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Structured Logging for Python

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makes logging in Python faster, less painful, and more powerful by adding structure to your log entries.

It's up to you whether you want

to take care about the output of your log entries or whether you prefer to forward them to an existing logging system like the standard library's

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Once you feel inspired to try it out, check out our friendly

Getting Started tutorial 
_ that also contains detailed installation instructions!

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If you prefer videos over reading, check out this DjangoCon Europe 2019 talk by

Markus Holtermann 
: "
Logging Rethought 2: The Actions of Frank Taylor Jr. 

Easier Logging

You can stop writing prose and start thinking in terms of an event that happens in the context of key/value pairs:

.. code-block:: pycon

from structlog import getlogger log = getlogger()"keyvaluelogging", outofthebox=True, effort=0) 2020-11-18 09:17.09 [info ] keyvaluelogging effort=0 outofthebox=True

Each log entry is a meaningful dictionary instead of an opaque string now!

Data Binding

Since log entries are dictionaries, you can start binding and re-binding key/value pairs to your loggers to ensure they are present in every following logging call:

.. code-block:: pycon

log = log.bind(user="anonymous", somekey=23) log = log.bind(user="hynek", anotherkey=42)"user.loggedin", happy=True) 2020-11-18 09:18.28 [info ] user.loggedin anotherkey=42 happy=True somekey=23 user=hynek

Powerful Pipelines

Each log entry goes through a

processor pipeline 
_ that is just a chain of functions that receive a dictionary and return a new dictionary that gets fed into the next function. That allows for simple but powerful data manipulation:

.. code-block:: python

def timestamper(logger, logmethod, eventdict): """Add a timestamp to each log entry.""" eventdict["timestamp"] = time.time() return eventdict

There are

plenty of processors 
_ for most common tasks coming with
  • Collectors of
    call stack information 
    _ ("How did this log entry happen?"),
  • …and
    _ ("What happened‽").
  • Unicode encoders/decoders.
  • Flexible


is completely flexible about how the resulting log entry is emitted. Since each log entry is a dictionary, it can be formatted to any format:
  • A colorful key/value format for
    local development 
  • JSON 
    _ for easy parsing,
  • or some standard format you have parsers for like nginx or Apache httpd.

Internally, formatters are processors whose return value (usually a string) is passed into loggers that are responsible for the output of your message.

comes with multiple useful formatters out-of-the-box.


is also very flexible with the final output of your log entries:
  • A built-in lightweight printer like in the examples above. Easy to use and fast.
  • Use the standard library's or Twisted's logging modules for compatibility. In this case
    works like a wrapper that formats a string and passes them off into existing systems that won't ever know that
    even exists. Or the other way round:
    comes with a
    formatter that allows for processing third party log records.
  • Don't format it to a string at all!
    passes you a dictionary and you can do with it whatever you want. Reported uses cases are sending them out via network or saving them in a database.

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Getting Help

Please use the

tag on
_ to get help.

Answering questions of your fellow developers is also a great way to help the project!

Project Information

is dual-licensed under
Apache License, version 2 
_ and
, available from
, the source code can be found on
_, the documentation at

We collect useful third party extension in

our wiki 

targets Python 3.6 and newer, and PyPy3.

If you need support for older Python versions, the last release with support for Python 2.7 and 3.5 was

_. The package meta data should ensure that you get the correct version.

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