DeepEval vs PyRIT

DeepEval is a free, open-source framework for building evaluation pipelines to test AI systems. Its Pytest-native evaluations run in CI/CD or as Python scripts, and the framework lists more than 50 research-backed metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity and bias. It evaluates text, images and audio, including conversational and voice interactions, using methods such as G-Eval, DAG, QAG and JevEval. DeepEval can generate synthetic goldens from a knowledge base and model conversations across user personas. It also traces agent steps so they can be graded and inspected in a terminal or test runner. Integrations cover agent frameworks including LangChain, Pydantic AI, OpenAI Agents, LangGraph, LlamaIndex and CrewAI, alongside a range of model providers. By default, it sends basic telemetry to PostHog, excluding personally identifiable information and stored results; users can opt out with an environment variable. The maker describes DeepEval OS as for pre-production testing, with results in local files and an engineer-owned test runner. Enterprise capabilities are offered through Confident AI Evals, with self-hosted or maker-cloud deployment options.

PyRIT is a free, MIT-licensed framework for security professionals and engineers assessing risks in generative AI systems. It supports single-turn and multi-turn red-team strategies such as Crescendo, TAP, and Skeleton Key. Scenario evaluations can be repeated across hundreds of objectives, including content harms, psychosocial risks, and data leakage. CoPyRIT provides a web interface for interacting with AI systems, recording findings, and collaborating with a team. Documented targets include OpenAI, Azure, Anthropic, Google, HuggingFace, custom HTTP endpoints and WebSockets, and web apps tested with Playwright. PyRIT stores conversations, scores, and attack results in SQLite or Azure SQL and can export results. Scoring supports true/false, Likert, classification, and custom scorers, including LLM-based and Azure AI Content Safety methods. Scanner, GUI, Framework, pyrit_scan, and pyrit_shell modes are documented. Installation is available through Docker, pip, or uv, with Python 3.13 recommended locally.