DeepEval

DeepEval is a comprehensive LLM evaluation framework used by leading AI companies including OpenAI, Google, Adobe, and Walmart. It provides a native Pytest integration that fits directly into CI/CD workflows, enabling unit-testing for LLMs with over 50 research-backed metrics. The framework supports single and multi-turn evaluations, multi-modal test cases (text, images, audio), synthetic data generation, and automatic prompt optimization.

• Unit-Testing for LLMs — Install via pip install deepeval and integrate natively with Pytest to run evaluations in your CI/CD pipeline.
• LLM-as-a-Judge Metrics — Access 50+ research-backed metrics including G-Eval (chain-of-thought criteria scoring), DAG (directed acyclic graph for multi-step scoring), and QAG (question-answer generation scoring).
• Single and Multi-Turn Evaluations — Evaluate any use case and system architecture, including multi-turn conversational agents.
• Native Multi-Modal Support — Evaluate text, images, and audio with built-in multi-modal test cases.
• Synthetic Data Generation — Generate synthetic test datasets and simulate conversations when no test data is available.
• Auto-Optimize Prompts — Automatically optimize prompts without manual tweaking using DeepEval's built-in prompt optimization.
• Confident AI Cloud Platform — Use DeepEval on Confident AI for team-wide collaborative AI testing, regression testing, dataset management, observability, tracing, online monitoring, and human annotations.
• Wide Framework Integrations — Integrates with OpenAI, LangChain, LlamaIndex, LangGraph, Pydantic AI, CrewAI, Anthropic, and OpenAI Agents.