Evidently AI

What is Evidently AI?

Evidently AI is an open-source platform built for teams who need to test, evaluate, and monitor AI systems—especially LLMs, RAG pipelines, and multi-agent workflows—in real-world production environments. Unlike traditional software, AI models can fail in unpredictable ways: they hallucinate, leak sensitive data, or break under cleverly crafted prompts. Evidently helps you catch these issues early with automated testing, continuous monitoring, and clear visual reports.

Whether you're a startup shipping your first chatbot or an enterprise managing dozens of AI services, Evidently gives you the tools to ensure your AI stays safe, reliable, and high-performing after every update. Built on a trusted open-source Python library with over 35 million downloads, it’s designed by AI practitioners for AI builders.

What are the features of Evidently AI?

• LLM Testing Platform: Evaluate output quality, safety, factuality, and adherence to guidelines across thousands of test cases.
• RAG Evaluation: Measure retrieval accuracy and reduce hallucinations by checking how well responses align with retrieved context.
• Adversarial Testing: Simulate attacks like jailbreaks, PII leaks, and toxic prompts to uncover hidden risks before bad actors do.
• AI Agent Testing: Validate complex, multi-step agent workflows—including tool use, reasoning chains, and decision logic.
• ML Monitoring: Track data drift, feature anomalies, and model performance degradation over time for both traditional ML and generative AI.
• Synthetic Test Data Generation: Automatically create realistic edge cases and adversarial inputs tailored to your domain.
• Open-Source Foundation: Leverage the Evidently Python library (7,000+ GitHub stars) for full transparency, customization, and offline use.

What are the use cases of Evidently AI?

• Testing a customer support chatbot for hallucinations and brand-compliant tone before launch
• Monitoring a RAG-powered internal knowledge assistant to ensure retrieved documents match user queries
• Running red-team simulations on a public-facing AI to prevent prompt injection or data leakage
• Tracking data drift in a loan approval ML model after a major economic shift
• Validating a travel-planning AI agent that books flights, hotels, and activities in sequence
• Generating compliance-ready evaluation reports for auditors or product stakeholders
• Comparing fine-tuned LLM versions during A/B testing to pick the best performer

How to use Evidently AI?

• Install the open-source Evidently Python library via pip install evidently to start local testing
• Define your evaluation criteria using built-in metrics (e.g., toxicity, PII detection) or custom LLM-as-a-judge prompts
• Generate synthetic test datasets that mimic real user inputs—including edge cases and adversarial examples
• Run batch evaluations on model outputs and get interactive HTML reports highlighting failures
• Integrate with CI/CD pipelines to automatically test new model versions before deployment
• Deploy the Evidently UI for live dashboards that track performance, drift, and quality over time