funlog is a tiny but useful package that offers a few Python decorators to log or tally function calls, with good control over what gets logged and when.

We all do quick print debugging sometimes. Sometimes this is via log statements or other times simply with print().

Logging decorators are a nice compromise between the simplicity of print debugging with more complex or careful log statements:

@log_calls()
def add(a, b):
    return a + b

Then in the logs you will have:

INFO:≫ Call: __main__.add(5, 5)
INFO:≪ Call done: __main__.add() took 0.00ms: 10

In addition to logging function calls, funlog decorators can log arguments briefly but clearly, abbreviating arguments like long strings or dataclasses and also time the function call (and logs it in a friendly way in milliseconds or seconds).

It's not always a good idea to do logging this way, but sometimes it saves time or typing compared to an equivalent manual log statement (see the mini FAQ).

In addition to logging calls, it lets you do very simple profiling by having warnings in production when certain functions are take more than a specified amount of time. Finally, you can use the decorators to get tallies of function calls and runtimes per function after a program runs a while or at exit.

It deliberately has zero dependencies and is a single file with ~500 lines of code.

I'm publishing it standalone since I have found over the years I frequently want to drop it into projects, even if only for faster debugging.

Add the funlog package to your environment in the usual way with pip install funlog, poetry add funlog, or uv add funlog.

Or if for some reason you prefer not to change the dependencies of your project at all, just copy the single file funlog.py.

A more realistic example is the small lint script from this project:

import subprocess
from rich import print as rprint
from funlog import log_calls

@log_calls(level="warning", show_timing_only=True)
def run(cmd: list[str]) -> int:
    rprint()
    rprint(f"[bold green]❯ {' '.join(cmd)}[/bold green]")
    errcount = 0
    try:
        subprocess.run(cmd, text=True, check=True)
    except subprocess.CalledProcessError as e:
        rprint(f"[bold red]Error: {e}[/bold red]")
        errcount = 1

    return errcount

It has the output

❯ codespell --write-changes src tests devtools README.md
⏱ Call to run took 88.78ms

❯ ruff check --fix src tests devtools
All checks passed!
⏱ Call to run took 310ms

❯ ruff format src tests devtools
4 files left unchanged
⏱ Call to run took 10.81ms

❯ basedpyright src tests devtools
0 errors, 0 warnings, 0 notes
⏱ Call to run took 2.07s

Here is a contrived example to illustrate recursive calls and tallies:

import time
import logging
from funlog import log_calls, log_tallies, tally_calls

# Set up logging however you like.
logging.basicConfig(level=logging.DEBUG, format="%(levelname)s:%(message)s", force=True)


@log_calls()
def add(a, b):
    return a + b


@tally_calls()
def sleep(n):
    time.sleep(0.01 * n)


@tally_calls()
def fibonacci(n):
    if n <= 1:
        return n
    sleep(n)
    return fibonacci(n - 1) + fibonacci(n - 2)


@log_calls()
def long_range(n):
    time.sleep(0.01 * n)
    return " ".join(str(i) for i in range(int(n)))


# Now call the functions:
long_range(fibonacci(add(add(5, 5), 2)))

# And then log tallies of all calls:
log_tallies()

Running that gives you:

INFO:≫ Call: __main__.add(5, 5)
INFO:≪ Call done: __main__.add() took 0.00ms: 10
INFO:≫ Call: __main__.add(10, 2)
INFO:≪ Call done: __main__.add() took 0.00ms: 12
INFO:⏱ __main__.sleep() took 125ms, now called 1 times, 125ms avg per call, total time 125ms
INFO:⏱ __main__.sleep() took 114ms, now called 2 times, 119ms avg per call, total time 239ms
INFO:⏱ __main__.sleep() took 95.03ms, now called 4 times, 109ms avg per call, total time 438ms
INFO:⏱ __main__.sleep() took 55.05ms, now called 8 times, 89.25ms avg per call, total time 714ms
INFO:⏱ __main__.fibonacci() took 0.00ms, now called 1 times, 0.00ms avg per call, total time 0.00ms
INFO:⏱ __main__.fibonacci() took 0.00ms, now called 2 times, 0.00ms avg per call, total time 0.00ms
INFO:⏱ __main__.fibonacci() took 25.22ms, now called 3 times, 8.41ms avg per call, total time 25.22ms
INFO:⏱ __main__.fibonacci() took 59.16ms, now called 5 times, 16.88ms avg per call, total time 84.38ms
INFO:⏱ __main__.fibonacci() took 128ms, now called 9 times, 26.41ms avg per call, total time 238ms
INFO:⏱ __main__.fibonacci() took 243ms, now called 15 times, 37.59ms avg per call, total time 564ms
INFO:⏱ __main__.sleep() took 33.76ms, now called 16 times, 60.92ms avg per call, total time 975ms
INFO:⏱ __main__.fibonacci() took 429ms, now called 25 times, 49.00ms avg per call, total time 1.23s
INFO:⏱ __main__.fibonacci() took 741ms, now called 41 times, 61.40ms avg per call, total time 2.52s
INFO:⏱ __main__.sleep() took 32.68ms, now called 32 times, 48.04ms avg per call, total time 1.54s
INFO:⏱ __main__.fibonacci() took 23.35ms, now called 75 times, 67.37ms avg per call, total time 5.05s
INFO:⏱ __main__.sleep() took 24.54ms, now called 64 times, 43.64ms avg per call, total time 2.79s
INFO:⏱ __main__.fibonacci() took 60.07ms, now called 129 times, 78.67ms avg per call, total time 10.15s
INFO:⏱ __main__.fibonacci() took 55.71ms, now called 223 times, 91.26ms avg per call, total time 20.35s
INFO:⏱ __main__.sleep() took 44.42ms, now called 128 times, 40.64ms avg per call, total time 5.20s
INFO:⏱ __main__.fibonacci() took 2.07s, now called 396 times, 107ms avg per call, total time 42.19s
INFO:≫ Call: __main__.long_range(144)
INFO:≪ Call done: __main__.long_range() took 1.45s: '0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 …' (465 chars)
INFO:⏱ Function tallies:
    __main__.fibonacci() was called 465 times, total time 59.73s, avg per call 128ms
    __main__.sleep() was called 232 times, total time 9.11s, avg per call 39.25ms

There are several other options. See docstrings and test_examples.py for more docs and examples on all the options.

• Isn't it better to do real logging? Decorator-based logging isn't much different from regular logging; think of these decorators as simply as a syntactic convenience. Also, they are only handy in certain situations, not something to overuse. The biggest benefit is it's less typing than a full log statement and worrying about formatting: it handles the name of the function, the args and return values, etc and it also truncates values so large values aren't logged by accident.

• If you want to trace function calls, shouldn't you use a debugger? Decorator logging doesn't replace a debugger. It's just one more tool where you want some visibility with little effort.

• Doesn't this create tons of spam in your logs? Again, this has all the usual considerations of regular logging. It will spam logs if you use it on functions that are called a lot. In production, it tends to be useful either for slower or less frequently called functions (like making an API call that takes a few seconds and consumes resources anyway) or with the if_slower_than_sec option so it only logs unexpectedly slow calls or with the tally options.

• Is this just a poor version of tracing? The goal is to be as simple as possible, even useful on little command-line apps. If you're wanting proper visibility into function calls on production cloud-deployed apps, you probably want something more appropriate, like OpenTelemetry.

• What about when you only want to log sometimes, not on every call? Probably just use a regular log statement. Or only log tallies or use the if_slower_than_sec option.

The log_calls() decorator is simple with reasonable defaults but is also fully customizable with optional arguments to the decorator. You can control whether to show arg values and return values:

• show_args to log the function arguments (truncating at truncate_length)

• show_return_value to log the return value (truncating at truncate_length)

By default both calls and returns are logged, but this is also customizable:

• show_calls_only=True to log only calls

• show_returns_only=True to log only returns

• show_timing_only=True only logs the timing of the call very briefly

If if_slower_than_sec is set, only log calls that take longer than that number of seconds.

By default, it uses standard logging with the given level, but you can pass in a custom log_func to override that.

Also by default, it shows values using quote_if_needed(), which is brief and very readable. You can pass in a custom repr_func to change that.

This is intended to only be a tiny library. It doesn't replace any other logging framework or telemetry system.

The main alternative that is similarly simple is logdecorator. It has similar use cases but a more explicit usage style, where you give the messages to the decorator itself. It seems like a good option but personally, I find that if I'm writing the log message, I'd often rather just use a regular log statement. (Also it does not offer tallies or timings like funlog does.)

Eliot is an interesting and more ambitious logging framework that includes somewhat similar decorator-based logging.

The main benefit of funlog is it is very simple to drop into a project when you want it and it's quick to add or remove a log decoration.

This project was built from simple-modern-uv.