RAMen adds AI-agent memory and semantic caching to a Redis-like store

RAMen is an in-memory data store that works like Redis for common caching tasks, but adds features aimed at AI apps and AI agents. Existing Redis clients can often connect to it with little more than a connection setting change because it supports the Redis protocol for common commands. Its main features are a normal cache, vector search, semantic cache, agent memory, a live dashboard, and a built-in MCP server.

The semantic cache can return a saved answer when a new question has the same meaning as an earlier one, which can reduce repeated AI model calls and lower cost. That cost-saving feature needs an embeddings provider, and it can use local Ollama or OpenAI embeddings. AI agents such as Claude can use RAMen through the MCP server to read, write, search, and remember data.

It can be installed with Go, run through Docker, or built from source, and it opens a data port plus a local dashboard by default. It is not yet meant to replace a large production Redis setup because it does not yet support clustering, replication, failover, or stronger durability.