hmem — Humanlike Memory for AI Agents

Example Workflow

• Well
• it claims that it can't pinpoint timestamps. But that's not true. It just cant see them (due to token efficiency) :)

• Hierarchical retrieval — lazy loading of detail levels saves tokens
• True tree structure — multiple siblings at the same depth (not just one chain)
• Compact output — child IDs render as .7 instead of P0029.7; dates shown only when differing from parent
• Persistent across sessions — agents remember previous work even after restart
• Editable without deletion — update_memory and append_memory modify entries in place; content is optional when toggling flags
• Markers — [♥] favorite, [P] pinned, [!] obsolete, [-] irrelevant, [*] active, [s] secret — on root entries and sub-nodes
• Pinned entries — super-favorites that show all children titles in bulk reads (not just the latest); use for reference entries you need in full every session
• Hashtags — cross-cutting tags (#hmem, #security) for filtering and discovering related entries across prefixes
• Import/Export — export_memory as Markdown or .hmem SQLite clone (excluding secrets); import_memory with L1 deduplication, sub-node merge, and automatic ID remapping on conflict
• Obsolete chain resolution — mark entries/sub-nodes obsolete with [✓ID] reference; read_memory auto-follows the chain to the current version
• Access-count promotion — most-accessed entries get expanded automatically ([★]); most-referenced sub-nodes shown as "Hot Nodes"
• Session cache — bulk reads suppress already-seen entries with Fibonacci decay; two modes: discover (newest-heavy) and essentials (importance-heavy)

Requirements