AIエージェント
A-mem-sys
Agentic memory for LLM agents, from a published research project.
★ 401 スターMIT
ガイドを見る →SimpleMemは、aiming-labが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: aiming-lab/SimpleMem）。GitHubスターは3,823、フォークは402で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年7月24日です。
Research-backed lifelong memory for LLM agents, text and multimodal.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Designed for very long horizons.
Handles text and other modalities.
Compression keeps memory tractable.
Tied to an ICML 2026 paper.
Structured memory representation.
Free to build on.
Download the source and move into the project folder.
git clone https://github.com/aiming-lab/SimpleMem.git
cd SimpleMemCreate a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pyIt is an open-source project at aiming-lab/SimpleMem with 402 forks. Check the repo's contributors and recent commits to judge how active maintenance is.
Yes - it is MIT licensed, so you can use, modify, and distribute it freely, including in commercial projects.
It depends on your setup - self-hosted storage can be free, while hosted tiers or embedding APIs may cost money. Check the README for options.
SimpleMem is a research system with a paper to read; mem0 and cognee are production-oriented platforms. Choose SimpleMem to study and extend the ideas, the others to ship memory features.
Clone the repo, read the README's quickstart, and run the provided example - most projects get you to a first result in minutes.
Agentic memory for LLM agents, from a published research project.
Microsoft's R&D-Agent: LLM agents that automate data-driven research, Kaggle modelling and LLM fine-tuning.
Persistent memory layer for AI agents, backed by Markdown and Milvus.
Stateful AI agents with memory that learns and improves.
MCP server letting AI agents run 150+ cybersecurity tools for authorized pentesting and research.
Mobile-Agent — multimodal GUI agents that operate phones and PCs from screenshots and instructions.
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — SimpleMemが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でSimpleMemとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
Tutti — a real-time shared workspace where people and multiple AI agents build in tune, sharing context, files, apps and tasks
A portable AI agent runtime for the JVM — write one @Agent, run it on any Java AI stack.
A low-code tool for building multi-agent LLM apps — chatbots, RAG and fine-tuning in one flow.
Build voice-based LLM agents with a modular, open-source toolkit.
OpenAI's lightweight TypeScript framework for multi-agent workflows and voice agents.
A rigorous benchmark for LLMs acting as agents.