DeepEval

DeepEval is an open-source LLM evaluation framework for Python that treats testing AI systems the way Pytest treats testing code. You write test cases for your RAG pipelines, agents, and chatbots and score them with a large library of ready-to-use metrics — G-Eval, answer relevancy, faithfulness, hallucination, task completion, and more — powered by LLM-as-a-judge and NLP models that can run locally on your machine.

It integrates with LangChain, LlamaIndex, OpenAI, and other stacks, and helps you choose the best models, prompts, and architectures, catch regressions, and safely migrate between providers. Results can optionally sync to Confident AI for reporting and iteration comparison.

What You Get

• A large catalog of ready-to-use LLM metrics (G-Eval, answer relevancy, faithfulness, hallucination, contextual precision/recall, task completion, and more)
• Pytest-style test cases and a deepeval test run CLI for evaluating LLM apps in CI
• Custom metric support via G-Eval and DAG so you can encode your own evaluation criteria
• Benchmarks and dataset tooling for synthetic data generation and red-teaming
• Integrations with LangChain, LlamaIndex, OpenAI, and other LLM stacks
• Optional sync to Confident AI for shared reports and iteration comparison

Common Use Cases

• Unit-testing a RAG pipeline’s faithfulness and answer relevancy against a golden dataset
• Regression-testing prompts and models in CI to catch quality drops before release
• Comparing models or providers (e.g. OpenAI vs Claude) with objective metrics before switching
• Evaluating agent task completion and tool-use correctness

Under The Hood

Architecture - DeepEval centers on LLMTestCase/Golden objects (input, actual output, expected output, retrieval context) that are scored by Metric implementations. Metrics like G-Eval prompt a judge LLM with a rubric and parse a structured score with reasoning; statistical and NLP-model metrics run locally. An evaluate() entry point and a Pytest plugin (deepeval test run) drive test cases, while dataset, synthesizer, and benchmark modules support data generation and standardized evals. Tech Stack - Pure Python packaged via a modern pyproject.toml, with Pydantic for typed data models, Pytest for the test-runner integration, and pluggable LLM backends (OpenAI and many others). Code Quality - A large, very actively developed codebase (16k+ stars, frequent releases) with an extensive test suite, typed models, and modular metric/benchmark packages. API Design - The framework mirrors Pytest idioms so Python developers are immediately productive: define a test case, pick metrics, assert. High-level evaluate() and decorators reduce boilerplate, and metrics return human-readable reasons alongside scores for debuggability.