# Evidently AI

- Category: Models & resources
- Official site: https://www.evidentlyai.com/
- HTML: https://www.88z.ai/en/tools/evidently-ai
- Markdown: https://www.88z.ai/en/tools/evidently-ai.md

Evidently AI is a leading open-source AI evaluation and observability framework designed to help teams ensure their AI systems are safe, reliable, and ready.

## What is Evidently AI?

Evidently AI is a leading open-source AI evaluation and observability framework designed to help teams ensure their AI systems are safe, reliable, and ready.Addressing the unique challenges of non-deterministic AI such as hallucinations, data drift, and security risks, Evidently provides end-to-end solutions from test case generation to continuous monitoring, validating model performance with every update.

The framework uniformly supports the evaluation of LLMs, AI Agents, RAG applications, and traditional predictive ML systems.Licensed under Apache 2.0, it has garnered over 7,500 GitHub stars and 40 million downloads, trusted by thousands of companies worldwide, including DeepL and Wise, for data quality testing and model performance monitoring in production environments.

## Key features

- **LLM & Agent Eval**：Designed for chatbots, RAG apps, and AI Agents, evaluating hallucinations, factuality, context relevance, and adherence to guidelines.
- **Predictive ML Mo**：Built-in metrics for tracking predictive performance, data drift, and data quality to maintain the robustness of traditional ML models.
- **Automated Eval P**：Integrate evaluations into CI/CD pipelines to systematically measure AI output quality, safety, and reliability with shareable visual reports.
- **Synthetic Data G**：Create realistic edge-case or adversarial inputs tailored to your use case, ranging from harmless prompts to hostile attacks, to test system resilience.
- **Continuous Real-**：Track evaluation results and ongoing quality checks via live dashboards to catch drift, regressions, and emerging risks early.
- **100+ Built-in Me**：Extensive library of pre-built metrics covering PII detection, sentiment, toxicity, and custom rules, supporting classifiers and LLM-as-a-Judge approaches.

## How to use it

1. 1.Install Evidently: Install the open-source library via pip or check the official docs for integration guides.
2. 2.Choose Evaluation Scenario: Determine if you are evaluating LLM/RAG applications or predictive ML models, and select corresponding templates or metrics.
3. 3.Configure Test Cases: Use built-in metrics or define custom rules, generating synthetic data if needed to cover edge cases.
4. 4.Integrate into Pipeline: Add evaluation steps to your CI/CD flow or monitoring DAGs for automated testing.
5. 5.Visualize & Monitor: View results through generated reports or live dashboards, setting alerts for data drift or quality degradation.

## Where it fits

- **RAG Application**：Evaluate context relevance and factuality in Retrieval-Augmented Generation systems to prevent hallucinations and misinformation.
- **Production Model**：Continuously monitor business-critical ML models in MLOps platforms to promptly detect data distribution shifts and performance decay.
- **AI Safety & Comp**：Detect PII leaks, toxic outputs, and jailbreak attempts to ensure AI systems adhere to safety guidelines and regulatory requirements.

## Pricing and free quota

Pricing and any free quota follow the official site.

## Who it is for

- MLOps Engineers
- Data Scientists
- AI Product Managers
- ML Platform Teams

## FAQ

### What types of models does Evidently AI support?

It supports LLMs, AI Agents, RAG applications, and traditional predictive ML models, offering tailored evaluation metrics for each.

### Is it completely free?

The core framework is open-source and free (Apache 2.0).The official site may offer additional enterprise features or support; please check their website for details.

### How can I detect hallucinations in LLMs?

Evidently provides specific metrics for factuality and hallucination detection, and can integrate LLM-as-a-Judge methods for automated evaluation.

## Tags

AI Observability · LLM Evaluation · MLOps · Open Source Tools

Features, pricing and availability follow the official site. Except for 88z.ai’s own API pages, this is a third-party product; 88z.ai only provides a directory introduction.