hydra-core
A framework for elegantly configuring complex applications
Decision gist · record as of 2026-08-14
Yes. Hydra is actively maintained, has no known vulnerabilities, installs with low friction, and is widely adopted in the ML and data science communities. The MIT license is permissive. Install it if your application needs to separate configuration from code or if you're building experiments that require frequent parameter variation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with four lightweight runtime dependencies (omegaconf, antlr4-python3-runtime, importlib-resources, packaging).
- Actively maintained—last commit 2026-08-13, release 9 days old, 10595 GitHub stars.
License · maintenance · safety
MIT (permissive) — MIT license (permissive): you can use, modify, and distribute Hydra freely in commercial and private projects with minimal restrictions—just include the license notice.
last release 2026-08-05 (9 days) · last repo commit 2026-08-13 · 10,595 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 22,144,063 downloads/mo, #984 on PyPI
Alternatives
Verify before relying
pip install hydra-core
from hydra import compose, initialize_config_dir
from omegaconf import DictConfig
@hydra.main(version_base=None, config_path="conf", config_name="config")
def my_app(cfg: DictConfig) -> None:
print(cfg)- Whether the package requires a specific minimum Python version (requires_python is unspecified in metadata).
- Exact scope of configuration composition features and whether they cover your use case.
What it is and what it does
Hydra is a configuration framework that separates application logic from settings, allowing you to define defaults in YAML files, override them from the command line, and compose configurations dynamically. It works by decorating your main function with @hydra.main, which intercepts command-line arguments and merges them with your config hierarchy—so you can run the same code with different settings without changing code.
The framework is built on omegaconf for structured configuration objects and includes tab-completion support and ANTLR-based parsing for advanced config syntax. It's widely used in machine learning and data science workflows where experiments require frequent parameter sweeps and reproducible configuration management. The active maintenance, permissive license, and low install friction make it a stable choice for projects that need to move configuration out of code.
Use it for
- Machine learning experiments: define model hyperparameters in YAML, override them per run from the CLI without code changes.
- Multi-environment deployments: maintain separate config files for dev, staging, and production, composed at runtime.
- Data pipeline configuration: parameterize data sources, processing steps, and output paths in a single declarative structure.
- Research reproducibility: capture full experimental settings in config files alongside code for easy replication.
- Complex application settings: organize deeply nested configuration hierarchies with inheritance and defaults.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Hydra is actively maintained, has no known vulnerabilities, installs with low friction, and is widely adopted in the ML and data science communities. The MIT license is permissive. Install it if your application needs to separate configuration from code or if you're building experiments that require frequent parameter variation.
Install
hydra-core on PyPI
Before you install
Low friction: pure Python wheel with four lightweight runtime dependencies (omegaconf, antlr4-python3-runtime, importlib-resources, packaging). Actively maintained—last commit 2026-08-13, release 9 days old, 10595 GitHub stars.
License in practice
MIT license (permissive): you can use, modify, and distribute Hydra freely in commercial and private projects with minimal restrictions—just include the license notice.
Quickstart
pip install hydra-core
from hydra import compose, initialize_config_dir
from omegaconf import DictConfig
@hydra.main(version_base=None, config_path="conf", config_name="config")
def my_app(cfg: DictConfig) -> None:
print(cfg)Verify before relying
- Whether the package requires a specific minimum Python version (requires_python is unspecified in metadata).
- Exact scope of configuration composition features and whether they cover your use case.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesomegaconfantlr4-python3-runtimeimportlib-resourcespackaging |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 22,144,063 / month, #984 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: hydra_core-1.3.5-py3-none-any.whl
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See also hydra-optuna-sweeper · hydra-colorlog · hydra-submitit-launcher · omegaconf · yacs · dora-search · mpich · dynaconf · hierarchical-conf