Memori is agent-native memory infrastructure from Memori Labs: a layer that turns what agents do and say into structured, persistent state that survives across sessions and systems. It is LLM-agnostic, datastore-agnostic and framework-agnostic, so it plugs into the stack you already run instead of forcing a rip-and-replace. The project positions memory as something built from agent execution and conversation together, and it reports large efficiency gains over naive full-context prompting. Memori has earned roughly 17,000 stars on GitHub and was under active development in 2026.
Most agents forget everything the moment a session ends, or stuff ever-growing transcripts back into the prompt. Memori takes a different approach: it observes agent runs and conversations, extracts structured memory from them, and stores it in your existing data infrastructure, so recall stays fast and token budgets stay lean. It offers Python and TypeScript SDKs, works with frameworks and agent tools such as Claude Code, and can be deployed as a managed cloud service, in a single-tenant cloud, inside your VPC, or fully on-premises, which makes it viable for enterprises with strict data controls.
Memori’s core is open source and free to self-host. Memori Labs also offers Memori Cloud, a zero-config hosted version you start with an API key, plus enterprise deployments, so the overall offering is freemium.
Memori is for teams shipping production agents that need reliable long-term memory across sessions and users, especially enterprises that must keep memory inside their own cloud or on-premises infrastructure.