DeepEval

DeepEvalSince 2026-01

DeepEval is an open-source LLM evaluation framework built by Confident AI that feels like 'Pytest for LLMs'. It allows AI engineers to write unit tests for prompts, RAG pipelines, and conversational agents with production-ready evaluation metrics that execute seamlessly in local terminals and CI/CD pipelines. DeepEval includes 14+ research-backed evaluation metrics covering hallucination, G-Eval, toxicity, answer relevancy, bias, summarization quality, and tool correctness.

DeepEval tests can be run directly using the standard 'pytest' command line. It integrates with Confident AI's cloud dashboard to track metric drift over time, analyze test run regressions, and debug failing test cases with root-cause explanations. It supports custom LLM judges, synthetic dataset synthesis, and synthetic edge-case generation to stress-test AI systems prior to enterprise deployment.

vLLMSince 2023-06

vLLM is the industry-standard open-source LLM serving and inference engine designed for ultra-high throughput and minimal memory waste. Developed by UC Berkeley researchers, vLLM introduced PagedAttention—a revolutionary memory management algorithm that manages attention key-value (KV) cache like virtual memory in operating systems, virtually eliminating memory fragmentation. Capable of delivering 2x to 4x higher throughput than Hugging Face TGI and standard PyTorch runtimes, vLLM powers production AI inference infrastructure across enterprise cloud clusters and high-volume API providers worldwide.

vLLM features state-of-the-art inference optimizations including continuous request batching, Chunked Prefill, speculative decoding, prefix caching, and native quantization support (AWQ, GPTQ, FP8, INT4, SqueezeLLM). It provides drop-in OpenAI-compatible REST API endpoints, supports multi-GPU distributed tensor parallelism with Ray/NCCL, and serves all major model architectures including DeepSeek-V3, Llama 3.3, Mistral, Qwen 2.5, and Command R+.