traceflow-py 1.5.2

A lightweight decorator for visually tracing Python function calls with indented call trees, timing, variable tracking, and async support.

TraceFlow

TraceFlow is a lightweight, zero-dependency Python decorator library for visually tracing function execution. It renders beautiful, indented call trees directly to your console — making debugging recursive, nested, and async code effortless.

fibonacci(3)
├── fibonacci(2)
│   ├── fibonacci(1)
│   │   └── return 1 [0.0003s]
│   ├── fibonacci(0)
│   │   └── return 0 [0.0000s]
│   └── return 1 [0.0006s]
├── fibonacci(1)
│   └── return 1 [0.0000s]
└── return 2 [0.0008s]

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Features

Feature	Description
Call Trees	Nested, indented tree visualization for every function call
Execution Time	Per-call timing with [0.0042s] annotations
Truncation	Smart character-limit truncation for large arguments and returns
Exception Tracing	Captures and renders exceptions inline in the call tree
Async Support	Safely traces async/await functions and asyncio.gather
Depth Limiting	Cap tracing depth with max_depth to reduce noise
File Export	Redirect trace output to a file instead of the console
Global Toggle	traceflow.disable() / traceflow.enable() to control all tracing at runtime
Variable Tracking	track_vars=True to log every local variable assignment inside a function
Capture Prints	capture_prints=True to intercept print() calls and log them inline in the tree
Memory Profiling	track_memory=True to append [+1.2000 MB] memory allocation to the return line
Python Logging	logger=my_logger to emit trace lines through the standard logging module
Class Decorator	@watch_class to automatically wrap all methods in a class

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Installation

pip install traceflow-py

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Quick Start

from traceflow import watch

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

fibonacci(3)

Output:

fibonacci(3)
├── fibonacci(2)
│   ├── fibonacci(1)
│   │   └── return 1 [0.0003s]
│   ├── fibonacci(0)
│   │   └── return 0 [0.0000s]
│   └── return 1 [0.0006s]
├── fibonacci(1)
│   └── return 1 [0.0000s]
└── return 2 [0.0008s]

---

Feature Guide

1. Execution Time Tracking

Every traced call automatically measures wall-clock time.

from traceflow import watch
import time

@watch(track_time=True)
def slow_add(a, b):
    time.sleep(0.05)
    return a + b

slow_add(10, 20)

slow_add(10, 20)
└── return 30 [0.0502s]

Set track_time=False to disable timing:

@watch(track_time=False)
def add(a, b):
    return a + b

add(1, 2)

add(1, 2)
└── return 3

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2. Argument & Return Truncation

Prevent massive data structures from flooding your console with truncate_len.

@watch(truncate_len=30)
def process(data):
    return [x * 2 for x in data]

process(list(range(50)))

process([0, 1, 2, 3, 4, 5, 6, 7, 8,...)
└── return [0, 2, 4, 6, 8, 10, 12, 14,... [0.0000s]

The character limit applies to both arguments and return values. Set truncate_len=None or truncate_len=0 to disable truncation entirely.

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3. Exception Tracing

Exceptions are captured, rendered in the tree, and re-raised — so your error handling works normally while you get full visibility.

@watch()
def divide(a, b):
    return a / b

@watch()
def safe_math(x, y):
    return divide(x, y)

try:
    safe_math(10, 0)
except ZeroDivisionError:
    pass

safe_math(10, 0)
├── divide(10, 0)
│   └── ZeroDivisionError: division by zero [0.0000s]
└── ZeroDivisionError: division by zero [0.0000s]

The exception propagates through each level of the call tree, showing exactly where it originated and how it bubbled up.

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4. Max Depth Limiting

Reduce noise in deeply recursive functions by capping the trace depth.

@watch(max_depth=2)
def deep_fib(n):
    if n <= 1:
        return n
    return deep_fib(n - 1) + deep_fib(n - 2)

deep_fib(4)

deep_fib(4)
├── deep_fib(3)
│   └── return 2 [0.0000s]
├── deep_fib(2)
│   └── return 1 [0.0000s]
└── return 3 [0.0000s]

Calls beyond max_depth still execute normally — they just aren't traced.

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5. Async Function Support

TraceFlow natively supports async functions. Concurrent tasks from asyncio.gather are traced cleanly.

import asyncio
from traceflow import watch

@watch()
async def fetch_data(id):
    await asyncio.sleep(0.01)
    return f"data_{id}"

@watch()
async def fetch_all():
    return await asyncio.gather(fetch_data(1), fetch_data(2), fetch_data(3))

asyncio.run(fetch_all())

fetch_all()
├── fetch_data(1)
│   └── return 'data_1' [0.0123s]
├── fetch_data(2)
│   └── return 'data_2' [0.0123s]
├── fetch_data(3)
│   └── return 'data_3' [0.0123s]
└── return ['data_1', 'data_2', 'data_3'] [0.0126s]

---

6. File Export

Redirect all trace output to a text file instead of the console.

@watch(export_path="trace_output.txt")
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

fibonacci(3)
# -> Trace written to trace_output.txt

The file is appended to, so multiple runs accumulate in the same file.

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7. Global Enable / Disable

Turn all tracing on or off at runtime without removing decorators. Functions still execute normally when tracing is disabled — only the output is suppressed.

import traceflow
from traceflow import watch

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

add(1, 2)           # Trace is printed

traceflow.disable()
add(3, 4)           # No trace output, but returns 7 normally

traceflow.enable()
add(5, 6)           # Trace resumes

add(1, 2)
└── return 3 [0.0000s]

add(5, 6)
└── return 11 [0.0000s]

Also available: traceflow.is_enabled() to check current state.

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8. Variable State Tracking

See exactly how local variables change inside a function, line by line. Parameters are captured as a baseline and not logged (they're already visible in the call signature).

@watch(track_vars=True)
def compute(x, y):
    total = x + y
    doubled = total * 2
    message = f"Result: {doubled}"
    return doubled

compute(5, 3)

compute(5, 3)
│   · total = 8
│   · doubled = 16
│   · message = 'Result: 16'
└── return 16 [0.0002s]

Variable tracking inside a loop:

@watch(track_vars=True)
def sum_list(items):
    total = 0
    for val in items:
        total += val
    return total

sum_list([10, 20, 30])

sum_list([10, 20, 30])
│   · total = 0
│   · val = 10
│   · total = 10
│   · val = 20
│   · total = 30
│   · val = 30
│   · total = 60
└── return 60 [0.0001s]

Note: Variable tracking uses sys.settrace and is only available for synchronous functions. It adds overhead and is best used for targeted debugging, not production code.

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9. Capture Prints (Console Interception)

If your function contains standard print() statements, they normally break the trace tree formatting. By enabling capture_prints=True, TraceFlow will temporarily hijack sys.stdout and inject your print statements directly into the tree hierarchy.

@watch(capture_prints=True)
def process_data():
    print("Loading data...")
    print("Processing...")
    return True

process_data()

process_data()
│   · print: Loading data...
│   · print: Processing...
└── return True [0.0000s]

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10. Python logging Integration

For enterprise applications that require all output to be routed through standard logging channels instead of sys.stdout, you can pass a standard Python logger to the decorator. You can optionally specify the log_level (it defaults to logging.DEBUG).

import logging
from traceflow import watch

logger = logging.getLogger("my_app")
logger.setLevel(logging.DEBUG)

@watch(logger=logger, log_level=logging.INFO)
def authenticate_user(user_id):
    return True

authenticate_user(42)

INFO:my_app:authenticate_user(42)
INFO:my_app:└── return True [0.0102s]

---

11. Memory Profiling

Leverage Python's built-in tracemalloc to track the memory allocated during the execution of your function. TraceFlow will append the memory delta directly to the return line.

@watch(track_memory=True)
def load_large_dataset():
    return [x for x in range(10000)]

load_large_dataset()

load_large_dataset()
└── return [0, 1, 2, 3, ... [0.0026s] [+0.3786 MB]

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11. Class-Level Decorator (@watch_class)

Instead of manually decorating every single method, you can use @watch_class to automatically apply the @watch logic to all methods (including __init__, while safely ignoring other magic dunder methods). It accepts all the same configuration arguments as @watch.

from traceflow import watch_class

@watch_class(track_time=False)
class DataProcessor:
    def __init__(self, data):
        self.data = data

    def process(self):
        return self._transform(self.data)

    def _transform(self, data):
        return [x * 2 for x in data]

processor = DataProcessor([1, 2, 3])
processor.process()

__init__(<__main__.DataProcessor object at ...>, [1, 2, 3])
└── return None
process(<__main__.DataProcessor object at ...>)
├── _transform(<__main__.DataProcessor object at ...>, [1, 2, 3])
│   └── return [2, 4, 6]
└── return [2, 4, 6]

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12. Keyword Arguments

TraceFlow displays both positional and keyword arguments.

@watch()
def greet(name, greeting="Hello", punctuation="!"):
    return f"{greeting}, {name}{punctuation}"

greet("Aarav", greeting="Hey", punctuation="!!")

greet('Aarav', greeting='Hey', punctuation='!!')
└── return 'Hey, Aarav!!' [0.0000s]

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Configuration Reference

Parameter	Type	Default	Description
track_time	bool	True	Append [0.0042s] execution time to each return line
truncate_len	int	50	Max characters for argument/return repr. 0 or None to disable
max_depth	int	None	Stop tracing beyond this call depth. None for unlimited
export_path	str	None	Write trace to a file instead of stdout
track_vars	bool	False	Log local variable assignments inside the function (sync only)
capture_prints	bool	False	Intercept print() calls and log them inline in the tree
track_memory	bool	False	Track memory allocation delta and append to return line
logger	logging.Logger	None	A standard Python logger to emit trace lines to
log_level	int	logging.DEBUG	The logging level to use if logger is provided

Global Functions

Function	Description
traceflow.enable()	Enable all tracing (default state)
traceflow.disable()	Disable all tracing — decorated functions still run normally
traceflow.is_enabled()	Returns True if tracing is currently active

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Author

Aarav Agarwal

License

This project is licensed under the MIT License.