Provides AI agents with persistent, hierarchical memory, preventing loss of context and duplicated work across sessions.
Sponsored
AI agents often lose all context and progress when a session ends or work shifts across different environments. Hmem addresses this by providing agents with persistent, humanlike memory, structured into five lazy-loaded levels of detail. This innovative approach allows agents to retrieve exactly the amount of context needed—from broad summaries to fine-grained specifics—ensuring continuity, eliminating duplicate effort, and preserving hard-won insights across all sessions and machines.
Key Features
01Hierarchical memory retrieval with lazy loading across five levels of detail
020 GitHub stars
03True tree structure for memory, allowing multiple siblings at the same depth
04Persistent memory across sessions, ensuring agents retain context after restarts
05Per-agent memory stored in individual SQLite files, with an option for shared company knowledge
06Native Model Context Protocol (MCP) compatibility, working with tools like Claude Code and OpenCode
Use Cases
01Maintaining continuous context for AI agents across multiple development sessions or machines
02Enabling multi-agent AI systems to share and build upon persistent knowledge
03Optimizing token usage by providing AI agents with context-aware, lazy-loaded memory on demand
