memorylayer

✓Verified·Scanned 2/18/2026

MemoryLayer provides a JS/Python client for semantic agent memory that calls the service at https://memorylayer.clawbot.hk and exposes remember, search, get_context, and stats. The skill reads credentials from MEMORYLAYER_API_KEY, MEMORYLAYER_EMAIL, and MEMORYLAYER_PASSWORD, makes network requests to https://memorylayer.clawbot.hk, and includes shell setup commands like export MEMORYLAYER_EMAIL=... and pip install memorylayer.

from clawhub.ai·vba32654·43.3 KB·0 installs
Scanned from 1.0.0 at ba32654 · Transparency log ↗
$ vett add clawhub.ai/khli01/memorylayer

MemoryLayer ClawdBot Skill

Semantic memory for AI agents with 95% token savings.

🎯 What is MemoryLayer?

MemoryLayer provides semantic long-term memory for AI agents, replacing bloated file-based memory systems with efficient vector search.

The Problem:

  • Dumping entire chat history = 10,500+ tokens per request
  • Keyword search misses semantic matches
  • File-based memory doesn't scale
  • Cost: $945/month at 30K requests

The Solution:

  • Semantic search via embeddings
  • 95% token reduction (10.5K → 500 tokens)
  • <200ms retrieval
  • Cost: $45/month at 30K requests

Savings: $900/month 💰

🚀 Quick Start

Install

clawdbot skill install memorylayer

Note for developers: If cloning from GitHub, run npm install first to install dependencies.

Setup

# Sign up for FREE account at https://memorylayer.clawbot.hk
# Then configure credentials:

export MEMORYLAYER_EMAIL=your@email.com
export MEMORYLAYER_PASSWORD=your_password

Usage

JavaScript:

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"

// Get formatted context for prompt injection
const context = await memory.get_context('user preferences', 5);
// Returns: "## Relevant Memories\n- User prefers dark mode..."

Python:

from memorylayer import memory

# Store
memory.remember(
    "User prefers dark mode UI",
    memory_type="semantic",
    importance=0.8
)

# Search
results = memory.recall("UI preferences")
for r in results:
    print(f"{r.relevance_score:.2f}: {r.memory.content}")

📊 Token Savings Example

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

🌟 Features

  • Semantic Search - Find by meaning, not keywords
  • Multi-tenant - Isolated memory per agent
  • Fast - <200ms average search time
  • Memory Types - Episodic, semantic, procedural
  • FREE Plan - 10,000 operations/month
  • Dual Language - JavaScript + Python support

📖 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

💰 Pricing

FREE Plan

  • 10,000 operations/month
  • 1GB storage
  • Community support
  • Perfect for side projects

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

🔗 Links

📝 Examples

See the examples/ directory for:

  • basic-usage.js - Simple remember + search demo
  • agent-integration.js - Agent workflow integration
  • token-savings-demo.js - Before/after ROI comparison

🤝 Support

📄 License

MIT

Built by QuantechCo | Powered by MemoryLayer