Persistent memory for AI assistants
An MCP server that gives AI assistants memory that survives between sessions. Local Redis, vector search, and knowledge graphs with zero configuration.
npx @n3wth/r3
Without r3
> What's my preferred stack? I don't have any information about your preferences.
With r3
> What's my preferred stack? Based on our past conversations: React + TypeScript, Tailwind, Postgres with Drizzle ORM.
How it works
r3 runs a local Redis server with vector search. Your AI stores memories as embeddings and retrieves them by meaning, not just keywords.
See it in action
r3 storing and retrieving memories across sessions.
Antigravity CLI setup
agy mcp add r3 npx -y @n3wth/r3
agy mcp listStart a session and use /mcp to inspect the connection.
Get started
MCP Desktop Clients
Add r3 to your MCP config file.
json// MCP client config (e.g. .gemini/settings.json){"mcpServers": {"r3": {"command": "npx","args": ["@n3wth/r3"]}}}
MCP CLI Tools
Add with a single command.
bash# MCP CLI toolsgemini mcp add r3 npx -y @n3wth/r3gemini mcp list
What you get
r3 runs entirely on your machine. Embedded Redis, vector search, and knowledge graphs with no external services.
Semantic Search
Cosine similarity ranking across 384-dimension vectors. Query by meaning, not keywords.
Knowledge Graph
Automatic entity extraction links memories into a traversable graph of relationships.
Fast local reads
Embedded Redis serves as both cache layer and vector store. Local embedding generation, no API calls.
MCP compatible
Works with any MCP-compatible client. Desktop apps, CLI tools, and custom integrations.
TypeScript SDK
Typed memory operations, search results, and configuration. Ships its own type declarations.
Fully local
Embedded Redis server, local vector store. No cloud services, no API keys required.
Your AI forgets everything between sessions.
One command adds persistent memory.
npx @n3wth/r3