I built MOS (MemoryOS) – a lightweight, self-hosted memory microservice for LLMs using Node.js, pgvector, and local embeddings.
The developer built MOS (MemoryOS), a lightweight, self-hosted memory microservice for LLMs using Node.js, pgvector, and local embeddings. MOS aims to efficiently manage long-term context windows for LLM applications. The service is designed to be decoupled and lightweight, addressing the need for efficient memory management in LLM applications. You can find the code on GitHub.
Key takeaways
- MOS uses Node.js, TypeScript, and Express for the backend.
- PostgreSQL with pgvector is used for the database.
- The service is designed to separate I/O-heavy API operations from CPU-heavy ML tasks.
I built MOS (MemoryOS) – a lightweight, self-hosted memory microservice for LLMs using Node.js, pgvector, and local embeddings.
The developer built MOS (MemoryOS), a lightweight, self-hosted memory microservice for LLMs using Node.js, pgvector, and local embeddings. MOS aims to efficiently manage long-term context windows for LLM applications. The service is designed to be decoupled and lightweight, addressing the need for efficient memory management in LLM applications. You can find the code on GitHub.
Key takeaways
- MOS uses Node.js, TypeScript, and Express for the backend.
- PostgreSQL with pgvector is used for the database.
- The service is designed to separate I/O-heavy API operations from CPU-heavy ML tasks.