$ loading_
Give AI agents persistent memory across sessions with efficient on-demand context loading.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "hmem" yet — see the docs or source repo.
Use hmem to configure persistent memory for my customer support AI: automatically capture user preferences, past issues, and resolutions from each session, then lazily load relevant context at the start of new sessions.
A memory setup or tool result that lets the support AI retain context across sessions and reduce repeated questions.
Use hmem to save my research assistant's working memory, including project goals, literature notes, to-dos, and unfinished hypotheses, so I can resume seamlessly on another device.
A portable memory state that allows the research assistant to quickly restore task context in a new environment.
Use hmem to design a layered memory strategy for my coding agent: always prioritize core project rules, load recent task history next, and lazily fetch older sessions only when needed to reduce token usage.
A layered memory and loading strategy that balances continuity for the coding agent with lower context costs.
Provide structured memory and intent capture for AI agents with lower token costs.
Provide persistent memory for AI agents across sessions and tasks.
Give AI coding agents persistent cross-project memory and connected context retrieval.
Provide AI agents with traceable bi-temporal long-term memory in SQLite.
Give AI agents persistent semantic memory with searchable cross-session context.
Manage directory-scoped memory for AI coding agents to cut token usage.