Claude Skill
MemoriLabs/Memori
Memori is an agent-native memory infrastructure that turns agent execution and conversation into structured, persistent state. LLM-agnostic, enterprise-ready, and deployable on cloud, VPC, or on-pr...
Overview
Stars15,527
Forks2,767
LanguagePython
Last pushed2026-06-15
Last synced2026-07-04
Repository
OwnerMemoriLabs
RepositoryMemori
Full nameMemoriLabs/Memori
Repo ID1,025,381,911
GitHub URLhttps://github.com/MemoriLabs/Memori
Install this Skill
git clone https://github.com/MemoriLabs/Memori.gitRegistry
TypeUnknown
Quality scoreUnknown
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Summary
Memori is an agent-native memory infrastructure that provides a LLM-agnostic layer to transform agent execution and conversation into structured, persistent state for production systems. Built for enterprise, it integrates with existing data infrastructure without rip-and-replace and supports deployment across managed cloud, single-tenant cloud, VPC, and on-premises environments.
Chinese description
Memori 是智能体原生记忆基础设施。它是一个与LLM无关的层，能将智能体执行与对话转化为生产系统所需的、结构化的持久化状态。Memori 专为企业构建，可与您现有的数据基础设施协同工作，无需推倒重来，并支持在托管云、单租户云、VPC 及本地环境部署。
Key features
- LLM-agnostic memory layer for agents
- Structured, persistent state from execution and conversation
- Enterprise-grade with no rip-and-replace integration
- Multi-environment deployment: cloud, VPC, on-premises
- Works with existing data infrastructure
Use cases
- Production agent memory management
- Enterprise conversational AI with persistent state
- Multi-tenant agent systems requiring isolation
- On-premises AI deployments for compliance
- Stateful agent workflows in regulated industries