DeepEval

DeepEvalSince 2026-01

DeepEval is an open-source LLM evaluation framework built by Confident AI that feels like 'Pytest for LLMs'. It allows AI engineers to write unit tests for prompts, RAG pipelines, and conversational agents with production-ready evaluation metrics that execute seamlessly in local terminals and CI/CD pipelines. DeepEval includes 14+ research-backed evaluation metrics covering hallucination, G-Eval, toxicity, answer relevancy, bias, summarization quality, and tool correctness.

DeepEval tests can be run directly using the standard 'pytest' command line. It integrates with Confident AI's cloud dashboard to track metric drift over time, analyze test run regressions, and debug failing test cases with root-cause explanations. It supports custom LLM judges, synthetic dataset synthesis, and synthetic edge-case generation to stress-test AI systems prior to enterprise deployment.

RagasSince 2026-01

Ragas (Retrieval Augmented Generation Assessment) is the industry-standard evaluation framework designed specifically to measure the performance of RAG pipelines without requiring human-annotated ground truth datasets. Ragas evaluates RAG systems across critical dimensions: Faithfulness (hallucination detection), Answer Relevance (query alignment), Context Precision (signal-to-noise ratio in retrieved chunks), and Context Recall (measuring whether all necessary information was retrieved).

Ragas also includes powerful synthetic test data generation capabilities (Ragas Testset Generation), creating diverse multi-hop questions, reasoning challenges, and adversarial probes from raw document corpora automatically. It integrates natively with LangChain, LlamaIndex, Haystack, and DSPy, enabling continuous evaluation loops in production monitoring and pre-deployment automated CI gates.