Article URL: https://github.com/DaqulaLin/MemStitch Comments URL: https://news.ycombinator.com/item?id=48901051 Points: 6 # Comments: 1

In multi-agent collaborative workflows, separate agents often process the same long text context sequentially. For example: Under standard inference engines, Agent B is forced to repeat the expensive prefill phase, duplicate GPU activations, and suffer from high Time-to-First-Token (TTFT) latency. Below is the benchmark analysis of Context-Stitcher compared to standard vLLM cold-prefills when executing consecutive agents over a shared 200-page document: Context-Stitcher includes a responsive developer portal to monitor physical cache block states (idle, private allocations, shared/stitched pages, security alarms) in real time. Context-Stitcher supports Python SDK Decorators and OpenAI-Compatible REST APIs for cross-application integrations: If your agent pipelines are written in Python, you can utilize the StitcherMesh and @stitch_agent decorators to link context memory: The gateway exposes standard OpenAI endpoints. Point your LLM client base URL to Context-Stitcher to activate sharing. You can inspect, add, or revoke access authorization rules dynamically between agents.