GPT-6 Astra paired with a seven-repo open-source cache stack
A cache loop built from LMCache, vLLM, Redis, Qdrant, LangGraph, LiteLLM and Langfuse lets Astra handle only the reasoning that deserves to enter context.
GPT 6 Astra + GitHub cache stack is f...cking fast 7 open source repos fit the system in this video almost perfectly 01 LMCache ▸https://github.com/LMCache/LMCache → reuses KV cache instead of recomputing the same context again 02 vLLM ▸https://github.com/vllm-project/vllm → serves the model and keeps inference throughput high 03 Redis ▸ https://github.com/redis/redis → holds hot task state, counters and fast session data 04 Qdrant ▸ https://github.com/qdrant/qdrant → keeps long lived retrieval memory outside the active context 05 LangGraph ▸https://github.com/langchain-ai/langgraph → controls task state when work moves between fetch, tag, evict and flush 06 LiteLLM ▸ https://github.com/BerriAI/litellm → routes model traffic and gives the stack one gateway 07 Langfuse ▸https://github.com/langfuse/langfuse → shows cache hits, model calls, latency and failures across the run EVICT should kill dead context early FLUSH should happen before stale state poisons the next task That is the part most agent stacks still waste money on They keep feeding the model context that should have been cached, compressed or dropped three steps ago GPT 6 Astra does the reasoning the cache ring decides what deserves to reach it
This post quotes another post: x.com/marlenuii/status/2109673372091126158