KI-Agenten
motorhead
A memory and retrieval server built for LLMs.
★ 917 SterneApache-2.0
Anleitung ansehen →MemOS ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von MemTensor (MemTensor/MemOS auf GitHub). Es hat 11.763 Sterne auf GitHub und 1.069 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 8. Oktober 2026 auf GitHub aktualisiert.
Self-evolving memory OS for LLMs and agents: persistent graph memory, hybrid retrieval, skill reuse.
Choose the hosted Cloud API for zero infrastructure, self-hosting for your own servers, or the local plugin for 100% on-device memory in OpenClaw, Hermes or DeepSeek Harness.
For self-hosting, clone the repo, copy docker/.env.example to .env and fill in your LLM and embedder API keys; for cloud, create an API key in the dashboard.
Bring up MemOS with Docker Compose (API, Neo4j and Qdrant) and confirm it answers on port 8000.
Create a memory cube, add conversation messages through the API, then search them back and watch recall improve future agent turns.
One API to add, retrieve, edit and delete memory, structured as an inspectable graph rather than an opaque embedding store.
Natively stores text, images, tool traces and personas, retrieved and reasoned over together in one system.
Manage multiple knowledge bases as composable cubes with isolation and controlled sharing across users, projects and agents.
The MemScheduler runs memory operations asynchronously, keeping latency low under high concurrency for production stability.
Refine memory with natural-language feedback - correcting, supplementing or replacing memories over time, which underpins its self-evolving behavior.
Official cloud and local plugins for OpenClaw, Hermes Agent and DeepSeek Harness add automatic recall before tasks and capture after them, with a local Memory Viewer.
Clone MemOS for self-hosting; cloud and plugin users can skip the clone.
git clone https://github.com/MemTensor/MemOS.git
cd MemOSCopy the example environment file and fill in your LLM provider and embedder API keys.
cp docker/.env.example .env # fill in your API keys in .envLaunch MemOS with Docker Compose - it starts the API together with Neo4j and Qdrant - then call it on port 8000.
cd docker
docker compose upMemOS is developed by MemTensor and contributors; the research is described in the paper 'MemOS: A Memory OS for AI System' (arXiv 2507.03724).
The open-source code is Apache-2.0 licensed and self-hosting is free. The hosted MemOS Cloud API is a managed service with its own key and terms.
Self-hosting needs your own LLM and embedder keys (set in .env) for memory extraction and search; the cloud API uses a MemOS API key instead. The local plugin keeps data on-device.
All three give agents long-term memory; MemOS centers on a graph-structured memory OS with composable memory cubes and an async scheduler, and ships first-party plugins for OpenClaw, Hermes and DeepSeek Harness.
For the fastest path, get a cloud API key from the MemOS dashboard and call the add/search endpoints; for self-hosting, clone, fill .env, and docker compose up.
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.