Get ultra-persistent memory and token savings with MemOS, for founders using LLM and AI agents, backed by 10k+ GitHub stars.
10,146 stars924 forksTypeScriptQuality 9/10Updated 7/9/2026100% free ยท open source
What it does
MemOS provides ultra-persistent memory and token savings for founders using large language models (LLMs) and AI agents, enabling efficient and cost-effective operation of these models.
Install / run
git clone https://github.com/MemTensor/MemOS.git && cd MemOS
When to use it
โขWhen building conversational AI applications that require remembering context across multiple interactions
โขWhen optimizing LLMs for token usage to reduce costs and improve performance
โขWhen integrating AI agents into workflows that require persistent memory and skill reuse across tasks
Quick start
1Run `npm install` to install dependencies
2Configure the `memos.config.json` file to set up the memory and retrieval settings
3Start the MemOS server with `npm run start`
4Use the `memos_client.ts` example to test the MemOS API and demonstrate ultra-persistent memory and token savings
5Explore the `examples` directory for more advanced use cases and integrations with LLMs and AI agents
Ready-to-paste prompt
Use the following command to test the MemOS API: `node memos_client.ts -m 'What is the capital of France?'`
Heads up: Make sure to set the `HYBRID_RETRIEVAL` environment variable to enable hybrid-retrieval, which is required for ultra-persistent memory and token savings, by running `export HYBRID_RETRIEVAL=true` before starting the MemOS server
Saves to your device
Topics
agent
agentic-ai
ai
ai-agents
chatgpt
claude
hermes
llm
long-term-memory
mcp
memory
memory-management
multi-agent
openclaw
python
rag
self-evolving
self-hosted
skills
token-savings
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.