---
id: configspace
version: "1.2.2"
license: # ConfigSpace Copyright (c) 2014-2021, ConfigSpace developers Matthias Feurer Katharina Eggensperger and others (see commit history). All rights reserved. Redistribution and use in source and binary… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# ConfigSpace — Creation and manipulation of parameter configuration spaces for automated algorithm configuration and hyperparameter tuning. 

License: permissive · Maintenance: active · Downloads: 241.7K/mo

## 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 above — verify before relying.

ConfigSpace defines and manipulates configuration spaces for hyperparameter optimization and algorithm configuration, supporting continuous, discrete, and categorical parameters with sampling and constraint handling.

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

pip install configspace
uv add configspace
poetry add configspace

## Installing ConfigSpace

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: # ConfigSpace Copyright (c) 2014-2021, ConfigSpace developers Matthias Feurer Katharina Eggensperger and others (see commit history). All rights reserved. Redistribution and use in source and binary… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 241.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

hyperparameter optimization configuration, algorithm configuration space, parameter space definition, hyperparameter tuning framework, configuration domain language, automated algorithm configuration, parameter sampling and constraints, hyperparameter-optimization, automl, algorithm-configuration

[View on SkillFed](https://skillfed.io/packages/configspace) · [View on PyPI](https://pypi.org/project/configspace/)