deepeval

Documentation | Metrics and Features | Getting Started | Integrations | Confident AI

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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, voice agents, multi-turn chatbots, 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.

🔥 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

AI Agent Metrics

- [Task Completion](https://deepeval.com/docs/metrics-task-completion) — evaluate whether an AI agent accomplished its goal
- [Tool Correctness](https://deepeval.com/docs/metrics-tool-correctness) — check if the right tools were called with the right arguments
- [Goal Accuracy](https://deepeval.com/docs/metrics-goal-accuracy) — measure how accurately the agent achieved the intended goal
- [Step Efficiency](https://deepeval.com/docs/metrics-step-efficiency) — evaluate whether the agent took unnecessary steps
- [Plan Adherence](https://deepeval.com/docs/metrics-plan-adherence) — check if the agent followed the expected plan
- [Plan Quality](https://deepeval.com/docs/metrics-plan-quality) — evaluate the quality of the agent's plan
- [Tool Use](https://deepeval.com/docs/metrics-tool-use) — measure quality of tool usage
- [Argument Correctness](https://deepeval.com/docs/metrics-argument-correctness) — validate tool call arguments

</details>

Voice Agent Metrics

- [Agent Responsiveness](https://deepeval.com/docs/metrics-agent-responsiveness) — check whether the voice agent answered when it was spoken to
- [Turn-Taking Naturalness](https://deepeval.com/docs/metrics-turn-taking-naturalness) — measure response latency and interruptions across the call
- [Voice Naturalness](https://deepeval.com/docs/metrics-voice-naturalness) — evaluate how natural the voice agent sounds
- [Transcription Accuracy](https://deepeval.com/docs/metrics-transcription-accuracy) — check whether the voice agent's speech-to-text heard the caller correctly
- [Audio Glitch](https://deepeval.com/docs/metrics-audio-glitch) — detect audio artifacts in the voice agent's speech

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Multi-Turn Metrics

- [Knowledge Retention](https://deepeval.com/docs/metrics-knowledge-retention) — evaluate whether the chatbot retains factual information throughout a conversation
- [Conversation Completeness](https://deepeval.com/docs/metrics-conversation-completeness) — measure whether the chatbot satisfies user needs throughout a conversation
- [Turn Relevancy](https://deepeval.com/docs/metrics-turn-relevancy) — evaluate whether the chatbot generates consistently relevant responses throughout a conversation
- [Turn Faithfulness](https://deepeval.com/docs/metrics-turn-faithfulness) — check if the chatbot's responses are factually grounded in retrieval context across turns
- [Role Adherence](https://deepeval.com/docs/metrics-role-adherence) — evaluate whether the chatbot adheres to its assigned role throughout a conversation

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RAG Metrics

- [Answer Relevancy](https://deepeval.com/docs/metrics-answer-relevancy) — measure how relevant the output is to the input
- [Faithfulness](https://deepeval.com/docs/metrics-faithfulness) — evaluate whether the RAG pipeline's output factually aligns with the retrieval context
- [Contextual Recall](https://deepeval.com/docs/metrics-contextual-recall) — measure how well the RAG pipeline's retrieval context aligns with the expected output
- [Contextual Precision](https://deepeval.com/docs/metrics-contextual-precision) — evaluate whether relevant nodes in the RAG pipeline's retrieval context are ranked higher
- [Contextual Relevancy](https://deepeval.com/docs/metrics-contextual-relevancy) — measure the overall relevance of the RAG pipeline's retrieval context to the input
- [RAGAS](https://deepeval.com/docs/metrics-ragas) — average of answer relevancy, faithfulness, contextual precision, and contextual recall

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MCP Metrics

- [MCP Task Completion](https://deepeval.com/docs/metrics-mcp-task-completion) — evaluate how effectively an MCP-based agent accomplishes a task
- [MCP Use](https://deepeval.com/docs/metrics-mcp-use) — measure how effectively an agent uses its available MCP servers
- [Multi-Turn MCP Use](https://deepeval.com/docs/metrics-multi-turn-mcp-use) — evaluate MCP server usage across conversation turns

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Multimodal Metrics

- [Text to Image](https://deepeval.com/docs/multimodal-metrics-text-to-image) — evaluate image generation quality based on semantic consistency and perceptual quality
- [Image Editing](https://deepeval.com/docs/multimodal-metrics-image-editing) — evaluate image editing quality based on semantic consistency and perceptual quality
- [Image Coherence](https://deepeval.com/docs/multimodal-metrics-image-coherence) — measure how well images align with their accompanying text
- [Image Helpfulness](https://deepeval.com/docs/multimodal-metrics-image-helpfulness) — evaluate how effectively images contribute to user comprehension of the text
- [Image Reference](https://deepeval.com/docs/multimodal-metrics-image-reference) — evaluate how accurately images are referred to or explained by accompanying text

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Other Metrics

- [Hallucination](https://deepeval.com/docs/metrics-hallucination) — check whether the LLM generates factually correct information against provided context
- [Summarization](https://deepeval.com/docs/metrics-summarization) — evaluate whether summaries are factually correct and include necessary details
- [Bias](https://deepeval.com/docs/metrics-bias) — detect gender, racial, or political bias in LLM outputs
- [Toxicity](https://deepeval.com/docs/metrics-toxicity) — evaluate toxicity in LLM outputs
- [JSON Correctness](https://deepeval.com/docs/metrics-json-correctness) — check whether the output matches an expected JSON schema
- [Prompt Alignment](https://deepeval.com/docs/metrics-prompt-alignment) — measure whether the output aligns with instructions in the prompt template

</details>

• 🎯 Supports both end-to-end and component-level LLM evaluation.
• 🧩 Build your own custom metrics that are automatically integrated with DeepEval's ecosystem.
• 🔮 Generate single and multi-turn synthetic datasets, and simulate live voice calls against your voice agent.
• 🔗 Integrates seamlessly with ANY CI/CD environment.
• 🧬 Optimize prompts automatically based on evaluation results.
• 🏆 Easily benchmark ANY LLM on popular LLM benchmarks in under 10 lines of code., including MMLU, HellaSwag, DROP, BIG-Bench Hard, TruthfulQA, HumanEval, GSM8K.

🔌 Integrations