by zetaalphavector · Agent Tool · ★ 129
Elo-based RAG Agent evaluator RAGElo[^1] is a streamlined toolkit for evaluating Retrieval Augmented Generation (RAG)-powered Large Language Models (LLMs) question answering agents using the Elo rating system. While it has become easier to prototype and incorporate generative LLMs in production, evaluation is still the most challenging part of the solution. Comparing different outputs from multiple prompt and pipeline variations to a "gold standard" is not easy. Still, we can ask a powerful LLM to judge between pairs of answers and a set of questions.
| Stars | 129 |
| Forks | 7 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 59.7501736526283/100 |
| Open Issues | 8 |
| Last Updated | 2026-08-12 |
| Created | 2023-10-10 |
| Platforms | python |
| Est. Tokens | ~19k |
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RAGElo is RAGElo is a set of tools that helps you selecting the best RAG-based LLM agents by using an Elo ranker. It is categorized as a Agent Tool with 129 GitHub stars.
RAGElo is primarily written in Python.
You can find installation instructions and usage details in the RAGElo GitHub repository at github.com/zetaalphavector/RAGElo. The project has 129 stars and 7 forks, indicating an active community.
RAGElo is released under the Apache-2.0 license, making it free to use and modify according to the license terms.