🐢 Open-Source Evaluation & Testing library for LLM Agents
A comprehensive Python framework for evaluating LLM-extracted structured data against ground truth labels. Supports binary classification, scalar values, and list fields with detailed performance metrics, confidence-based evaluation, and statistical uncertainty quantification via non-parametric bootstrap confidence intervals.
Evaluation & testing framework for computer vision models
Build confidence in your AI with systematic slice-based testing
Lightweight Python library for automated data leakage detection in ML datasets. Detects duplicate rows, ID overlap, target leakage, and temporal leakage.
Per-layer ONNX export parity validator: PyTorch + ONNX Runtime cross-checked layer by layer in a C++ comparator with Python fallback
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