Overview
Compare RAGAS (RAG evaluation specialist) and DeepEval (general LLM evaluation) across evaluation metrics, testing methods, and CI/CD integration.
Python · Apache-2.0
Ragas is a framework for evaluating RAG (Retrieval Augmented Generation) systems. It provides various evaluation metrics including faithfulness, answer relevance, context precision, helping developers optimize RAG application performance.
Python · Apache-2.0
DeepEval is an open-source evaluation framework for LLM applications. It provides rich evaluation metrics and tools, supporting unit testing and integration testing to help developers build reliable LLM applications.
| Best for | Ragas | DeepEval |
|---|---|---|
| Core positioning | Specialized in RAG system evaluation, provides RAG-specific metrics like Context Precision/Recall, Answer Relevancy, Faithfulness | General-purpose LLM evaluation framework covering RAG, Agent, conversation, hallucination, bias and other evaluation scenarios |
| Evaluation metrics | Built-in 10+ RAG metrics, includes LLM-as-judge and traditional metrics (BLEU, ROUGE), custom metrics need Python writing | Built-in 30+ evaluation metrics, includes G-Eval, Hallucination, Toxicity, Bias etc., supports custom metrics via plugin mechanism |
| Testing methods | Synthetic test set generation based on real business datasets, integration with RAG frameworks like LangChain, LlamaIndex | Supports synthetic data generation, A/B testing, regression testing, CI/CD integration, Confident AI platform provides cloud dashboard |
| Integration and ecosystem | Integration via Python SDK, with LangSmith, Weights & Biases, MLflow visualization integration | Native CI/CD integration (GitHub Actions, GitLab CI), seamless integration with mainstream LLM frameworks |
| Metric | Ragas | DeepEval |
|---|---|---|
| Stars | 14.9k | 17.0k |
| Forks | 1.6k | 1.7k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last commit | February 24, 2026 | July 20, 2026 |
Choose based on your primary workflow, language ecosystem, and integration needs. Review each project's documentation and recent GitHub activity before adopting it in production.