Humanlike Memory for AI Agents

Example Use Cases

• 1

  Persistent memory across multiple sessions and machines.

• 2

  That require hierarchical, selective retrieval of context to optimize token usage.

• 3

  That benefit from editable, curated, and tagged memory entries to improve decision-making.

Description

hmem is a Model Context Protocol server that enables AI agents to retain and retrieve memory in a five-level nested hierarchy, mimicking human memory. It solves the problem of AI agents forgetting previous sessions by allowing selective loading of memory detail levels, reducing token waste and improving consistency. The system supports editing, tagging, obsolescence marking, and memory curation, making AI agents more context-aware and efficient across multiple machines and sessions.