Automated evaluations
Measure AI output quality, safety, and reliability, and run evaluation workflows as part of a pipeline.
Evidently AI
Freemium
Open-source framework for evaluating and monitoring LLM applications, AI agents, and ML models.
What is Evidently AI?
Evidently AI is an open-source framework for evaluating, testing, and monitoring LLM applications, RAG systems, AI agents, and predictive machine-learning models. It helps teams assess output quality, safety, and reliability during development and track changes after deployment. Evaluations can combine built-in metrics with custom rules, classifiers, prompts, or models, while reports and monitoring views make results available for review.
What can Evidently AI do?
Built-in and custom metrics
Use a library of more than 100 built-in metrics or define evaluations with custom prompts, models, and rules.
Synthetic test data
Generate realistic, edge-case, or adversarial inputs tailored to an application, including benign and hostile prompts.
Evaluation reports
Explore evaluation results in visual reports that can be shared with others.
Ongoing monitoring
Track evaluation results and quality checks in a live dashboard to identify drift, regressions, and emerging risks.
Coverage for LLM and predictive ML systems
Evaluate LLM behavior, retrieval quality, and safety alongside predictive performance, data drift, and data quality.
Use Cases
“Validate a RAG application”
Assess retrieval quality and context relevance, and check generated answers for factuality or hallucinations before release.
“Test an AI agent workflow”
Use edge-case and adversarial inputs to evaluate agent responses and expose risks such as unsafe output or prompt-based attacks.
“Add evaluations to a development pipeline”
Run quality and safety checks during application updates, then review results in visual reports.
“Monitor predictive models in production”
Track model performance, data quality, and data drift as production data changes.
Frequently Asked Questions
What systems can Evidently evaluate?
It supports LLM-powered products such as chatbots, RAG applications, AI agents, and copilots, as well as predictive machine-learning systems.
Can I define evaluations for my own requirements?
Yes. The framework offers more than 100 built-in metrics and supports custom evaluations using prompts, models, or rules, including combinations of rules, classifiers, and LLM-based evaluations.
Can evaluations run as part of a pipeline?
Yes. Evidently describes running evaluations in a pipeline and reviewing the results through shareable visual reports.
What kinds of checks are available for LLM applications?
Examples include guideline and format adherence, hallucinations and factuality, PII detection, retrieval quality and context relevance, sentiment, toxicity, tone, and trigger words.
Quick Facts
- Product category
- Open-source AI evaluation and observability framework
- License
- Apache 2.0
- Supported systems
- LLM applications, RAG systems, AI agents, and predictive ML models
- Evaluation methods
- Built-in metrics, rules, classifiers, and LLM-based evaluations
- Website
- evidentlyai.com
Evidently AI Traffic Analysis
Traffic data is for reference only.
- Domain Rating
- 73
Evidently AI Alternatives
Langfuse
langfuse.com
Langfuse is an open-source AI platform for tracing, evaluating, and improving LLM applications and agents with observability, prompts, experiments, and human review.
Hamming AI
hamming.ai
Testing, red teaming, and production monitoring for teams building voice and chat agents.
Akto
akto.io
AI security platform for discovering, testing, governing, and protecting LLMs, agents, MCPs, and GenAI applications.
Future AGI
futureagi.com
Evaluation, simulation, and monitoring tools for teams building AI agents.
Dynamo AI
dynamo.ai
Enterprise AI controls for evaluating, securing, monitoring, and governing generative and agentic AI.
OpenSearch
opensearch.org
OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite for ingesting, searching, visualizing, and analyzing data. It supports application search, observability, security analytics, and machine learning workflows.