Gliding Horse – a Rust Agent OS with CPU-Like Memory Architecture
MESI coherence for agent memory is clever, but LangGraph and AutoGen already own this space.
When two agents share state, one of them is usually reading a stale copy. agent-coherence makes that visible.
Applies CPU cache coherence protocols to multi-agent LLM synchronization—clever analogy.
AI engineers building multi-agent systems
AutoGen · LangGraph · CrewAI
MESI coherence for agent memory is clever, but LangGraph and AutoGen already own this space.
Persists KV cache to SSD—makes local LLMs actually usable for real coding.
RDMA-backed distributed KV cache cuts prefill latency 3.1× where vLLM's built-in caching maxes out.
Keeps a single cached store of repos and gives you an interactive CLI to link them into .llm/reference so you can check a small dotllm.json into projects and run dotllm sync on fresh clones. It’s a pragmatic, low-fuss alternative to submodules or ad-hoc scripts — nicely opinionated — but the project could use clearer docs around cross-platform behaviour, conflict resolution, and scalability.
Keeps agent memory at 8 KB constant size while KV caches bloat to 156 MB.
Modular context folders beat monolithic prompts for scaling AI agent instructions.