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

• Homepage: https://memorylayer.clawbot.hk
• Dashboard: https://dashboard.memorylayer.clawbot.hk
• API Docs: https://memorylayer.clawbot.hk/docs
• ClawdHub: https://clawhub.ai/skills/memorylayer

📝 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

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

📄 License

MIT

---

Built by QuantechCo | Powered by MemoryLayer