Enables LLM evaluation and observability by uploading documents, building test sets, running RAG pipelines, and automatically scoring answers for groundedness, hallucination risk, retrieval quality, latency, and cost, with tools exposed to MCP-compatible clients.
Enables evaluation of AI-generated code across 45 specialized dimensions using deterministic pattern matching and optional LLM-powered deep review, acting as an independent quality gate.
Enables AI assistants to reflect on, critique, and continuously improve their performance using Mandoline's evaluation framework. Provides tools for creating custom evaluation metrics and scoring prompt/response pairs to measure AI assistant quality.
AI safety evaluation toolkit that scores text for care-centered alignment, detects threats like jailbreaks, and certifies AI responses against a 16-probe framework. It enables users to analyze relationship health, predict burnout risk, and ensure ethical AI interactions.
Evaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.
Enables testing AI safety classifier robustness against query decomposition, obfuscation, and multi-agent attacks. Provides tools for full evaluation pipelines, query previews, and status checks.
