  Large language models (LLMs) are increasingly used for complex tasks that
require multiple generation calls, advanced prompting techniques, control flow,
and structured inputs/outputs. However, efficient systems are lacking for
programming and executing these applications. We introduce SGLang, a system for
efficient execution of complex language model programs. SGLang consists of a
frontend language and a runtime. The frontend simplifies programming with
primitives for generation and parallelism control. The runtime accelerates
execution with novel optimizations like RadixAttention for KV cache reuse and
compressed finite state machines for faster structured output decoding.
Experiments show that SGLang achieves up to 6.4x higher throughput compared to
state-of-the-art inference systems on various large language and multi-modal
models on tasks including agent control, logical reasoning, few-shot learning
benchmarks, JSON decoding, retrieval-augmented generation pipelines, and
multi-turn chat. The code is publicly available at
https://github.com/sgl-project/sglang
