ConfigSpace
Creation and manipulation of parameter configuration spaces for automated algorithm configuration and hyperparameter tuning.
Decision gist · record as of 2026-08-14
Yes. ConfigSpace is a stable, actively maintained library with low install friction, no security issues, and a permissive license. It is purpose-built for hyperparameter optimization and algorithm configuration workflows. Install it if you are working on AutoML, hyperparameter tuning, or algorithm configuration tasks and need a structured way to define and sample parameter spaces.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with a pure-Python wheel and five common scientific dependencies (numpy, scipy, pyparsing, typing_extensions, more_itertools).
- Actively maintained with recent commits and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — BSD 3-clause permissive license with an exception: files in ConfigSpace.nx are copied from networkx and licensed under a separate BSD license. Both are permissive and suitable for commercial and private use.
last release 2025-12-19 (238 days) · last repo commit 2026-07-21 · 225 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 241,655 downloads/mo, #8,872 on PyPI
Alternatives
Verify before relying
from ConfigSpace import ConfigurationSpace
cs = ConfigurationSpace(
name="myspace",
space={
"a": (0.1, 1.5),
"b": (2, 10),
"c": ["mouse", "cat", "dog"],
},
)
configs = cs.sample_configuration(2)- Whether ConfigSpace integrates with specific hyperparameter optimization frameworks beyond SMAC
- Performance characteristics when handling very large configuration spaces
- Support for conditional parameters or hierarchical constraints
What it is and what it does
ConfigSpace is a Python library for defining and working with configuration spaces—structured representations of algorithm parameters and hyperparameters. It provides a domain-specific language to declare parameters as continuous ranges, discrete integers, or categorical choices, and then sample valid configurations from that space. The library is commonly used in automated machine learning and hyperparameter optimization workflows, where you need to systematically explore parameter combinations.
The package depends on numpy, scipy, and pyparsing for numerical operations and parsing, plus typing_extensions and more_itertools for compatibility and iteration utilities. It supports Python 3.9 and later, is actively maintained, and carries no known security vulnerabilities. The library is permissive-licensed under BSD 3-clause, with a subset of code (the nx subpackage) also under BSD from NetworkX.
Use it for
- Define hyperparameter spaces for machine learning model tuning in AutoML pipelines
- Sample random or structured configurations for algorithm benchmarking and empirical evaluation
- Represent parameter constraints and dependencies in black-box optimization frameworks like SMAC
- Build configuration search spaces for neural architecture search or algorithm selection tasks
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ConfigSpace is a stable, actively maintained library with low install friction, no security issues, and a permissive license. It is purpose-built for hyperparameter optimization and algorithm configuration workflows. Install it if you are working on AutoML, hyperparameter tuning, or algorithm configuration tasks and need a structured way to define and sample parameter spaces.
Install
configspace on PyPI
Before you install
Low friction installation with a pure-Python wheel and five common scientific dependencies (numpy, scipy, pyparsing, typing_extensions, more_itertools). Actively maintained with recent commits and no known vulnerabilities.
License in practice
BSD 3-clause permissive license with an exception: files in ConfigSpace.nx are copied from networkx and licensed under a separate BSD license. Both are permissive and suitable for commercial and private use.
Quickstart
from ConfigSpace import ConfigurationSpace
cs = ConfigurationSpace(
name="myspace",
space={
"a": (0.1, 1.5),
"b": (2, 10),
"c": ["mouse", "cat", "dog"],
},
)
configs = cs.sample_configuration(2)Verify before relying
- Whether ConfigSpace integrates with specific hyperparameter optimization frameworks beyond SMAC
- Performance characteristics when handling very large configuration spaces
- Support for conditional parameters or hierarchical constraints
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpypyparsingscipytyping_extensionsmore_itertools |
| Maintenance | Actively maintained 238 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 241,655 / month, #8,872 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseNatural Language :: EnglishOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development |
Evidence: configspace-1.2.2-py3-none-any.whl
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