Ratary Memory MCP

Name: Ratary Memory MCP
Author: ontorata

by ontorataversion 1.1.1i★ –iRuns on your computer

Registry nameio.github.ontorata/ratary

Install

Overview

Description from the maker (original English)

Persistent coding memory for AI assistants. MCP stdio, hybrid search, graph, context.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: RATARY_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: RATARY_API_KEY, RATARY_BASE_URL

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "ratary": {
      "command": "npx",
      "args": [
        "-y",
        "@ratary/mcp-server"
      ],
      "env": {
        "RATARY_API_KEY": "your-value",
        "RATARY_BASE_URL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add ratary --env RATARY_API_KEY=your-value --env RATARY_BASE_URL=your-value -- npx -y @ratary/mcp-server

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "ratary": {
      "command": "npx",
      "args": [
        "-y",
        "@ratary/mcp-server"
      ],
      "env": {
        "RATARY_API_KEY": "your-value",
        "RATARY_BASE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "ratary": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@ratary/mcp-server"
      ],
      "env": {
        "RATARY_API_KEY": "your-value",
        "RATARY_BASE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

RATARY_API_KEY secret required env
Ratary API key (aic_...)
RATARY_BASE_URL env
Ratary REST base URL (default https://ratary.ontorata.com)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account ontorata, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7275 servers in this category
Published to the registry more recently than 20% of the other 7275 servers in this category
Basic checks equal to or better than 100% of the other 7275 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Ratary Memory MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: RATARY_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.1.1) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Ratary

Build AI that remembers.

Ratary is the open-source AI Brain Platform — persistent memory,

structured knowledge, and intelligent retrieval for every model and agent you run.

Get started → · Star on GitHub · Website · Docs

Ratary is where AI remembers. · Built by Ontorata

Quick start · Ecosystem · Architecture · How it works · Capabilities · Docs

The problem

*Why does AI forget between sessions?*

Every AI session starts from zero.

Your model forgets yesterday's architecture decisions. Your agent drops customer context between runs. Your coding assistant can't recall why you chose Postgres over DynamoDB. Teams paste the same background into Cursor, Claude, ChatGPT, and custom bots — and knowledge still drifts.

Vector databases store chunks. RAG pipelines retrieve documents. Agent frameworks orchestrate tools.

None of them give AI a durable brain.

Why Ratary exists

*Why build a brain layer now?*

AI models are getting cheaper. Context windows are getting larger. Agents are getting capable.

But AI still forgets.

The bottleneck is no longer reasoning. It's memory — durable, structured, retrievable, and owned by you.

Every serious application eventually needed a database. Every serious AI system will need a brain layer: persistent intelligence that sits between your models and your storage — independent of any single vendor, IDE, or agent framework.

AI should remember.

Developers should own that memory.

Ratary exists to be that layer. Applications bring models. Ratary brings the brain.

What Ratary is

*What is Ratary?*

Ratary is an AI Brain Platform — infrastructure that gives AI:

Persistent memory — durable, owner-scoped, versioned
Structured knowledge — metadata, relations, graph traversal
Intelligent retrieval — hybrid search + bounded context assembly
Protocol access — Ratary MCP, REST, optional gRPC

It sits between AI clients and storage. One brain, many surfaces — Cursor, Claude Code, custom agents, enterprise APIs, and remote MCP hosts.

The runnable deployment is [Ratary Server](#quick-start) — this repository. Ratary is the product; Ratary Server is what you clone and run.

Bring your model. Ratary brings the memory.

Quick start

*How do I run Ratary Server locally?*

Ratary is the product. Ratary Server is the open-source deployment you run — ontorata/ratary (this repository). @ratary/sdk, @ratary/cli, and Ratary MCP connect to it; sibling Ontorata products use the same source of truth.

Prerequisites: Node.js 24 · SQL metadata store (pick one — Postgres is the template default)

Path A — PostgreSQL (npm + local or Docker)

git clone https://github.com/ontorata/ratary.git

cd ratary && npm install cp .env.example .env # Set AUTH_SECRET + DATABASE_URL — see .env.example QUICK START npm run db:apply-postgres-schema npm run setup # wire Ratary MCP for Cursor, Claude Code, … npm run dev

Or use Docker: docker compose --profile postgres up --build — see docs/DOCKER.md.

Path B — Cloudflare D1

git clone https://github.com/ontorata/ratary.git

cd ratary && npm install cp .env.example .env # Set AUTH_SECRET + SQL_PROVIDER=d1 + CLOUDFLARE_* / D1_* npm run db:migrate npm run setup npm run dev

Details: [docs/GUIDE.md](docs/GUIDE.md#1-setup) · [docs/CONFIGURATION.md](docs/CONFIGURATION.md)

→ API http://localhost:9876 · Swagger /docs

First REST call: bootstrap once to get an API key (aic_...) — see GUIDE — First REST API key.

# Save your first memory

curl -X POST http://localhost:9876/api/v1/memory \ -H "Authorization: Bearer aic_YOUR_KEY" \ -H "Content-Type: application/json" \ -d '{"title":"Hello brain","project":"demo","content":"Ratary remembers this."}'

Full guide: [docs/GUIDE.md](docs/GUIDE.md) · SDK & MCP examples in docs/examples/

npm packages (`@ratary`)

Client libraries ship on npm under the `@ratary` scope — product name, published by Ontorata. No server clone required for SDK, CLI, or hosted MCP.

npm install @ratary/sdk@1.1.0

npx @ratary/mcp-server@1.1.3 # remote REST → stdio MCP npm install -g @ratary/cli@1.1.0 # operator CLI

Package	Version	Install	Role
`@ratary/sdk`	1.1.0	`npm install @ratary/sdk`	Typed REST client + `client.admin.*`
`@ratary/cli`	1.1.0	`npm install -g @ratary/cli`	Operator commands (`admin`, `connectors`)
`@ratary/mcp-server`	1.1.3	`npx @ratary/mcp-server`	IDE MCP → hosted API

Set RATARY_BASE_URL and RATARY_API_KEY (aic_...). Details: [packages/README.md](packages/README.md) · remote MCP install.

Ecosystem

*Which repository owns what?*

The Visual architecture diagram shows logical layers inside Ratary Server. This diagram shows repository and product relationships — what ships in this repo, what connects to it, and what lives in sibling Ontorata repositories. Both views describe the same platform from different angles.

Throughout this README, Ratary MCP means the official memory MCP implementation (stdio in this repo · npm @ratary/mcp-server for hosted REST). It is not the same as Ontorata MCP (ecosystem gateway — separate repo).

┌─────────────────────────────────────────────────────────────┐

│ Ratary Cloud (opt-in) │ │ optional hosted deployment · not this repo │ └──────────────────────────────┬──────────────────────────────┘ │ ┌──────────────────────────────▼──────────────────────────────┐ │ Ratary Server ← ontorata/ratary (this repo) │ └──────────────────────────────┬──────────────────────────────┘ │ │ │ ▼ ▼ ▼ @ratary/sdk @ratary/cli @ratary/mcp-server (SDK) (CLI) (Ratary MCP · npm) │ │ │ └──────────────┴──────────────┘ │ ┌─────────────────────┴─────────────────────┐ ▼ ▼ ▼ Ontorata MCP Ontorata Studio Ontory Runtime ontorata/ontorata-mcp ontorata/Ontorata-Studio ontorata/ontory-runtime ecosystem product ecosystem product (ADR-2119; was ontorata/ontory)

Infrastructure (ships from ontorata/ratary — server plus client packages):

Component	Repository	Role
Ratary Server	ontorata/ratary	Memory engine — REST, persistence, Ratary MCP stdio. You run this.
Ratary SDK	npm `@ratary/sdk` · `packages/sdk`	Typed REST client for Ratary Server.
Ratary CLI	npm `@ratary/cli` · `packages/cli`	Operator commands; delegates to Ratary SDK.
Ratary MCP	npm `@ratary/mcp-server` · stdio in repo	Memory MCP — full stdio in clone · npm proxy for hosted REST.

Ecosystem products (separate repositories — connect to Ratary Server; not bundled here):

Product	Repository	Role
Ontorata MCP	ontorata/ontorata-mcp	Ecosystem MCP gateway — Ratary MCP plus additional Ontorata tools.
Ontorata Studio	ontorata/Ontorata-Studio	Operator UI — uses `@ratary/sdk` only.
Ontory	ontorata/ontory-runtime	Ontory Runtime — AI orchestration (ADR-2119). Legacy URL `ontorata/ontory` redirects.

Ratary Server does not depend on ecosystem product repositories.

Ratary MCP vs Ontorata MCP

*Which MCP should I install?*

	Ratary MCP	Ontorata MCP
Layer	Ratary infrastructure	Ontorata ecosystem product
What it is	Official memory protocol for Ratary Server	Ecosystem gateway for Ontorata products
Scope	Memory — CRUD, search, context, graph	Ratary memory plus additional Ontorata tools
Repository	ontorata/ratary · npm `@ratary/mcp-server`	ontorata/ontorata-mcp
Typical `mcp.json` key	`ratary`	`ontorata`

Use Ratary MCP for direct memory access. Use Ontorata MCP for one MCP entry point across the Ontorata stack. Both use Ratary Server as source of truth.

What Ratary is not

*How is Ratary different from alternatives?*

	Vector DB	Memory API	RAG	Agent framework	Ratary
Primary job	Similarity search	Key-value recall	Document Q&A	Tool orchestration	Durable AI memory
Structured knowledge & graph	❌	⚠️	❌	⚠️	✅
MCP-native IDE integration	❌	⚠️	❌	⚠️	✅
Token-efficient context assembly	❌	⚠️	❌	⚠️	✅
Self-host & data sovereignty	✅	⚠️	⚠️	⚠️	✅
Clear agent boundary	N/A	⚠️	N/A	❌ bundled	✅ substrate only

Ratary complements your stack — it does not replace pgvector, LangGraph, or your agent of choice. See the [Capability matrix](#capability-matrix) for a feature-level comparison.

Why not just pgvector, Mem0, Letta, LangGraph, or RAG?

If you only use…	You get…	What you miss
pgvector	Embedding similarity	Structured memory, graph, MCP, context packing

| Mem0 | Fast hosted memory API | Full self-host, hybrid retrie

Only the beginning is shown; read the rest in the repo

Ratary Memory MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

The problem

Why Ratary exists

What Ratary is

Quick start

Path A — PostgreSQL (npm + local or Docker)

Path B — Cloudflare D1

npm packages (@ratary)

Ecosystem

Ratary MCP vs Ontorata MCP

What Ratary is not

npm packages (`@ratary`)