Sharing a production MemPalace + OpenClaw memory system (84 days, 7,635 drawers)

[CarsonLRS]

Sharing: Production MemPalace + OpenClaw Memory System (84 days, 7,635 drawers)

Hi @milla-jovovich and MemPalace community,

I've been running MemPalace in a fully local, production-grade OpenClaw agent memory system for 84 days. Wanted to share what we built and see if there's interest in contributing parts of it back upstream.

What we built

A complete L0-L4 memory pipeline on top of MemPalace, running on a personal Mac:

Dialogue → L0 JSONL archive (never lost)
         → L1 Semantic extraction (DeepSeek API, ADD/UPDATE/NOOP dedup)
         → L2 Scene blocks (24h window self-audit)
         → L3 User persona (7-day evolution)
         → L4 Knowledge graph (entities + triples + conflict resolution)
              ↓
         All landing in MemPalace as single source of truth

Key highlights:

• Shallow/deep memory split: daily context via ChromaDB vector (<200ms), deep recall triggered by natural language ("认真回忆…")
• bge-m3 1024-dim embedding replacing default 384-dim English model — night-and-day difference for Chinese semantics
• 8 launchd cron jobs, 84 days unattended, self-healing from Mac sleep gaps
• Crash-proof: survived ChromaDB full rebuild + OpenClaw version rollback, zero memory loss

Relationship to Issue #206

I noticed #206 proposes an OpenClaw skill (active MCP tool calling). We approached it from the other direction — a memory slot plugin (kind: "memory") that enables automatic recall injection, not just on-demand search. These two are complementary: skill for explicit retrieval, memory slot for background context injection.

We haven't built the slot plugin yet (currently using MemPalace MCP via active-memory config), but we've validated the entire pipeline works at production scale.

Data points

Metric	Value
Runtime	84 days (Apr 30 – Jul 23)
Structured memories	7,635 drawers
Knowledge graph	6,077 active triples
Total storage	194 MB
Retrieval latency	<200ms shallow, <500ms deep (fusion)
Retrieval cost	Zero — all local embedding + local search

Longer write-up (in Chinese) with architecture details: https://juejin.cn/post/7665530652353445894

What we'd like to contribute

If the community is interested, we're happy to upstream:

1. bge-m3 integration guide — replacing the default embedder with 1024-dim bge-m3, including the dimension migration script (ChromaDB won't let you change collection dimension after creation — we solved this)
2. Pipeline scripts — save_dialogue, memory_pipeline, auto_extract for L0-L4 automation
3. Self-healing launchd orchestration — Mac sleep recovery, preflight health checks
4. OpenClaw memory slot plugin — a thin wrapper registering memory_recall tool, enabling automatic background recall (fills the gap between MemPalace MCP and active-memory auto-injection)

All pure Python / shell, MIT-compatible. Happy to discuss the right contribution path — integrations/ subdirectory? Standalone repo with cross-reference? Whatever works best for the project.

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Thanks for building MemPalace — it's genuinely the most well-architected local memory system I've found for agent use cases.