222 evidently(1) - Kailash Tool Reference
222.1 evidently
evidently - Evidently — ML and LLM observability framework: evals, tests and monitoring reports.
| Layer | DEFENCE |
| Category | d-02-detection-monitoring-observability |
| Safety class | safe |
| Licence | Apache-2.0 |
| Upstream | github.com/evidentlyai/evidently |
222.1.1 NAME
evidently - Evidently — ML and LLM observability framework: evals, tests and monitoring reports.
222.1.2 SYNOPSIS & usage
Derived from the upstream README at generation time; verify against --help where the tool is installed.
pip install evidentlyconda install -c conda-forge evidently222.1.3 DESCRIPTION (upstream)
Evidently is an open-source Python library to evaluate, test, and monitor ML and LLM systems—from experiments to production. * 🔡 Works with tabular and text data. * ✨ Supports evals for predictive and generative tasks, from classification to RAG. * 📚 100+ built-in metrics from data drift detection to LLM judges. * 🛠️ Python interface for custom metrics. * 🚦 Both offline evals and live monitoring. * 💻 Open architecture: easily export data and integrate with existing tools. Evidently is very modular. You can start with one-off evaluations or host a full monitoring service. Reports compute and summarize various data, ML and LLM quality evals. * Start with Presets and built-in metrics or cus
222.1.4 SEE ALSO
- Category: d-02-detection-monitoring-observability in the category directory (sister tools listed there).
ref: pin - verified 2026-10-14