Automated evaluation
Measure AI output quality, safety, and reliability as part of a pipeline, then review results through shareable visual reports.
Evidently AI
RevendiquerEvidently AI is an open-source framework for evaluating and monitoring LLMs, RAG apps, AI agents, and predictive ML systems with tests, drift tracking, and reports.
Open-source AI evaluation and observability
Evidently AI is an open-source framework for AI evaluation and observability. It is designed to help teams test, monitor, and understand LLMs, RAG applications, AI agents, and predictive machine learning models in one place.
The product focuses on production quality: generating test cases, running automated checks, tracking drift and regressions, and presenting the results in visual reports or dashboards. The source material describes it as fully open-source under Apache 2.0 and positions it for both programmatic use and web-based review.
Core capabilities
Synthetic test data
Create realistic, edge-case, or adversarial inputs for your use case, including hostile prompts and other stress tests.
Continuous monitoring
Track evaluation results and ongoing checks in a live dashboard to surface drift, regressions, and emerging risks early.
Built-in and custom metrics
Use 100+ built-in metrics for LLMs and predictive ML, with support for custom rules, classifiers, prompts, and model-based checks.
Debugging and root-cause analysis
Investigate quality drops with pre-built summaries, plots, and performance views that help isolate what changed and where.
Collaborative review
Share findings with different stakeholders through custom views, reports, and a web interface or API workflow.
Common use cases
LLM application testing
Evaluate chatbots, copilots, RAG systems, AI agents, and other LLM-powered products before and after release using configurable templates and checks.
Predictive ML monitoring
Track data quality, data drift, and model performance for classification, regression, ranking, and recommender systems in production.
Adversarial quality testing
Generate synthetic prompts and adversarial examples to stress-test safety, factuality, PII handling, and other quality dimensions.
Shared analysis and debugging
Use dashboards, summaries, and custom views to diagnose quality changes and explain results to engineers, product managers, and domain experts.
Pros and Cons
Pros
- Covers both LLM workflows and predictive ML monitoring in one framework.
- Supports automated evaluation, synthetic data generation, and continuous monitoring.
- Includes 100+ built-in metrics and allows custom checks.
- Offers both programmatic and web-interface workflows for different teams.
- Provides visual reports, dashboards, and summaries that support collaboration and debugging.
Cons
- The provided pages do not confirm pricing, plan structure, or deployment options.
- Integration details with third-party tools are not shown in the source material.
FAQ
What kinds of systems does Evidently AI support?
Evidently AI provides open-source tools for evaluating and monitoring LLMs, RAG applications, AI agents, and predictive ML systems. The source material does not show a separate setup flow, but it does describe using the library in a pipeline or through the web interface.
What outputs can teams get from the platform?
The product supports automated evaluation, synthetic data generation for testing, continuous monitoring, and report generation. For ML use cases, it also offers model cards, performance reports, and pre-built summaries and plots for root cause analysis.
Can different team members use Evidently AI together?
The source says checks can be run programmatically or through the web interface, and results can be shared as visual reports or custom views. It is described as built for engineers, product managers, and domain experts to collaborate on AI quality.
What information is not confirmed in the source material?
The source emphasizes evaluation, testing, monitoring, debugging, and sharing results, but it does not show pricing, deployment options, or supported third-party integrations on the pages provided.
Quick Facts
- Category
- AI Evaluation and Observability
- Platform
- Open-source framework
- License
- Apache 2.0
- Primary users
- AI, ML, and MLOps teams
- Source domain
- evidentlyai.com
- Workflow
- Programmatic checks and web-interface review