confident-ai/deepeval

The LLM Evaluation Framework

What it solves

DeepEval is an open-source evaluation framework designed to solve the difficulty of testing and validating Large Language Model (LLM) applications. It provides a structured way to perform unit testing for AI apps—similar to Pytest—to ensure quality, prevent prompt drifting, and allow developers to confidently switch models or architectures.

How it works

DeepEval uses a variety of metrics to evaluate LLM outputs. It employs an "LLM-as-a-judge" approach, using other LLMs, statistical methods, or local NLP models to score responses. It supports both black-box end-to-end testing and component-level evaluation, where it can trace the complete trajectory of an agent's decisions, tool calls, and retrieval steps.

Who it’s for

It is built for developers building AI agents, RAG (Retrieval-Augmented Generation) pipelines, and chatbots using frameworks like LangChain, OpenAI, CrewAI, or LlamaIndex.

Highlights

  • Diverse Metric Library: Includes specialized metrics for RAG (faithfulness, answer relevancy), Agentic behavior (task completion, tool correctness), and Multi-turn conversations (knowledge retention, role adherence).
  • Custom Metrics: Users can build their own custom metrics using G-Eval or the graph-based DAG builder.
  • Synthetic Data Generation: Ability to generate single and multi-turn synthetic datasets for testing.
  • Broad Integration: Plugs into major LLM frameworks and CI/CD environments.
  • Benchmarking: Allows benchmarking LLMs against popular datasets like MMLU and GSM8K in minimal code.

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