bocoel — LLM Plugin by rentruewang

by rentruewang · LLM Plugin · ★ 290

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About bocoel

🫙 Archive This has topped the show HN front page link. This was born as a research project, and I think I have achieved most of what I set out to create. So today (2025/09/14) I'm archiving this repository. Nowadays I'm mainly working on , a deep learning algorithm compiler! Check it out. ☂️ BoCoEL Bayesian Optimization as a Coverage Tool for Evaluating Large Language Models ![Python 3.12](https://img.shields.io/badge/python-3.12-blue

bayesian-optimizationbenchmarkingevaluationlanguage-modelllmmachine-learning

Quick Facts

Stars290
Forks16
LanguagePython
CategoryLLM Plugin
LicenseBSD-3-Clause
Quality Score64.4795522551245/100
Open Issues6
Last Updated2026-05-16
Created2023-12-16
Platformspython
Est. Tokens~482k

bocoel alternative? Top 6 similar tools

Looking for a bocoel alternative? If you're comparing bocoel with other llm plugin tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is bocoel?

bocoel is Bayesian Optimization as a Coverage Tool for Evaluating LLMs. Accurate evaluation (benchmarking) that's 10 times faster with just a few lines of modular code.. It is categorized as a LLM Plugin with 290 GitHub stars.

What programming language is bocoel written in?

bocoel is primarily written in Python. It covers topics such as bayesian-optimization, benchmarking, evaluation.

How do I install or use bocoel?

You can find installation instructions and usage details in the bocoel GitHub repository at github.com/rentruewang/bocoel. The project has 290 stars and 16 forks, indicating an active community.

What license does bocoel use?

bocoel is released under the BSD-3-Clause license, making it free to use and modify according to the license terms.

What are the best alternatives to bocoel?

The top alternatives to bocoel on Agent Skills Hub include Awesome-LLM-Eval, create-llm, Hegelion. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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