Token导航 LogoToken导航TokenDH.com
研究检索敏感数据clawhub未标认证来源可访问clear审计提醒

memorylayermemorylayer 搜索

Agent Skill

memorylayer 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

59,946

周安装

2,575

GitHub Stars

7

下载量

21,012
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:memorylayer(memorylayer 搜索)
来源仓库:https://github.com/khli01/memorylayer
安装命令:
openclaw skills install memorylayer
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 memorylayer

简介

memorylayer 使用矢量搜索实现 AI 代理的语义记忆,节省约 95% 令牌消耗。

  • 适用于 OpenClaw 中需要低成本高效检索大规模记忆库的场景。
  • 通过 clawhub 安装后,支持近似最近邻搜索与缓存优化。
  • 安装命令:openclaw skills install memorylayer;需预训练嵌入模型。
  • 建议监控令牌节省效果与召回率平衡点。

SKILL.md

slug
memorylayer
name
MemoryLayer
description
Semantic memory for AI agents. 95% token savings with vector search.
homepage
https://memorylayer.clawbot.hk
metadata
clawdbot
emoji
🧠

MemoryLayer

Semantic memory infrastructure for AI agents that actually scales.

Features

  • 95% Token Savings - Retrieve only relevant memories
  • Semantic Search - Find memories by meaning, not keywords
  • Sub-200ms - Lightning-fast memory retrieval
  • Multi-tenant - Isolated memory per agent instance

Setup

1. Sign up for FREE account

Visit https://memorylayer.clawbot.hk and sign up with Google. You'll get:

  • 10,000 operations/month
  • 1GB storage
  • Community support

2. Configure credentials

# Option 1: Email/Password
export MEMORYLAYER_EMAIL=your@email.com
export MEMORYLAYER_PASSWORD=your_password

# Option 2: API Key (recommended for production)
export MEMORYLAYER_API_KEY=ml_your_api_key_here

3. Install Python SDK (if not using skill wrapper)

pip install memorylayer

Usage

Basic Example

// In your Clawdbot agent
const memory = require('memorylayer');

// Store a memory
await memory.remember(
  'User prefers dark mode UI',
  { type: 'semantic', importance: 0.8 }
);

// Search memories
const results = await memory.search('UI preferences');
console.log(results[0].content); // "User prefers dark mode UI"

Python Example

from plugins.memorylayer import memory

# Store
memory.remember(
    "Boss prefers direct reporting with zero bullshit",
    memory_type="semantic",
    importance=0.9
)

# Search
results = memory.recall("What are Boss's preferences?")
for r in results:
    print(f"{r.relevance_score:.2f}: {r.memory.content}")

Token Savings

Before MemoryLayer:

# Inject entire memory files
context = open('MEMORY.md').read()  # 10,500 tokens
prompt = f"{context}\
\
User: What are my preferences?"

After MemoryLayer:

# Inject only relevant memories
context = memory.get_context("user preferences", limit=5)  # ~500 tokens
prompt = f"{context}\
\
User: What are my preferences?"

Result: 95% token reduction, $900/month savings at scale

API Reference

memory.remember(content, options)

Store a new memory.

Parameters:

  • content (string): Memory content
  • options.type (string): 'episodic' | 'semantic' | 'procedural'
  • options.importance (number): 0.0 to 1.0
  • options.metadata (object): Additional tags/data

Returns: Memory object with id

memory.search(query, limit)

Search memories semantically.

Parameters:

  • query (string): Search query (natural language)
  • limit (number): Max results (default: 10)

Returns: Array of SearchResult objects

memory.get_context(query, limit)

Get formatted context for prompt injection.

Parameters:

  • query (string): What context do you need?
  • limit (number): Max memories (default: 5)

Returns: Formatted string ready for prompt

memory.stats()

Get usage statistics.

Returns: Object with total_memories, memory_types, operations_this_month

Advanced

Memory Types

Episodic - Events and experiences

memory.remember('Deployed MemoryLayer on 2026-02-03', { type: 'episodic' });

Semantic - Facts and knowledge

memory.remember('Boss prefers concise reports', { type: 'semantic' });

Procedural - How-to and processes

memory.remember('To restart server: ssh root@... && systemctl restart...', { type: 'procedural' });

Metadata Tagging

memory.remember('User likes blue', {
  type: 'semantic',
  metadata: {
    category: 'preferences',
    subcategory: 'colors',
    source: 'user_profile'
  }
});

Usage Tracking

const stats = await memory.stats();
console.log(`Total memories: ${stats.total_memories}`);
console.log(`Operations this month: ${stats.operations_this_month}`);
console.log(`Plan: ${stats.plan} (${stats.operations_limit}/month)`);

Pricing

FREE Plan (Current)

  • 10,000 operations/month
  • 1GB storage
  • Community support

Pro Plan ($99/mo)

  • 1M operations/month
  • 10GB storage
  • Email support
  • 99.9% SLA

Enterprise (Custom)

  • Unlimited operations
  • Unlimited storage
  • Dedicated support
  • Self-hosted option
  • Custom SLA

Support

  • Documentation: https://memorylayer.clawbot.hk/docs
  • API Reference: https://memorylayer.clawbot.hk/api
  • Community: Discord (link in docs)
  • Issues: GitHub (link in docs)

Links

  • Homepage: https://memorylayer.clawbot.hk
  • Dashboard: https://dashboard.memorylayer.clawbot.hk
  • API Docs: https://memorylayer.clawbot.hk/docs
  • Python SDK: https://pypi.org/project/memorylayer (when published)

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。

平台分布

OpenClaw

83.02%
按下载量换算17,444

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills