nanoargs 0.2.0

A tiny config loader and CLI framework built around Pydantic models

NanoArgs

A tiny, opinionated configuration loader and CLI framework built around Pydantic models.

Features

• YAML Configuration Loading — load and merge multiple files with left-to-right precedence
• Smart Imports — !import loads one file, path resolved relative to the importing file
• Composable Merging — !merge deep-merges imports and inline mappings left to right
• Runtime Overrides — JSONPath-like syntax via --override
• Pydantic Validation — automatic validation and type coercion
• Schema Generation — --print-schema renders a formatted JSON schema; --print-config dumps the merged result
• Rich CLI Interface — styled help, errors, and schema output via Rich
• Subcommands — multi-command CLIs via Nested; each command owns its own config model
• Type Safety — pyright strict, modern Python type hints

Installation

pip install git+https://github.com/cusp-ai-oss/nanoargs.git

Requires Python 3.10+. Dependencies: lark, pydantic, pyyaml, rich, rich-argparse.

Quick Start

from pydantic import BaseModel
from nanoargs import NanoArgs

class Config(BaseModel):
    mode: str = "train"
    batch_size: int = 32
    learning_rate: float = 0.001

config = NanoArgs(Config).parse()

python app.py config.yaml                                    # load a YAML file
python app.py base.yaml exp.yaml                             # merge multiple files
python app.py config.yaml --override '$.batch_size=128'      # JSONPath override
echo 'batch_size: 128' | python app.py -                     # read from stdin
python app.py --print-schema                                 # inspect the schema
python app.py config.yaml --print-config                     # inspect merged result

Subcommands

Use Nested to define multi-command CLIs. Fields typed as T | None = None become subcommands:

from nanoargs import NanoArgs, Nested

class TrainConfig(BaseModel):
    lr: float = 0.001
    epochs: int = 10

class EvalConfig(BaseModel):
    checkpoint: str

class CLI(Nested):
    verbose: bool = False          # pre-command parent field
    train: TrainConfig | None = None
    evaluate: EvalConfig | None = None

result = NanoArgs(CLI).parse()

python app.py train config.yaml --override '$.lr=0.01'
python app.py --override '$.verbose=true' evaluate config.yaml
python app.py --help                   # lists available commands
python app.py train --print-schema     # schema for a specific command

Nested composes naturally — a field whose type is itself Nested creates a sub-group (app data preprocess config.yaml).

See the Subcommands guide for full details.

Documentation

• Getting Started — full walkthrough
• Configuration Guide — import system, merge rules, override syntax, CLI flags
• Subcommands — Nested patterns, parent fields, multi-level nesting
• Path Syntax — JSONPath-like grammar reference
• API Reference
• Changelog

Gotchas

• YAML booleans in config files: yes, no, on, off are booleans in YAML. Quote them for strings: "yes". This does not affect --override values.
• List merging: config file merging replaces lists entirely — it does not append.
• Hyphenated keys: --override paths require bracket syntax: --override '$["learning-rate"]=0.001'.
• Negative array indices: $[-1] works Python-style (last element). Negative slice bounds are also supported: $[-3:], $[:-1].
• Bracket notation quotes: both $["key"] and $['key'] are supported.
• --print-config without config files: prints Pydantic-validated defaults and exits.
• Unknown fields: overriding a path that doesn't match any model field is silently ignored by Pydantic.
• Auto-vivification: $.a.b.c=1 creates intermediate dicts for missing keys or None values.
• @file resolution: from the CLI, @file resolves relative to the working directory; inside YAML (!override), relative to the YAML file. Use @@ to escape a literal @.

License

Apache 2.0