Best AI Evaluation Tools Tools

Explore 8 AI evaluation tools tools to find the perfect solution.

Evaluation Tools

8 tools

Lighteval

An all-in-one toolkit for evaluating LLMs on multiple backends, offering detailed sample-by-sample performance metrics and task customization options.

DeepEval

DeepEval is an open-source evaluation toolkit for AI models that provides advanced metrics for both text and multimodal outputs. It supports features like multimodal G-Eval, conversational evaluation using a list of Turns, and integrates platform support along with comprehensive documentation.

Dataset-to-Model Monitor

A tool that monitors datasets and tracks models trained on them, helping users manage and oversee AI model performance.

seismometer

seismometer is an open-source Python package for evaluating AI model performance with a focus on healthcare. It provides templates and tools to analyze statistical performance, fairness, and the impact of interventions on outcomes using local patient data. Although designed for healthcare applications, it can be used to validate models in any field.

Dataset to Model Monitor

A Hugging Face Space tool that automatically tracks and notifies users when new models are trained on a specified dataset (HuggingFaceM4/VQAv2). It leverages the librarian bot to post alerts in a discussion thread, enabling developers and researchers to keep up-to-date with models built on this dataset.

OpenAI Evals

OpenAI Evals is an open-source framework for evaluating large language models (LLMs) and LLM systems. It offers a registry of benchmarks and tools for developers and researchers to run, customize, and manage evaluations to assess model performance and behavior.

EvoMaster

AI-driven tool for automatically generating system-level test cases and fuzzing for web/enterprise applications and APIs (REST/GraphQL/RPC).

alpha-beta-CROWN

alpha-beta-CROWN is an efficient, scalable, and GPU accelerated neural network verifier that uses linear bound propagation and branch-and-bound methods to provide provable robustness guarantees against adversarial attacks and verify properties like Lyapunov stability. It is a winning solution in VNN-COMP from 2021 to 2024.