# Ratary Memory

> Ratary Memory — ontorata-ratary. Use this tool when you need to enhance AI capabilities with persistent and structured memory, solving problems of knowledge retention and retrieval in AI systems. Ratary Memory provides inputs such as metadata and relations, and outputs like versioned knowledge and intelligent search results, through interfaces including Ratary MCP, REST, and gRPC. It is ideal for use cases requiring durable and owner-scoped memory, such as AI brain platforms and custom agents.

Canonical page: https://skillsregistry.net/skills/ontorata-ratary  
JSON: https://api.skillsregistry.net/v1/skills/ontorata-ratary

## Description

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, enter

## Trust

- **Trust score (0–1):** 0.39
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/iib5dfw70e)
- **Repository:** <https://github.com/ontorata/ratary>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ontorata-ratary"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ontorata-ratary` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ontorata-ratary/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt