How does the test set generation work?

Ragas builds a knowledge graph from your documents, simulates user queries (single-hop, multi-hop) based on different personas, and generates synthetic question-answer-context triplets for evaluation.

Can I use Ragas without an expensive LLM like GPT-4?

Yes, Ragas is LLM-agnostic via adapters. You can use open-source models via HuggingFace or other providers, though metric accuracy may vary. Configuration is done via the RunConfig.

How do I evaluate a custom AI agent?

Use the agent-specific metrics like Tool Call Accuracy, Topic Adherence, and Agent Goal Accuracy. Ragas can parse agent traces to evaluate the sequence of tool calls and final output.

Is there a way to reduce evaluation costs?

Utilize the built-in caching mechanism to store LLM responses for repeated queries. You can also use smaller, cheaper LLMs for certain metrics where high precision isn't critical.

How does Ragas handle non-English data?

Ragas supports non-English evaluation through language adaptation of prompts and metrics. You can customize prompts for different languages and use appropriate multilingual embedding models.

What is the difference between the `evaluate()` and `aevaluate()` functions?

The `evaluate()` function is synchronous, while `aevaluate()` is the asynchronous version for use in async environments like FastAPI or Jupyter notebooks with asyncio, allowing for concurrent execution.

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Ragas

Ragas is an open-source framework for evaluating Large Language Model (LLM) applications, designed to replace subjective 'vibe checks' with systematic, reproducible evaluation loops. It provides a comprehensive toolkit for measuring performance across key dimensions like faithfulness, answer relevancy, context precision, and recall, using both LLM-driven and traditional NLP metrics. The core philosophy is experimentation-first, allowing developers to run evaluations as part of their development pipeline, track results over time, and iterate with confidence. It integrates seamlessly with popular AI frameworks like LangChain and LlamaIndex, and supports custom metric creation. Ragas also includes tools for generating synthetic test datasets, particularly for RAG (Retrieval-Augmented Generation) and agent-based systems, to stress-test applications. Targeted at AI engineers, ML researchers, and technical founders building LLM-powered products, Ragas provides the metrics and infrastructur

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Sep 22, 2026

LLM evaluation libraryRAG metricsAgent metricsTestset generationExperiment tracking

Product information

Everything worth knowing about Ragas

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Experiments-first approach

Evaluate changes consistently with experiments. Make changes, run evaluations, observe results, and iterate to improve your LLM application.

Ragas Metrics Library

Use a library of pre-built metrics for RAG, agents, SQL, and general tasks, or create custom metrics with simple decorators.

Framework Integration

Built-in integration with popular frameworks like LangChain, LlamaIndex, Haystack, and more for easy evaluation workflows.

Testset Generation

Automatically generate test data for RAG, agents, and other AI applications to create evaluation datasets.

Technical capabilities

Metric Customization

Modify metric prompts, adapt metrics to different languages, and train or align metrics to specific use cases.

Must watch videos

RAGAS: How to Evaluate a RAG Application Like a Pro for Beginners

8:37

Tutorial8:37

RAGAS: How to Evaluate a RAG Application Like a Pro for Beginners

Welcome to an in-depth tutorial on RAGAS, your go-to framework for evaluating and testing retrieval-augmented generation (RAG) applications. 🌐🛠️ Today, we're diving into how RAGAS can enhance your AI's performance by precisely assessing both the retrieval and generation aspects of language models. Evaluate a RAG application. RAGAS: How to Evaluate a RAG Application Like a Pro for Beginners 🔹 What is RAGAS? RAGAS stands for Retrieval-Augmented Generation Application System, designed to test the effectiveness of language and embedding models by simulating real-world queries and assessing the responses. 🔹 Tutorial Overview: Installation: Easy steps to get RAGAS up and running on your system. Core Components: Understanding the critical components of RAGAS, including data ingestion, chunk conversion, and response generation. Practical Demonstration: Walkthrough of a sample testing scenario to show how you can implement RAGAS to evaluate your AI models. Evaluation Metrics: Detailed explanation of key metrics like faithfulness and answer correctness, ensuring your model's outputs are accurate and reliable. 🔹 Why Watch This Video? Enhance AI Performance: Learn to pinpoint strengths and weaknesses in your AI models. Streamlined Testing: Discover how to systematically test and improve the retrieval and generation capabilities of your language models. 🔔 Don't forget to subscribe and hit the bell icon to stay updated on all things Artificial Intelligence. Like this video to help it reach more AI enthusiasts like you! 🔗 Resources: Sponsor a Video: https://mer.vin/contact/ Do a Demo of Your Product: https://mer.vin/contact/ Patreon: https://patreon.com/MervinPraison Ko-fi: https://ko-fi.com/mervinpraison Discord: https://discord.gg/nNZu5gGT59 Twitter / X : https://twitter.com/mervinpraison Code: https://mer.vin/2024/05/ragas-basics-to-evaluate-rag/ Timestamps: 0:00 - Introduction to RAGAS 0:38 - How RAGAS Works: From Data Ingestion to Response Generation 1:40 - Starting with RAGAS: Installation Guide 2:46 - Configuring RAGAS: Step-by-Step Code Walkthrough 3:47 - Evaluating AI with RAGAS: Understanding Metrics 5:30 - Practical Example: Running RAGAS in Action 6:01 - Results Analysis: Interpreting Output Data 7:31 - What's Next? Upcoming Detailed Tutorials on RAGAS #RAGAS #Evaluate #RAG #Application

19:41

Tutorial19:41

AI Agent Evaluation with RAGAS

RAGAS (RAG ASsessment) is an evaluation framework for RAG pipelines. Here, we see how to use RAGAS for evaluating an AI agent built using LangChain and using Anthropic's Claude 3, Cohere's embedding models, and the Pinecone vector database. 📌 Code: https://github.com/pinecone-io/examples/blob/master/learn/generation/better-rag/03-ragas-evaluation.ipynb 📕 Article: https://www.pinecone.io/learn/series/rag/ragas/ 🌟 Build Better Agents + RAG: https://platform.aurelio.ai (use "JBMARCH2025" coupon code for $20 free credits) 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ 00:00 RAG Evaluation 00:39 Overview of LangChain RAG Agent 03:04 RAGAS Code Prerequisites 03:40 Agent Output for RAGAS 05:14 RAGAS Evaluation Format 08:04 RAGAS Metrics 08:56 Understanding RAGAS Metrics 09:16 Retrieval Metrics 11:55 RAGAS Context Recall 14:43 RAGAS Context Precision 15:52 Generation Metrics 16:05 RAGAS Faithfulness 17:16 RAGAS Answer Relevancy 18:40 Metrics Driven Development #ai #artificialintelligence #nlp #chatbot #langchain

22:21

Tutorial22:21

Evaluate AI Agents in Python with Ragas

In this video we take a look at Ragas, a Python package made for evaluating AI agents and RAG systems. Code: https://github.com/NeuralNine/youtube-tutorials/tree/main/Ragas%20Tutorial ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾ 📚 Programming Books & Merch 📚 🐍 The Python Bible Book: https://www.neuralnine.com/books/ 💻 The Algorithm Bible Book: https://www.neuralnine.com/books/ 👕 Programming Merch: https://www.neuralnine.com/shop 💼 Services 💼 💻 Freelancing & Tutoring: https://www.neuralnine.com/services 🖥️ Setup & Gear 🖥️: https://neuralnine.com/extras/ 🌐 Social Media & Contact 🌐 📱 Website: https://www.neuralnine.com/ 📷 Instagram: https://www.instagram.com/neuralnine 🐦 Twitter: https://twitter.com/neuralnine 🤵 LinkedIn: https://www.linkedin.com/company/neuralnine/ 📁 GitHub: https://github.com/NeuralNine 🎙 Discord: https://discord.gg/JU4xr8U3dm

Who uses it

Real use cases, no hype

RAG Pipeline Evaluation

Systematically measure retrieval precision and generation faithfulness for retrieval-augmented generation systems.

Agent Behavior Analysis

Evaluate tool-use accuracy, goal adherence, and topic consistency in autonomous AI agents.

Prompt Iteration

A/B test prompt versions with consistent experiments to quantitatively measure improvements.

Benchmarking LLMs

Compare performance of different LLMs (e.g., GPT-4 vs. Llama) on custom evaluation datasets.

Summarization Quality Check

Assess factual correctness, relevance, and conciseness of generated summaries.

Multi-turn Conversation Analysis

Evaluate coherence, relevance, and consistency across conversational exchanges.

Text-to-SQL Validation

Measure semantic equivalence and execution correctness of generated SQL queries.

What's great

Rich library of specialized metrics for RAG, agents, and summarization.
Seamless integration with LangChain, LlamaIndex, and other major frameworks.
Built-in test set generation for RAG and agent workflows.
Active community and clear documentation for quick start.

Technical strengths

LLM-driven metrics capture nuance better than traditional NLP scores.
Experiments-first approach enables rigorous A/B testing of prompt/model changes.
Extensible via simple decorators for custom metric creation.

Where it falls short

Initial setup requires configuring LLM providers and embedding models.
Complex metric definitions may have a learning curve.
Can be overkill for simple, single-turn QA evaluation.

Technical limitations

Relies on LLM calls for metrics, adding latency and cost.
Dependency on external LLM APIs means potential rate limits or downtime.
Test set generation quality is highly dependent on the source data graph.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

Key metrics include Faithfulness (generation grounded in context), Context Precision (relevance of retrieved context), Response Relevancy (answer addresses the query), and Answer Correctness (factual accuracy). They are LLM-assessed.