QuickRecall - Zero-Dependency Memory Engine. 常用记忆优先出现。零依赖 AI 记忆引擎，纯 Node.js。/ Prioritizes frequently used memories. Zero deps.

Persistent memory engine for AI agents with semantic recall, hotness prioritization, importance weighting, time decay, and auto-compaction. Zero external dependencies. Pure Node.js.

CHEN-feng123@chen-feng123

Install

openclaw skills install @chen-feng123/quickrecall

Memory Enhancement Engine

记忆增加引擎 — Persistent memory engine with hotness-prioritized semantic recall.

Memories that are recalled more often appear first — not just keyword matches.

Quick Start

bash

# Node.js API
const { MemorySystem } = require('./memory-enhancement-engine/memory.js');
const mem = new MemorySystem();

# CLI tool (view / search / compact)
node memory-enhancement-engine/memo.cjs status
node memory-enhancement-engine/memo.cjs query "something to find"

Core Usage

javascript

const { MemorySystem } = require('./memory-enhancement-engine/memory.js');

// Create engine (stores to MEMORY_STORE.json automatically)
const mem = new MemorySystem({ decayHalfLifeHours: 2 });

// Write a memory
mem.add({
  content: "Paris is the capital of France.",
  importance: 1.5,
  metadata: { tags: ["geography", "fact"] }
});

// Semantic search
const results = mem.query("France capital");
console.log(results);

// Get recent memories
const recent = mem.recent(10);

// Compact old memories (summarize low-importance clusters)
mem.compact(5, 0.3);

API

Method	Description
`add(content, importance, metadata)`	Write a memory
`retrieve(query, k)`	Semantic search (returns sorted by score)
`getRecent(n)`	Get N most recent memories
`remove(predicate)`	Remove memories matching predicate
`compact(groupSize, minImportance)`	Compact old memories into summaries
`getStatus()`	Get engine stats (count, size, etc.)

Scoring Formula

text

score = similarity × 0.5 + recency × 0.3 + hotness × 0.2

Where hotness = log(1 + access_count) × exp(-time_delta / 86400)

Features

Hotness-Prioritized Recall — Frequently accessed memories get boosted scores
Semantic Search — Bigram overlap + character-level similarity
Importance Weighting — 0.0 (trivial) to 2.0 (critical)
Time Decay — Half-life configurable (default 2 hours)
Auto-Prune — Beyond 1000 entries, least important are pruned
Auto-Compaction — Merge low-importance groups into summaries
No Server Needed — Direct Node.js require, stores to local JSON

Installation

Method	Command
Copy	Copy `memory.js` + `memo.cjs` to your project
ClawHub	`clawhub install memory-enhancement-engine`

File Structure