GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026
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Decision brief
RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests.
Good fit when
- For tasks requiring robust handling of noisy data in Bayesian Optimization
- When needing integration with libraries like [george] and [pyrfr]
Avoid when
- Avoid if your project strictly requires open-source licenses other than BSD-3-Clause
- Not suitable for users not comfortable installing external dependencies manually
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (2712d since push)
- As of Oct 3, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 3, 2026
- Security (OSV)
- 15 low (15 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install RoBOSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
RoBO is a Python-based framework for conducting robust Bayesian Optimization, making use of Gaussian processes and random forests libraries for optimization tasks.
Capability facts
- Languages
- python
Source: github.language · Oct 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 3, 2026)
python setup.py installSource link
Tags
README
RoBO a Robust Bayesian Optimization framework. ================================================ Master Branch Installation RoBO uses the Gaussian processes library george and the random forests library pyrfr. In order to use these libraries make sure that libeigen and swig are in...
For agents
This page has a .md twin and JSON over the API.