deepeval Claude Code Plugin
Claude Code PluginClaude CodeDevelopmentAI Agents & AI App DevelopmentMonitoring & ObservabilityCode Quality, Review & TestingThe explanation below is AI-generated. Please verify it against the sources.
This plugin bundles skills that let an AI assistant add DeepEval-based evaluation and tracing to an AI application, including building datasets, generating Confident AI reports, and running iterative improvement loops based on the results. According to its README, DeepEval is an open-source, Python-based LLM evaluation framework similar in style to pytest, offering metrics such as G-Eval, RAG-focused metrics, agentic metrics, and multi-turn metrics, plus integrations with frameworks like OpenAI, LangChain, LangGraph, and CrewAI. Confident AI is a companion cloud and self-hosted platform from the same creators that layers on collaboration, production tracing, dataset curation, monitoring, and red-teaming, per the homepage. The exact extent of what is automated versus manual is not detailed in the sources, so it is marked unknown.
About the service
Confident AI is described on its homepage as an evaluation and observability platform for LLM applications, providing tracing, automatic dataset curation from production traces, AI red-teaming, and governance controls, available as both a managed cloud service and a self-hosted deployment.
What you can do with deepeval
- Write and run DeepEval test cases and metrics (e.g. G-Eval, RAG metrics, agentic metrics, multi-turn metrics) against an AI application
- Instrument application code to capture traces of LLM calls, tool calls, and agent steps
- Build or auto-generate evaluation datasets from traces
- Sync results to Confident AI for shareable reports, production monitoring, and team collaboration
- Iterate on failing metrics by adjusting prompts, models, or architecture based on evaluation results
Sources
History of deepeval
- Claude Code Plugin Added deepeval