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openclaw-memory-hubOpenClaw 记忆 HUB

Agent Skill

openclaw-memory-hub 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

979

周安装

40

GitHub Stars

公开资料未说明

下载量

317
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:openclaw-memory-hub(OpenClaw 记忆 HUB)
来源仓库:https://github.com/victorqr/openclaw-memory-hub
安装命令:
openclaw skills install openclaw-memory-hub
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install openclaw-memory-hub

简介

openclaw-memory-hub 提供 OpenClaw 的三层内存架构支持。

  • 适合 L0 运行时语义检索与 L1 工作内存协同场景。
  • 使用 Ollama bge-m3 模型和 SQLite-vec 向量存储实现高效检索。
  • 安装命令为 openclaw skills install openclaw-memory-hub,需安装向量数据库。
  • 注意向量维度与相似度阈值设置,影响召回精度与性能。

SKILL.md

name
openclaw-memory-hub
description
Three-tier memory architecture for OpenClaw AI agents. Provides L0 runtime semantic retrieval (Ollama bge-m3 + SQLite-vec vector store), L1 working memory (daily markdown logs), L2 long-term memory (curated base file), Dreaming pipeline for automatic insight promotion, and three-way sync (Cloud ↔ Markdown ↔ Vector). Use when setting up persistent agent memory, configuring memory plugins, or building multi-layered memory systems for OpenClaw.
version
1.7.0

OpenClaw Memory Hub

Three-tier memory architecture with automated Dreaming pipeline and three-way synchronization.

Overview

This architecture solves AI amnesia across sessions by layering memory at three levels:

TierLayerTechnologyPurpose
L0Runtime Retrievalmemory-core plugin (Ollama bge-m3 → SQLite + sqlite-vec)Real-time semantic + BM25 hybrid search
L0Cloud RecallMemOS Cloud plugin (*optional*)Cross-device memory capture and recall
L1Working Memorymemory/YYYY-MM-DD.md filesDaily summaries, todos, technical notes (30–90 day retention)
L2Long-term MemoryMEMORY.md (read-only base)Key facts, user profile, permanent decisions

Automated Pipelines

  • Dreaming (03:00 UTC daily): Scans conversation logs, evaluates candidates via DeepSeek analysis, promotes high-scoring insights to L2
  • Three-way Sync (18:00 / 20:00 / 22:00 CST): Keeps Cloud ↔ Markdown ↔ Vector stores in sync
  • **Wiki Compilation (04:00 UTC daily, *optional*)**: Extracts entities and concepts, writes structured wiki vault pages

Setup

One-command auto-setup

bash scripts/auto-setup.sh

This script handles everything interactively:

StepWhat it doesToggle
1Install Ollama (standard or Intel edition)--skip-ollama
2Download bge-m3 embedding model
3Plugin conflict check (auto-detect subconscious-personality-guardian)
4Check memory-core plugin status
5Insert memory-core config into openclaw.jsonAuto-insert on confirm
6Install and configure MemOS Cloud plugin with critical config--skip-memos
7Create memory/ directory, check AGENTS.md
8Set up Dreaming cron job (03:00 UTC daily)

Options

bash scripts/auto-setup.sh --skip-ollama   # Skip Ollama install (use your existing one)
bash scripts/auto-setup.sh --skip-memos    # Skip MemOS Cloud plugin entirely
bash scripts/auto-setup.sh --dry-run       # Preview without making changes

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
  • Setting up automatic Dreaming and promotion pipelines
  • Configuring three-way sync between cloud, files, and vector DB

Plugin Conflicts

❌ subconscious-personality-guardian ↔ memory-core

Incompatible. Both use the same OpenClaw memory slot. Installing both causes write conflicts and retrieval duplication.

Fix: auto-setup.sh detects and disables this automatically. Manual fix:

{
  "plugins": {
    "disabled": ["subconscious-personality-guardian"],
    "deny": ["subconscious-personality-guardian"]
  }
}

✅ memory-core + MemOS Cloud

Compatible — designed to work in layers.

They execute in sequence, not in competition:

User message
  → MemOS Cloud (before_agent_start hook)
      → Injects: static facts, preferences, profile
  → memory-core (runtime semantic query)
      → Injects: recent conversations, topical context
  → Agent receives layered memory

MemOS handles "who the user is" (long-term facts). memory-core handles "what we talked about" (recent history).

Critical config (MemOS Cloud):

{
  "recallFilterFailOpen": true,  // Don't block pipeline if API fails
  "asyncMode": true,             // Let memory-core run too
  "resetOnNew": true,            // Fresh context per session
  "hooks.allowConversationAccess": true  // Hooks need this
}

See references/architecture.md for the full recommended config.

Both should be enabled together.

⚠️ MemOS Cloud + ReMe

Potentially conflicting. File layer overlap and retrieval duplication. Choose one.

See references/architecture.md for full compatibility details.

Components

1. Memory Plugins (L0)

Configured via openclaw.json:

{
  "plugins": {
    "entries": {
      "memory-core": {
        "config": {
          "embeddingUrl": "http://127.0.0.1:11434/api/embed",
          "embeddingModel": "bge-m3",
          "dimension": 1024
        }
      },
      "memos-cloud-openclaw-plugin": {
        "config": {
          "url": "https://your-memos-server",
          "token": "your-token"
        }
      }
    }
  }
}

2. Memory Files (L1 + L2)

~/.openclaw/workspace/
├── memory/
│   ├── YYYY-MM-DD.md          # Daily working memory (auto-indexed)
│   ├── MEMORY_INDEX.md        # Vector BM25 cluster summaries
│   ├── memos-cloud-*.md       # Cloud-pulled memory entries
│   ├── .sync-cloud-state.json # Cloud pull cursor
│   └── .sync-push-state.json  # Push state (SHA256 tracking)
├── MEMORY.md                  # Long-term memory base (read-only)
├── AGENTS.md                  # Runtime context + memory rules
└── SOUL.md                    # Agent persona

3. Sync Scripts (*optional*)

Located at user_workspace/scripts/:

  • sync-cloud-pull.py — Pull from MemOS Cloud → Markdown files
  • sync-cloud-push.py — Push local markdown changes → Cloud (SHA256 diff)
  • sync-vector-index.py — Vector DB → MEMORY_INDEX.md (FTS5 BM25 clustering)
  • sync-all.sh — Orchestrator that runs all three

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 (recommended)

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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OpenClaw

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安装前确认

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