Skip to content
SPECIFICTION
Get listed

ARES

github.com

LinuxAPISelf-hosted
Visit github.com
Spec score 7.7 #14 of 29 in LLM Evaluation Tools

Spec sheet

Purpose
ARES is an automated framework for evaluating retrieval-augmented generation (RAG) systems.Read 9 Oct 2026 · github.com
Intended users
The project documentation describes ARES as a tool for evaluating RAG systems and comparing different RAG configurations.Read 9 Oct 2026 · github.com
Support
The README invites users with questions to contact [email protected] or [email protected].Read 9 Oct 2026 · github.com
License
The GitHub repository identifies its license as Apache-2.0.Read 9 Oct 2026 · github.com
Annotation guidance
The README recommends at least 50 annotated query, document, and answer examples for the human preference validation set, with several hundred ideal.Read 4 Oct 2026 · github.com
Compute requirements
For execution without the OpenAI API, the README lists over about 100 GB of available disk space and a GPU, and says an A100 should work.Read 9 Oct 2026 · github.com
Custom systems
ARES is model-agnostic and can evaluate queries and answers generated by custom RAG models.Read 4 Oct 2026 · ares-ai.vercel.app
Data requirements
ARES requires an in-domain prompts dataset and an unlabeled evaluation set; a labeled evaluation set is optional because PPI can create one using machine labels.Read 4 Oct 2026 · ares-ai.vercel.app
Dataset example
The README says the optional full Natural Questions dataset is 37.3 GB.Read 9 Oct 2026 · github.com
Datasets
ARES can retrieve KILT datasets including nq, hotpotqa, wow, and fever, and SuperGLUE datasets including record, rte, boolq, and multirc.Read 4 Oct 2026 · ares-ai.vercel.app
Evaluation criteria
ARES assesses context relevance, answer faithfulness, and answer relevance.Read 9 Oct 2026 · github.com
Evaluation method
ARES combines synthetic data generation, fine-tuned classifiers, and Prediction-Powered Inference to estimate evaluation results with statistical confidence.Read 9 Oct 2026 · github.com
Evaluation metrics
ARES evaluates context relevance, answer faithfulness, and answer relevance.Read 4 Oct 2026 · github.com
Hardware requirements
The README says local execution needs over about 100 GB of available disk space and a GPU, and names an A100 as a working example.Read 4 Oct 2026 · github.com
Input requirements
The README says evaluation uses a human preference validation set, few-shot examples, and a larger set of unlabeled query-document-answer triples; it recommends at least 50 validation examples.Read 9 Oct 2026 · github.com
Install
The documentation provides installation through `pip install ares-ai` or by cloning the GitHub repository and installing it locally.Read 4 Oct 2026 · ares-ai.vercel.app
Installation
The README gives `pip install ares-ai` as the installation command.Read 9 Oct 2026 · github.com
Local execution
ARES supports running models locally with vLLM, which the README says can enable offline operation and enhanced privacy.Read 9 Oct 2026 · github.com
Method
ARES generates synthetic training data, fine-tunes lightweight language models as judges, and uses prediction-powered inference with human-annotated examples to produce confidence intervals.Read 4 Oct 2026 · ares-ai.vercel.app
Model integrations
The README gives examples using OpenAI and TogetherAI API keys and shows local model execution with vLLM.Read 4 Oct 2026 · github.com
Model support
ARES describes itself as model-agnostic and shows examples using OpenAI, Together AI, and vLLM-hosted models.Read 9 Oct 2026 · github.com
Offline use
ARES says vLLM enables local model execution and offline operation.Read 4 Oct 2026 · github.com

Where Specifiction places it

Alternatives to ARESAll alternatives →