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AI Evaluation & Observability

Xrag

Xrag is a Python research framework for benchmarking foundational components of retrieval-augmented generation systems through a CLI and interactive WebUI.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Xrag — 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 Xrag when you need to configure and compare RAG pipeline components, inspect system responses and assess performance with retrieval and answer-evaluation metrics.

Who it is for

  • RAG developers comparing retrieval and pipeline components
  • Researchers evaluating RAG configurations and system outputs

Documented uses

  • Compare retrieval approaches such as vector, BM25, hybrid and tree-based retrieval.
  • Evaluate retrieval quality, response faithfulness and answer correctness.
  • Inspect retrieved results and generated responses during interactive testing.
  • Organize agentic RAG workflows with sequential, conditional, iterative, parallel or hybrid orchestrators.
Before you choose

Requirements & limits

The project is a public GitHub repository licensed under Apache-2.0. The README specifies Python 3.11 or later and pip installation.

  • The framework lists traditional retrieval metrics, LLM-based response measures and deeper contextual measures.
  • The README describes pluggable retrievers, embeddings and LLMs, but does not specify the complete supported component set or benchmark procedures.
  • The interactive workflow includes dataset configuration, index building, strategy selection, query testing and evaluation.
  • The README names OpenAI and local models such as Qwen and LLaMA; model setup and any required credentials depend on configuration.

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

Install XRAG and prepare its CLI configuration

Set up the Python package and prepare the configuration file before using XRAG commands.

Before you start

  • Python 3.11 or later
  • pip
  • A config.toml file to edit

Steps

  1. Install the package with pip install examinationrag.

  2. Install jury without dependencies using pip install jury --no-deps.

  3. Apply the README's listed package version adjustments for requests, urllib3 and jiwer.

  4. Edit config.toml to set the desired configuration.

Expected outcome

The README describes the xrag-cli command as available after installation and configuration preparation.

Guide limitations

  • This guide covers installation and configuration preparation only; it does not establish that a benchmark run succeeds.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
9 October 2026
Original website title
DocAILab/XRAG · Official homepage title · retained in its source language
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