by zetaalphavector · Agent Tool · ★ 128
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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. This led us to develop a simple tool for tournament-style Elo ranking of LLM outputs. By comparing answers from different RAG pipelines and prompts over multiple questions, RAGElo computes a ranking of the different settings, providing a good overview of what works (and what doesn't). ⚙️ Installation For using RAGElo as a Python library or as CLI, install it using pip: When working from source we recommend an isolated environment (e.g., ). The project's Python lives at . Environment variables and providers: OpenAI requires . Set it in your shell or load it via dotenv before invoking the CLI. Ollama is
| Stars | 128 |
| Forks | 6 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 32.75/100 |
| Open Issues | 8 |
| Last Updated | 2026-05-04 |
| Created | 2023-10-10 |
| Platforms | python |
| Est. Tokens | ~138k |
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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 128 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 128 stars and 6 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.
The top alternatives to RAGElo on Agent Skills Hub include agent-skills, awesome-android-agent-skills, claude-code-skill-factory. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.