Tsinghua Open-Sources AKV, a KV Cache Engineering Server for LLM Agents on SGLang

Researchers at Tsinghua University have released AKV, an open-source KV cache engineering server designed to optimize cache management for LLM agents running on the SGLang inference framework. KV cache efficiency is one of the primary bottlenecks in serving long-context, multi-turn agentic workloads, and AKV introduces structured controls for cache allocation, eviction, and reuse across agent sessions. The tool is particularly relevant for teams self-hosting large models and running high-concurrency agent workloads where cache thrashing degrades throughput. SGLang has been gaining traction as a high-performance inference framework, and AKV extends its capabilities specifically for agentic use cases. Developers running SGLang deployments should evaluate AKV as a drop-in optimization layer for agent serving infrastructure.