RAMen

README

RAMen

RAM + ramen. In memory, and fast to spin up.

RAMen is a fast in-memory data store, like Redis, but built for AI apps and AI agents.

It does three things:

1. It works as a drop-in cache. It speaks the same protocol as Redis, so your existing Redis client and code keep working with no changes.
2. It can store and search vectors, and it can cache AI answers by meaning. So if two prompts mean the same thing, RAMen can return the saved answer instead of calling the model again. That saves you money.
3. It has a built-in MCP server. That means AI agents (like Claude) can read, write, search, and remember data in RAMen directly as a tool. No extra glue code needed.

Redis was built for app servers. RAMen is built for AI agents.

Is this a Redis alternative?

Yes, for a lot of common use cases. If you use Redis (or Valkey) as a cache or a simple key value store, you can point your app at RAMen instead and it will just work, because RAMen speaks the Redis protocol (RESP2).

Where RAMen is different:

• It has vector search and a semantic cache built in. With Redis you would need an extra module or extra code for that.
• It has an MCP server built in, so AI agents can use it as a tool out of the box.
• It is one small single file (a single binary) with no extra dependencies. Easy to download and run.

What RAMen does NOT do yet (so you know what you are getting):

• No clustering, no replication, no failover.
• No append-only-file durability (it saves snapshots to disk instead).
• Not tuned to beat Redis or Valkey on raw speed.

So: great as a Redis-style cache and an AI data layer for one machine. Not yet a replacement for a big production Redis cluster.

Quick start (the simple version)

You need Go installed (version 1.25 or newer). Then run one of these.

Option 1: install with Go

go install github.com/Rohit-Dnath/RAMen/cmd/ramen@latest
ramen

Option 2: run with Docker

docker run -p 6379:6379 -p 8080:8080 ghcr.io/rohit-dnath/ramen:latest

Option 3: build from source

git clone https://github.com/Rohit-Dnath/RAMen
cd RAMen
go run ./cmd/ramen

When it starts you will see:

ramen: listening on [::]:6379
ramen: dashboard on http://localhost:8080

That is it. RAMen is now running on port 6379, and a live dashboard is at http://localhost:8080.

How to use it

Talk to it like Redis

If you have redis-cli installed, just connect to port 6379:

redis-cli -p 6379

> SET hello world
OK
> GET hello
"world"
> EXPIRE hello 60
(integer) 1

If you have an app that already uses Redis, point it at RAMen. Usually you only change one setting:

export REDIS_URL=redis://localhost:6379

Basic commands

These work just like Redis:

SET user:1 "Rohit"          store a value
GET user:1                  read it back
INCR visits                 count something
HSET person name Rohit      store fields under one key
RPUSH queue job1 job2       a list
SADD tags go ai             a set
ZADD board 100 alice        a ranked list
SUBSCRIBE news              listen for messages
PUBLISH news "hello"        send a message

Full list with examples: docs/commands.md.

Vector search

Store vectors and find the closest ones. Your app gives RAMen the numbers, no API key needed.

VSET docs d1 0.1 0.2 0.9 META "intro page"
VSET docs d2 0.9 0.1 0.0 META "pricing page"
VSEARCH docs 0.1 0.2 0.85 TOPK 1 WITHSCORES

Semantic cache (save money on AI calls)

Cache an AI answer once. Next time a similar question comes in, get the saved answer back instead of paying for another model call.

SCACHE.SET "What is the capital of France?" "Paris"
SCACHE.GET "whats the capital of france" THRESHOLD 0.9
> "Paris"

This needs an embeddings provider. It works with a free local Ollama or with OpenAI. See Configuration.

Agent memory

Let an AI agent remember things across turns:

REMEMBER session7 user_name Rohit
RECALL session7 user_name
> "Rohit"

Use it from Claude (MCP)

Start RAMen, then add this to your Claude config and Claude can use RAMen as a tool:

{
  "mcpServers": {
    "ramen": {
      "command": "ramen",
      "args": ["mcp", "--addr", "localhost:6379"]
    }
  }
}

Step by step guide: docs/mcp.md.

The dashboard

Open http://localhost:8080 in your browser while RAMen is running. You will see your keys, memory use, and the cache hit rate, updating live. Turn it off with --dashboard-addr "".

RAMen vs Redis vs Valkey

	RAMen	Redis	Valkey
Redis protocol (RESP2)	Yes	Yes	Yes
Works with existing Redis clients	Yes (common commands)	Yes	Yes
One single binary, no setup	Yes	No	No
Vector search built in	Yes	Needs a module	Needs a module
Semantic cache command	Yes	No	No
MCP server for AI agents	Yes	No	No
Clustering and replication	Not yet	Yes	Yes
Best raw speed on one node	Good	Best	Best
License	BSD-3-Clause	AGPL / RSAL (since 2024)	BSD-3-Clause

Configuration

Every flag also has a RAMEN_* environment variable.

Flag	Env var	Default	What it does
--addr	RAMEN_ADDR	:6379	Port to listen on
--auth	RAMEN_AUTH	empty	Require a password (empty means no password)
--snapshot-path	RAMEN_SNAPSHOT_PATH	ramen.snapshot	Where to save data (empty turns saving off)
--snapshot-interval	RAMEN_SNAPSHOT_INTERVAL	60s	How often to save to disk
--dashboard-addr	RAMEN_DASHBOARD_ADDR	:8080	Dashboard port (empty turns it off)
RAMEN_EMBED_URL	RAMEN_EMBED_URL	empty	Embeddings endpoint for the semantic cache
RAMEN_EMBED_MODEL	RAMEN_EMBED_MODEL	text-embedding-3-small	Which embedding model to use
RAMEN_EMBED_KEY	RAMEN_EMBED_KEY	empty	API key for the embeddings endpoint

Turn on the semantic cache for free with a local Ollama:

ollama pull nomic-embed-text
export RAMEN_EMBED_URL=http://localhost:11434/v1/embeddings
export RAMEN_EMBED_MODEL=nomic-embed-text
ramen

Or use OpenAI:

export RAMEN_EMBED_URL=https://api.openai.com/v1/embeddings
export RAMEN_EMBED_MODEL=text-embedding-3-small
export RAMEN_EMBED_KEY=sk-your-key
ramen

Roadmap

• Done: core Redis commands, strings, hashes, lists, sets, sorted sets, expiry, pub/sub, snapshot saving.
• Done: vector store, semantic cache, agent memory, MCP server, dashboard.
• Next: replication, stronger durability, clustering, faster vector index (HNSW).

Support the project

RAMen is free and open source, and it always will be. If it saves you time or you would like to help fund its development, you can support the work directly. It genuinely helps keep the project moving.

Not in a position to chip in? A star, a share, or a bug report helps just as much. Thank you.

Contributing

RAMen is open source and very new, which means this is a great time to get involved and make a real difference. Beginners are welcome. You do not need to be a Go expert.

Good first things to help with:

• Try it out and report bugs or confusing parts.
• Improve the docs or add examples.
• Add a missing Redis command.
• Test RAMen with your favourite Redis client and tell us what worked.

Good first issues

New here? These are small, self-contained, and well described. Pick one, comment to claim it, and you are off.

• Good first issues beginner-friendly, clearly scoped tasks.
• Help wanted things we would love a hand with.
• All open issues the full list.

Most open issues are missing Redis commands with the exact files and acceptance criteria already written down, so you can follow an existing handler as a template and open a PR the same day.

Then read the Contributing Guide to get started. If you are not sure where to begin, open an issue and say hi. Stars and shares also help a lot.

Contributors

Thanks to these wonderful people who help make RAMen better.

Rohit-Dnath

bayrambartu

tchalikanti1705

License

BSD-3-Clause. This is the same license family Redis used before 2024, and the one Valkey uses. Simple and permissive, no surprises.