MemoryLayer

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

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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

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