facebookresearch/nevergrad

Health v2 · maintenance only

Adoption not part of the score

4204 stars · 371 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Nevergrad is a Python toolbox for gradient-free (derivative-free) optimization, providing many optimization algorithms such as NGOpt and differential evolution. It supports optimizing continuous, discrete, and mixed parameter spaces, making it well suited for hyperparameter tuning and black-box function minimization.

Use cases

• tune machine learning hyperparameters without gradients
• minimize a black-box function with no derivative access
• optimize mixed continuous and discrete parameters
• run evolutionary algorithms like differential evolution in Python
• benchmark optimization algorithms
• optimize simulation or experiment parameters

When to choose

• your objective function is non-differentiable, noisy, or expensive to differentiate
• you need to tune hyperparameters of ML models or pipelines
• you need a variety of derivative-free optimizers behind one API
• your search space mixes continuous, integer, and categorical variables

When to avoid

• your problem is smooth and gradient-based methods (e.g., Adam, L-BFGS) work well
• you need large-scale deep learning training with backpropagation
• you need a general-purpose constraint solver rather than black-box optimization

Facets

library · maturity active

machine-learning benchmarking data-science machine-learning artificial-intelligence python cross-platform gradient-free-optimization derivative-free-optimization hyperparameter-tuning evolutionary-algorithms metaheuristic algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/nevergrad")