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 evidentlyto 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
