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ARES

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What ARES is

ARES is an open-source framework for evaluating retrieval-augmented generation systems, including custom systems and comparisons between configurations. It measures context relevance, answer faithfulness, and answer relevance. The framework creates synthetic training data, fine-tunes lightweight language models as judges, and uses Prediction-Powered Inference with human-annotated examples to estimate results with confidence intervals. An in-domain prompts dataset and an unlabeled evaluation set are required; a labeled evaluation set is optional. The README recommends at least 50 annotated query, document, and answer examples for the human preference validation set, with several hundred as an ideal. ARES can retrieve KILT and SuperGLUE datasets. Examples show use with OpenAI and TogetherAI API keys, as well as local model execution with vLLM, which can support offline operation. It is self-hosted and supports Linux. Installation is available through `pip install ares-ai` or by cloning and installing the repository locally. Local execution requires a GPU and over about 100 GB of available disk space; an A100 is given as a working example. The repository uses the Apache-2.0 license.

Who it suits

ARES suits teams evaluating custom RAG systems or comparing RAG configurations. Users need evaluation datasets and annotated examples, and local execution requires substantial disk space and a GPU.

What it does well

  • Evaluates three named aspects of RAG responses.
  • Supports custom, model-agnostic systems.
  • Can run locally with vLLM for offline operation.
  • Open-source under the Apache-2.0 license.
  • Installation is documented through pip.

What to know first

  • Requires an in-domain prompts dataset and unlabeled evaluation set.
  • Local execution needs a GPU.
  • Local execution needs over about 100 GB of disk space.
  • At least 50 validation examples are recommended.

Limits

Local execution requires a GPU and more than about 100 GB of available disk space. A human preference validation set is used, with at least 50 annotated examples recommended; a labeled evaluation set is optional because machine labels can be used.

Getting started

  1. Install with `pip install ares-ai` or clone the GitHub repository
  2. Prepare an in-domain prompts dataset
  3. Prepare an unlabeled query-document-answer evaluation set
  4. Create a human preference validation set, with at least 50 examples recommended
  5. Configure OpenAI or Together AI API access, or run a local model with vLLM
  6. Run the evaluation on Linux in your self-hosted environment

Verdict

ARES offers a defined evaluation method for RAG systems, with support for custom models and local execution. Its data and compute requirements are important to account for before setting up an evaluation.

Questions

Is ARES free?

Yes. ARES is free and open source under the Apache-2.0 license.

Which platforms does ARES support?

It supports an API on Linux and is deployed self-hosted.

Can ARES evaluate custom RAG systems?

Yes. ARES is model-agnostic and can evaluate queries and answers from custom RAG models.

Can it run without sending data to an external model API?

Yes. vLLM supports local model execution and can enable offline operation.

Which models and datasets are shown?

Examples use OpenAI, Together AI, and vLLM-hosted models. Dataset examples include KILT and SuperGLUE collections.

Facts from ARES's own pages

Annotation guidance
The README recommends at least 50 annotated query, document, and answer examples for the human preference validation set, with several hundred ideal. · github.com, 4 Oct 2026
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. · github.com, 9 Oct 2026
Custom systems
ARES is model-agnostic and can evaluate queries and answers generated by custom RAG models. · ares-ai.vercel.app, 4 Oct 2026
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. · ares-ai.vercel.app, 4 Oct 2026
Dataset example
The README says the optional full Natural Questions dataset is 37.3 GB. · github.com, 9 Oct 2026
Datasets
ARES can retrieve KILT datasets including nq, hotpotqa, wow, and fever, and SuperGLUE datasets including record, rte, boolq, and multirc. · ares-ai.vercel.app, 4 Oct 2026
Evaluation criteria
ARES assesses context relevance, answer faithfulness, and answer relevance. · github.com, 9 Oct 2026
Evaluation method
ARES combines synthetic data generation, fine-tuned classifiers, and Prediction-Powered Inference to estimate evaluation results with statistical confidence. · github.com, 9 Oct 2026
Evaluation metrics
ARES evaluates context relevance, answer faithfulness, and answer relevance. · github.com, 4 Oct 2026
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. · github.com, 4 Oct 2026
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. · github.com, 9 Oct 2026
Install
The documentation provides installation through `pip install ares-ai` or by cloning the GitHub repository and installing it locally. · ares-ai.vercel.app, 4 Oct 2026
Installation
The README gives `pip install ares-ai` as the installation command. · github.com, 9 Oct 2026
Intended users
The project documentation describes ARES as a tool for evaluating RAG systems and comparing different RAG configurations. · github.com, 9 Oct 2026
License
The GitHub repository identifies its license as Apache-2.0. · github.com, 9 Oct 2026
Local execution
ARES supports running models locally with vLLM, which the README says can enable offline operation and enhanced privacy. · github.com, 9 Oct 2026
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. · ares-ai.vercel.app, 4 Oct 2026
Model integrations
The README gives examples using OpenAI and TogetherAI API keys and shows local model execution with vLLM. · github.com, 4 Oct 2026
Model support
ARES describes itself as model-agnostic and shows examples using OpenAI, Together AI, and vLLM-hosted models. · github.com, 9 Oct 2026
Offline use
ARES says vLLM enables local model execution and offline operation. · github.com, 4 Oct 2026
Purpose
ARES is an automated framework for evaluating retrieval-augmented generation (RAG) systems. · github.com, 9 Oct 2026
Support
The README invites users with questions to contact [email protected] or [email protected]. · github.com, 9 Oct 2026

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