LogEye

Understand exactly what your code is doing in real time, no debugger needed.

LogEye is a frictionless runtime logger for Python that shows variable changes, function calls, and data mutations as they happen.

Think of it as "print debugging" just better - automated, structured, easy to drop into, and remove from any codebase

Installation

pip install logeye

Quick example

from logeye import log
@log
def add(a, b):
	return a + b
add(2, 3)
@log(mode="edu")
def add_edu(a, b):
	return a + b
add_edu(2, 3)

Output:

[0.000s] playground.py:7 (call) add args=(2, 3)
[0.000s] playground.py:5 (set) add.a = 2
[0.000s] playground.py:5 (set) add.b = 3
[0.000s] playground.py:5 (return) add args=(2, 3) -> 5
[0.000s] Calling add_edu(2, 3)
[0.000s] Defined add_edu.a = 2
[0.000s] Defined add_edu.b = 3
[0.000s] add_edu(2, 3) returned 5

Who is it for?
What does it do?
- What exactly does it track?
Quick start
Educational Mode
Logging functions
Advanced function logging
Logging objects
Messages
Utility functions
Output format
Some Usage Examples
Inspiration
Limitations
Contact
License

Who is it for?

LogEye helps you see how your code executes step by step.

Perfect for:

beginners learning programming
students studying algorithms
teachers explaining concepts

No more scattered print() calls. No debugger setup. Simply run your code and see everything.

Why keep doing this?

print(x)
print(y)
print(queue)

When a single | l suffices?

Added (1, 'B') to queue -> [(1, 'B')]
Sorted queue -> [(1, 'B'), (4, 'C')]
Popped (1, 'B') from queue

What does it do?

Core features:

educational mode for algorithm tracing
log values with automatic variable name inference
trace function calls, local variables, and returns
track object and data structure mutations in real time
format messages using f-string, template, or scope variables

Advanced features:

filter variables and control verbosity (level, filter)
log to files or stdout
recursively track nested structures
AST-based name inference (including multi-line assignments)

However, keep in mind that name inference is best-effort and may not be accurate in some more extreme cases.

What exactly does it track?

Without changing your code, LogEye shows:

function calls and returns
local variables inside functions
object attribute changes
list / dict / set mutations
nested structures
recursion and call depth

Quick start

from logeye import log
x = log(10)
message = log("Hello from {name}", name="Matt")
@log(level="call")
def add(a, b):
	something = 2 + 2 # Unused
	return a + b
add(2, 2)
name = "Matt"
message2 = log("Hello from $name")
config = log({"debug": True, "port": 8080})
config.port = 9090
config["debug"] = False

Example output:

[0.000s] playground.py:3 (set) x = 10
[0.024s] playground.py:4 (set) message = 'Hello from Matt'
[0.026s] playground.py:13 (call) add args=(2, 2)
[0.026s] playground.py:10 (return) add args=(2, 2) -> 4
[0.026s] playground.py:16 (set) message2 = 'Hello from Matt'
[0.027s] playground.py:18 (set) config = {'debug': True, 'port': 8080}
[0.027s] playground.py:19 (change) config.port = 9090
[0.027s] playground.py:20 (change) config.debug = False

Educational Mode!

Educational mode is designed to make algorithms read like a story instead of a trace. :)

Instead of raw internal logs, it shows:

clean function calls
meaningful variable changes
human-readable operations
minimal noise

Enable it with:

from logeye import log, set_mode
# Globally
set_mode("edu")
# Locally
@log(mode="edu")
def my_function(): ...

Before vs After

Default mode

[0.000s] demo_dijkstra.py:36 (call) dijkstra args=({'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}, 'A')
[0.001s] demo_dijkstra.py:8 (set) dijkstra.graph = {'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}
[0.001s] demo_dijkstra.py:8 (set) dijkstra.start = 'A'
[0.001s] demo_dijkstra.py:8 (set) dijkstra.node = 'A'
...
[0.005s] demo_dijkstra.py:15 (change) dijkstra.queue = {'op': 'pop', 'index': 0, 'value': (6, 'D'), 'state': []}
[0.005s] demo_dijkstra.py:17 (change) dijkstra.current_dist = 6
[0.005s] demo_dijkstra.py:31 (return) dijkstra args=({'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}, 'A') -> {'A': 0, 'B': 1, 'C': 3, 'D': 4}

Educational mode

[0.000s] demo_dijkstra.py:3 DIJKSTRA - SHORTEST PATH
[0.000s] Calling dijkstra({'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}, 'A')
[0.001s] Defined dijkstra.graph = {'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}
[0.001s] Defined dijkstra.start = 'A'
...
[0.004s] Sorted queue -> [(6, 'D')]
[0.005s] Popped (6, 'D') from queue
[0.005s] dijkstra.current_dist = 6
[0.005s] dijkstra({'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}, 'A') returned {'A': 0, 'B': 1, 'C': 3, 'D': 4}

What changes in educational mode

Function calls become readable:
```
Calling foo(1, b=2)
```
No raw args/kwargs dictionaries
Internal noise is removed:
- no <func ...>
- no test/module prefixes
- no irrelevant internals
Data structure operations are human-friendly:
```
Added 5 to arr -> [1, 2, 5]
```

Example - Educational Factorial

from logeye import log, l
l("FACTORIAL")
@log(mode="edu")
def factorial(n):
	if n == 1:
		return 1
	return n * factorial(n - 1)
factorial(5)

Output:

[0.000s] FACTORIAL
[0.000s] Calling factorial(5)
[0.000s] Defined factorial.n = 5
[0.001s] Calling factorial#2(4)
[0.001s] Defined factorial#2.n = 4
[0.002s] Calling factorial#3(3)
[0.002s] Defined factorial#3.n = 3
[0.002s] Calling factorial#4(2)
[0.003s] Defined factorial#4.n = 2
[0.003s] Calling factorial#5(1)
[0.003s] Defined factorial#5.n = 1
[0.003s] factorial#5(1) returned 1
[0.003s] factorial#4(2) returned 2
[0.003s] factorial#3(3) returned 6
[0.003s] factorial#2(4) returned 24
[0.003s] factorial(5) returned 120

It’s especially useful for:

learning recursion
understanding sorting algorithms
teaching data structures
quickly verifying logic without a debugger

Logging functions

Decorate a function or wrap it with log:

from logeye import log
@log
def add(a, b):
	total = a + b
	return total
add(2, 3)

[0.000s] playground.py:10 (call) add args=(2, 3)
[0.000s] playground.py:6 (set) add.a = 2
[0.000s] playground.py:6 (set) add.b = 3
[0.000s] playground.py:7 (set) add.total = 5
[0.000s] playground.py:7 (return) add args=(2, 3) -> 5

This will log:

the function call
local variable changes inside the function
the return value

Advanced function logging

You can customise how functions are logged using @log(...):

Control verbosity

from logeye import log
@log(level="call")
def foo():
	x = 10
	return x

Levels:

"call" - only function calls and returns
"state" - variable changes only (no call spam)
"full" - full tracing (default)

from logeye import log
@log(filter=["x"])
def foo():
	x = 10
	y = 20
	return x + y

Only selected variables will be logged.

from logeye import log
@log(filepath="logs/my_func.log")
def foo():
	x = 10
	return x

Logs for this function will be written to a file instead of stdout.

Combos

from logeye import log
@log(level="state", filter=["queue"], filepath="queue.log")
def process():
	queue = []
	queue.append(1)
	queue.append(2)

Logging objects

log() can wrap mappings and objects with __dict__ into a LoggedObject:

from logeye import log
settings = log({"theme": "dark", "volume": 3})
settings.theme = "light"
settings.volume += 1

You can also pass an object:

from logeye import *
@log
class User:
	def __init__(self):
		self.name = "Matt"
		self.active = True
user = l(User())
user.name = "For"

[0.000s] playground.py:11 (call) user <- User.__init__
[0.000s] playground.py:7 (set) user.name = 'Matt'
[0.000s] playground.py:8 (set) user.active = True
[0.000s] playground.py:12 (change) user.name = 'For'

Messages

Use log() with a string to emit a formatted message:

from logeye import log
name = "Matt"
email = "mattfor@relaxy.xyz"
log("Current user: $name\nEmail: $email")
# Also works like this!
log("Current user: {}\nEmail: {}", "Matt", "mattfor@relaxy.xyz")

[0.001s] demo9.py:5 
Current user: Matt
Email: mattfor@relaxy.xyz
[0.002s] demo9.py:7 
Current user: Matt
Email: mattfor@relaxy.xyz

str.format() is tried first. If that fails, the logger also tries caller globals / locals and template substitution.

Utility functions

`watch(value, name=None)`

Wraps a value for logging without changing its type unless needed.

`toggle_logs(True/False)`

Disable or enable logging globally.

`set_path_mode(mode)`

Controls how file paths are shown in output.

Accepted values:

absolute
project
file

`set_output_formatter(func)`

Replace the default formatter.

Signature:

func(elapsed, kind, name, value, filename, lineno)

`reset_output_formatter()`

Restore the built-in formatter.

Output format

By default, messages look like this:
(note, the path by default is relative to the run directory of the file you're launching the module in)

[0.123s] path/to/file.py:42 (set) x = 10

For plain messages:

[0.123s] path/to/file.py:42 some text

File logging

from logeye.config import set_global_log_file, toggle_global_log_file
set_global_log_file("logs/app.log")
toggle_global_log_file(True)

All logs will now be written to the specified file. To disable: toggle_global_log_file(False)

Some Usage Examples

Example 1: Master Demo, a bit of everything

Code

from logeye import (
	log,
	l,
	set_path_mode,
	toggle_logs,
	reset_output_formatter,
	set_output_formatter,
)
log("=== BASIC MESSAGES ===", show_time=False, show_file=False, show_lineno=False)
x = 5
log("value is {}", x)
log("value via template: $x")
log("file absolute: $apath")
log("file relative: $rpath")
log("file name: $fpath")
log("\n=== ASSIGNMENTS ===", show_time=False, show_file=False, show_lineno=False)
a = log(10)
b = l(20)
c = 30 | l
# tuple unpacking
d, e = log("hello"), log("world")
log("\n=== EXPRESSIONS ===", show_time=False, show_file=False, show_lineno=False)
f = (10 + 5) | l
g = l(100 + 200)
log("\n=== FUNCTIONS ===", show_time=False, show_file=False, show_lineno=False)
@log
def add(a, b):
	total = a + b
	total = total * 2
	return total
res = add(3, 4)
log("\n=== NESTED FUNCTIONS ===", show_time=False, show_file=False, show_lineno=False)
@log
def outer(x):
	def inner(y):
		z = y + 1
		return z
	return inner(x)
outer(10)
log("\n=== LAMBDAS ===", show_time=False, show_file=False, show_lineno=False)
f = lambda: log("lambda called")
f()
g = lambda v: v * 2
g = l(g) # wrap lambda
g(5)
log("\n=== OBJECT TRACKING ===", show_time=False, show_file=False, show_lineno=False)
obj = log({"x": 1, "nested": {"y": 2}})
obj.x = 10
obj.nested.y = 20
obj["x"] = 30
log("\n=== CLASS TRACKING ===", show_time=False, show_file=False, show_lineno=False)
@log
class User:
	def __init__(self, name):
		self.name = name
		self.active = True
user = l(User("Matt"))
user.name = "For"
user.active = False
log("\n=== PATH MODES ===", show_time=False, show_file=False, show_lineno=False)
set_path_mode("absolute")
log("absolute path mode")
set_path_mode("project")
log("project path mode")
set_path_mode("file")
log("file path mode")
log("\n=== CUSTOM FORMATTER ===", show_time=False, show_file=False, show_lineno=False)
def simple_formatter(elapsed, kind, name, value, filename, lineno):
	return f"{kind.upper()} -> {name}: {value}"
set_output_formatter(simple_formatter)
x = log(123)
log("formatted message")
reset_output_formatter()
log("\n=== ENABLE / DISABLE ===", show_time=False, show_file=False, show_lineno=False)
log("this should appear")
toggle_logs(False)
log("this should NOT appear")
toggle_logs(True)
log("logging back on")
log("\n=== MIXED USAGE ===", show_time=False, show_file=False, show_lineno=False)
value = (5 | l) * (10 | l)
log("final value is $value")

Output

=== BASIC MESSAGES ===
[0.001s] master_demo.py:13 value is 5
[0.002s] master_demo.py:14 value via template: 5
[0.003s] master_demo.py:15 file absolute: /home/mattfor/Programming/Python/LogEye/demos/master_demo.py
[0.004s] master_demo.py:16 file relative: master_demo.py
[0.005s] master_demo.py:17 file name: master_demo.py
=== ASSIGNMENTS ===
[0.006s] master_demo.py:21 (set) a = 10
[0.006s] master_demo.py:22 (set) b = 20
[0.007s] master_demo.py:23 (set) c = 30
[0.007s] master_demo.py:26 (set) d = 'hello'
[0.008s] master_demo.py:26 (set) e = 'world'
=== EXPRESSIONS ===
[0.016s] master_demo.py:30 (set) f = 15
[0.016s] master_demo.py:31 (set) g = 300
=== FUNCTIONS ===
[0.033s] master_demo.py:43 (call) add args=(3, 4)
[0.033s] master_demo.py:38 (set) add.a = 3
[0.033s] master_demo.py:38 (set) add.b = 4
[0.033s] master_demo.py:39 (set) add.total = 7
[0.033s] master_demo.py:40 (change) add.total = 14
[0.033s] master_demo.py:40 (return) add args=(3, 4) -> 14
=== NESTED FUNCTIONS ===
[0.049s] master_demo.py:57 (call) outer args=(10)
[0.050s] master_demo.py:50 (set) outer.x = 10
[0.050s] master_demo.py:50 (call) outer.inner
[0.050s] master_demo.py:50 (set) outer.inner = {'type': 'function', 'path': 'outer.inner', 'defaults': {'y': 10}}
[0.050s] master_demo.py:50 (set) outer.inner.y = 10
[0.050s] master_demo.py:52 (set) outer.inner.z = 11
[0.050s] master_demo.py:52 (return) outer.inner args=(10) -> 11
[0.050s] master_demo.py:54 (return) outer args=(10) -> 11
=== LAMBDAS ===
[0.058s] master_demo.py:62 (change) f = <function <lambda> at 0x7fe3a8fa7d70>
[0.066s] master_demo.py:61 lambda called
[0.066s] master_demo.py:65 (change) g = <function <lambda> at 0x7fe3a8fa7cc0>
[0.066s] master_demo.py:66 (change) g = <function <lambda> at 0x7fe3a8fa7ed0>
[0.074s] master_demo.py:66 (call) <lambda> args=(5)
[0.074s] master_demo.py:64 (set) <lambda>.v = 5
[0.074s] master_demo.py:64 (return) <lambda> args=(5) -> 10
=== OBJECT TRACKING ===
[0.083s] master_demo.py:70 (set) obj = {'x': 1, 'nested': {'y': 2}}
[0.083s] master_demo.py:72 (change) obj.x = 10
[0.083s] master_demo.py:73 (change) obj.nested.y = 20
[0.083s] master_demo.py:74 (change) obj.x = 30
=== CLASS TRACKING ===
[0.092s] master_demo.py:86 (call) user <- User.__init__ args=('Matt')
[0.092s] master_demo.py:82 (set) user.name = 'Matt'
[0.093s] master_demo.py:83 (set) user.active = True
[0.093s] master_demo.py:87 (change) user.name = 'For'
[0.093s] master_demo.py:88 (change) user.active = False
=== PATH MODES ===
[0.109s] /home/mattfor/Programming/Python/LogEye/demos/master_demo.py:93 absolute path mode
[0.118s] master_demo.py:96 project path mode
[0.126s] master_demo.py:99 file path mode
=== CUSTOM FORMATTER ===
[0.134s] master_demo.py:110 (set) x = 123
[0.143s] master_demo.py:111 formatted message
=== ENABLE / DISABLE ===
[0.159s] master_demo.py:117 this should appear
[0.167s] master_demo.py:123 logging back on
=== MIXED USAGE ===
[0.176s] master_demo.py:127 (set) value = 5
[0.176s] master_demo.py:127 (set) value = 10
[0.184s] master_demo.py:128 final value is 50

Example 2: Factorial

Code

from logeye import log, l
l("FACTORIAL - BY ITERATION")
# Iteration
@log
def factorial(n):
	result = 1
	for i in range(1, n + 1):
		result *= i
	return result
factorial(5)
l("FACTORIAL - BY RECURSION")
# Recursion
@log
def factorial(n):
	if n == 1:
		return 1
	return n * factorial(n - 1)
factorial(5)

Output

[0.000s] playground.py:3 FACTORIAL - BY ITERATION
[0.000s] playground.py:15 (call) factorial args=(5)
[0.000s] playground.py:9 (set) factorial.n = 5
[0.000s] playground.py:10 (set) factorial.result = 1
[0.000s] playground.py:11 (set) factorial.i = 1
[0.000s] playground.py:11 (change) factorial.i = 2
[0.000s] playground.py:10 (change) factorial.result = 2
[0.000s] playground.py:11 (change) factorial.i = 3
[0.000s] playground.py:10 (change) factorial.result = 6
[0.000s] playground.py:11 (change) factorial.i = 4
[0.000s] playground.py:10 (change) factorial.result = 24
[0.000s] playground.py:11 (change) factorial.i = 5
[0.000s] playground.py:10 (change) factorial.result = 120
[0.000s] playground.py:12 (return) factorial args=(5) -> 120
[0.001s] playground.py:17 FACTORIAL - BY RECURSION
[0.001s] playground.py:28 (call) factorial args=(5)
[0.001s] playground.py:23 (set) factorial.n = 5
[0.002s] playground.py:25 (call) factorial#2 args=(4)
[0.002s] playground.py:23 (set) factorial#2.n = 4
[0.004s] playground.py:25 (call) factorial#3 args=(3)
[0.004s] playground.py:23 (set) factorial#3.n = 3
[0.005s] playground.py:25 (call) factorial#4 args=(2)
[0.005s] playground.py:23 (set) factorial#4.n = 2
[0.006s] playground.py:25 (call) factorial#5 args=(1)
[0.006s] playground.py:23 (set) factorial#5.n = 1
[0.006s] playground.py:24 (return) factorial#5 args=(1) -> 1
[0.006s] playground.py:25 (return) factorial#4 args=(2) -> 2
[0.006s] playground.py:25 (return) factorial#3 args=(3) -> 6
[0.006s] playground.py:25 (return) factorial#2 args=(4) -> 24
[0.006s] playground.py:25 (return) factorial args=(5) -> 120

Example 3: Dijkstra

from logeye import log, l
l("DIJKSTRA - SHORTEST PATH")
@log
def dijkstra(graph, start):
	distances = {node: float("inf") for node in graph}
	distances[start] = 0
	visited = set()
	queue = [(0, start)]
	while queue:
		current_dist, node = queue.pop(0)
		if node in visited:
			continue
		visited.add(node)
		for neighbor, weight in graph[node].items():
			new_dist = current_dist + weight
			if new_dist < distances[neighbor]:
				distances[neighbor] = new_dist
				queue.append((new_dist, neighbor))
		queue.sort()
	return distances
graph = {"A": {"B": 1, "C": 4}, "B": {"C": 2, "D": 5}, "C": {"D": 1}, "D": {}}
dijkstra(graph, "A")

Output

[0.000s] playground.py:3 DIJKSTRA - SHORTEST PATH
[0.001s] playground.py:41 (call) dijkstra args=({'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}, 'A')
[0.001s] playground.py:8 (set) dijkstra.graph = {'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}
[0.001s] playground.py:8 (set) dijkstra.start = 'A'
[0.001s] playground.py:8 (set) dijkstra.node = 'A'
[0.001s] playground.py:8 (change) dijkstra.node = 'B'
[0.001s] playground.py:8 (change) dijkstra.node = 'C'
[0.001s] playground.py:8 (change) dijkstra.node = 'D'
[0.001s] playground.py:9 (set) dijkstra.distances = {'A': inf, 'B': inf, 'C': inf, 'D': inf}
[0.001s] playground.py:9 (change) dijkstra.distances.A = 0
[0.002s] playground.py:12 (set) dijkstra.visited = set()
[0.002s] playground.py:14 (set) dijkstra.queue = [(0, 'A')]
[0.002s] playground.py:15 (change) dijkstra.queue = {'op': 'pop', 'index': 0, 'value': (0, 'A'), 'state': []}
[0.002s] playground.py:17 (change) dijkstra.node = 'A'
[0.002s] playground.py:17 (set) dijkstra.current_dist = 0
[0.002s] playground.py:20 (change) dijkstra.visited = {'op': 'add', 'value': 'A', 'state': {'A'}}
[0.002s] playground.py:23 (set) dijkstra.neighbor = 'B'
[0.002s] playground.py:23 (set) dijkstra.weight = 1
[0.002s] playground.py:25 (set) dijkstra.new_dist = 1
[0.002s] playground.py:26 (change) dijkstra.distances.B = 1
[0.002s] playground.py:27 (change) dijkstra.queue = {'op': 'append', 'value': (1, 'B'), 'state': [(1, 'B')]}
[0.003s] playground.py:23 (change) dijkstra.neighbor = 'C'
[0.003s] playground.py:23 (change) dijkstra.weight = 4
[0.003s] playground.py:25 (change) dijkstra.new_dist = 4
[0.003s] playground.py:26 (change) dijkstra.distances.C = 4
[0.003s] playground.py:27 (change) dijkstra.queue = {'op': 'append', 'value': (4, 'C'), 'state': [(1, 'B'), (4, 'C')]}
[0.003s] playground.py:29 (change) dijkstra.queue = {'op': 'sort', 'args': (), 'kwargs': {}, 'state': [(1, 'B'), (4, 'C')]}
[0.003s] playground.py:15 (change) dijkstra.queue = {'op': 'pop', 'index': 0, 'value': (1, 'B'), 'state': [(4, 'C')]}
[0.003s] playground.py:17 (change) dijkstra.node = 'B'
[0.003s] playground.py:17 (change) dijkstra.current_dist = 1
[0.003s] playground.py:20 (change) dijkstra.visited = {'op': 'add', 'value': 'B', 'state': {'B', 'A'}}
[0.003s] playground.py:23 (change) dijkstra.weight = 2
[0.003s] playground.py:25 (change) dijkstra.new_dist = 3
[0.003s] playground.py:26 (change) dijkstra.distances.C = 3
[0.004s] playground.py:27 (change) dijkstra.queue = {'op': 'append', 'value': (3, 'C'), 'state': [(4, 'C'), (3, 'C')]}
[0.004s] playground.py:23 (change) dijkstra.neighbor = 'D'
[0.004s] playground.py:23 (change) dijkstra.weight = 5
[0.004s] playground.py:25 (change) dijkstra.new_dist = 6
[0.004s] playground.py:26 (change) dijkstra.distances.D = 6
[0.004s] playground.py:27 (change) dijkstra.queue = {'op': 'append', 'value': (6, 'D'), 'state': [(4, 'C'), (3, 'C'), (6, 'D')]}
[0.004s] playground.py:29 (change) dijkstra.queue = {'op': 'sort', 'args': (), 'kwargs': {}, 'state': [(3, 'C'), (4, 'C'), (6, 'D')]}
[0.004s] playground.py:15 (change) dijkstra.queue = {'op': 'pop', 'index': 0, 'value': (3, 'C'), 'state': [(4, 'C'), (6, 'D')]}
[0.004s] playground.py:17 (change) dijkstra.node = 'C'
[0.004s] playground.py:17 (change) dijkstra.current_dist = 3
[0.004s] playground.py:20 (change) dijkstra.visited = {'op': 'add', 'value': 'C', 'state': {'B', 'C', 'A'}}
[0.005s] playground.py:23 (change) dijkstra.weight = 1
[0.005s] playground.py:25 (change) dijkstra.new_dist = 4
[0.005s] playground.py:26 (change) dijkstra.distances.D = 4
[0.005s] playground.py:27 (change) dijkstra.queue = {'op': 'append', 'value': (4, 'D'), 'state': [(4, 'C'), (6, 'D'), (4, 'D')]}
[0.005s] playground.py:29 (change) dijkstra.queue = {'op': 'sort', 'args': (), 'kwargs': {}, 'state': [(4, 'C'), (4, 'D'), (6, 'D')]}
[0.005s] playground.py:15 (change) dijkstra.queue = {'op': 'pop', 'index': 0, 'value': (4, 'C'), 'state': [(4, 'D'), (6, 'D')]}
[0.005s] playground.py:17 (change) dijkstra.current_dist = 4
[0.005s] playground.py:15 (change) dijkstra.queue = {'op': 'pop', 'index': 0, 'value': (4, 'D'), 'state': [(6, 'D')]}
[0.005s] playground.py:17 (change) dijkstra.node = 'D'
[0.006s] playground.py:20 (change) dijkstra.visited = {'op': 'add', 'value': 'D', 'state': {'B', 'C', 'D', 'A'}}
[0.006s] playground.py:29 (change) dijkstra.queue = {'op': 'sort', 'args': (), 'kwargs': {}, 'state': [(6, 'D')]}
[0.006s] playground.py:15 (change) dijkstra.queue = {'op': 'pop', 'index': 0, 'value': (6, 'D'), 'state': []}
[0.006s] playground.py:17 (change) dijkstra.current_dist = 6
[0.006s] playground.py:31 (return) dijkstra args=({'A': {'B': 1, 'C': 4}, 'B': {'C': 2, 'D': 5}, 'C': {'D': 1}, 'D': {}}, 'A') -> {'A': 0, 'B': 1, 'C': 3, 'D': 4}

Inspiration

Idea came to be during Warsaw IT Days 2026. During the Python lecture "Logging module adventures".
I thought there definitely was an easier way to do it without repeating yourself constantly, and it turns out there was!

Limitations

variable name inference is best-effort and may fail in complex or highly dynamic expressions
some edge cases (e.g. deeply nested calls, chained expressions, unusual syntax) may fall back to a generic name like "set"
lambda functions are not automatically traced unless explicitly wrapped with log()
function tracing relies on sys.settrace() and may introduce overhead in performance-sensitive code
logging inside heavily recursive or multithreaded code may produce noisy or hard-to-follow output
AST-based analysis requires access to source files and may not work correctly in environments without source code ( e.g. compiled/obfuscated code, some REPLs)
tuple assignment tracking depends on call order and may behave unexpectedly in complex expressions
object wrapping only supports mappings and objects with __dict__
custom objects with unusual attribute behaviour may not be fully tracked
logging output is intended for debugging and introspection, not structured logging or production telemetry
local variables may be wrapped at runtime to enable mutation tracking, which can affect identity checks and edge-case behaviour

Contact

If you have questions, ideas, or run into issues:

please open an issue!
or email me mattfor@relaxy.xyz
or add me on discord @mattfor

Notable contributors

@OutSquareCapital Helped with typing and refactoring

License

See LICENSE for details.

Version 1.6.2

Name		Name	Last commit message	Last commit date
demos		demos
logeye		logeye
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
release.sh		release.sh

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LogEye

Installation

Quick example

Table of Contents

Who is it for?

What does it do?

What exactly does it track?

Quick start

Educational Mode!

Before vs After

Default mode

Educational mode

What changes in educational mode

Example - Educational Factorial

Logging functions

Advanced function logging

Control verbosity

Levels:

Combos

Logging objects

Messages

Utility functions

watch(value, name=None)

toggle_logs(True/False)

set_path_mode(mode)

set_output_formatter(func)

reset_output_formatter()

Output format

File logging

Some Usage Examples

Code

Output

Code

Output

Output

Inspiration

Limitations

Contact

Notable contributors

License

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages

`watch(value, name=None)`

`toggle_logs(True/False)`

`set_path_mode(mode)`

`set_output_formatter(func)`

`reset_output_formatter()`