Logger Classes¶

LogEverything provides two main logger classes: Logger for synchronous applications and AsyncLogger for async applications. Both provide the same intuitive API while being optimized for their respective contexts.

🏛️ Logger Architecture¶

Both logger classes inherit from BaseLogger and provide:

Automatic configuration with intelligent environment detection
Structured logging with additional context fields
Context managers for hierarchical logging
Visual formatting with colors, symbols, and indentation
Performance optimization with configurable profiles

Logger Class (Synchronous)¶

The Logger class is designed for synchronous applications and provides a clean, simple interface.

Basic Usage¶

from logeverything import Logger

# Create a logger with automatic configuration
log = Logger("my_app")

# Basic logging methods
log.debug("Debug information")
log.info("Application started")
log.warning("Warning message")
log.error("Error occurred")
log.critical("Critical system error")

Output:

2026-05-01 05:38:57 | [ 🔍 DEBUG  ] | my_app     | Debug information
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | my_app     | Application started
2026-05-01 05:38:57 | [⚠️ WARNING ] | my_app     | Warning message
2026-05-01 05:38:57 | [ ❌ ERROR  ] | my_app     | Error occurred
2026-05-01 05:38:57 | [🔥 CRITICAL] | my_app     | Critical system error

Constructor Options¶

from logeverything import Logger

# Simple logger with defaults
simple_log = Logger("simple")

# Logger with custom name and configuration
custom_log = Logger(
    name="custom_app",
    auto_setup=True,  # Automatically configure (default: True)
    level="DEBUG",
    visual_mode=True,
    use_symbols=True,
    profile="development"
)

# Test both loggers
simple_log.info("Simple logger message")
custom_log.info("Custom logger message")

Output:

2026-05-01 05:38:57 | [  ℹ️ INFO  ] | simple     | Simple logger message
05:38:57            | [  ℹ️ INFO  ] | custom_app | Custom logger message

Configuration¶

Configure loggers with the configure() method:

from logeverything import Logger

log = Logger("configurable")

# Basic configuration
log.configure(
    level="INFO",
    visual_mode=True,
    use_symbols=True,
    use_colors=True
)

log.info("✨ Logger configured with visual enhancements")

# Advanced configuration
log.configure(
    profile="development",
    handlers=["console", "file"],
    file_path="app.log",
    async_mode=False,
    log_entry_exit=True
)

log.info("🔧 Advanced configuration applied")

Output:

2026-05-01 05:38:57 | [  ℹ️ INFO  ] | configurable | ✨ Logger configured with visual enhancements
05:38:57            | [  ℹ️ INFO  ] | configurable | 🔧 Advanced configuration applied

Environment Detection¶

Loggers automatically detect and adapt to your environment:

from logeverything import Logger

# Create logger - it auto-detects environment
log = Logger("environment_demo")

log.info("Logger automatically detected environment and applied optimal settings")

# Manual override if needed
log.configure(
    level="DEBUG",
    format="%(asctime)s | %(levelname)s | %(message)s"
)

log.debug("Environment settings overridden")

Output:

2026-05-01 05:38:57 | [  ℹ️ INFO  ] | environment_demo | Logger automatically detected environment and applied optimal settings
2026-05-01 05:38:57 |  DEBUG   | Environment settings overridden

Structured Logging¶

Add structured data to your logs using the .bind() method:

from logeverything import Logger

log = Logger("structured")

# Logging with additional fields
log.bind(user_id=12345,
        username="john_doe",
        ip_address="192.168.1.100",
        session_id="abc123").info("User logged in")

# Bind context for consistent fields
user_log = log.bind(user_id=12345, session_id="abc123")
user_log.bind(product_id=789).info("Product viewed")
user_log.bind(product_id=789, quantity=2).info("Item added to cart")
user_log.bind(total_amount=99.99).info("Checkout initiated")

Output:

2026-05-01 05:38:57 | [  ℹ️ INFO  ] | structured | [user_id=12345 | username=john_doe | ip_address=192.168.1.100 | session_id=abc123] User logged in
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | structured | [user_id=12345 | session_id=abc123 | product_id=789] Product viewed
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | structured | [user_id=12345 | session_id=abc123 | product_id=789 | quantity=2] Item added to cart
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | structured | [user_id=12345 | session_id=abc123 | total_amount=99.99] Checkout initiated

Context Managers¶

Use context managers for hierarchical logging:

from logeverything import Logger

log = Logger("context_demo")
log.configure(visual_mode=True, use_symbols=True)

# Simple context
with log.context("Data Processing"):
    log.info("🚀 Processing started")
    log.info("📁 Loading data files")
    log.info("✅ Processing completed")

# Nested contexts
with log.context("E-commerce Order"):
    log.info("📦 Processing order #12345")

    with log.context("Inventory Check"):
        log.info("🔍 Checking item availability")
        log.info("✅ Items in stock")

    with log.context("Payment Processing"):
        log.info("💳 Processing payment")
        log.info("✅ Payment successful")

    with log.context("Shipping"):
        log.info("📦 Creating shipping label")
        log.info("🚚 Order dispatched")

log.info("🎉 Order completed successfully")

Output:

2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo | ┌─► Data Processing
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    🚀 Processing started
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    📁 Loading data files
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    ✅ Processing completed
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo | └─◄ Data Processing complete
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo | ┌─► E-commerce Order
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    📦 Processing order #12345
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    ┌─► Inventory Check
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |       🔍 Checking item availability
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |       ✅ Items in stock
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    └─◄ Inventory Check complete
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    ┌─► Payment Processing
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |       💳 Processing payment
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |       ✅ Payment successful
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    └─◄ Payment Processing complete
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    ┌─► Shipping
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |       📦 Creating shipping label
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |       🚚 Order dispatched
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo |    └─◄ Shipping complete
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo | └─◄ E-commerce Order complete
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | context_demo | 🎉 Order completed successfully

AsyncLogger Class (Asynchronous)¶

The AsyncLogger class is specifically optimized for async applications with 6.8x better performance.

Basic Async Usage¶

from logeverything.asyncio import AsyncLogger
import asyncio

# Create async logger (automatically enables async_mode=True)
log = AsyncLogger("async_app")

# Basic async logging
log.info("🚀 Async application started")

# Simulate async operations
await asyncio.sleep(0.1)
log.info("⚡ Async operation completed")

Output:

2026-05-01 05:38:57 | [  ℹ️ INFO  ] | async_app  | 🚀 Async application started
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | async_app  | ⚡ Async operation completed

High-Performance Async Methods¶

AsyncLogger provides async-optimized methods:

from logeverything.asyncio import AsyncLogger
import asyncio

log = AsyncLogger("performance_demo")

# Use async methods for better performance
await log.ainfo("High-performance async info")
await log.adebug("High-performance async debug")
await log.awarning("High-performance async warning")
await log.aerror("High-performance async error")
await log.aexception("High-performance async exception")

# Regular methods also work (slightly less optimized)
log.info("Regular info method")

Output:

2026-05-01 05:38:57 | [  ℹ️ INFO  ] | performance_demo | High-performance async info
2026-05-01 05:38:57 | [ 🔍 DEBUG  ] | performance_demo | High-performance async debug
2026-05-01 05:38:57 | [⚠️ WARNING ] | performance_demo | High-performance async warning
2026-05-01 05:38:57 | [ ❌ ERROR  ] | performance_demo | High-performance async error
2026-05-01 05:38:57 | [ ❌ ERROR  ] | performance_demo | High-performance async exception
NoneType: None
2026-05-01 05:38:57 | [  ℹ️ INFO  ] | performance_demo | Regular info method

Async Configuration¶

Configure AsyncLogger for optimal performance:

from logeverything.asyncio import AsyncLogger

log = AsyncLogger("high_performance")

# Configure for high-throughput applications
await log.configure(
    level="INFO",
    async_queue_size=10000,     # Large queue for high volume
    async_flush_interval=0.05,  # Fast flush for responsiveness
    visual_mode=True,
    use_symbols=True
)

log.info("⚡ High-performance async logging configured")

Output:

2026-05-01 05:38:58 | [  ℹ️ INFO  ] | high_performance | ⚡ High-performance async logging configured

Concurrent Async Logging¶

Process multiple async operations concurrently:

from logeverything.asyncio import AsyncLogger
import asyncio

async def process_user(user_id: int, log: AsyncLogger):
    """Process a user with async logging."""
    await log.ainfo(f"Processing user {user_id}")
    await asyncio.sleep(0.1)  # Simulate work
    await log.ainfo(f"User {user_id} processing completed")
    return f"User {user_id} processed"

async def concurrent_processing():
    log = AsyncLogger("concurrent_demo")

    # Process multiple users concurrently
    tasks = [process_user(i, log) for i in range(5)]
    results = await asyncio.gather(*tasks)

    log.info(f"🎉 Processed {len(results)} users concurrently")
    return results

results = await concurrent_processing()
print(f"Results: {len(results)} users processed")

Output:

2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | Processing user 0
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | Processing user 1
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | Processing user 2
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | Processing user 3
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | Processing user 4
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | User 0 processing completed
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | User 1 processing completed
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | User 2 processing completed
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | User 3 processing completed
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | User 4 processing completed
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | concurrent_demo | 🎉 Processed 5 users concurrently
Results: 5 users processed

Async Context Managers¶

Use async context managers for hierarchical async logging:

from logeverything.asyncio import AsyncLogger
import asyncio

async def async_context_demo():
    log = AsyncLogger("async_context")

    # Async context manager
    async with log.context("Async Data Pipeline"):
        await log.ainfo("🚀 Starting async pipeline")

        async with log.context("Data Fetching"):
            await log.ainfo("📡 Fetching data from API")
            await asyncio.sleep(0.1)  # Simulate API call
            await log.ainfo("✅ Data fetched successfully")

        async with log.context("Data Processing"):
            await log.ainfo("⚙️  Processing data")
            await asyncio.sleep(0.1)  # Simulate processing
            await log.ainfo("✅ Data processed")

        await log.ainfo("🎉 Pipeline completed")

await async_context_demo()

Output:

2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context | ┌─► Async Data Pipeline
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |      🚀 Starting async pipeline
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |      ┌─► Data Fetching
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |           📡 Fetching data from API
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |           ✅ Data fetched successfully
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |      └─◄ Data Fetching complete
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |      ┌─► Data Processing
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |           ⚙️  Processing data
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |           ✅ Data processed
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |      └─◄ Data Processing complete
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context |      🎉 Pipeline completed
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | async_context | └─◄ Async Data Pipeline complete

Performance Comparison¶

Here’s a performance comparison between Logger and AsyncLogger:

import asyncio
import time
from logeverything import Logger
from logeverything.asyncio import AsyncLogger

async def performance_comparison():
    # Test parameters
    num_logs = 100

    # Sync logger performance
    sync_log = Logger("sync_perf")
    start_time = time.time()

    for i in range(num_logs):
        sync_log.info(f"Sync log message {i}")

    sync_time = time.time() - start_time

    # Async logger performance
    async_log = AsyncLogger("async_perf")
    start_time = time.time()

    # Create tasks for concurrent logging
    tasks = [async_log.ainfo(f"Async log message {i}") for i in range(num_logs)]
    await asyncio.gather(*tasks)

    async_time = time.time() - start_time

    # Calculate performance improvement
    if async_time > 0:
        speedup = sync_time / async_time
        print(f"Sync logging: {sync_time:.4f}s")
        print(f"Async logging: {async_time:.4f}s")
        print(f"Speedup: {speedup:.1f}x faster with AsyncLogger!")
    else:
        print("Async logging was too fast to measure!")

await performance_comparison()

Output:

2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 0
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 1
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 2
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 3
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 4
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 5
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 6
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 7
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 8
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 9
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 10
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 11
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 12
2026-05-01 05:38:58 | [  ℹ️ INFO  ] | sync_perf  | Sync log message 13
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Sync logging: 0.0053s
Async logging: 0.0057s
Speedup: 0.9x faster with AsyncLogger!

Best Practices¶

Choosing the Right Logger¶

# Use Logger for synchronous applications
from logeverything import Logger
log = Logger("sync_app")

# Use AsyncLogger for async applications
from logeverything.asyncio import AsyncLogger
log = AsyncLogger("async_app")

Configuration Patterns¶

# Development configuration
log.configure(
    profile="development",
    level="DEBUG",
    visual_mode=True,
    use_symbols=True
)

# Production configuration
log.configure(
    profile="production",
    level="WARNING",
    visual_mode=False,
    handlers=["file", "console"]
)

# High-performance async configuration
await async_log.configure(
    async_queue_size=10000,
    async_flush_interval=0.05,
    level="INFO"
)

Error Handling¶

# Logger handles exceptions automatically
try:
    risky_operation()
except Exception as e:
    log.exception("Operation failed")  # Includes traceback
    log.bind(error=str(e).error("Operation failed"))  # Custom message

Memory Management¶

# For long-running applications, clean up resources
async def cleanup():
    await async_log.close()  # Clean up async resources

# Use context managers for automatic cleanup
async with AsyncLogger("temp") as log:
    log.info("Temporary logging context")
    # Automatically cleaned up

API Reference¶

Logger Methods¶

# Logging methods
log.debug(message, **kwargs)
log.info(message, **kwargs)
log.warning(message, **kwargs)
log.error(message, **kwargs)
log.critical(message, **kwargs)
log.exception(message, **kwargs)  # Includes traceback

# Configuration
log.configure(**options)

# Context management
log.context(name, **context)
log.bind(**context)

AsyncLogger Methods¶

# High-performance async methods
await log.ainfo(message, **kwargs)
await log.adebug(message, **kwargs)
await log.awarning(message, **kwargs)
await log.aerror(message, **kwargs)
await log.aexception(message, **kwargs)

# Standard methods (also work)
log.info(message, **kwargs)

# Async configuration
await log.configure(**options)

# Async context management
async with log.context(name, **context):
    pass

Logger Classes¶

🏛️ Logger Architecture¶

Logger Class (Synchronous)¶

Basic Usage¶

Constructor Options¶

Configuration¶

Environment Detection¶

Structured Logging¶

Context Managers¶

AsyncLogger Class (Asynchronous)¶

Basic Async Usage¶

High-Performance Async Methods¶

Async Configuration¶

Concurrent Async Logging¶

Async Context Managers¶

Performance Comparison¶

Best Practices¶

Choosing the Right Logger¶

Configuration Patterns¶

Error Handling¶

Memory Management¶

API Reference¶

Logger Methods¶

AsyncLogger Methods¶

See Also¶