Evidently AI

Evidently is an Apache 2.0 Python library built by Elena Samuylova and Emeli Dral, who previously worked together at Yandex Data Factory, Russia's enterprise AI division, before co-founding an industrial AI startup and then launching Evidently AI in 2020 with Y Combinator backing. The library predates the LLM era by three years, originally focused on data drift detection and traditional ML model monitoring for classifiers, regression models, and recommendation systems. When LLM applications became production workloads, Evidently extended the same framework to cover RAG evaluation, agent testing, and LLM safety checks rather than building a separate product. That breadth distinguishes Evidently from every other tool in the Testing category: teams running both classical ML pipelines and LLM applications can instrument both through a single library with over 20 million downloads.

Key Capabilities

• 100+ pre-built metrics spanning ML and LLM: Metrics cover data drift, model performance degradation, text quality, semantic similarity, retrieval relevance, summarization quality, toxicity, PII detection, and LLM-as-judge scoring, with a custom metric API for project-specific evaluation criteria

• Data drift detection: Identifies distribution shifts between training and production data for tabular ML models, triggering alerts or pipeline actions before model performance visibly degrades in user-facing applications

• RAG and agent testing: Validates retrieval accuracy and hallucination rates in RAG pipelines and checks multi-step reasoning, tool use, and workflow completion in agent applications

• Adversarial testing: Probes LLM applications for jailbreaks, PII leakage, and harmful content generation before deployment, with auto-generated test conditions based on historical examples

• CI/CD integration with automated test suites: Structured test runs with configurable pass/fail thresholds integrate into existing deployment pipelines, blocking releases when drift or quality checks fail

• Evidently Cloud managed platform: A commercial layer on top of the OSS library that adds team collaboration, role-based access control, live dashboards, and alerting without requiring teams to self-host the monitoring backend