humanlog 0.1.0

One-line progress + logging for humans.

humanlog

One-line logging + progress for humans.
Works in terminals, CI, and Jupyter — no setup, no configuration.

from humanlog import log

log.step("Downloading data")
...
log.done(rows=120_000)

log.info("Cleaning finished")
log.warn("Missing values detected", cols=3)

Why humanlog?

Most logging tools are either:

• too low-level (stdlib logging),
• too heavy (structured logging frameworks),
• or great at one thing (progress bars) but awkward for everything else.

humanlog is opinionated and small:

• readable output
• automatic timing
• graceful behavior everywhere
• zero configuration

It’s for applications, scripts, CLIs, and data workflows.

Install

pip install humanlog

Python 3.9+ (no dependencies).

The core idea

Steps feel like steps

log.step("Training model")
train()
log.done(loss=0.031)

Output (terminal):

→ Training model …
✓ Training model (loss=0.031, time=2.41s)

No manual timers. No formatting. No state.

Informational messages

log.info("Loading dataset", rows=120_000)
log.warn("Missing values detected", cols=3)
log.error("Failed to connect to database")

Output:

[12:40:03] ℹ Loading dataset (rows=120000)
[12:40:04] ⚠ Missing values detected (cols=3)
[12:40:05] ✖ Failed to connect to database

Works everywhere

Terminal (TTY)

• Animated steps
• Clean overwrite
• Human-friendly symbols

CI (GitHub Actions, GitLab, etc.)

• No carriage-return garbage
• Timestamped, line-by-line logs

Jupyter notebooks

• No broken progress bars
• Clean output cells

humanlog automatically detects where it’s running and adapts.

Set HUMANLOG_NO_ANIMATE=1 (or NO_COLOR=1) to force non-animated output.

Zero configuration

No handlers.
No log levels to wire up.
No global logging side effects.

Just import and go.

API

log.step(message: str)

Start a timed step.

If another step is already active, humanlog automatically finishes it first so you never end up with overlapping step state.

You can also use it as a context manager to auto-complete the step:

with log.step("Training model"):
    train()

If an exception escapes the block, the step is automatically closed as failed:

[12:40:05] ✖ Training model (error=RuntimeError, time=0.73s)

log.done(**info)

Finish the current step and print timing + optional key/value info.

log.info(message: str, **info)

Print an informational message.

If a step is active, it is automatically completed before the info line is printed.

log.warn(message: str, **info)

Print a warning (to stderr).

If a step is active, it is automatically completed before the warning is printed.

log.error(message: str, **info)

Print an error (to stderr).

If a step is active, it is automatically completed before the error is printed.

Key/value info is always optional and rendered inline:

log.info("Loaded batch", batch=3, rows=1024)

Example: a real script

from humanlog import log

log.step("Downloading data")
download()
log.done(rows=120_000)

log.step("Cleaning data")
clean()
log.done(dropped=341)

log.step("Training model")
train()
log.done(epochs=10, loss=0.031)

log.info("All done")

Readable. Intentional. Calm.

Non-goals (by design)

humanlog is not:

• a replacement for structured logging
• a metrics system
• a tracing framework
• a progress-bar DSL

If you need JSON logs or OpenTelemetry, use those tools.

If you want pleasant, readable output, use humanlog.

Roadmap (likely, but optional)

• log.track(iterable, label="Processing")
• optional Rich-powered rendering
• stdlib logging bridge (opt-in)

The core will stay small.

Philosophy

humanlog optimizes for:

• clarity over flexibility
• defaults over configuration
• humans over machines

Logs are for people first.

Contributing

Contributions are welcome. See CONTRIBUTING.md for local setup and quality checks.

Changelog

Release history is tracked in CHANGELOG.md.

Releasing

Maintainers can follow RELEASE.md for packaging, verification, and publish steps.

License

MIT