KI-Agenten
SimpleMem
Research-backed lifelong memory for LLM agents, text and multimodal.
★ 3,8 Tsd. SterneMIT
Anleitung ansehen →A-mem-sys ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von WujiangXu (WujiangXu/A-mem-sys auf GitHub). Es hat 401 Sterne auf GitHub und 62 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 15. März 2026 auf GitHub aktualisiert.
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.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls A-mem-sys nicht das Richtige ist.
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.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die A-mem-sys bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
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.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.