logust 0.3.0

A fast, Rust-powered Python logging library with free-threaded support, inspired by loguru

Logust

A fast, Rust-powered Python logging library inspired by loguru.

Features

• Blazing Fast - Rust-powered core optimized for formatting, serialization, and file I/O
• Beautiful by Default - Colored output with zero configuration needed
• Caller Information - Automatic module, function, and line number in every log
• Flexible Sinks - Output to console, files, or any callable (lambda, function)
• Simple API - loguru-compatible interface for easy migration
• File Management - Size/time-based rotation, retention policies, gzip compression
• JSON Support - Built-in serialization for structured logging
• Context Binding - Attach metadata to log records with bind()
• Exception Handling - Automatic traceback capture with catch() decorator
• Custom Levels - Define your own log levels with colors and icons
• Color Markup - Inline <red>color</red> tags in log messages
• Async Writing - Optional thread-safe async file writes with enqueue=True
• Free-Threaded Python - Release automation builds wheels for CPython 3.13t and 3.14t on Linux and macOS
• Zero-Config Integrations - Built-in support for standard logging, FastAPI, and function timing

Benchmarks

A recent release-build run of the included throughput suite (benchmarks/bench_throughput.py, 10,000 messages) produced the following snapshot:

Scenario	logging	loguru	logust
File write (sync)	963.57 ms	2676.74 ms	15.93 ms
Formatted messages	966.38 ms	2710.67 ms	15.65 ms
JSON serialize	N/A	2717.99 ms	14.91 ms
With context (sync)	N/A	2600.08 ms	14.29 ms
File write (async + complete)	N/A	3019.49 ms	16.50 ms

In the same run, logust stayed in the 14-17 ms range for JSON serialization, context-bound logging, and async file writes, while loguru took roughly 2.6-3.1 s for those scenarios.

See benchmarks/README.md for reproduction steps and docs/comparison.md for the full comparison.

Installation

pip install logust

Quick Start

import logust

logust.info("Hello, Logust!")
logust.debug("Debug message")
logust.warning("Warning message")
logust.error("Error message")
logust.success("Success message")

Output:

2025-12-25 12:00:00.123 | INFO     | __main__:<module>:3 - Hello, Logust!
2025-12-25 12:00:00.123 | DEBUG    | __main__:<module>:4 - Debug message
2025-12-25 12:00:00.123 | WARNING  | __main__:<module>:5 - Warning message
2025-12-25 12:00:00.123 | ERROR    | __main__:<module>:6 - Error message
2025-12-25 12:00:00.123 | SUCCESS  | __main__:<module>:7 - Success message

The default format includes caller information (module, function, line) for easy debugging.

Log Levels

8 built-in levels from lowest to highest severity:

from logust import logger

logger.trace("Trace message")     # TRACE (5)
logger.debug("Debug message")     # DEBUG (10)
logger.info("Info message")       # INFO (20)
logger.success("Success message") # SUCCESS (25)
logger.warning("Warning message") # WARNING (30)
logger.error("Error message")     # ERROR (40)
logger.fail("Fail message")       # FAIL (45)
logger.critical("Critical!")      # CRITICAL (50)

Level Filtering

from logust import logger, LogLevel

# Only show WARNING and above
logger.set_level(LogLevel.Warning)
# Or using string
logger.set_level("warning")

# Check current level
current = logger.get_level()

# Check if level is enabled
if logger.is_level_enabled("DEBUG"):
    logger.debug("This runs only if DEBUG is enabled")

File Output

from logust import logger

# Basic file output
logger.add("app.log")

# With rotation and retention
logger.add("app.log", rotation="500 MB", retention="10 days")

# Time-based rotation with compression
logger.add("app.log", rotation="daily", compression=True)

# Error-only file with specific level
logger.add("error.log", level="ERROR")

# Async writes for high-throughput scenarios
logger.add("async.log", enqueue=True)

Rotation Options

• Size-based: "100 KB", "500 MB", "1 GB"
• Time-based: "hourly", "daily"

Retention Options

• Time-based: "7 days", "30 days"
• Count-based: 5 (keep last 5 files)

Console Output

import sys
from logust import logger

# Remove default console handler
logger.remove()

# Add stdout with colors
logger.add(sys.stdout, colorize=True)

# Add stderr with JSON output
logger.add(sys.stderr, serialize=True)

# Multiple outputs with different formats
logger.add(sys.stdout, colorize=True, format="{level} | {message}")
logger.add(sys.stderr, serialize=True)  # JSON to stderr
logger.add("app.log", rotation="daily")  # File for archival

JSON Output

from logust import logger

# JSON file output
logger.add("app.json", serialize=True)
logger.info("Structured log")
# Output: {"time":"2025-12-25 12:00:00.123","level":"INFO","message":"Structured log"}

# With context binding
user_logger = logger.bind(user_id="123", session="abc")
user_logger.info("User action")
# Output: {"time":"...","level":"INFO","message":"User action","extra":{"user_id":"123","session":"abc"}}

Custom Format

from logust import logger

# Custom format template
logger.add("custom.log", format="[{level}] {message}")

# Available placeholders:
# {time}       - Timestamp
# {level}      - Log level name
# {level:<8}   - Level with width specifier
# {message}    - Log message
# {name}       - Module name
# {module}     - Module name (alias for {name})
# {function}   - Function name
# {line}       - Line number
# {file}       - Source file name
# {elapsed}    - Time since logger start (HH:MM:SS.mmm)
# {thread}     - Thread name:id
# {process}    - Process name:id
# {extra[key]} - Extra context fields

Callable Sinks

Send logs to any callable (function, lambda, method):

from logust import logger

# Lambda sink
messages = []
logger.add(lambda msg: messages.append(msg))

# Function sink with formatting
def my_sink(msg):
    print(f"[LOG] {msg}")

logger.add(my_sink, format="{level} | {message}")

# With level filter
logger.add(lambda msg: send_to_slack(msg), level="ERROR")

# With JSON serialization
logger.add(lambda msg: send_to_elasticsearch(msg), serialize=True)

Context Binding

from logust import logger

# Permanent binding - returns new logger
user_logger = logger.bind(user_id="123", session="abc")
user_logger.info("User logged in")

# Temporary binding with context manager
with logger.contextualize(request_id="req-456"):
    logger.info("Processing request")  # includes request_id
logger.info("Request done")  # no request_id

Exception Handling

The catch() Decorator

from logust import logger

@logger.catch(ValueError, level="WARNING")
def risky_function():
    raise ValueError("Something went wrong")

risky_function()  # Logs the exception, doesn't re-raise

@logger.catch(reraise=True)
def critical_function():
    raise RuntimeError("Critical error")

critical_function()  # Logs and re-raises

The exception() Method

try:
    result = 1 / 0
except:
    logger.exception("Division failed")
    # Logs ERROR with full traceback automatically

Using opt(exception=True)

try:
    risky_operation()
except:
    logger.opt(exception=True).error("Operation failed")

Color Markup

from logust import logger

# Inline color tags in messages
logger.info("<red>Error</red> in <blue>module</blue>")
logger.warning("<bold><yellow>Warning:</yellow></bold> Check this")

# Available tags:
# Colors: red, green, yellow, blue, magenta, cyan, white, black
# Bright: bright_red, bright_green, bright_blue, etc.
# Styles: bold (b), italic (i), underline (u), dim, strike (s)

Custom Levels

from logust import logger

# Register a custom level
logger.level("NOTICE", no=25, color="cyan", icon="!")

# Use the custom level
logger.log("NOTICE", "Custom level message")
logger.log(25, "Using numeric level")

Lazy Evaluation

Defer expensive computations until the message is actually emitted:

def expensive_computation():
    # Only runs if DEBUG level is enabled
    return complex_calculation()

logger.opt(lazy=True).debug("Result: {}", expensive_computation)

Enhanced Tracebacks

try:
    a = 10
    b = 0
    result = a / b
except:
    # Show variable values at each frame
    logger.opt(diagnose=True).error("Calculation failed")
    # Output includes: a = 10, b = 0

    # Extended backtrace beyond catch point
    logger.opt(backtrace=True).error("Full stack trace")

Callbacks

from logust import logger

def my_callback(record):
    # Send to external service, metrics, etc.
    print(f"Log received: {record['level']} - {record['message']}")

# Add callback
callback_id = logger.add_callback(my_callback, level="ERROR")

logger.error("This triggers the callback")

# Remove callback
logger.remove_callback(callback_id)

Handler Management

from logust import logger

# Add handler and get ID
handler_id = logger.add("app.log")

# Remove specific handler
logger.remove(handler_id)

# Remove ALL handlers (including console)
logger.remove()

# Disable/enable console output
logger.disable()
logger.enable()

# Check if console is enabled
if logger.is_enabled():
    logger.info("Console is active")

# Flush all pending writes
logger.complete()

Configure from Dict

from logust import logger

logger.configure(
    handlers=[
        {"sink": "app.log", "level": "INFO", "rotation": "1 day"},
        {"sink": "debug.log", "level": "DEBUG"},
        {"sink": "app.json", "serialize": True},
    ],
    levels=[
        {"name": "NOTICE", "no": 25, "color": "cyan"},
    ],
    extra={"app": "myapp", "version": "1.0"},
)

Log Parsing

Parse existing log files:

from logust import parse, parse_json

# Parse text logs with regex pattern
for record in parse("app.log", r"(?P<time>[\d-]+ [\d:.]+) \| (?P<level>\w+) \| (?P<message>.*)"):
    print(record["level"], record["message"])

# Parse JSON logs
for record in parse_json("app.json"):
    print(record["level"], record["message"])

Filter by Handler

from logust import logger

# Only log errors from specific module
logger.add("errors.log", filter=lambda r: "database" in r.get("message", ""))

# Level-based filter
logger.add("warnings.log", filter=lambda r: r.get("level") == "WARNING")

Integrations

Zero-config utilities in logust.contrib:

Intercept Standard Logging

from logust.contrib import intercept_logging

# Redirect ALL standard logging to logust
intercept_logging()

# Now third-party libraries use logust too
import logging
logging.info("This goes through logust!")

Function Timing Decorators

from logust.contrib import log_fn, debug_fn

@log_fn
def process_data(items):
    return [x * 2 for x in items]

process_data([1, 2, 3])
# Logs: "Called process_data with elapsed_time=0.001"

@debug_fn
async def fetch_user(user_id):
    return await db.get(user_id)

FastAPI / Starlette Middleware

from fastapi import FastAPI
from logust.contrib.starlette import setup_fastapi

app = FastAPI()
setup_fastapi(app, skip_routes=["/health"])

# That's it! You get:
# - Request/response logging with timing
# - Request IDs in all logs
# - Standard logging redirected to logust

Complete API Reference

Logger Methods

Method	Description
trace/debug/info/success/warning/error/fail/critical(message)	Log at specific level
log(level, message)	Log at any level (name or number)
exception(message)	Log ERROR with current traceback
add(sink, **options)	Add handler (file, console, or callable)
remove(handler_id)	Remove handler
bind(**kwargs)	Create logger with bound context
contextualize(**kwargs)	Temporary context (context manager)
catch(exception, **options)	Exception catching decorator
opt(**options)	Per-message options
patch(patcher)	Create logger with record patcher
level(name, no, color, icon)	Register custom level
set_level(level)	Set minimum console level
get_level()	Get current console level
is_level_enabled(level)	Check if level is enabled
enable()/disable()	Toggle console output
complete()	Flush all handlers
add_callback(fn, level)	Add log callback
remove_callback(id)	Remove callback
configure(**options)	Configure from dicts

Handler Options (add())

Option	Type	Description
level	str | LogLevel	Minimum level for handler
format	str	Custom format template
rotation	str	Rotation strategy (files only)
retention	str | int	Retention policy (files only)
compression	bool	Gzip rotated files (files only)
serialize	bool	JSON output
filter	callable	Filter function
enqueue	bool	Async writes (files only)
colorize	bool	ANSI colors (console only, auto-detect if None)

Opt Options (opt())

Option	Description
lazy=True	Defer callable evaluation
exception=True	Auto-capture traceback
backtrace=True	Extended stack trace
diagnose=True	Show variable values
depth=N	Stack frame adjustment

Development

# Clone the repository
git clone https://github.com/yamaaaaaa31/logust.git
cd logust

# Create virtual environment
uv venv && source .venv/bin/activate

# Install maturin and dev dependencies
uv pip install maturin prek

# Development build
maturin develop

# Set up git hooks
prek install
prek install --hook-type pre-push

# Run tests
cargo test                # Rust tests
pytest tests/             # Python tests

# Run benchmarks
python benchmarks/bench_throughput.py

# Test installation
python -c "import logust; logust.info('It works!')"

Pre-commit Hooks

This project uses prek to maintain code quality. The following checks run automatically:

Hook	Stage	Description
cargo fmt	pre-commit	Rust formatting
cargo clippy	pre-commit	Rust linting
cargo check	pre-commit	Rust compilation check
cargo test	pre-push	Rust tests
ruff	pre-commit	Python linting + auto-fix
ruff-format	pre-commit	Python formatting
mypy	pre-commit	Python type checking

Run all checks manually:

prek run --all-files

Requirements

• Python >= 3.10
• Rust (for development)

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Quick Start for Contributors

git clone https://github.com/yamaaaaaa31/logust.git
cd logust
uv venv && source .venv/bin/activate
uv pip install maturin prek
maturin develop
prek install

License

This project is licensed under the MIT License.

The MIT License is a permissive license that allows:

• Commercial use
• Modification
• Distribution
• Private use

The only requirement is to include the license and copyright notice in copies of the software.

See LICENSE for the full text.

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Logust - Fast logging for Python, powered by Rust.