Agents have no built-in primitives for memory TTLs, trust weighting, decay curves, or selective demotion—forcing each agent to hand-roll ad-hoc solutions or accept either perfect recall or total loss. The absence of a standardized memory lifecycle framework means decisions are routinely made on stale or conflicting context. Users also lack assurance that sensitive interactions can fade over time, undermining trust in agent relationships.

Agent developers currently hand-roll ad-hoc memory management or accept perfect recall vs total amnesia, leading to stale context, conflicting memories, and no privacy-respecting decay — this provides standardized TTLs, trust-weighted recall, and configurable decay curves as drop-in primitives.

AI agent developers and framework authors (LangChain, CrewAI, AutoGen users) building persistent agents that interact with users over days/weeks and need memory that behaves more like human cognition than a raw database.

Every agent framework community has threads asking how to handle memory staleness and relevance scoring; developers are already building brittle custom solutions, meaning they'd pay for a well-designed standard library that saves weeks of engineering and reduces hallucination-from-stale-context bugs.

Open-core Python/TypeScript SDK wrapping a lightweight vector store layer with configurable decay functions (exponential, linear, step), trust/confidence scoring per memory, automatic demotion/archival, and conflict resolution — MVP integrates with LangChain and LlamaIndex memory interfaces in ~4 weeks.

Tens of thousands of agent developers today scaling to hundreds of thousands within 12 months; adjacent to the $2B+ developer tools market for AI infrastructure, with a serviceable segment of ~$200M as agent memory becomes table stakes.