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Simplemem

将 SimpleMem 集成到 OpenClaw，为 LLM Agent 提供跨会话持久记忆：把交互压缩为记忆单元、合并相关记忆并按意图检索；提供 Python 封装与 PowerShell 命令，未配置 API Key 时回退为 JSON 关键词检索。

nantes
nantes
热度 80.40
最近更新 2026-05-11 17:26:34
分类：知识依赖：代码执行 / 读写文件 / 第三方 API / 内部调用大模型应用场景：笔记整理 / 摘要总结

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add simplemem
Metadata
name
simplemem
version
1.0.0
description
Efficient Lifelong Memory for LLM Agents - semantic compression, cross-session memory, and intent-aware retrieval
metadata
{"openclaw":{"emoji":"🧠","requires":{"bins":["python"],"env":["OPENAI_API_KEY"]},"primaryEnv":"OPENAI_API_KEY","homepage":"https://github.com/aiming-lab/SimpleMem"}}

SimpleMem Skill

Integrates SimpleMem: Efficient Lifelong Memory for LLM Agents into OpenClaw.

What it does

SimpleMem provides semantic memory compression and retrieval for agents:

  • Store: Compresses interactions into compact memory units
  • Synthesize: Merges related memories on-the-fly
  • Retrieve: Intent-aware planning for efficient context retrieval

Installation

# Install Python dependency
pip install simplemem

# Or via repo
git clone https://github.com/aiming-lab/SimpleMem.git
cd SimpleMem
pip install -r requirements.txt

Configuration (Optional - Full Features)

For full SimpleMem features, set your OpenAI API key:

$env:OPENAI_API_KEY = "your-openai-key"

Without API key: Uses JSON fallback (basic keyword search) With API key: Uses full SimpleMem with semantic embeddings

Usage

PowerShell Script

# Agregar memoria
.\simplemem.ps1 -Action add -Content "El usuario prefiere cafe con leche de avena"

# Buscar memorias
.\simplemem.ps1 -Action search -Query "cafe"

# Ver estadisticas
.\simplemem.ps1 -Action stats

Python API

from simplemem import SimpleMemSystem, set_config, SimpleMemConfig

# With API key (full features)
config = SimpleMemConfig()
config.openai_api_key = "your-key"
set_config(config)
system = SimpleMemSystem()

# Add memory
system.add("User preference: coffee with oat milk", user_id="user1")

# Retrieve
results = system.retrieve("What does user like?", user_id="user1")

Key Features

  • Cross-session memory: Persistent across conversations (64% better than Claude-Mem)
  • Semantic compression: 43.24% F1 on LoCoMo benchmark
  • Fast retrieval: 388ms average retrieval time
  • Multi-index: Semantic + Lexical + Symbolic layers
  • Fallback: JSON-based storage when no API key available

Files

  • simplemem.py - Main Python wrapper
  • simplemem.ps1 - PowerShell CLI script
  • data/ - Storage directory (created on first use)

Credits