rag-harness is an open-source command-line tool that helps developers evaluate and compare different Retrieval-Augmented Generation (RAG) systems. It provides standardized metrics and benchmarking capabilities to assess retrieval quality, generation accuracy, and overall RAG pipeline performance. Primarily used by AI engineers and researchers building or optimizing RAG applications.
rag-harness sits in PulseGate's AI & ML category. It focuses on evaluating and benchmarking different RAG (Retrieval-Augmented Generation) pipelines and configurations. It is built as an open-source project for developers. The project is open source (MIT). It runs on the command line.
Behind rag-harness is Bevin Katti, and it first shipped in 2026. Development happens publicly on GitHub with 14 commits in the last 90 days. Key capabilities include RAG Evaluation, benchmarking, and Metrics Comparison.
Summary written by a language model from the project’s public pages.
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