AI agents operating across multi-turn or long-horizon tasks lose the majority of learned context within a small number of interactions, with reported forgetting rates as high as 94%, leading to knowledge loss, repeated errors, and hallucinated continuity. Current mitigations such as external logging are insufficient without a unified memory architecture that prevents confabulation and maintains coherent state. This gap degrades agent reliability in any task requiring sustained context and makes long-running autonomous agents fundamentally untrustworthy.

Agents lose 94% of context across turns and confabulate continuity, making long-running autonomous tasks fundamentally unreliable and untrustworthy.

AI agent developers and orchestration platforms (e.g., LangChain, CrewAI, AutoGPT builders) shipping production agents that must maintain state across sessions, tools, and multi-step workflows.

Every serious agent builder hits the memory wall within days of prototyping; vector DB + RAG hacks are duct tape that still hallucinates. A drop-in memory protocol with anti-confabulation guarantees (hash-verified recall, provenance chains) would be table-stakes infra developers pay for like they pay for Supabase or Pinecone.

MVP is an open-source memory server with a simple SDK (store/recall/verify) that wraps structured episodic + semantic memory with cryptographic provenance stamps so agents can distinguish real memories from generated ones; ship as a hosted API with a free tier to drive adoption, integrate with LangChain/CrewAI in week one.

AI agent infra market is projected at $10B+ by 2027; persistent memory is as fundamental as databases were to web apps, targeting every agent deployment.