Confident AI
DeepEval-powered LLM evaluation platform to test, benchmark, and safeguard apps
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Evaluate, test, and monitor ML & LLM systems effortlessly
A Python library that provides 100+ built‑in metrics, customizable evaluations, and a monitoring UI for both tabular and generative AI models, supporting offline analysis and live production tracking.
Evidently is a Python library designed for evaluating, testing, and monitoring machine‑learning and large‑language‑model pipelines. It ships with more than a hundred ready‑to‑use metrics covering data quality, drift detection, classification, regression, ranking, and LLM‑specific judges. Users can generate interactive Reports, turn them into Test Suites with pass/fail thresholds, and export results as JSON, HTML, or Python dictionaries.
The framework works locally via a lightweight UI that can be self‑hosted, or through Evidently Cloud for a managed experience with alerts and dataset management. Installation is a single pip install evidently (or Conda) command, after which reports and monitoring dashboards can be launched from a notebook or a terminal. Custom metrics are added through a simple Python interface, making the library adaptable to any domain‑specific evaluation need.
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Detect data drift between training and production
Early alerts when feature distributions shift, preventing model degradation
Automate LLM response quality checks in CI
Pass/fail test suites ensure new releases meet predefined quality thresholds
Generate interactive reports for model debugging
Visual summaries of metrics help pinpoint performance bottlenecks
Deploy a live monitoring dashboard for production models
Continuous visibility and alerting on key performance indicators
Run `pip install evidently` or `conda install -c conda-forge evidently`.
Yes, reports can be executed in Python and exported as JSON, HTML, or dictionaries.
The OSS UI is self‑hosted; Cloud provides managed hosting, alerting, and additional admin features.
Implement a Python class following Evidently’s metric interface and include it in a Report.
Yes, Test Suites let you define `gt` (greater than) or `lt` (less than) conditions for any metric.
Project at a glance
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