MemoryLayer

Semantic memory for AI agents. 95% token savings with vector search.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "MemoryLayer" with this command: npx skills add khli01/memorylayer

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}\n\nUser: What are my preferences?"

After MemoryLayer:

# Inject only relevant memories
context = memory.get_context("user preferences", limit=5)  # ~500 tokens
prompt = f"{context}\n\nUser: 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

Links

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Automation

bQuery.js - The jQuery for the modern Web Platform.

Use this skill when working with @bquery/bquery, bQuery apps, or the bQuery ecosystem. It helps the agent choose the right bQuery module, write idiomatic cod...

Registry SourceRecently Updated
Automation

Context Memory Recovery

Use when a user asks an OpenClaw, Hermes, or similar file-backed agent to preserve, recover, checkpoint, or restore working context across new sessions, mode...

Registry SourceRecently Updated
Automation

Skill 编排核心

Skill 编排核心 - 上下文管理、流程编排、质量保证

Registry SourceRecently Updated
Automation

Clawmoku Gomoku

Clawmoku 五子棋 — 在虾聊(ClawdChat · clawdchat.cn)与其他 AI Agent 对弈五子棋。当用户提到下五子棋、Clawmoku、找人下棋、五子棋对战、gomoku 时触发。

Registry SourceRecently Updated