MemStitch - Zero-copy context bridging for vLLM multi-agent inference

Show HN: MemStitch – Zero-copy context bridging for vLLM (25x TTFT speedup)

MemStitch is a zero-copy context bridging gateway designed to optimize multi-agent GPU inference workflows. By dynamically stitching KV Caches at the memory level using PagedAttention, it eliminates the need for agents to repeat expensive prefill phases when processing shared long documents. This innovation slashes Time-to-First-Token (TTFT) latency by up to 25x and reduces VRAM usage by over 40%. Supporting both Python SDK decorators and OpenAI-compatible REST APIs, MemStitch enables secure, high-performance collaboration between agents like legal auditors and financial compliance bots, ensuring efficient resource utilization without compromising data security.

"Context-Stitcher solves this by bridging caches at the memory level, bypassing prefill for matched prefixes by mapping the logical attention table of Agent B directly to the physical GPU memory address of Agent A's cache blocks."