Ragas iconRagas

open-source Free Star14k

Open-source framework for evaluating RAG pipelines with reference-free LLM-as-judge metrics

python
14.2K+ GitHub Stars
2M+ PyPI Downloads
14+ Built-in Metrics

Overview

Ragas (Retrieval Augmented Generation Assessment) is an open-source framework that provides reference-free evaluation metrics for RAG pipelines. Rather than requiring ground-truth labels, Ragas uses LLM-as-judge techniques to assess retrieval quality, generation faithfulness, and answer relevancy. Originally developed by Exploding Gradients, the framework has become the de facto standard for RAG evaluation in production systems. It integrates seamlessly with LangChain, LlamaIndex, and other orchestration frameworks, making it easy to add evaluation to existing RAG applications.

The Verdict

Who Should Use Ragas?

Best For

  • Teams building RAG applications needing quality metrics
  • Production systems requiring automated evaluation
  • Developers comparing retrieval strategies
  • CI/CD pipelines needing regression testing
  • Research teams benchmarking RAG approaches

Not Ideal For

  • General LLM evaluation (not RAG-specific) - try DeepEval
  • Teams needing a managed platform - try TruLens Cloud
  • Non-Python environments
  • Applications requiring human-in-the-loop evaluation
  • Cost-sensitive projects (requires LLM API calls)

What's Great

  • Reference-free metrics - no ground truth labels needed
  • RAG-specific metrics (faithfulness, context relevancy, answer relevancy)
  • Easy integration with LangChain, LlamaIndex, Haystack
  • Synthetic test data generation for cold starts
  • Active community and rapid development
  • Well-documented with extensive examples
GitHub · Official Docs

Watch Out For

  • Evaluation costs can add up (LLM API calls for each metric)
  • Metrics can be inconsistent across different judge LLMs
  • Limited support for multi-turn conversations
  • No built-in dashboard (need external visualization)
  • Some metrics require specific data formats
GitHub Issues · Community Feedback

Pricing

Open Source
Free
Full framework, all metrics, unlimited usage
LLM Costs
~$0.01-0.05
Per evaluation (varies by provider)
View all features & details

Core Metrics

  • Faithfulness - factual consistency with context
  • Answer Relevancy - response matches question
  • Context Precision - relevant chunks ranked higher
  • Context Recall - retrieves all necessary info
  • Context Relevancy - retrieved docs are pertinent
  • Answer Correctness - accuracy vs ground truth
  • Answer Similarity - semantic match scoring
  • Harmfulness - safety and toxicity detection

Advanced Features

  • Synthetic test data generation
  • Custom metric creation
  • Async evaluation support
  • Batch processing
  • Multi-modal evaluation (experimental)
  • Agent/tool evaluation metrics
  • Aspect-based critique

Integrations

  • LangChain
  • LlamaIndex
  • Haystack
  • OpenAI, Anthropic, Azure OpenAI
  • Hugging Face models
  • Arize Phoenix
  • LangSmith
  • Weights & Biases

Platforms & Requirements

  • Python 3.8+
  • pip install ragas
  • Works on macOS, Linux, Windows
  • Jupyter notebook support
  • CI/CD pipeline compatible

How It Compares

Feature Ragas DeepEval TruLens
Focus RAG-specific General LLM RAG + General
Reference-free Yes Yes Yes
Built-in Metrics 14+ 14+ 10+
Test Generation Yes Yes No
Managed Platform No Confident AI TruLens Cloud
LangChain Integration Native Yes Yes
Pytest Integration No Native No
Cost Free Free / Paid Free / Paid
Best For RAG pipelines CI/CD testing Observability

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