shouterlog 0.3.1

A custom logging tool that expands normal logger with additional formatting and debug capabilities.

Shouterlog

This is an alternative logging module with extra capabilities. It provides a method to output various types of lines and headers, with customizable message and line lengths, traces additional information and provides debug capabilities and optional persistence based on that. Its purpose is to be integrated into other classes that also use logger, primarily based on attrsx, including support for injecting any initialized logger.

from shouterlog import Shouter
import logging

1. Initialize Shouter Class

Shouter is the core logging class in shouterlog—a drop-in logger wrapper with richer formatting, traceback-aware records, and optional persistence/plotting hooks. Initialization lets you configure things like supported classes for readable tracebacks, formatting length/styles, whether to show function or traceback info, and paths for saving log records or environment snapshots.

Common settings include supported_classes, dotline_length, show_function, show_traceback, tears_persist_path, and persist_env, but you can keep defaults and customize only what you need.

1.1 Shouter options (reference)

This table lists the main Shouter configuration fields and what they control. Fields managed by attrsx or handler setup are marked accordingly.

Option	What it controls	Notes
supported_classes	Tuple of classes Shouter should recognize when building readable tracebacks.	Include classes you want shown in the call chain; if empty, it falls back to the immediate caller.
available_actions	List of LogAction items callable from logger methods via actions=[...].	add_actions() can append custom actions or built-ins like Langfuse.
dotline_length	Width for line/header styles (line, dline, pline, headers).	Controls the visual length of separators and headers.
auto_output_type_selection	Whether output type is chosen automatically based on traceback depth.	If True and output_type is None, Shouter picks a style.
show_function	Prefixes output with the current function/class name.	Useful to keep basic context in each line.
show_traceback	Prefixes output with the traceback chain (leaf -> root).	When False, tracebacks are still recorded in tears.
show_idx	Whether to show the internal log index in output.	Index is always stored in each tear.
tears_persist_path	File path for persisted JSON log records.	Appended to on error/fatal/critical or persist_state().
env_persist_path	File path for persisted environment snapshot.	Written with dill when persist_env=True.
datetime_format	Timestamp format stored in each tear.	Default is %Y-%m-%d %H:%M:%S.
persist_env	Enables writing a serializable locals snapshot on error/fatal/critical.	Use for post-mortem debugging.
log_records	In-memory list of all tears captured so far.	Internal; populated automatically.
lock	Thread lock used during persistence.	Internal; created in __attrs_post_init__.
last_traceback	Storage for last traceback (legacy/local).	Internal; ContextVars hold the active chain.
logger	The underlying logging.Logger instance.	Provided by attrsx logger chaining; can be injected.
loggerLvl	Logger level used by attrsx logger setup.	attrsx-managed; can be overridden via attrsx config.
logger_name	Logger name used by attrsx logger setup.	None defaults to module/class naming.
logger_format	Log format string for the underlying logger.	Defaults to %(levelname)s:%(name)s:%(message)s.
log_plotter_h	LogPlotter instance used for plotting.	Initialized lazily by show_sequence_diagram().
log_plotter_class	Class used to build the plotter handler.	attrsx handler spec.
log_plotter_params	Parameters passed when constructing the plotter handler.	attrsx handler spec.
langfuse_h	Langfuse handler instance for trace exporting.	Only present if Langfuse is initialized.
langfuse_class	Class used to build the Langfuse handler.	attrsx handler spec.
langfuse_params	Parameters passed when constructing Langfuse handler.	attrsx handler spec.

shouter = Shouter(
    # optional/ required
    supported_classes = (),
    # optionally 
    ## Formatting settings
    dotline_length = 50,
    auto_output_type_selection = True,
    show_function = False,
    show_traceback = False,
    # For saving records
    tears_persist_path = 'log_records.json',
    datetime_format = "%Y-%m-%d %H:%M:%S",
    # For saving env
    persist_env = False,
    env_persist_path = 'environment.dill',
    ## Logger settings
    logger = None,
    logger_name = 'Shouter',
    loggerLvl = logging.DEBUG,
    logger_format = '(%(asctime)s) : %(name)s : [%(levelname)s] : %(message)s'
)

2. Basic usage like logging

In basic usage, Shouter behaves like a standard Python logger, so calling .info, .debug, etc. produces familiar log output out of the box. You can then layer on extras (labels, output types, tracebacks) as needed.

shouter.debug(
    # optional
    dotline_length=30)
shouter.debug("This is a debug message!")
shouter.info("This is an info message!")
shouter.warning("This is a warning message!")
shouter.error("This is an error message!")
shouter.fatal("This is a fatal message!")
shouter.critical("This is a critical message!")

(2026-01-28 00:38:47,911) : Shouter : [DEBUG] : ==============================
(2026-01-28 00:38:47,914) : Shouter : [DEBUG] : This is a debug message!
(2026-01-28 00:38:47,915) : Shouter : [INFO] : This is an info message!
(2026-01-28 00:38:47,918) : Shouter : [WARNING] : This is a warning message!
(2026-01-28 00:38:47,919) : Shouter : [ERROR] : This is an error message!
(2026-01-28 00:38:47,921) : Shouter : [CRITICAL] : This is a fatal message!
(2026-01-28 00:38:47,923) : Shouter : [CRITICAL] : This is a critical message!

3. Using different output types

Shouterlog supports multiple output types (lines, headers, titles, and warning styles) that change how the message is formatted. You can either set output_type explicitly for manual formatting, or leave it None and enable auto_output_type_selection so Shouter chooses a style based on the current traceback depth.

Manual examples include output_type='HEAD1', output_type='line', or output_type='subtitle2'. Automatic formatting is useful when you want nested calls to visually indent/scale without specifying a format on every log.

# Different types of outputs
shouter.info(output_type="dline")
shouter.info(output_type="HEAD1", mess="Header Message")

(2026-01-28 00:38:47,929) : Shouter : [INFO] : ==================================================
(2026-01-28 00:38:47,931) : Shouter : [INFO] : 
==================================================
-----------------Header Message----------------- 
==================================================

4. Custom logger configuration

Shouterlog is built with attrsx, so—like other attrsx-based classes—you can replace the built-in logger setup with any already-initialized logging.Logger instance. This lets you plug Shouter into existing logging configurations without changing your logger wiring.

import logging

# Custom logger
custom_logger = logging.getLogger("CustomLogger")
custom_logger.setLevel(logging.INFO)

# Shouter with custom logger
shouter_with_custom_logger = Shouter(supported_classes=(), logger=custom_logger)
shouter_with_custom_logger.info(mess="Message with custom logger")

5. Backwards compatibility with existing loggers

Backwards compatibility means Shouter keeps the standard logger method signatures (.info, .debug, etc.) while allowing extra keyword args for its custom features. When used inside attrsx, any extra parameters that the standard logger does not accept are treated as non-breaking and ignored, so integration does not change existing behavior.

In practice, features like save_vars, label, or custom output_type only take effect when Shouter is active; otherwise they are safely dropped rather than raising errors.

import logging
import attrsx

@attrsx.define
class ExampleClass:

            
    def print_debug(self):

        a = 0

        self.logger.debug("This is a debug message!", save_vars = ["a"])
        
    def print_info(self):
        
        self.logger.info("This is a info message!")
        
    def print_warning(self):
        
        self.logger.warning("This is a warning message!")
        
    def print_error(self):
        
        self.logger.error("This is a error message!", label = "TEST")
        
    def print_critical(self):
        
        self.logger.critical("This is a critical message!")
        
    def perform_action_chain_1(self):
        
        self.logger.debug("Action 1")
        self.print_debug()
                
        self.logger.debug("Action 2")
        self.print_error()
        
    def perform_action_chain_2(self):
                
        a = 1
        b = 'b'
        c = ['list']
        d = {'key' : 'value'}
        e = Shouter()
        
        self.logger.info("Logging vars", save_vars = ["a","b","e"])
        self.logger.error("Saving env", label = "TEST")

ec = ExampleClass()

ec.print_debug()
ec.print_info()
ec.print_warning()
ec.print_error()
ec.print_critical()

INFO:ExampleClass:This is a info message!
WARNING:ExampleClass:This is a warning message!
ERROR:ExampleClass:This is a error message!
CRITICAL:ExampleClass:This is a critical message!

shouter_for_example_class = Shouter(
    supported_classes = (ExampleClass,),
    tears_persist_path = 'log_records.json'
)

ec = ExampleClass(logger=shouter_for_example_class)

ec.print_debug()
ec.print_info()
ec.print_warning()
ec.print_error()
ec.print_critical()
ec.perform_action_chain_1()

INFO:Shouter:ExampleClass.print_info:This is a info message!
WARNING:Shouter:ExampleClass.print_warning:This is a warning message!
ERROR:Shouter:ExampleClass.print_error:This is a error message!
CRITICAL:Shouter:ExampleClass.print_critical:This is a critical message!
ERROR:Shouter:ExampleClass.print_error:+ This is a error message!

6. Built-in records from Shouter usage

Each log call creates a built-in record (a "tear") that captures structured metadata: timestamp, level, function/traceback, line numbers, label, and optional environment data. These records accumulate in Shouter.log_records so you can inspect or export them later.

You can access the full list with Shouter.return_logged_tears() or filter by id via show_logs_by_id(). The records are also the input for plotting and persistence features.

shouter_for_example_class = Shouter(
    supported_classes = (ExampleClass,),
    tears_persist_path = 'log_records.json'
)

ec = ExampleClass(logger=shouter_for_example_class)

ec.print_debug()
ec.perform_action_chain_1()

ERROR:Shouter:ExampleClass.print_error:+ This is a error message!

import pandas as pd

pd.DataFrame(ec.logger.return_logged_tears())

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	idx	call_id	datetime	level	function	mess	line	lines	is_proc	proc_name	traceback	label	env
0	1	140329803945952	2026-01-28 00:38:48	debug	ExampleClass.print_debug	This is a debug message!	12	[12]	False	Task-2	[ExampleClass.print_debug]	None	{'a': 0}
1	2	140329802125440	2026-01-28 00:38:48	debug	ExampleClass.perform_action_chain_1	Action 1	32	[32]	False	Task-2	[ExampleClass.perform_action_chain_1]	None	{}
2	3	140329803946384	2026-01-28 00:38:48	debug	ExampleClass.print_debug	This is a debug message!	12	[12, 33]	False	Task-2	[ExampleClass.print_debug, ExampleClass.perfor...	None	{'a': 0}
3	4	140329802125440	2026-01-28 00:38:48	debug	ExampleClass.perform_action_chain_1	Action 2	35	[35]	False	Task-2	[ExampleClass.perform_action_chain_1]	None	{}
4	5	104240188118496	2026-01-28 00:38:48	error	ExampleClass.print_error	This is a error message!	24	[24, 36]	False	Task-2	[ExampleClass.print_error, ExampleClass.perfor...	TEST	{}

7. Debugging errors with Shouter

Debugging support has two persistence paths: (1) on error/fatal/critical, Shouter persists the log records and can dump a serializable snapshot of locals so you can inspect state even if the program breaks afterward; and (2) at any log level, save_vars lets you attach selected serializable locals directly to each log record (the "tear"), so they travel with the in-memory log list.

Use save_vars=["name", "obj.attr"] on any log call to store specific variables in the tear. For crash-time persistence, set persist_env=True (or call persist_state() manually) so the environment snapshot is dumped alongside the JSON log records.

shouter_for_example_class = Shouter(
    supported_classes = (ExampleClass,),
    tears_persist_path = 'log_records.json',
    persist_env = True,
    env_persist_path = 'environment.dill'
)

ec = ExampleClass(logger=shouter_for_example_class)

ec.print_debug()
ec.perform_action_chain_2()

INFO:Shouter:ExampleClass.perform_action_chain_2:Logging vars
ERROR:Shouter:ExampleClass.perform_action_chain_2:Saving env

ec.logger.return_last_words(
    # optional
    env_persist_path = 'environment.dill'
)

{'a': 1,
 'b': 'b',
 'c': ['list'],
 'd': {'key': 'value'},
 'e': Shouter(supported_classes=(), available_actions=[], dotline_length=50, auto_output_type_selection=True, show_function=True, show_traceback=False, show_idx=False, tears_persist_path='log_records.json', env_persist_path='environment.dill', datetime_format='%Y-%m-%d %H:%M:%S', log_records=[], persist_env=False, lock=<unlocked _thread.lock object at 0x7fa0dcf56600>, last_traceback=[], log_plotter_h=None, log_plotter_class=<class 'shouterlog.shouterlog.LogPlotter'>, log_plotter_params={}, langfuse_h=None, langfuse_class=<class 'shouterlog.shouterlog.LangfuseHandler'>, langfuse_params={}, loggerLvl=20, logger_name=None, logger_format='%(levelname)s:%(name)s:%(message)s')}

8. Plotting execution flow and reviewing steps

The plotting feature turns recorded tracebacks into a sequence diagram, showing how execution moves between classes/functions over time. It uses LogPlotter to render lanes for each class and arrows for calls/events, making it easy to visualize flow from the collected log records.

To use it, call Shouter.show_sequence_diagram() (or instantiate LogPlotter directly) with a list of log records such as Shouter.return_logged_tears(). You can customize labels, sizing, and proc grouping via the plotter parameters when calling the method.

import attrsx

@attrsx.define(handler_specs={
    "example1" : ExampleClass
},
    logger_chaining={
    'logger' : True
})
class MainExampleClass:

    def __attrs_post_init__(self):

        self._initialize_example1_h()

    def print_example_print(self):

        self.logger.debug("Printing from example!", label = "START")

        for i in range(5):

            self.example1_h.print_debug()

        self.logger.debug("Printing from example!", label = "END")

shouter_for_main_example_class = Shouter(
    supported_classes = (MainExampleClass, ExampleClass,),
    tears_persist_path = 'log_records2.json',
    loggerLvl=logging.DEBUG
)

mec = MainExampleClass(logger=shouter_for_main_example_class)

mec.print_example_print()

DEBUG:Shouter:MainExampleClass.print_example_print:Printing from example!
DEBUG:Shouter:ExampleClass.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass.print_debug:+ This is a debug message!
DEBUG:Shouter:MainExampleClass.print_example_print:Printing from example!

mec.logger.show_sequence_diagram()

mec.logger.show_logs_by_id(ids = [1,2])

[{'idx': 1,
  'call_id': 140328889495328,
  'datetime': '2026-01-28 00:38:48',
  'level': 'debug',
  'function': 'MainExampleClass.print_example_print',
  'mess': 'Printing from example!',
  'line': 17,
  'lines': [17],
  'is_proc': False,
  'proc_name': 'Task-2',
  'traceback': ['MainExampleClass.print_example_print'],
  'label': 'START',
  'env': {}},
 {'idx': 2,
  'call_id': 104240187936336,
  'datetime': '2026-01-28 00:38:48',
  'level': 'debug',
  'function': 'ExampleClass.print_debug',
  'mess': 'This is a debug message!',
  'line': 12,
  'lines': [12, 21],
  'is_proc': False,
  'proc_name': 'Task-2',
  'traceback': ['ExampleClass.print_debug',
   'MainExampleClass.print_example_print'],
  'label': None,
  'env': {'a': 0}}]

9. Using actions from logger

Actions are post-log hooks that run after a log call. Each action is a named function with optional input validation and preprocessing that can merge the log record ("tear") with your parameters. This lets a log statement trigger side effects like emitting Langfuse traces, flushing buffers, or invoking custom handlers without changing your main logging flow.

To add a new action, create a LogAction with a unique name, a callable, and optional input_model/input_prep_func, then pass it to Shouter.add_actions([...]). Default actions (like the Langfuse handler) are only registered if the handler has been initialized; once it is, calling .add_actions() will append those built-ins to available_actions, making them callable from logger methods.

import attrsx

@attrsx.define
class ExampleClass2:

            
    def print_debug(self):

        a = 0

        self.logger.debug("This is a debug message!", save_vars = ["a"], actions = [{
            "name" : "langfuse.log_trace",
            "params" : {
                "input" : {"a" : a}
            }
        }])

@attrsx.define(handler_specs={
    "example1" : ExampleClass2
},
    logger_chaining={
    'logger' : True
})
class MainExampleClass2:

    def __attrs_post_init__(self):

        self._initialize_example1_h()

    def print_example_print(self):

        self.logger.debug("Printing from example!", label = "START", actions = [{
            "name" : "langfuse.log_trace"
        }])

        for i in range(5):

            self.example1_h.print_debug()

        self.logger.debug("Printing from example!", label = "END", actions = [{
            "name" : "langfuse.log_trace"
        }])

from dotenv import load_dotenv
load_dotenv("../../.langfuse.env")

shouter_with_langfuse = Shouter(
    supported_classes = (MainExampleClass2, ExampleClass2,),
    tears_persist_path = 'log_records2.json',
    loggerLvl=logging.DEBUG
)

shouter_with_langfuse._initialize_langfuse_h(uparams = {
    "params" : {
        "host" : "http://localhost:3000"
    },
    "env_mapping" : {
        "secret_key" : "SECRET_KEY",
        "public_key" : "PUBLIC_KEY",
    }
})

shouter_with_langfuse.add_actions()

mec2 = MainExampleClass2(logger=shouter_with_langfuse)

mec2.print_example_print()

DEBUG:Shouter:MainExampleClass2.print_example_print:Printing from example!
DEBUG:Shouter:ExampleClass2.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass2.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass2.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass2.print_debug:+ This is a debug message!
DEBUG:Shouter:ExampleClass2.print_debug:+ This is a debug message!
DEBUG:Shouter:MainExampleClass2.print_example_print:Printing from example!