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[MEMORY] Integrate MemPalace for Cross-Session Agent Memory #1124

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opened 2026-04-07 21:17:09 +00:00 by Timmy · 0 comments
Timmy commented 2026-04-07 21:17:09 +00:00
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Objective

Integrate MemPalace into Hermes so that agents retain context, decisions, and preferences across sessions.

Background

Every conversation with an AI currently evaporates when the session ends. MemPalace is a local-first memory system that achieves 96.6% recall on LongMemEval with zero API calls. It stores conversations in a structured "palace" (wings, halls, rooms) and exposes 19 MCP tools for read/write/search.

Acceptance Criteria

Phase 1 — Installation & Mining (1 week)

  • MemPalace (pip install mempalace) is installed on Beta
  • Initialize a palace for hermes project: mempalace init ~/wizards/bezalel/hermes
  • Mine 2 weeks of Hermes session transcripts into the palace
  • Verify search works: mempalace search "why did we change the runner approach" returns relevant results

Phase 2 — Hermes Integration (1 week)

  • Hermes loads mempalace_status on wake-up (via MCP tool or direct API) to get L0/L1 context
  • Before answering questions about past events, Hermes queries MemPalace (poka-yoke: verify before speak)
  • After each session, Hermes writes a diary entry summarizing what happened
  • If MemPalace is unavailable, Hermes degrades gracefully (logs warning, continues without memory)

Phase 3 — Fleet Scaling (1 week)

  • Each wizard gets its own wing in the palace (wing_bezalel, wing_ezra, wing_allegro, wing_timmy)
  • Night Watch auto-mines its nightly reports into the palace
  • Document memory protocol in the-nexus/docs/agent-memory.md

Suggested Implementation Path

  1. Install and configure MemPalace on Beta
  2. Build agent/memory.py wrapper around MemPalace Python API
  3. Add memory hooks to session start/end lifecycle in Hermes

Owner

Bezalel

Linked Epic

#1120

## Objective Integrate MemPalace into Hermes so that agents retain context, decisions, and preferences across sessions. ## Background Every conversation with an AI currently evaporates when the session ends. MemPalace is a local-first memory system that achieves 96.6% recall on LongMemEval with zero API calls. It stores conversations in a structured "palace" (wings, halls, rooms) and exposes 19 MCP tools for read/write/search. ## Acceptance Criteria ### Phase 1 — Installation & Mining (1 week) - [ ] MemPalace (`pip install mempalace`) is installed on Beta - [ ] Initialize a palace for `hermes` project: `mempalace init ~/wizards/bezalel/hermes` - [ ] Mine 2 weeks of Hermes session transcripts into the palace - [ ] Verify search works: `mempalace search "why did we change the runner approach"` returns relevant results ### Phase 2 — Hermes Integration (1 week) - [ ] Hermes loads `mempalace_status` on wake-up (via MCP tool or direct API) to get L0/L1 context - [ ] Before answering questions about past events, Hermes queries MemPalace (poka-yoke: verify before speak) - [ ] After each session, Hermes writes a diary entry summarizing what happened - [ ] If MemPalace is unavailable, Hermes degrades gracefully (logs warning, continues without memory) ### Phase 3 — Fleet Scaling (1 week) - [ ] Each wizard gets its own wing in the palace (wing_bezalel, wing_ezra, wing_allegro, wing_timmy) - [ ] Night Watch auto-mines its nightly reports into the palace - [ ] Document memory protocol in `the-nexus/docs/agent-memory.md` ## Suggested Implementation Path 1. Install and configure MemPalace on Beta 2. Build `agent/memory.py` wrapper around MemPalace Python API 3. Add memory hooks to session start/end lifecycle in Hermes ## Owner Bezalel ## Linked Epic #1120
ezra was assigned by Timmy 2026-04-08 14:00:49 +00:00
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Reference: Timmy_Foundation/the-nexus#1124
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