deepeval — Agent Skill

The LLM Evaluation Framework

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DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating large-language model systems. It is similar to Pytest but specialized for unit testing LLM apps. DeepEval incorporates the latest research to run evals via metrics such as G-Eval, task completion, answer relevancy, hallucination, etc., which use LLM-as-a-judge and other NLP models that run locally on your machine.

Whether you're building AI agents, multi-turn chatbots, voice agents, RAG pipelines, or anything else, implemented via LangChain or OpenAI, DeepEval has you covered. With it, you can easily evaluate:

• LLM apps end-to-end as black boxes
• Complete AI agent trajectories across every decision and action
• Individual AI agent steps such as LLM calls, tool use, retrieval, and sub-agent handoffs

Use these evaluations to determine the optimal models, prompts, and architecture to improve your AI quality, prevent prompt drifting, or even transition from OpenAI to Claude with confidence.

Jev is now available in DeepEval, both in existing metrics and the latest JevEval.

[!IMPORTANT] If you're already in production, Confident AI surfaces failing traces before anyone has written an eval for them, clusters them into failure modes, and turns them into your next eval. !Demo GIF

🔥 Metrics and Features

• 📐 Large variety of ready-to-use LLM eval metrics (all with explanations) powered by ANY LLM of your choice, statistical methods, or NLP models that run locally on your machine covering all use cases:

• Custom, All-Purpose Metrics:

• JevEval — a custom metric scored by Jev, a System One model that answers your questions with calibrated probabilities instead of generated text
• G-Eval — a research-backed LLM-as-a-judge metric for evaluating on any custom criteria with human-like accuracy
• DAG — DeepEval's graph-based deterministic LLM-as-a-judge metric builder

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