The Best Awadb Alternatives in 2026

Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

WhichLLM tests local language models on a user’s hardware and ranks them using recent benchmarks.

Turbostream analyzes real time streaming data with large language models.

LLMeBench provides a flexible framework for benchmarking large language models.

This repository implements YOLOv3 variants in PyTorch for real time object detection, training, validation, inference, and export.

Aider LLM Leaderboards compares language models on code editing benchmarks.

Vearch provides distributed vector search capabilities for applications built around artificial intelligence.

This repository catalogs AI models across providers with pricing, context, modality, and capability data.

skill-optimizer benchmarks, evaluates, and optimizes skills to check their performance across large language models.

tsfresh automatically extracts relevant features from time series data.

Giskard provides open source evaluation and testing tools for LLM agents.

NALP covers Natural Adversarial Language Processing through a dedicated library.

neuropredict evaluates predictive performance using neuroimaging features.

Adala automates data labeling through an autonomous agent framework for organizing labeled datasets.

Cornac provides a framework for comparing multimodal recommender systems and recommendation algorithms.

Open-RAG-Eval evaluates retrieval-augmented generation without requiring golden answers.

nilearn provides Python tools for machine learning and neuroimaging analysis.

pytorch tabular provides a unified framework for deep learning models on tabular data.

Captum explains model behavior and supports interpretability analysis for PyTorch systems.

AutoViz automatically creates visualizations for datasets of different sizes from a single line of code.

PyTorch Frame provides a library for applying deep learning models to tabular data.

A Python package to assess and improve fairness of machine learning models.

The Featuretools Python library supports automated feature engineering and is published as open source.

Snorkel helps generate training data programmatically through weak supervision.