Skip to main content
Free
RAGAS logo
free

RAGAS

Verified 6 hours ago
Compare Alternatives Claim this listing
About RAGAS 17s read
1 min read

RAGAS (Retrieval Augmented Generation Assessment) is an open-source system for evaluating RAG pipelines using reference-free metrics. It assesses faithfulness, answer relevancy, context precision, and context recall automatically using LLMs, without requiring ground truth labels. RAGAS has become a standard benchmarking system for RAG pipeline quality and is integrated into LangChain and LlamaIndex.

Best for: RAG developers wanting automated, reference-free evaluation of their retrieval and generation quality using standard community benchmarks
Screenshot
Key Features
  • Reference-free RAG evaluation
  • Faithfulness & relevancy metrics
  • Context precision & recall scoring
  • LangChain & LlamaIndex integration
  • Custom metric support
Pros & Cons
What we like
No ground truth labels required
Standard metrics used across the RAG research community
Open-source and easy to integrate
What to consider
Evaluation quality depends on the evaluator LLM
Metrics can be gamed with poor retrieval
Questions and Answers

No questions yet. Be the first to ask!

Log in to ask a question.

User Reviews

No reviews yet. Be the first to review RAGAS!

Log in to leave a review.

You might also like
Hypotenuse AI H
Hypotenuse AI
freemium
Braze B
Braze
paid
Sprinklr S
Sprinklr
paid
User Reviews

No reviews yet. Be the first to leave a review!

Log in to leave a review.

Was this page helpful?

AI Glossary

The simulation of human intelligence in machines programmed to think, learn, and problem-solve. AI encompasses machine learning, natural language processing, computer vision, and more.

A deep learning model trained on vast text datasets to understand and generate human-like language. Examples include GPT-4, Claude, and Gemini.

AI systems that create new content - text, images, audio, video, or code - based on patterns learned during training rather than retrieving existing data.

The practice of designing and refining input text (prompts) to guide an AI model toward producing more accurate, relevant, or creative outputs.

A computing architecture inspired by the human brain, consisting of interconnected layers of nodes that learn to recognise patterns in data.

A branch of AI where algorithms improve automatically through experience and exposure to data, without being explicitly programmed for each task.

AI technology that enables computers to understand, interpret, and generate human language - the foundation of chatbots, translation tools, and voice assistants.

The process of further training a pre-trained AI model on a specific, smaller dataset to specialise it for a particular task or domain.

When an AI model generates plausible-sounding but factually incorrect or entirely fabricated information, often presenting it with false confidence.

The units of text (roughly 4 characters or ¾ of a word in English) that LLMs process. Model costs and context limits are measured in tokens.

The maximum amount of text (measured in tokens) an LLM can process in a single interaction - both input and output combined.

A technique that enhances LLM outputs by fetching relevant external documents at query time, grounding responses in up-to-date or proprietary data.

Numerical vector representations of text, images, or other data that capture semantic meaning, enabling AI to measure similarity and retrieve relevant content.

The process of running a trained AI model to produce predictions or outputs from new input data - distinct from the training phase.

An AI system that autonomously plans and executes multi-step tasks by combining reasoning, tool use (web search, code execution, APIs), and memory.

AI models that can process and generate multiple types of data - such as text, images, audio, and video - within a single unified system.

A large-scale AI model trained on broad data that serves as a base for many downstream applications via fine-tuning or prompting.

A pricing model where core features are available for free, with premium features, higher usage limits, or advanced capabilities offered via paid plans.

A set of protocols that allows developers to integrate an AI tool's capabilities directly into their own applications and workflows.

Zero-shot means the model handles a task without any examples; few-shot means it is given a small number of examples in the prompt to guide its response.
Trending Now
Claude AI Learning Brain OpenAI Operator
Tool Info
Verified 6 hours ago
Pricing free
Added Jun 02, 2026
Source Manual Entry
Visit Website Back to Tools See Alternatives
Share:
Pricing Notes

AI tool pricing changes frequently. The pricing shown was accurate at our last editorial review. We recommend checking the tool's website directly for current plans.

Last reviewed: Oct 23, 2026
Check Current Pricing
Related Tools
Hypotenuse AI freemium Braze paid Sprinklr paid Buoy Health free
Compare

See how RAGAS stacks up against alternatives.

vs Hypotenuse AI vs Braze vs Sprinklr
Pricing
Free
Visit Tool
Best For

RAG developers wanting automated, reference-free evaluation of their retrieval and generation quality using standard community benchmarks

Embed this tool

Add a link badge to your website or blog:

People Also Viewed

Hypotenuse AI
Hypotenuse AI
Freemium
 Braze
Braze
Paid
 Sprinklr
Sprinklr
Paid
 Buoy Health
Buoy Health
Free