Documentation   •   pip install tyro

tyro.cli() is a tool for generating CLI interfaces in Python.

We can define configurable scripts using functions:

"""A command-line interface defined using a function signature.

Usage: python script_name.py --foo INT [--bar STR]
"""

import tyro

def main(
    foo: int,
    bar: str = "default",
) -> None:
    ...  # Main body of a script.

if __name__ == "__main__":
    # Generate a CLI and call `main` with its two arguments: `foo` and `bar`.
    tyro.cli(main)

Or instantiate config objects defined using tools like dataclasses, pydantic, and attrs:

"""A command-line interface defined using a class signature.

Usage: python script_name.py --foo INT [--bar STR]
"""

from dataclasses import dataclass
import tyro

@dataclass
class Config:
    foo: int
    bar: str = "default"

if __name__ == "__main__":
    # Generate a CLI and instantiate `Config` with its two arguments: `foo` and `bar`.
    config = tyro.cli(Config)

    # Rest of script.
    assert isinstance(config, Config)  # Should pass.

Other features include helptext generation, nested structures, subcommands, and shell completion. For examples and the API reference, see our documentation.

Why tyro?

1. Define things once. Standard Python type annotations, docstrings, and default values are parsed to automatically generate command-line interfaces with informative helptext.

2. Static types. Unlike tools dependent on dictionaries, YAML, or dynamic namespaces, arguments populated by tyro benefit from IDE and language server-supported operations — tab completion, rename, jump-to-def, docstrings on hover — as well as static checking tools like pyright and mypy.

3. Modularity. tyro supports hierarchical configuration structures, which make it easy to decentralize definitions, defaults, and documentation.

In the wild

tyro is designed to be lightweight enough for throwaway scripts, while facilitating type safety and modularity for larger projects. Examples:

nerfstudio-project/nerfstudio	Open-source tools for neural radiance fields.
Sea-Snell/JAXSeq	Train very large language models in Jax.
kevinzakka/obj2mjcf	Interface for processing OBJ files for Mujoco.
blurgyy/jaxngp	CUDA-accelerated implementation of instant-ngp, in JAX.
NVIDIAGameWorks/kaolin-wisp	PyTorch library for neural fields.
autonomousvision/sdfstudio	Unified framework for surface reconstruction.
openrlbenchmark/openrlbenchmark	Collection of tracked experiments for reinforcement learning.
vwxyzjn/cleanrl	Single-file implementation of deep RL algorithms.

Alternatives

tyro is an opinionated library. If any design decisions don't make sense, feel free to file an issue!

You might also consider one of many alternative libraries. Some that we particularly like:

• simple-parsing and jsonargparse, which provide deeper integration with configuration file formats like YAML and JSON.
• clipstick, which focuses on simplicity + generating CLIs from Pydantic models.
• datargs, which provides a minimal API for dataclasses.
• defopt, which has similarly comprehensive type annotation support.
• fire and clize, which support arguments without type annotations.

We also have some notes on tyro's design goals and other alternatives in the docs here.