DeepEval

DeepEval is an open-source LLM evaluation framework designed to help developers and teams test and iterate on large language model applications. It offers a wide range of metrics for evaluating LLM outputs, supports synthetic data generation, and includes red-teaming capabilities for identifying safety vulnerabilities. Integrated with the Confident AI cloud platform, it provides tools for managing the full LLM evaluation lifecycle, including dataset curation, benchmarking, and debugging.

DeepEval is an open-source LLM evaluation framework that directly addresses the core needs of the OpenAI Platform concept. It provides extensive metrics for evaluating LLM outputs, including conversational metrics, and supports both end-to-end and component-level evaluation. The platform offers features like synthetic data generation, red-teaming for safety vulnerabilities, and benchmarking of LLMs, which align with the safety and alignment framework and research aspects. While DeepEval itself is an evaluation framework, its integration with 'Confident AI' provides a cloud platform for managing the full evaluation lifecycle, including enterprise-grade features like data curation, benchmarking, and debugging via LLM traces, which aligns with enterprise solutions. The mention of 'Codex-based model endpoints for generating, explaining, and debugging code' in the concept's 'code-generation' feature is partially met by DeepEval's focus on evaluating LLM outputs, which includes code-related LLM applications, and its ability to debug evaluation results via LLM traces. The API access is inherent in its nature as a framework for developers, and the ability to build custom metrics aligns with fine-tuning and custom models.