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Documentation Index

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Answers to frequently asked questions about Chromologger’s behavior, limitations, and roadmap.
Chromologger supports Python 3.8 and later, including 3.8, 3.10, 3.11, 3.12, 3.13, and 3.14 as declared in pyproject.toml.
Python 2 is not supported. The library relies on modern Python features including pathlib, inspect, and type hints that are unavailable in Python 2.
Not yet. Automatic log rotation — by file size or by date — is on the roadmap for a future release.Workaround: Use date-stamped filenames to create a new log file each day manually.
import os
from datetime import datetime
from chromologger import Logger

os.makedirs('./logs', exist_ok=True)
logger = Logger(f'./logs/app_{datetime.now().strftime("%Y%m%d")}.log')
This produces files like app_20250115.log, giving you one file per day without any additional tooling.
No. Chromologger does not implement any internal locking mechanism. Concurrent writes from multiple threads to the same Logger instance are not guaranteed to be safe and may produce corrupted or interleaved log entries.Safe alternative: Create a separate Logger instance with a distinct file path per thread.
import threading
from chromologger import Logger
import os

os.makedirs('./logs', exist_ok=True)

def worker(thread_id):
    logger = Logger(f'./logs/worker_{thread_id}.log')
    logger.log(f'Thread {thread_id} started')
    # ... work ...
    logger.close()

threads = [threading.Thread(target=worker, args=(i,)) for i in range(4)]
for t in threads:
    t.start()
for t in threads:
    t.join()
Currently, only two levels are implemented:
MethodLevel written to file
logger.log(msg)[INFO]
logger.log_e(e)[ERROR]
Support for WARNING, DEBUG, and CRITICAL levels is planned for a future release.
In the meantime, you can prefix your messages manually — e.g. logger.log('[WARNING] Disk usage above 90%') — though this won’t affect filtering or formatting behavior.
Not yet. __enter__ / __exit__ support is on the roadmap. Until then, use a try/finally block to guarantee that close() is always called, even if an exception occurs.
from chromologger import Logger
import os

os.makedirs('./logs', exist_ok=True)
logger = Logger('./logs/app.log')

try:
    logger.log('Work started')
    # ... your work ...
    logger.log('Work finished')
finally:
    logger.close()  # always runs, even on exception
This pattern is equivalent to what a context manager would do, and it works today in all supported Python versions.
Both Logger instances open the file in append mode, so entries written by each will be appended sequentially to the same file. No data is lost under sequential access patterns.However, under concurrent workloads (multiple threads or processes writing simultaneously), entries may be interleaved in unexpected order because Chromologger does not coordinate between instances. For concurrent scenarios, prefer separate files per logger — see the thread-safety question above.
When Chromologger itself encounters an error — for example, if it cannot open the user-specified log file — it writes diagnostics to a log.log file located inside the Chromologger package directory (not your project directory).The exact path is printed to the console by chromolog whenever an internal error occurs:
ERROR  Revise el archivo "log" que se encuentra en esta ruta: /path/to/chromologger/log.log
Check that file for detailed error messages, including exception type, source file, and line number.
Console output via chromolog.Print is currently always enabled. Each call to logger.log() prints a message pointing to the log file path, and internal errors also print to the console.A debug-mode flag to suppress this output is on the roadmap. At present, there is no official way to silence it.
If noisy console output is blocking your workflow, consider wrapping calls in a redirect temporarily — but be aware this is a workaround, not a supported configuration.
Not natively — structured export formats are a planned feature. As a workaround, serialize your data to a JSON string before passing it to log().
import json
from chromologger import Logger
import os

os.makedirs('./logs', exist_ok=True)
logger = Logger('./logs/events.log')

logger.log(json.dumps({'event': 'purchase', 'amount': 99.99, 'user_id': 42}))
logger.close()
This produces a log entry like:
[INFO][2025-01-15 10:30:00.123456] - {"event": "purchase", "amount": 99.99, "user_id": 42}
You can then parse these lines back to JSON for analysis or ingestion into a log aggregation tool.
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