dataforge-07-evals is an open-source CLI tool for evaluating and benchmarking data-quality repair agents. It provides an agent-agnostic framework for running standardized tests and benchmarks on data repair workflows, aimed at data scientists and researchers.
dataforge-07-evals sits in PulseGate's LLM eval & observability category. It focuses on evaluating and benchmarking data-quality repair agents in a standardized, agent-agnostic way. It is built as an open-source project for data scientists. dataforge-07-evals is open source under the Apache-2.0 license. dataforge-07-evals is available on the command line, and it can be self-hosted.
Behind dataforge-07-evals is Aegis15, and it first shipped in 2026. Development happens publicly on GitHub with 97 commits in the last 90 days. Among its 5 catalogued features are evaluation harness, data-quality benchmarks, and agent-agnostic.
Summary written by a language model from the project’s public pages.
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