AI Agents
SimpleMem
Research-backed lifelong memory for LLM agents, text and multimodal.
★ 3.8k starsMIT
View guide →Agentic memory for LLM agents, from a published research project.
A-mem-sys is an open-source ai agents project by WujiangXu (WujiangXu/A-mem-sys on GitHub). It has 401 GitHub stars and 62 forks, is released under the MIT license, and was last updated on GitHub on 15 March 2026.
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.
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Free and open — grab the source, star the repo, and start creating.