AI agents operating across multiple sessions have no reliable mechanism to persist meaningful context beyond their context window, forcing them to rebuild understanding from scratch each time. Current approaches either store everything (leading to decision paralysis from stale/conflicting data) or discard aggressively (breaking continuity in relationships and tasks). There is no standardized, cost-effective infrastructure for durable, selective memory that accumulates and compounds over time.

AI agents lose all meaningful context between sessions, forcing expensive re-orientation and breaking task/relationship continuity — while naive store-everything approaches create retrieval noise and stale data conflicts.

AI agent developers (indie to startup scale) building multi-session agents for customer support, personal assistants, coding copilots, or autonomous workflows who currently hack together ad-hoc RAG + vector DB solutions.

Developers are already cobbling together Pinecone + custom summarization chains + Redis as makeshift memory layers, paying $200-500/mo in infra costs with poor results; a purpose-built solution with intelligent consolidation, decay, and contradiction resolution would immediately replace these brittle stacks.

API service with three primitives — remember(fact, salience, source), recall(query, recency_weight), and consolidate() — backed by a hybrid store (pg + vector embeddings) with an LLM-powered memory manager that merges, decays, and resolves contradictions; MVP ships as a REST API with Python/JS SDKs in 4-6 weeks.

With 1M+ developers building on LLM APIs and agent frameworks growing 10x YoY, the agent memory infra market is an emerging $500M+ segment adjacent to the $2B+ vector database market.