Why it matters
- Provides objective measurement for RAG systems that otherwise require expensive human evaluation.
- Identifies which part of a RAG pipeline is failing — retrieval quality vs. generation quality — enabling targeted improvement.
- LLM-as-a-judge evaluation scales to thousands of test cases automatically.
- Framework-agnostic: evaluates any RAG system built with LangChain, LlamaIndex, or custom code.
Key capabilities
- Faithfulness: Measures whether every claim in the answer is supported by retrieved context — detects hallucinations.
- Answer Relevancy: Scores how well the answer addresses the user's original question.
- Context Recall: Evaluates whether the retrieval step found all information needed to answer correctly.
- Context Precision: Measures how relevant the retrieved documents are — penalizes noisy retrieval.
- Answer Correctness: When ground truth is available, measures factual accuracy.
- Aspect Critique: Define custom evaluation criteria for domain-specific quality requirements.
- LangChain integration: Direct evaluator integration for LangChain-based RAG chains.
- LlamaIndex integration: Evaluate LlamaIndex query pipelines with RAGAS metrics.
- Ragas Cloud: Managed dashboard for tracking evaluation results over time and across experiments.
Technical notes
- License: Apache 2.0 (open source)
- GitHub: github.com/explodinggradients/ragas (7K+ stars)
- Install:
pip install ragas - Evaluation LLM: GPT-4 (default); any LangChain-compatible LLM
- Frameworks: LangChain, LlamaIndex, custom RAG
- Output: Per-metric 0–1 scores; dataset-level aggregate statistics
- Pricing: Free (library); Ragas Cloud (managed) pricing available
Ideal for
- ML engineers building RAG applications who need to measure and improve retrieval and generation quality.
- Teams iterating on RAG pipelines who need automated evaluation to track improvement across iterations.
- Research teams comparing different embedding models, chunking strategies, or retrieval approaches quantitatively.
Not ideal for
- Evaluating non-RAG LLM applications — use TruLens or Promptfoo for general LLM evaluation.
- Real-time monitoring of production LLM calls — RAGAS is designed for offline batch evaluation.
- Replacing human evaluation entirely — LLM-as-a-judge has known biases and limitations for subjective quality.