Stateless agents and memoryless copilots are everywhere — because there’s no clean, scoped, compliant way to persist memory across time.
Support bots forget past tickets. Sales agents repeat themselves. Tutors re-teach old lessons. Teams try patching it with vector DBs, RAG chains, or hardcoded context windows—but it’s fragile, siloed, and non-compliant by default.
LLM memory (like OpenAI's) is locked inside their UI. Vector DBs are dumb storage. Agent frameworks like LangChain or MemGPT force you to build custom logic. Scoped, semantic, multi-user memory? Everyone’s rebuilding the same stack.
What are you using today to give your AI apps long-term memory? How are you handling scope, TTL, and compliance?
We built Recallio as a drop-in memory API—semantic, scoped, exportable, and fast. It works with any model or agent and gives you memory that acts like infrastructure, not duct tape.
Would love to hear: where does memory still suck for you? What should Recallio handle next?