/install victor-memory-hub
Victor Memory Hub
Four-tier memory architecture with automated Dreaming pipeline, three-way synchronization, Active Memory recall, and two-stage auto-extraction.
Overview
| Tier | Layer | Technology | Purpose |
|---|---|---|---|
| L0 | Runtime Retrieval | memory-core plugin (Ollama bge-m3 → SQLite + sqlite-vec) | Real-time semantic + BM25 hybrid search |
| L0 | Cloud Recall | MemOS Cloud plugin (optional) | Cross-device memory capture and recall |
| L0 | Active Memory | Built-in OpenClaw plugin | Pre-reply sub-agent memory search + context injection |
| L1 | Working Memory | memory/YYYY-MM-DD.md files |
Daily summaries, todos, technical notes |
| L2a | Long-term Index | MEMORY.md (~90 lines, read-only base) |
Bidirectional index → ARCHIVE.md, 0% Auto-Extracted pollution |
| L2b | Detailed Archive | ARCHIVE.md (~220 lines) |
Full records with ← MEMORY.md:XX reverse references |
| L2c | Structured KB | facts.sqlite (entity/key/value) |
Precise lookup for IPs, ports, versions; activation/decay tracking |
Automated Pipelines
| Pipeline | Schedule | Description |
|---|---|---|
| Dreaming | 03:00 UTC daily | Scan logs → DeepSeek analysis → promote to L2 |
| Three-way Sync | 18:00 / 20:00 / 22:00 CST | Cloud ↔ Markdown ↔ Vector alignment |
| auto-memory v3 | 18:30 / 22:30 CST | MemOS Cloud facts → qwen3 filter → Stage1: ARCHIVE.md (SHA-256 dedup) → Stage2: MEMORY.md (summary) |
| session-extract | 22:00 CST | Scan session JSONL → MemOS extractor/reranker → .learnings/ + memory/YYYY-MM-DD.md (two-pass) |
Memory Flow (Unified Daily Pipeline)
agent_end → MemOS Cloud (cloud extraction)
↓
┌─ 18:00 sync-pull (Cloud → local cache) ─┐
│ 18:30 auto-memory.py │
│ → Stage 1: ARCHIVE.md (SHA-256 dedup) │
│ → Stage 2: MEMORY.md (one-line summary) │
│ │
│ 20:00 sync-push + reindex │
│ 22:00 session-extract.py │
│ → Pass 1: .learnings/ERRORS.md │
│ → Pass 2: memory/YYYY-MM-DD.md │
│ → archive analyzed → trash │
└───────────────────────────────────────────┘
before_agent_start → MemOS Cloud recall
+ Active Memory sub-agent
+ memory-core (bge-m3 + BM25)
↓
Layered context injected before reply
Scripts
| Script | Purpose |
|---|---|
auto_memory_extract.py |
v3 two-stage pipeline: ARCHIVE.md archive → MEMORY.md summary |
session-extract.py |
Scan session JSONL → .learnings/ + memory/ (two-pass) |
seed-facts-db.py |
Initialize facts.sqlite from ARCHIVE.md |
facts_activation.py |
Hebbian activation + daily decay + Hot/Warm/Cool |
daily-memory-pipeline.sh |
6-stage unified daily pipeline |
write_file.py |
Cross-platform text writer (UTF-8/BOM/CRLF) |
auto-setup.sh |
One-command Ollama + memory-core + MemOS Cloud setup |
Setup
One-command auto-setup
bash scripts/auto-setup.sh
Options
bash scripts/auto-setup.sh --skip-ollama # Skip Ollama install
bash scripts/auto-setup.sh --skip-memos # Skip MemOS Cloud
bash scripts/auto-setup.sh --dry-run # Preview only
Manual setup
See references/setup-guide.md for step-by-step manual configuration.
When to Use
- Setting up OpenClaw memory for the first time
- Configuring memory-core plugin with local Ollama embedding
- Installing MemOS Cloud plugin for cross-device sync
- Enabling Active Memory for pre-reply context injection
- Setting up auto-memory pipeline for MEMORY.md curation
- Installing cross-platform-writer for cross-OS file compatibility
- Configuring automatic Dreaming and promotion pipelines
- Setting up three-way sync between cloud, files, and vector DB
Plugin Conflicts
❌ subconscious-personality-guardian ↔ memory-core
Incompatible. Both use the same OpenClaw memory slot.
Fix: Disable in openclaw.json:
{
"plugins": {
"disabled": ["subconscious-personality-guardian"],
"deny": ["subconscious-personality-guardian"]
}
}
✅ memory-core + MemOS Cloud
Compatible — designed to work in layers.
User message → MemOS Cloud (static facts) → memory-core (recent context)
✅ Active Memory + MemOS Cloud + memory-core
Compatible — triple-layer recall.
User message
→ Active Memory sub-agent (searches all memory stores)
→ MemOS Cloud (injects long-term facts & preferences)
→ memory-core (semantic + BM25 hybrid retrieval)
→ Agent receives layered context
✅ auto-memory + MemOS Cloud + memos-extractor
Designed to work together. MemOS captures at agent_end, syncs to local files, then auto-memory reads those files for MEMORY.md curation. v1.10 adds a second channel: memos-extractor-0.6b API (MemOS self-developed model) returns structured facts + preferences, cross-validated against qwen3 output.
Components
1. Memory Plugins (L0)
See references/architecture.md for full configuration.
2. Memory Files (L1 + L2)
~/.openclaw/workspace/
├── memory/
│ ├── YYYY-MM-DD.md # Daily working memory (auto-indexed)
│ ├── MEMORY_INDEX.md # Vector BM25 cluster summaries
│ └── .sync-*.json # Sync state files
├── MEMORY.md # Long-term memory index (~90 lines)
├── ARCHIVE.md # Detailed archive (~220 lines, ← bidirectional refs)
├── AGENTS.md # Runtime context + memory rules
└── user_workspace/
├── memos-cloud-cache/ # Cloud-pulled memory (isolated from index)
│ └── memos-cloud-*.md
├── scripts/
│ └── sync-*.py # Sync scripts (pull/push/vector)
└── skills/
└── victor-memory-hub/ # Installed from ClawHub
└── scripts/ # Pipeline scripts (auto-memory, session-extract, etc.)
3. Pipeline Scripts (in victor-memory-hub/scripts/)
auto_memory_extract.py— v3 two-stage pipeline (ARCHIVE.md → MEMORY.md)session-extract.py— Session JSONL scan → .learnings/ + memory/seed-facts-db.py— Initialize facts.sqlite from ARCHIVE.mdfacts_activation.py— Hebbian activation + daily decaydaily-memory-pipeline.sh— 6-stage unified daily pipelinewrite_file.py— Cross-platform text file writerauto-setup.sh— One-command Ollama + memory-core + MemOS Cloud setup
See references/sync-api.md for MemOS Cloud API details.
File Reference
references/architecture.md— Detailed architecture documentationreferences/setup-guide.md— Complete manual setup guide with templatesreferences/sync-api.md— MemOS Cloud API referencescripts/auto-setup.sh— One-command interactive setupscripts/auto_memory_extract.py— auto-memory v2 scriptscripts/write_file.py— Cross-platform text file writerscripts/auto_memory_extract.py— v3 two-stage memory extraction
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install victor-memory-hub - 安装完成后,直接呼叫该 Skill 的名称或使用
/victor-memory-hub触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Victor Memory Hub 是什么?
四层记忆架构 (Four-tier memory architecture for OpenClaw AI agents). 提供 L0 运行时语义检索 (Ollama bge-m3 + SQLite-vec 向量库)、L1 工作记忆 (每日 Markdown 日志)、L2 长期记忆 (MEMORY.md 索引... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 40 次。
如何安装 Victor Memory Hub?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install victor-memory-hub」即可一键安装,无需额外配置。
Victor Memory Hub 是免费的吗?
是的,Victor Memory Hub 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Victor Memory Hub 支持哪些平台?
Victor Memory Hub 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Victor Memory Hub?
由 VictorQR(@victorqr)开发并维护,当前版本 v1.12.5。