Open-RAG-Eval
Open-RAG-Eval is an open-source evaluation framework for Retrieval-Augmented Generation (RAG) systems, with a special focus on agentic RAG—where RAG components are connected to an AI agent. Developed by Vectara, it eliminates the need for manually created golden answers by enabling automatic assessment of RAG pipeline quality using a suite of metrics. The framework allows users to compare different RAG setups, chunking strategies, and retriever-generator combinations. It integrates with various data sources and LLMs, providing a standardized way to benchmark and iterate on RAG performance. At its core, Open-RAG-Eval evaluates the faithfulness, relevance, and accuracy of generated responses without reference ground-truth answers. It measures how well a RAG system retrieves relevant context and generates factually aligned answers. The tool can be used to test both standalone RAG pipelines and AI agents that employ RAG as a tool for decision-making or question-answering. It supports streaming and multi-turn interactions, making it suitable for real-world agentic workflows. The framework is designed for extensibility: users can plugin custom metrics, retrieval sources, and evaluation datasets. It includes pre-defined metrics such as hallucination rate, context coverage, and answer completeness. A configurable YAML-based setup allows defining evaluation experiments, and results are presented in comparative reports. The project also offers containerized deployment via Docker and a Python package for easy integration into ML pipelines. Open-RAG-Eval is particularly valuable for teams building RAG-powered applications who need continuous quality monitoring, for researchers comparing novel RAG approaches, and for AI agent developers who want to ensure their agents provide reliable retrieved information. Its open-source nature encourages community contributions and transparency in evaluation methodologies.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
Evaluating RAG systems and agentic RAG pipelines is challenging without having pre-written gold-standard answers for every query. Open-RAG-Eval solves this by providing automatic metrics that assess retrieval and generation quality without requiring reference answers. This enables developers and researchers to quickly gauge how well their AI agents and RAG tools perform, identify weaknesses, and iterate on improvements.
Use cases
- Benchmarking different RAG architectures (e.g., dense vs. sparse retrieval) using automated metrics.
- Evaluating how chunking strategies affect retriever performance in a RAG pipeline.
- Assessing the reliability of AI agents that use RAG to answer user queries or execute tasks.
- Integrating RAG quality checks into CI/CD pipelines for continuous deployment of LLM applications.
- Comparing the faithfulness and relevance of outputs from various RAG-powered chatbots.
- Research experiments on novel retriever-generator fusion techniques without crafting manual test sets.
Audience
- Developers
- ML Engineers
- Data Scientists
- Researchers
Pros
- No need for golden answers; automatic evaluation saves time and effort.
- Built with agentic RAG scenarios in mind, evaluating end-to-end agent workflows.
- Extensible framework supports custom metrics and data sources.
- Open-source and community-driven (Apache 2.0 license expected).
- Containerized deployment simplifies setup and integration.
Cons
- Heavy reliance on the specific metrics implemented; may not cover all quality dimensions.
- Setup and configuration for custom RAG backends may require additional effort.
- Currently focused on text-based RAG; multimodal evaluation may not be supported.
Official website: https://github.com/vectara/open-rag-eval
Canonical LambdaBase page: https://www.lambdabase.com/tools/open-rag-eval