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Simplemem

Efficient Lifelong Memory for LLM Agents - semantic compression, cross-session memory, and intent-aw

Rating: 3.8 (121 reviews)
Downloads: 28,497 downloads
Version: 1.0.0

Overview

Efficient Lifelong Memory for LLM Agents - semantic compression, cross-session memory, and intent-aware retrieval.

✨Key Features

Store: Compresses interactions into compact memory units

Synthesize: Merges related memories on-the-fly

Retrieve: Intent-aware planning for efficient context retrieval

Complete Documentation

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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

powershell

# 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:

powershell

$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

powershell

# 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

python

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

Repo: https://github.com/aiming-lab/SimpleMem
Paper: https://arxiv.org/abs/2601.02553
Discord: https://discord.gg/KA2zC32M

Installation

Terminal bash


openclaw install simplemem

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💻Code Examples

pip install -r requirements.txt

pip-install--r-requirementstxt.txt

## Configuration (Optional - Full Features)

For full SimpleMem features, set your OpenAI API key:

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

envopenaiapikey--your-openai-key.txt

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

## Usage

### PowerShell Script

example.txt

# 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

example.txt

# 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

example.py

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")