How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024
The main features of evalplus/evalplus are: Benchmarks and Datasets, Evaluation Frameworks, Model Evaluation and Benchmarking.
Open-source alternatives to evalplus/evalplus include: openai/simple-evals — This project is a language model evaluation framework and benchmarking tool designed to measure the accuracy and… rllm-org/rllm — rllm is an asynchronous reinforcement learning framework for training language agents. It provides a unified pipeline… giskard-ai/giskard — Giskard is an evaluation framework, testing library, and quality monitoring system for large language models and AI… confident-ai/deepeval — Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for… eleutherai/lm-evaluation-harness — This project is a standardized framework for benchmarking large language models across a wide range of academic and… comet-ml/opik — Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It…
This project is a language model evaluation framework and benchmarking tool designed to measure the accuracy and performance of models across diverse datasets. It provides a system for implementing model-based graders, running standardized tests for mathematical reasoning, coding, and factuality, and calculating quantified performance metrics such as precision, recall, F1 scores, and pass-at-k. The framework utilizes model-based grading and rubrics to validate response quality against expert-defined criteria. It includes a multi-model benchmarking loop and a model-agnostic API interface to co
rllm is an asynchronous reinforcement learning framework for training language agents. It provides a unified pipeline that runs the same agent code for both evaluation and training, automatically capturing traces for gradient computation. The framework supports distributed reinforcement learning across multiple GPUs and nodes using pluggable backends, and executes agents in isolated sandboxes—either locally or in the cloud—for safe and scalable rollout collection. It trains agents built with LangGraph, SmolAgents, OpenAI Agents SDK, or custom frameworks without requiring core logic changes. T
This project is a standardized framework for benchmarking large language models across a wide range of academic and reasoning datasets. It provides a platform for executing automated evaluation tasks to measure model accuracy and performance, ensuring consistent assessment through a structured configuration schema. The framework distinguishes itself by incorporating a dedicated utility for data decontamination, which identifies and removes overlapping training samples from evaluation sets to prevent data leakage. It also features a flexible task builder that allows users to define custom benc
Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes. The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn