ИИ-агенты
SimpleMem
Research-backed lifelong memory for LLM agents, text and multimodal.
★ 3,8 тыс. звёздMIT
Смотреть руководство →A-mem-sys — проект с открытым исходным кодом из категории «ИИ-агенты» от WujiangXu (WujiangXu/A-mem-sys на GitHub). У него 401 звезда на GitHub и 62 форка, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 15 марта 2026.
Agentic memory for LLM agents, from a published research project.
Start with one concrete task rather than a grand architecture.
Clone the repo and check the examples folder for a quickstart.
Install packages, set environment variables, and point it at your model.
Execute the demo workflow, then adapt it to your own data.
Memory the agent manages itself.
Tied to a published paper.
Readable research implementation.
Free to build on.
Explores memory curation ideas.
Good for learning the area.
Download the source and move into the project folder.
git clone https://github.com/WujiangXu/A-mem-sys.git
cd A-mem-sysCreate 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.pyWujiangXu/A-mem-sys is its home on GitHub, with 62 forks so far. The contributors tab and recent commits show how actively it is maintained.
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.
A-MEM is research code exploring agentic memory; mem0 is production memory infrastructure. Study A-MEM for ideas, deploy mem0 (or similar) for products.
Follow the README quickstart, then browse the examples directory for patterns that match what you want to build.
Research-backed lifelong memory for LLM agents, text and multimodal.
Give your coding agents a persistent memory that survives every session.
Microsoft's R&D-Agent: LLM agents that automate data-driven research, Kaggle modelling and LLM fine-tuning.
Make your agents learn from experience with a dedicated context engine.
A memory management kit for agents — remember me, refine me.
Self-hosted AI vulnerability research: agents find, validate, deduplicate, and rank real security issues.
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если A-mem-sys вам не подходит.
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.
Соседние проекты из категории «ИИ-агенты» в каталоге, ближайшие по числу звёзд к A-mem-sys — продолжайте изучать полку по обе стороны от него.
Safety testing for agents, right in pytest.
Benchmark computer-use agents on long, real-world desktop tasks.
The tougher, more robust successor to the MMLU benchmark.
WorkArena: ServiceNow's benchmark for how well web agents handle everyday knowledge-work tasks.
A harness-oriented framework for production-grade agent applications.
A complete evaluation toolkit for AI agents.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.