Mem0A persistent memory layer designed to provide long-term, personalized context for large language models and AI agents.

Mem0 is an AI memory layer that integrates with AI assistants and agents to provide personalized and continuous context across interactions. It aims to solve the challenge of maintaining user preferences, adapting to individual needs, and enabling continuous learning for AI systems. The tool utilizes a distinct memory algorithm that focuses on a single-pass, add-only extraction approach, meaning new information is added without overwriting existing memories. Key to its operation are agent-generated facts, which are treated as first-class information. Mem0 also incorporates entity linking, where entities are extracted, embedded, and interconnected across memories to enhance retrieval accuracy. Furthermore, it employs multi-signal retrieval, combining semantic, BM25 keyword, and entity matching to fuse diverse scoring signals, alongside temporal reasoning for time-aware retrieval. Mem0 offers core capabilities such as multi-level memory management, handling User, Session, and Agent states with adaptive personalization. It provides a developer-friendly experience with an intuitive API and cross-platform SDKs for Python and Node.js. Applications include AI assistants requiring consistent, context-rich conversations, customer support chatbots recalling past interactions, healthcare systems tracking patient preferences, and adaptive experiences in productivity tools and gaming. Deployment options are flexible, including a Python/npm library for testing and prototyping, a self-hosted server for teams managing their own infrastructure, and a fully managed cloud platform for zero-operations production use. The platform also reports high benchmark scores on memory evaluation frameworks like LoCoMo, LongMemEval, and BEAM, highlighting its efficiency and recall capabilities.