Provides advanced evaluation tools for assessing AI safety, alignment, and performance of LLM outputs. Enables programmatic evaluation of quality, safety metrics like toxicity and PII detection, and operational metrics including carbon footprint and cost estimation.
Prediction stability engine for AI agents. Evaluate model stability, detect ghosts, probe any LLM for instability, monitor fleet drift. 20 tools + 1 resource. Works with Claude, Cursor, VS Code.
Enables language models to run data-quality checks and profiling on local files, using dbt-style assertions like not_null, unique, relationships, and accepted_values.
A local, agentic AI pipeline that analyzes tabular data, detects anomalies, and generates interpretive summaries using local LLMs orchestrated via the Model Context Protocol.
Multi-dimensional data quality validation and statistical anomaly detection for LLM training data, with auto-fix pipeline and MCP tools for AI IDE integration.
