Maintaining vLLM Prefix Cache Warmth Across AI Agent Turns

A discussion in the local LLM community highlights methods for keeping vLLM's prefix cache active between sequential turns in multi-step AI agent workflows. The focus is on preventing cache evictions so that repetitive context does not need to be reprocessed during multi-turn agent interactions. Multi-step AI agents frequently re-send long system prompts and interaction histories, causing substantial latency if the prompt key-value (KV) cache is evicted between turns. Keeping the prefix cache warm drastically improves Time-to-First-Token (TTFT) latency and overall serving throughput for complex agent systems. vLLM's Automatic Prefix Caching enables KV-cache block sharing across requests with identical prompt prefixes, but idle turns or high GPU memory pressure can trigger cache eviction. Optimizations include sending periodic keep-alive requests, customizing cache allocation policies, or pinning specific agent prefix blocks in GPU memory.