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Victor Memory Hub

作者 VictorQR · GitHub ↗ · v1.12.5 · MIT-0
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在 OpenClaw 中安装
/install victor-memory-hub
功能描述
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...
使用说明 (SKILL.md)

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.md
  • facts_activation.py — Hebbian activation + daily decay
  • daily-memory-pipeline.sh — 6-stage unified daily pipeline
  • write_file.py — Cross-platform text file writer
  • auto-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 documentation
  • references/setup-guide.md — Complete manual setup guide with templates
  • references/sync-api.md — MemOS Cloud API reference
  • scripts/auto-setup.sh — One-command interactive setup
  • scripts/auto_memory_extract.py — auto-memory v2 script
  • scripts/write_file.py — Cross-platform text file writer
  • scripts/auto_memory_extract.py — v3 two-stage memory extraction
安全使用建议
Install this only if you deliberately want a durable memory layer for your agent. Before enabling cloud sync or cron jobs, review what conversation/session data will be captured, where tokens are stored, which files may be pushed to cloud services, and how you will delete or correct unwanted memories.
功能分析
Type: OpenClaw Skill Name: victor-memory-hub Version: 1.12.5 The skill bundle implements a highly invasive memory architecture that performs system-level modifications and transmits sensitive data to an external service. Key indicators include the `auto-setup.sh` script, which requests `sudo` privileges to install software and intentionally disables the `subconscious-personality-guardian` plugin. Furthermore, `session-extract.py` and `auto_memory_extract.py` read all agent session logs and exfiltrate extracted facts and conversation context to a third-party API (`memos.memtensor.cn`). While these actions are documented as part of a 'Cloud Sync' feature, the requirement to disable security-oriented plugins and the broad collection of session history for external processing represent significant security and privacy risks.
能力标签
requires-sensitive-credentials
能力评估
Purpose & Capability
The broad memory capture, local retrieval, cloud sync, and active recall capabilities are aligned with the stated purpose of a persistent OpenClaw memory hub, but they are inherently sensitive.
Instruction Scope
The artifacts describe automatic session extraction, long-term promotion, and pre-reply context injection, but do not clearly show per-item user approval, deletion controls, or strong exclusions for sensitive conversations.
Install Mechanism
The setup script is user-directed and interactive, but it can download and run installers, modify OpenClaw configuration, and configure plugins/cron jobs outside a formal install spec.
Credentials
The skill processes conversation/session data into local files, SQLite/vector stores, and optional cloud memory; this is purpose-aligned but broad and only partially bounded by the visible documentation.
Persistence & Privilege
The system is designed to create persistent memory files, scheduled pipelines, lifecycle hooks, and future context injection, which can affect later agent behavior after the original setup task.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install victor-memory-hub
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /victor-memory-hub 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.12.5
Renamed to victor-memory-hub
元数据
Slug victor-memory-hub
版本 1.12.5
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

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。

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