{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/19"}],"enrichment":{"capability":"Provides structured JSON logging with integrated exception handling that tracks calling context automatically, allowing you to log messages with named parameters and chain exceptions without creating custom exception types.","skillfed_tags":["structured-logging","exception-handling","json-output"],"use_cases":["Build microservices or distributed systems where structured JSON logs feed into centralized log aggregation and analysis tools.","Debug complex call chains by attaching extra properties to all subsequent log messages, including those from spawned threads.","Replace standard Python logging in existing codebases by changing import statements without rewriting exception handling logic.","Log parametric data safely without risk of accidentally including sensitive local variables (the library discourages locals() usage).","Identify root causes in exception chains without creating custom exception classes\u2014exceptions are identified by their message templates."],"what_it_does":"mo-logs is a structured logging library that outputs all logs as JSON and tightly integrates exception handling into the logging flow. Instead of using Python's standard logging module, you import a single logger object and call methods like logger.info() or logger.error() with message templates and named parameters. The library automatically captures calling context (file, line, method, thread, machine details) without requiring you to create logger instances per module.\n\nThe key design principle is that exceptions and logging are inseparable: logger.error() raises an exception that can be caught by calling code, and only logs if it propagates uncaught. You chain exceptions using a cause parameter rather than raise-from syntax, and can test whether a specific exception message template exists anywhere in the causal chain using the in operator. All parameters are kept separate from the template string to preserve structure for downstream log analysis tools.","worth_installing":"Yes, if you are building systems that require structured logging and want exception handling semantics tightly integrated with logging. The low install friction, active maintenance, and zero known vulnerabilities make it safe to adopt. The copyleft license is a minor constraint if you plan to keep the library proprietary, but acceptable for most open-source and internal projects. Not necessary if you are satisfied with standard Python logging or do not need JSON-structured output."},"id":"mo-logs","links":{"html":"https://skillfed.io/packages/mo-logs","md":"https://skillfed.io/packages/mo-logs.md","pypi":"https://pypi.org/project/mo-logs/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-02","license_spdx":null,"license_treatment":"copyleft","name":"mo-logs","python_support":"unspecified","summary":"More Logs! Structured Logging and Exception Handling"},"popularity":{"monthly_downloads":117824,"position":12147,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"8.703.26061"}