Enables AI applications to use advanced memory management capabilities through the memU AI framework. Supports storing conversation memories, semantic retrieval, multi-user management, and memory statistics via standardized MCP protocol.
Provides persistent long-term memory for AI agents with semantic search and activation-based decay. Enables AI systems to remember across sessions through layered memory architecture and automatic context-aware retrieval.
Drop-in memory layer for AI coding agents, upgrading them from chat history to a governed Memory OS with hybrid search, contradiction detection, and safe governance.
Enables AI agents to store and retrieve memories with user-specific context using Mem0, allowing them to maintain conversation history and make informed decisions based on past interactions.
A long-term memory system built for AI Agents. Agent wakes up already knowing who he is, not querying "who am I?" every session. Every turn calling back accurate memory context. Achieving accurate memory hits while also preventing memory from expanding at scale. No compression, no forgetting.