I built a cognitive layer for AI agents that learns without LLM calls

The problem Every time your agent starts a conversation, it starts from zero. Sure, you can stuff a summary into the system prompt. You can use RAG. You can call Mem0 or Zep. But all of these have the same problem: they need LLM calls to learn . To extract facts, to build a user profile, to understand what matters — you're paying per token, adding latency, and depending on a cloud service. What if the learning happened locally, automatically, without any LLM involvement? What AuraSDK does differently AuraSDK is a cognitive layer that runs alongside any LLM. It observes interactions and — without any LLM calls — builds up a structured understanding of patterns, causes, and behavioral rules. from aura import Aura , Level brain = Aura ( " ./agent_memory " ) brain . enable_full_cognitive_stack () # store what happens brain . store ( " User always deploys to staging first " , level = Level . Domain , tags = [ " workflow " ]) brain . store ( " Staging deploy prevented 3 production incidents