SGLang

SGLang is a high-performance serving framework that reduces the compute overhead of LLM inference through a co-design of its front-end programming language and back-end runtime. It introduces RadixAttention, a memory management technique that aggressively caches and reuses the KV (key-value) states across prompts. This approach delivers significant performance improvements over standard serving engines such as vLLM in scenarios with high prefix overlap. For teams building complex autonomous agents, relying on long system prompts or using extensive few-shot prompting with shared examples, SGLang can provide substantial gains in latency and efficiency.