GitHub's MemStitch slashes multi-agent AI inference latency by 25x, saves 40% GPU memory; zero-copy KV cache stitching bypasses redundant prefill when agents share context, cuts time-to-first-token from 1200ms to 48ms using cryptographic block mapping and secure memory bridging.

GitHub - DaqulaLin/MemStitch: Zero-Copy Context Bridging Gateway for Multi-Agent GPU Inference. Bypasses the expensive prefill phase by dynamically stitching KV Caches at the memory level using PagedAttention. Cuts TTFT latency by up to 25x and saves 40%+ VRAM for collaborative LLM workflows.

Context-Stitcher 🪡 Zero-Copy Context Bridging Gateway for Multi-Agent GPU Inference. 💡 Core Value Proposition In multi-agent collaborative workflows, separate agents often process the same long text context sequentially. For example: Agent A (Legal Auditor): Reads a 200-page contract and runs compliance analyses (populating the GPU KV Cache). Agent B (Financial Compliance): Reads the same 200-page contract and audits financial liabilities. Under standard inference engines, Agent B is forced to...