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-**MemPalace memory backend** — Optional upgrade layering ChromaDB vector search + temporal knowledge graph on top of OpenClaw's built-in `memory_search`. Dual embedding models (embeddinggemma-300m-qat 300M + all-MiniLM-L6-v2 22M) catch different semantic matches.
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-**Python bridge** (`scripts/python/mempalace_bridge.py`) — JSON contract between Node.js and MemPalace SDK v3.0.0 via spawn(). Supports search, mine, wake-up, status, init, as-of, and export commands. Full stdout suppression preserves JSON contract.
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-**Shared bridge utility** (`scripts/lib/bridge-call.mjs`) — Single source of truth for Python bridge communication. Configurable `rejectOnError` for strict vs graceful degradation modes.
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-**Memory backend abstraction** (`scripts/lib/memory-backend.mjs`) — Factory pattern with `MemPalaceBackend` and `FileBackend`. Async dynamic imports, graceful fallback.
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-**FileBackend** (`scripts/lib/file-backend.mjs`) — Grep-based search over memory/*.md files. Read-only fallback when MemPalace is not installed.
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-**MemPalaceBackend** (`scripts/lib/mempalace-backend.mjs`) — Full ChromaDB + temporal KG access via Python bridge.
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**Benefit:** Your agent handles MOR tokens and private keys safely. The same security patterns used by professional custody solutions, adapted for AI agents.
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### 🧠 MemPalace — Enhanced Memory (Optional)
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| Component | What It Does |
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|-----------|-------------|
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|**ChromaDB Vector Search**| Semantic search across all memory files using all-MiniLM-L6-v2 embeddings |
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|**Temporal Knowledge Graph**| Query entity relationships at specific points in time ("What did we know about X on date Y?") |
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|**Obsidian Vault Export**| Export palace as browsable vault with frontmatter, wikilinks, MOCs, and timeline pages |
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|**Dual Embedding Models**| Complements OpenClaw's built-in embeddinggemma-300m-qat — different models catch different semantic matches |
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|**Migration Tool**| Idempotent one-time import of existing memory/*.md files into MemPalace |
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**Benefit:** Your agent gets deeper memory recall across thousands of files. Two embedding models (300M + 22M params) searching independently means better coverage. The Obsidian export lets you browse and graph-visualize your agent's entire knowledge base. Requires `pip install mempalace` — EverClaw works without it.
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---
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## Available Models
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-**macOS or Linux** — macOS Keychain or libsecret for native key storage; encrypted file fallback works everywhere
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-**age, zstd, jq** — for backup/restore features (auto-installed by `install.sh`)
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-**node-llama-cpp** — for local memory search embeddings (auto-installed by `setup.mjs` and `install.sh`)
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-**mempalace** (optional) — `pip install mempalace` for enhanced ChromaDB + temporal KG memory. Not required — EverClaw works without it.
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That's it. No external accounts. No API keys. No subscriptions.
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## 23. Enhanced Memory with MemPalace (v2026.4.8)
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Optional upgrade to EverClaw's memory backend using [MemPalace](https://github.com/AiEnigma-Labs/MemPalace) — a local-first memory system with ChromaDB vector search, temporal knowledge graph, and hierarchical organization (wings/rooms/drawers).
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### Why MemPalace?
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- **Dual embedding models**: OpenClaw's built-in `memory_search` uses embeddinggemma-300m-qat (300M); MemPalace uses all-MiniLM-L6-v2 (22M). Different models catch different semantic matches.
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- **Temporal awareness**: Query what was known about an entity at a specific date (`as-of` queries).
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- **Wing/room hierarchy**: Organize memories by project, topic, or time period.
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- **Obsidian export**: Browse your agent's memory as a full Obsidian vault with wikilinks and frontmatter.
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- **Zero external APIs**: Everything runs locally. No data leaves the machine.
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### Install
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```bash
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pip install mempalace
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```
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### Migration (one-time import of existing memory files)
npm run test:memory # 28 tests: backend, factory, bridge, regression
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```
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### Privacy
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MemPalace stores data locally in `~/.mempalace/`. Exported vaults may contain PII — consider encrypting the folder or using Obsidian's encryption plugin.
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