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Agent Skill · 研究检索

memclawz(memclawz 搜索)

memclawz 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。

memclawz 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 12,759。

总安装 9,996周安装 425GitHub Stars 公开资料未说明下载 3,502核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
6.0.1

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:memclawz(memclawz 搜索)
来源仓库:https://github.com/yoniassia/memclawz
安装命令:
openclaw skills install memclawz
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 memclawz

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

memclawz 是一个基于 Qdrant + Mem0 + Neo4j/Graphiti 的 AI 代理车队存储系统。适用于多代理协作环境下的知识图谱构建与联合记忆管理。支持复合评分、压缩引擎和时间知识图等高级功能。使用前需规划数据存储架构并确认各组件版本兼容性。建议先在小规模测试环境中验证稳定性和扩展能力。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install memclawz;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

memclawz 怎么安装?

在终端执行 openclaw skills install memclawz。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

memclawz 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

memclawz 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

memclawz 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

memclawz 用的人多吗?

公开数据是累计安装 9,996 次,近一周安装 425 次,下载 3,502 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

memclawz 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
memclawz

memclawz 搜索 · 本页

9,996—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
memclawz
description
AI agent fleet memory system — Qdrant + Mem0 + Neo4j/Graphiti. Composite scoring, compaction engine, temporal knowledge graph, multi-claw federation, sleep-time reflection, routing engine, MCP server. Use when you need to install, configure, manage, search, route, compact, or upgrade the agent memory system.
metadata
openclaw
requires
bins
["python3", "pip3"]

MemClawz v6 🧠

Fleet memory system for OpenClaw agents with composite scoring, compaction engine, Graphiti temporal knowledge graph, multi-claw federation, and sleep-time reflection.

What's New in v6

  • Composite Scoring — Weighted blend of semantic similarity + recency decay + importance + access frequency
  • Compaction Engine — Session/daily/weekly compaction with LLM extraction
  • Graphiti Integration — Neo4j temporal knowledge graph for entity relationships and contradiction detection
  • Multi-Claw Federation — HTTP push/pull protocol for sharing memories across fleet
  • Sleep-Time Reflection — LLM-driven pattern detection, insight generation, and MEMORY.md update proposals
  • Enhanced MCP Server — New tools: compact_session, reflect, memory_stats

Quick Install

Prerequisites

  • Python 3.10+
  • Qdrant running (Docker or binary)
  • Neo4j running (for Graphiti; optional but recommended)
  • OpenAI API key (for embeddings)
  • Anthropic API key (for classification)

Install Qdrant

# Docker (preferred)
docker run -d --name qdrant -p 6333:6333 -p 6334:6334 \
  -v ~/.openclaw/qdrant-storage:/qdrant/storage \
  --restart unless-stopped qdrant/qdrant

# Or binary (no Docker)
curl -sL https://github.com/qdrant/qdrant/releases/latest/download/qdrant-x86_64-unknown-linux-musl.tar.gz | tar xz
./qdrant --storage-path ~/.openclaw/qdrant-storage &

Install MemClawz

cd ~
git clone https://github.com/yoniassia/memclawz.git
cd memclawz
pip3 install -r requirements.txt

Configure

cat > ~/memclawz/.env << EOF
OPENAI_API_KEY=<your-key>
ANTHROPIC_API_KEY=<your-key>
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_COLLECTION=yoniclaw_memories
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=
GRAPHITI_ENABLED=true
FEDERATION_ENABLED=true
FEDERATION_ROLE=master
WORKSPACE_DIR=/home/yoniclaw/.openclaw/workspace
EOF

Deploy Services

cp ~/memclawz/systemd/*.service ~/.config/systemd/user/
systemctl --user daemon-reload
systemctl --user enable --now neo4j memclawz-api memclawz-watcher memclawz-cron

Verify

curl http://localhost:3500/health
# {"status":"ok","version":"6.0.0","qdrant":"ok","neo4j":"ok","graphiti":"ok","federation":"ok",...}

API Reference

Core (v5 compatible)

# Search with composite scoring
curl "http://localhost:3500/api/v1/search?q=eToro+SuperApp&limit=10"
# Use raw cosine: &use_composite=false

# Add memory (feeds both Qdrant AND Graphiti)
curl -X POST "http://localhost:3500/api/v1/add" \
  -H "Content-Type: application/json" \
  -d '{"content":"BTC hit 100K on March 1","agent_id":"tradeclaw","memory_type":"event"}'

# List by agent
curl "http://localhost:3500/api/v1/memories?agent_id=tradeclaw&limit=20"

# Stats / Agents
curl http://localhost:3500/api/v1/stats
curl http://localhost:3500/api/v1/agents

Graph Search (v6)

# Search temporal knowledge graph
curl "http://localhost:3500/api/v1/graph/search?q=eToro+deployment"

# Get entity relationships
curl "http://localhost:3500/api/v1/graph/entity/YoniClaw"

Compaction (v6)

# Trigger session compaction
curl -X POST "http://localhost:3500/api/v1/compact/session" \
  -H "Content-Type: application/json" \
  -d '{"session_id":"main:whatsapp:direct:+35794329522","agent_id":"main"}'

# Generate daily digest
curl -X POST "http://localhost:3500/api/v1/compact/daily"

# Run weekly merge
curl -X POST "http://localhost:3500/api/v1/compact/weekly"

# Check compaction status
curl "http://localhost:3500/api/v1/compact/status"

Reflection (v6)

# Trigger reflection (analyzes last 24h of memories)
curl -X POST "http://localhost:3500/api/v1/reflect" \
  -H "Content-Type: application/json" \
  -d '{"hours":24,"max_memories":100}'

Federation (v6)

# Register a remote node
curl -X POST "http://localhost:3500/api/v1/federation/register" \
  -H "Content-Type: application/json" \
  -d '{"node_id":"clawdet","node_url":"http://188.34.197.212:3500","node_key":"shared-secret"}'

# Push memories from remote
curl -X POST "http://localhost:3500/api/v1/federation/push" \
  -H "Content-Type: application/json" \
  -d '{"node_id":"clawdet","node_key":"shared-secret","memories":[{"content":"...","type":"fact","agent":"main"}]}'

# Pull memories to remote
curl -X POST "http://localhost:3500/api/v1/federation/pull" \
  -H "Content-Type: application/json" \
  -d '{"node_id":"clawdet","node_key":"shared-secret","since":"2026-03-13T00:00:00Z","limit":100}'

# Federation status
curl "http://localhost:3500/api/v1/federation/status"

Composite Scoring

score = (w_semantic × similarity + w_recency × decay + w_importance × weight) × access_boost
  • Semantic: 50% weight (cosine from Qdrant)
  • Recency: 30% weight (exponential, 90-day half-life)
  • Importance: 20% weight (type-based: decisions > preferences > facts > events)
  • Access boost: up to 1.5× for frequently accessed memories
  • Persistent types (decisions, preferences, relationships): 40% recency floor

Memory Types

  • fact — factual statement about a person, project, system
  • decision — a choice that was made
  • preference — user preference or style choice
  • procedure — steps to accomplish something
  • relationship — info about a person or org relationship
  • event — something that happened at a specific time
  • insight — learned lesson, pattern, or strategic insight

Canonical Memory Order

  1. Local canonical files first — MEMORY.md, memory/*.md, memory/people/*, memory/sessions/*, knowledge/*.md
  2. MemClawz second — Qdrant + Mem0 + Neo4j/Graphiti + API + MCP
  3. LCM/transcripts third — raw capture and extraction layer

Services

ServicePortDescription
memclawz-api3500REST API (v6)
memclawz-watcher—LCM auto-extract (+ Graphiti feed)
memclawz-cron—Compaction scheduler (30-min cycle)
memclawz-mcpstdioMCP server (v6 tools)
Neo4j7474/7687Graph database (Graphiti)
Qdrant6333Vector database

MCP Integration

{
  "mcpServers": {
    "memclawz": {
      "command": "python3",
      "args": ["/path/to/memclawz/memclawz/mcp_server.py"],
      "env": {"OPENAI_API_KEY": "<key>", "ANTHROPIC_API_KEY": "<key>"}
    }
  }
}

MCP tools: search_memory, add_memory, get_agent_memories, compact_session, reflect, memory_stats

Architecture

LCM → Watcher → Classify → Mem0 → Qdrant + Graphiti/Neo4j
                                    ↑↓            ↑↓
Fleet Agents ←→ REST API :3500  ←→ Qdrant    Neo4j
MCP Clients  ←→ MCP Server     ←→ Qdrant
Remote Claws ←→ Federation API ←→ Qdrant
Cron         →  Compactor/Reflection → Files + Qdrant + Graphiti

平台分布

OpenClaw

92.9%
按下载量换算3,253

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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