Memori
LLM-agnostic layer turning agent execution into structured, persistent state.
External enrichment · as of 2026-08-29
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LLM-agnostic layer turning agent execution into structured, persistent state.
Memori is agent-native memory infrastructure: an LLM-agnostic layer that transforms agent execution and conversation history into structured, persistent state for production systems. Unlike conversational memory wrappers or vector retrieval, Memori captures tool calls, decisions, traces, and user context as durable, queryable state that agents rely on across sessions, models, and workflows. Let Memori handle agent memory so your team can improve accuracy and cut inference costs instead of building and operating a memory system themselves. Benchmarked on LoCoMo, a benchmark from Snap Research measuring how well systems remember and retrieve details. Memori delivers industry-leading results: 87% accuracy using 721 tokens, roughly 2.8% of full-context cost, enabling teams to reduce inference spend by up to 97.2%. Built for enterprise, Memori works with the infra you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.