Ratary Memory MCP

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

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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.

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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.

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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.

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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

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

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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):

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

Ratary Server does not depend on ecosystem product repositories.

Ratary MCP vs Ontorata MCP

*Which MCP should I install?*

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.

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What Ratary is not

*How is Ratary different from alternatives?*

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?

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