DC Logger

A flexible, async-first structured logging library for Python with support for multiple handlers, cloud providers, distributed tracing, and multi-tenancy.

Features

• Async-first design - Non-blocking logging for high-performance applications
• Structured logging - JSON-based logs with entities, actions, and context
• Multiple handlers - Console, file, and cloud output simultaneously
• Cloud integrations - Datadog, AWS CloudWatch, GCP, Azure support
• LangSmith observability - Stream structured logs alongside LangChain traces
• Distributed tracing - Built-in correlation with trace/span IDs
• Decorator logging - Automatic function entry/exit logging with @log_call
• Multi-tenant support - Tenant context for SaaS applications
• Colored console output - ANSI colors for better visibility

Installation

pip install dc_logger

# With Datadog support
pip install dc_logger[datadog]

# With LangSmith support
pip install dc_logger[langsmith]

# With all extras
pip install dc_logger[all]

Quick Start

import asyncio
from dc_logger import DCLogger, ConsoleLogConfig, LogLevel

async def main():
    config = ConsoleLogConfig(level=LogLevel.INFO)
    logger = DCLogger(config, "my_app")
    
    await logger.info("Application started")
    await logger.warning("This is a warning")
    await logger.error("Something went wrong")
    
    await logger.close()

asyncio.run(main())

Using the Decorator

from dc_logger import log_call

@log_call(action_name="process", include_params=True)
async def process_data(data_id: str):
    # Automatically logs entry, exit, duration, and errors
    return {"status": "processed"}

Structured Logging with Context

from dc_logger import LogEntity

entity = LogEntity(type="dataset", id="ds_123", name="Sales Data")

await logger.info(
    "Dataset processed",
    entity=entity,
    action="process_dataset",
    duration_ms=1500,
    extra={"rows": 10000}
)

Multi-Handler Configuration

from dc_logger import create_console_file_config

config = create_console_file_config(
    file_path="logs/app.log",
    level=LogLevel.INFO,
    pretty_print=True
)
logger = DCLogger(config, "my_app")

Documentation

For AI Agents

See .ai/context.md for a quick reference guide optimized for AI agents.

Key Components

Log Levels

LogLevel.DEBUG      # Detailed debugging information
LogLevel.INFO       # General informational messages
LogLevel.WARNING    # Warning messages for potential issues
LogLevel.ERROR      # Error messages for failures
LogLevel.CRITICAL   # Critical errors requiring immediate attention

Cloud Integrations

Datadog

from dc_logger import create_console_datadog_config

config = create_console_datadog_config(
    datadog_api_key="your-api-key",
    datadog_service="my-service",
    datadog_env="production"
)

LangSmith

from dc_logger import create_console_langsmith_config

config = create_console_langsmith_config(
    api_key="your-langsmith-api-key",
    project_name="observability",
    tags=["dc_logger", "staging"]
)

logger = DCLogger(config, "my_app")

Architecture

See documentation/architecture.md for design details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Jae Wilson (@jaewilson07)