RAGAs

Evaluation framework for your RAG pipelines

Overview

RAGAs (Retrieval-Augmented Generation Assessment) is a dedicated, open-source framework designed specifically for evaluating the performance of RAG pipelines. It provides a set of metrics that assess each component of the RAG system—the retriever, the generator, and the overall pipeline. By measuring aspects like faithfulness, answer relevance, and context precision, RAGAs helps developers understand the bottlenecks in their RAG applications and provides actionable insights for improvement. It is designed to work with minimal to no ground truth labels.

✨ Key Features

Specialized RAG Evaluation
Component-level Metrics (Retriever, Generator)
Faithfulness & Answer Relevance Scoring
Context Precision & Recall
Open Source
Works without Ground Truth Labels
Integration with other frameworks

🎯 Key Differentiators

Highly specialized for RAG pipeline evaluation
Ability to evaluate without requiring human-annotated ground truth
Focus on component-level metrics for targeted improvements
Strong adoption within the open-source community

Unique Value: RAGAs provides a specialized, open-source framework to scientifically evaluate and debug RAG pipelines, enabling developers to build more accurate and reliable retrieval-augmented generation systems.

🎯 Use Cases (4)

Evaluating the performance of a RAG-based chatbot or search engine Comparing different retrieval strategies or LLMs within a RAG pipeline Identifying whether the retriever or the generator is the source of poor performance Automating the testing of RAG systems in a CI/CD workflow

            ✅ Best For
            Benchmarking different embedding models for a RAG retriever
Measuring the factual consistency (faithfulness) of a RAG system's answers
Evaluating the relevance of retrieved context for a given query

        

💡 Check With Vendor

Verify these considerations match your specific requirements:

Evaluating non-RAG LLM applications
General-purpose LLM observability and monitoring

🏆 Alternatives

UpTrain DeepEval Arize AI LangSmith

While many general-purpose evaluation tools have added RAG metrics, RAGAs is built from the ground up for this specific task. Its ability to work without ground truth data makes it particularly valuable for rapid iteration and development.

💻 Platforms

Python Library

✅ Offline Mode Available

🔌 Integrations

LangChain LlamaIndex Hugging Face OpenAI

💰 Pricing

Contact for pricing

Free Tier Available

Free tier: RAGAs is a completely free and open-source project.

Visit RAGAs Website →

RAGAs

Overview

✨ Key Features

🎯 Key Differentiators

🎯 Use Cases (4)

✅ Best For

💡 Check With Vendor

🏆 Alternatives

💻 Platforms

🔌 Integrations

💰 Pricing

🔄 Similar Tools in LLM Evaluation & Testing

Arize AI

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Weights & Biases

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