May 09, 2026
SGLang (Structured Generation Language) is designed to make LLM inference faster and more structured. Developed at LMSYS, it provides a high-performance runtime and a programming interface that allows for complex, multi-step LLM workflows with incredible efficiency.
The core innovation of SGLang is RadixAttention, which allows for the automatic reuse of KV caches across different requests. This is particularly powerful for multi-turn conversations, RAG systems, and few-shot prompting, as it eliminates redundant computation and significantly reduces latency.
SGLang allows developers to write "programs" that direct how LLMs should generate output. It supports advanced features like parallel decoding, constrained generation (JSON/Regex), and control flow, making it easy to build complex agents that are both fast and predictable.