Reasoning reuse: the deepest LLM saving nobody talks about
Reasoning reuse: the deepest LLM saving nobody talks about. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Notes from the nest · 980 posts
Engineering notes written by the people building Crowkis. Comparisons, use cases, economics, internals, security, operations, and nothing written just to rank.
Reasoning reuse: the deepest LLM saving nobody talks about. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Tool-result caching (CTOOLSET), caches a deterministic tool call keyed by tool plus exact args, so a swarm's duplicate lookups become one call. Here's how Crowkis does it and why it matters for cost and safety.
We ran Crowkis memory against two public, hostile retrieval benchmarks, SNAP's LoCoMo and LongMemEval, on a laptop with no cloud calls. Here are the recall numbers, by question type, with the reranker on and off.
Multi-agent systems fan out, and they ask overlapping questions constantly. Without a shared cache, that overlap is pure waste, the same answer, bought once per agent.
Building RAG document search on Rig (Rust)? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Building legal document assistants on the Mistral SDK? Add a semantic cache so the same clauses and questions across matters stop costing full price.
Building data analysis agents on LiteLLM? Add a semantic cache so repeated tool calls and the same analytical questions stop costing full price.
Building contract analysis tools on Haystack? Add a semantic cache so the same clause questions across documents stop costing full price.
Building customer support bots on LangChain? Add a semantic cache so repeat questions from every customer, all day stop costing full price.
Multimodal caching (image + text), caches image-plus-text lookups, so a repeated vision question is a hit instead of an expensive re-run. Here's how Crowkis does it and why it matters for cost and safety.
The binary is the whole product, server, REPL, doctor, bench, and the inspect tools. A tour of the crowkis command line, from cold start to debugging a missed hit.
Embeddings sound like math you need a PhD for. The core idea is simpler and more useful than that, and it's the reason a cache can tell that two different sentences mean the same thing.
Building internal copilots on Rig (Rust)? Add a semantic cache so employees asking overlapping questions of the same knowledge base stop costing full price.
Building education tutors on the Mistral SDK? Add a semantic cache so students asking the same concepts thousands of times stop costing full price.
Building HR assistants on LiteLLM? Add a semantic cache so the same policy questions from every employee stop costing full price.
Building onboarding assistants on Haystack? Add a semantic cache so every new hire asking the same first questions stop costing full price.
Building coding assistants on LangChain? Add a semantic cache so the same explanations and boilerplate reasoning, dozens of times a day stop costing full price.
Streaming response caching, serves cached answers chunk by chunk, so a hit feels like live typing and the seam between hit and miss disappears. Here's how Crowkis does it and why it matters for cost and safety.
Prompt injection rarely arrives in plain English. CGUARD normalizes the evasion first, whitespace, leetspeak, zero-width characters, then scans for jailbreaks, overrides, and system-prompt exfiltration.
CSET writes an answer into the semantic cache, running the five-stage anti-poisoning pipeline before it accepts anything.