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AI Evaluation & Observability

Prometheus Eval

Prometheus Eval is an open-source Python library for evaluating language-model outputs on generation tasks. It supports grading instruction-and-response pairs, including absolute scores from 1 to 5, and can use Prometheus locally through vLLM or evaluator models accessed through LLM APIs with LiteLLM.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Prometheus Eval — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a Python-based way to score generated responses against a prompt and rubric, or want to configure a judge model through local inference or an LLM API.

Who it is for

  • Developers assessing generated answers against instructions and scoring rubrics.
  • Teams comparing evaluator models or building LLM-as-a-judge evaluation workflows.

Documented uses

  • Assign rubric-based absolute grades to generated responses.
  • Evaluate language-model output using Prometheus or an API-accessible evaluator such as GPT-4.
  • Choose between local inference and an LLM API pathway for evaluation.
Before you choose

Requirements & limits

The repository identifies the project as open source under the Apache-2.0 license. Its README describes the library as beta.

  • Local Prometheus inference uses vLLM; the README separately documents API inference through LiteLLM.
  • The API examples include Prometheus served through vLLM or Hugging Face TGI and GPT-4 through an API.
  • The example absolute-grading output is a score from 1 to 5.
  • The repository labels the library beta.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Prometheus Eval for local inference

Install the library and the documented local inference dependency.

Before you start

  • Python environment for installing packages with pip.

Steps

  1. Install the library with the command below.

    Documented command
    pip install prometheus-eval

  2. For local Prometheus inference, install vLLM with the command below.

    Documented command
    pip install vllm

  3. Continue with the repository's Quick Start example to see the documented evaluator and grading pattern.

Expected outcome

The library and its stated local inference dependency are installed; the README provides a Quick Start example for setting up an evaluator and absolute grading.

Guide limitations

  • The repository labels the library beta.
  • These installation steps do not establish successful execution or compatibility on a particular system.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Prometheus EvalAssign rubric-based absolute grades to generated responses.Linux, macOS, WindowsThe repository identifies the project as open source under the Apache-2.0 license. Its README describes the library as beta.
FMBenchCompare inference latency and transactions per minute across model-serving configurations.CLI, CloudFMBench is distributed as a Python package and used from the command line. The project is published under the MIT-0 license.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
4 November 2026
Original website title
prometheus-eval/prometheus-eval · Official homepage title · retained in its source language
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