a custom function designed according to your preferences, you must be very careful not to use the logger within your function. Otherwise, there is a risk that your program hang because of a deadlock.

The color markups

To add colors to your logs, you just have to enclose your format string with the appropriate tags (e.g. <red>some message</red>). These tags are automatically removed if the sink doesn’t support ansi codes. For convenience, you can use </> to close the last opening tag without repeating its name (e.g. <red>another message</>).

The special tag <level> (abbreviated with <lvl>) is transformed according to the configured color of the logged message level.

Tags which are not recognized will raise an exception during parsing, to inform you about possible misuse. If you wish to display a markup tag literally, you can escape it by prepending a \ like for example \<blue>. If, for some reason, you need to escape a string programmatically, note that the regex used internally to parse markup tags is r"\\?</?((?:[fb]g\s)?[^<>\s]*)>".

Note that when logging a message with opt(colors=True), color tags present in the formatting arguments (args and kwargs) are completely ignored. This is important if you need to log strings containing markups that might interfere with the color tags (in this case, do not use f-string).

Here are the available tags (note that compatibility may vary depending on terminal):

Color (abbr)

Styles (abbr)

Black (k)

Bold (b)

Blue (e)

Dim (d)

Cyan (c)

Normal (n)

Green (g)

Italic (i)

Magenta (m)

Underline (u)

Red (r)

Strike (s)

White (w)

Reverse (v)

Yellow (y)

Blink (l)

Hide (h)






Basic colors

<red>, <r>

<GREEN>, <G>

Light colors

<light-blue>, <le>


8-bit colors

<fg 86>, <fg 255>

<bg 42>, <bg 9>

Hex colors

<fg #00005f>, <fg #EE1>

<bg #AF5FD7>, <bg #fff>

RGB colors

<fg 0,95,0>

<bg 72,119,65>


<bold>, <b>, <underline>, <u>

The environment variables

The default values of sink parameters can be entirely customized. This is particularly useful if you don’t like the log format of the pre-configured sink.

Each of the add() default parameter can be modified by setting the LOGURU_[PARAM] environment variable. For example on Linux: export LOGURU_FORMAT="{time} - {message}" or export LOGURU_DIAGNOSE=NO.

The default levels’ attributes can also be modified by setting the LOGURU_[LEVEL]_[ATTR] environment variable. For example, on Windows: setx LOGURU_DEBUG_COLOR "<blue>" or setx LOGURU_TRACE_ICON "🚀". If you use the set command, do not include quotes but escape special symbol as needed, e.g. set LOGURU_DEBUG_COLOR=^<blue^>.

If you want to disable the pre-configured sink, you can set the LOGURU_AUTOINIT variable to False.

On Linux, you will probably need to edit the ~/.profile file to make this persistent. On Windows, don’t forget to restart your terminal for the change to be taken into account.


>>> logger.add(sys.stdout, format="{time} - {level} - {message}", filter="sub.module")
>>> logger.add("file_{time}.log", level="TRACE", rotation="100 MB")
>>> def debug_only(record):
...     return record["level"].name == "DEBUG"
>>> logger.add("debug.log", filter=debug_only)  # Other levels are filtered out
>>> def my_sink(message):
...     record = message.record
...     update_db(message, time=record["time"], level=record["level"])
>>> logger.add(my_sink)
>>> level_per_module = {
...     "": "DEBUG",
...     "third.lib": "WARNING",
...     "anotherlib": False
... }
>>> logger.add(lambda m: print(m, end=""), filter=level_per_module, level=0)
>>> async def publish(message):
...     await api.post(message)
>>> logger.add(publish, serialize=True)
>>> from logging import StreamHandler
>>> logger.add(StreamHandler(sys.stderr), format="{message}")
>>> class RandomStream:
...     def __init__(self, seed, threshold):
...         self.threshold = threshold
...         random.seed(seed)
...     def write(self, message):
...         if random.random() > self.threshold:
...             print(message)
>>> stream_object = RandomStream(seed=12345, threshold=0.25)
>>> logger.add(stream_object, level="INFO")

Remove a previously added handler and stop sending logs to its sink.


handler_id (int or None) – The id of the sink to remove, as it was returned by the add() method. If None, all handlers are removed. The pre-configured handler is guaranteed to have the index 0.


ValueError – If handler_id is not None but there is no active handler with such id.


>>> i = logger.add(sys.stderr, format="{message}")
>>> logger.info("Logging")
>>> logger.remove(i)
>>> logger.info("No longer logging")

Wait for the end of enqueued messages and asynchronous tasks scheduled by handlers.

This method proceeds in two steps: first it waits for all logging messages added to handlers with enqueue=True to be processed, then it returns an object that can be awaited to finalize all logging tasks added to the event loop by coroutine sinks.

It can be called from non-asynchronous code. This is especially recommended when the logger is utilized with multiprocessing to ensure messages put to the internal queue have been properly transmitted before leaving a child process.

The returned object should be awaited before the end of a coroutine executed by asyncio.run() or loop.run_until_complete() to ensure all asynchronous logging messages are processed. The function asyncio.get_running_loop() is called beforehand, only tasks scheduled in the same loop that the current one will be awaited by the method.


awaitable – An awaitable object which ensures all asynchronous logging calls are completed when awaited.


>>> async def sink(message):
...     await asyncio.sleep(0.1)  # IO processing...
...     print(message, end="")
>>> async def work():
...     logger.info("Start")
...     logger.info("End")
...     await logger.complete()
>>> logger.add(sink)
>>> asyncio.run(work())
>>> def process():
...     logger.info("Message sent from the child")
...     logger.complete()
>>> logger.add(sys.stderr, enqueue=True)
>>> process = multiprocessing.Process(target=process)
>>> process.start()
>>> process.join()
Message sent from the child
catch(exception=<class 'Exception'>, *, level='ERROR', reraise=False, onerror=None, exclude=None, default=None, message="An error has been caught in function '{record[function]}', process '{record[process].name}' ({record[process].id}), thread '{record[thread].name}' ({record[thread].id}):")[source]

Return a decorator to automatically log possibly caught error in wrapped function.

This is useful to ensure unexpected exceptions are logged, the entire program can be wrapped by this method. This is also very useful to decorate Thread.run() methods while using threads to propagate errors to the main logger thread.

Note that the visibility of variables values (which uses the great better_exceptions library from @Qix-) depends on the diagnose option of each configured sink.

The returned object can also be used as a context manager.

  • exception (Exception, optional) – The type of exception to intercept. If several types should be caught, a tuple of exceptions can be used too.

  • level (str or int, optional) – The level name or severity with which the message should be logged.

  • reraise (bool, optional) – Whether the exception should be raised again and hence propagated to the caller.

  • onerror (callable, optional) – A function that will be called if an error occurs, once the message has been logged. It should accept the exception instance as it sole argument.

  • exclude (Exception, optional) – A type of exception (or a tuple of types) that will be purposely ignored and hence propagated to the caller without being logged.

  • default (Any, optional) – The value to be returned by the decorated function if an error occurred without being re-raised.

  • message (str, optional) – The message that will be automatically logged if an exception occurs. Note that it will be formatted with the record attribute.


decorator / context manager – An object that can be used to decorate a function or as a context manager to log exceptions possibly caught.


>>> @logger.catch
... def f(x):
...     100 / x
>>> def g():
...     f(10)
...     f(0)
>>> g()
ERROR - An error has been caught in function 'g', process 'Main' (367), thread 'ch1' (1398):
Traceback (most recent call last):
  File "program.py", line 12, in <module>
    └ <function g at 0x7f225fe2bc80>
> File "program.py", line 10, in g
    └ <function f at 0x7f225fe2b9d8>
  File "program.py", line 6, in f
    100 / x
          └ 0
ZeroDivisionError: division by zero
>>> with logger.catch(message="Because we never know..."):
...    main()  # No exception, no logs
>>> # Use 'onerror' to prevent the program exit code to be 0 (if 'reraise=False') while
>>> # also avoiding the stacktrace to be duplicated on stderr (if 'reraise=True').
>>> @logger.catch(onerror=lambda _: sys.exit(1))
... def main():
...     1 / 0
opt(*, exception=None, record=False, lazy=False, colors=False, raw=False, capture=True, depth=0, ansi=False)[source]

Parametrize a logging call to slightly change generated log message.

Note that it’s not possible to chain opt() calls, the last one takes precedence over the others as it will “reset” the options to their default values.

  • exception (bool, tuple or Exception, optional) – If it does not evaluate as False, the passed exception is formatted and added to the log message. It could be an Exception object or a (type, value, traceback) tuple, otherwise the exception information is retrieved from sys.exc_info().

  • record (bool, optional) – If True, the record dict contextualizing the logging call can be used to format the message by using {record[key]} in the log message.

  • lazy (bool, optional) – If True, the logging call attribute to format the message should be functions which will be called only if the level is high enough. This can be used to avoid expensive functions if not necessary.

  • colors (bool, optional) – If True, logged message will be colorized according to the markups it possibly contains.

  • raw (bool, optional) – If True, the formatting of each sink will be bypassed and the message will be sent as is.

  • capture (bool, optional) – If False, the **kwargs of logged message will not automatically populate the extra dict (although they are still used for formatting).

  • depth (int, optional) – Specify which stacktrace should be used to contextualize the logged message. This is useful while using the logger from inside a wrapped function to retrieve worthwhile information.

  • ansi (bool, optional) – Deprecated since version 0.4.1: the ansi parameter will be removed in Loguru 1.0.0, it is replaced by colors which is a more appropriate name.


Logger – A logger wrapping the core logger, but transforming logged message adequately before sending.


>>> try:
...     1 / 0
... except ZeroDivisionError:
...    logger.opt(exception=True).debug("Exception logged with debug level:")
[18:10:02] DEBUG in '<module>' - Exception logged with debug level:
Traceback (most recent call last, catch point marked):
> File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero
>>> logger.opt(record=True).info("Current line is: {record[line]}")
[18:10:33] INFO in '<module>' - Current line is: 1
>>> logger.opt(lazy=True).debug("If sink <= DEBUG: {x}", x=lambda: math.factorial(2**5))
[18:11:19] DEBUG in '<module>' - If sink <= DEBUG: 263130836933693530167218012160000000
>>> logger.opt(colors=True).warning("We got a <red>BIG</red> problem")
[18:11:30] WARNING in '<module>' - We got a BIG problem
>>> logger.opt(raw=True).debug("No formatting\n")
No formatting
>>> logger.opt(capture=False).info("Displayed but not captured: {value}", value=123)
[18:11:41] Displayed but not captured: 123
>>> def wrapped():
...     logger.opt(depth=1).info("Get parent context")
>>> def func():
...     wrapped()
>>> func()
[18:11:54] DEBUG in 'func' - Get parent context

Bind attributes to the extra dict of each logged message record.

This is used to add custom context to each logging call.


**kwargs – Mapping between keys and values that will be added to the extra dict.


Logger – A logger wrapping the core logger, but which sends record with the customized extra dict.


>>> logger.add(sys.stderr, format="{extra[ip]} - {message}")
>>> class Server:
...     def __init__(self, ip):
...         self.ip = ip
...         self.logger = logger.bind(ip=ip)
...     def call(self, message):
...         self.logger.info(message)
>>> instance_1 = Server("")
>>> instance_2 = Server("")
>>> instance_1.call("First instance") - First instance
>>> instance_2.call("Second instance") - Second instance

Bind attributes to the context-local extra dict while inside the with block.

Contrary to bind() there is no logger returned, the extra dict is modified in-place and updated globally. Most importantly, it uses contextvars which means that contextualized values are unique to each threads and asynchronous tasks.

The extra dict will retrieve its initial state once the context manager is exited.


**kwargs – Mapping between keys and values that will be added to the context-local extra dict.


context manager / decorator – A context manager (usable as a decorator too) that will bind the attributes once entered and restore the initial state of the extra dict while exited.


>>> logger.add(sys.stderr, format="{message} | {extra}")
>>> def task():
...     logger.info("Processing!")
>>> with logger.contextualize(task_id=123):
...     task()
Processing! | {'task_id': 123}
>>> logger.info("Done.")
Done. | {}

Attach a function to modify the record dict created by each logging call.

The patcher may be used to update the record on-the-fly before it’s propagated to the handlers. This allows the “extra” dict to be populated with dynamic values and also permits advanced modifications of the record emitted while logging a message. The function is called once before sending the log message to the different handlers.

It is recommended to apply modification on the record["extra"] dict rather than on the record dict itself, as some values are used internally by Loguru, and modify them may produce unexpected results.

The logger can be patched multiple times. In this case, the functions are called in the same order as they are added.


patcher (callable) – The function to which the record dict will be passed as the sole argument. This function is in charge of updating the record in-place, the function does not need to return any value, the modified record object will be re-used.


Logger – A logger wrapping the core logger, but which records are passed through the patcher function before being sent to the added handlers.


>>> logger.add(sys.stderr, format="{extra[utc]} {message}")
>>> logger = logger.patch(lambda record: record["extra"].update(utc=datetime.utcnow())
>>> logger.info("That's way, you can log messages with time displayed in UTC")
>>> def wrapper(func):
...     @functools.wraps(func)
...     def wrapped(*args, **kwargs):
...         logger.patch(lambda r: r.update(function=func.__name__)).info("Wrapped!")
...         return func(*args, **kwargs)
...     return wrapped
>>> def recv_record_from_network(pipe):
...     record = pickle.loads(pipe.read())
...     level, message = record["level"], record["message"]
...     logger.patch(lambda r: r.update(record)).log(level, message)
level(name, no=None, color=None, icon=None)[source]

Add, update or retrieve a logging level.

Logging levels are defined by their name to which a severity no, an ansi color tag and an icon are associated and possibly modified at run-time. To log() to a custom level, you should necessarily use its name, the severity number is not linked back to levels name (this implies that several levels can share the same severity).

To add a new level, its name and its no are required. A color and an icon can also be specified or will be empty by default.

To update an existing level, pass its name with the parameters to be changed. It is not possible to modify the no of a level once it has been added.

To retrieve level information, the name solely suffices.

  • name (str) – The name of the logging level.

  • no (int) – The severity of the level to be added or updated.

  • color (str) – The color markup of the level to be added or updated.

  • icon (str) – The icon of the level to be added or updated.


Level – A namedtuple containing information about the level.


ValueError – If there is no level registered with such name.


>>> level = logger.level("ERROR")
>>> print(level)
Level(name='ERROR', no=40, color='<red><bold>', icon='❌')
>>> logger.add(sys.stderr, format="{level.no} {level.icon} {message}")
>>> logger.level("CUSTOM", no=15, color="<blue>", icon="@")
Level(name='CUSTOM', no=15, color='<blue>', icon='@')
>>> logger.log("CUSTOM", "Logging...")
15 @ Logging...
>>> logger.level("WARNING", icon=r"/!\")
Level(name='WARNING', no=30, color='<yellow><bold>', icon='/!\\')
>>> logger.warning("Updated!")
30 /!\ Updated!

Disable logging of messages coming from name module and its children.

Developers of library using Loguru should absolutely disable it to avoid disrupting users with unrelated logs messages.

Note that in some rare circumstances, it is not possible for Loguru to determine the module’s __name__ value. In such situation, record["name"] will be equal to None, this is why None is also a valid argument.


name (str or None) – The name of the parent module to disable.


>>> logger.info("Allowed message by default")
[22:21:55] Allowed message by default
>>> logger.disable("my_library")
>>> logger.info("While publishing a library, don't forget to disable logging")

Enable logging of messages coming from name module and its children.

Logging is generally disabled by imported library using Loguru, hence this function allows users to receive these messages anyway.

To enable all logs regardless of the module they are coming from, an empty string "" can be passed.


name (str or None) – The name of the parent module to re-allow.


>>> logger.disable("__main__")
>>> logger.info("Disabled, so nothing is logged.")
>>> logger.enable("__main__")
>>> logger.info("Re-enabled, messages are logged.")
[22:46:12] Re-enabled, messages are logged.
configure(*, handlers=None, levels=None, extra=None, patcher=None, activation=None)[source]

Configure the core logger.

It should be noted that extra values set using this function are available across all modules, so this is the best way to set overall default values.

To load the configuration directly from a file, such as JSON or YAML, it is also possible to use the loguru-config library developed by @erezinman.

  • handlers (list of dict, optional) – A list of each handler to be added. The list should contain dicts of params passed to the add() function as keyword arguments. If not None, all previously added handlers are first removed.

  • levels (list of dict, optional) – A list of each level to be added or updated. The list should contain dicts of params passed to the level() function as keyword arguments. This will never remove previously created levels.

  • extra (dict, optional) – A dict containing additional parameters bound to the core logger, useful to share common properties if you call bind() in several of your files modules. If not None, this will remove previously configured extra dict.

  • patcher (callable, optional) – A function that will be applied to the record dict of each logged messages across all modules using the logger. It should modify the dict in-place without returning anything. The function is executed prior to the one possibly added by the patch() method. If not None, this will replace previously configured patcher function.

  • activation (list of tuple, optional) – A list of (name, state) tuples which denotes which loggers should be enabled (if state is True) or disabled (if state is False). The calls to enable() and disable() are made accordingly to the list order. This will not modify previously activated loggers, so if you need a fresh start prepend your list with ("", False) or ("", True).


list of int – A list containing the identifiers of added sinks (if any).


>>> logger.configure(
...     handlers=[
...         dict(sink=sys.stderr, format="[{time}] {message}"),
...         dict(sink="file.log", enqueue=True, serialize=True),
...     ],
...     levels=[dict(name="NEW", no=13, icon="¤", color="")],
...     extra={"common_to_all": "default"},
...     patcher=lambda record: record["extra"].update(some_value=42),
...     activation=[("my_module.secret", False), ("another_library.module", True)],
... )
[1, 2]
>>> # Set a default "extra" dict to logger across all modules, without "bind()"
>>> extra = {"context": "foo"}
>>> logger.configure(extra=extra)
>>> logger.add(sys.stderr, format="{extra[context]} - {message}")
>>> logger.info("Context without bind")
>>> # => "foo - Context without bind"
>>> logger.bind(context="bar").info("Suppress global context")
>>> # => "bar - Suppress global context"
static parse(file, pattern, *, cast={}, chunk=65536)[source]

Parse raw logs and extract each entry as a dict.

The logging format has to be specified as the regex pattern, it will then be used to parse the file and retrieve each entry based on the named groups present in the regex.

  • file (str, pathlib.Path or file-like object) – The path of the log file to be parsed, or an already opened file object.

  • pattern (str or re.Pattern) – The regex to use for logs parsing, it should contain named groups which will be included in the returned dict.

  • cast (callable or dict, optional) – A function that should convert in-place the regex groups parsed (a dict of string values) to more appropriate types. If a dict is passed, it should be a mapping between keys of parsed log dict and the function that should be used to convert the associated value.

  • chunk (int, optional) – The number of bytes read while iterating through the logs, this avoids having to load the whole file in memory.


dict – The dict mapping regex named groups to matched values, as returned by re.Match.groupdict() and optionally converted according to cast argument.


>>> reg = r"(?P<lvl>[0-9]+): (?P<msg>.*)"    # If log format is "{level.no} - {message}"
>>> for e in logger.parse("file.log", reg):  # A file line could be "10 - A debug message"
...     print(e)                             # => {'lvl': '10', 'msg': 'A debug message'}
>>> caster = dict(lvl=int)                   # Parse 'lvl' key as an integer
>>> for e in logger.parse("file.log", reg, cast=caster):
...     print(e)                             # => {'lvl': 10, 'msg': 'A debug message'}
>>> def cast(groups):
...     if "date" in groups:
...         groups["date"] = datetime.strptime(groups["date"], "%Y-%m-%d %H:%M:%S")
>>> with open("file.log") as file:
...     for log in logger.parse(file, reg, cast=cast):
...         print(log["date"], log["something_else"])
trace(_Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity 'TRACE'.

debug(_Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity 'DEBUG'.

info(_Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity 'INFO'.

success(_Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity 'SUCCESS'.

warning(_Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity 'WARNING'.

error(_Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity 'ERROR'.

critical(_Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity 'CRITICAL'.

exception(_Logger__message, *args, **kwargs)[source]

Convenience method for logging an 'ERROR' with exception information.

log(_Logger__level, _Logger__message, *args, **kwargs)[source]

Log message.format(*args, **kwargs) with severity level.

start(*args, **kwargs)[source]

Deprecated function to add() a new handler.


Deprecated since version 0.2.2: start() will be removed in Loguru 1.0.0, it is replaced by add() which is a less confusing name.

stop(*args, **kwargs)[source]

Deprecated function to remove() an existing handler.


Deprecated since version 0.2.2: stop() will be removed in Loguru 1.0.0, it is replaced by remove() which is a less confusing name.