Ratary Memory MCP server
io.github.ontorata/ratary·v1.1.1·1 starsPersistent coding memory for AI assistants. MCP stdio, hybrid search, graph, context.
1 stars37 downloads/wk
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If you have run it, two minutes of your experience saves the next person an afternoon.
Ratary Memory MCP tools (6, 1 write)
write = sends, deletes, buys or postsRead from the package source without running it. The installed server may list more.
get_capabilitiesGet deployment capability manifest
get_contextBuild context for a task
get_memoryGet memory by ID
list_agent_clientsList certified external agent client profiles
save_memorywrite actionCreate a new memory
search_memorySearch memories by query
Public scan report
scanner v0.1.9 · 2026-09-26 · same rubric, same numbers if you re-run it
- Code scan7 source files scanned25/25
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitystatic API keys via environment variables6/15
- Maintenancelast push 47 days ago12/15
- Maintainer identityregistry namespace matches repository owner6/10
How Ratary Memory MCP compares
Against every graded Web search MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 75/100
- higher than 45% of 3,470 Web search servers
- Tools
- 6
- the typical Web search server has 5
- Tools that change things
- 17%
- typical: 0%. Fewer write tools means less an agent can break
- GitHub stars
- 1
- more than 55% of 1,786 Web search servers
- Downloads a week
- 37
- more than 23% of 827 Web search servers with a package
What its tools let an agent do: 5 read · 1 create
Install Ratary Memory MCP in Claude Code, Cursor or VS Code
Runs npx -y @ratary/mcp-server on your machine. Read the scan report first; the gateway never runs local packages.
claude mcp add ratary -- npx -y @ratary/mcp-server
What the publisher says
From the Ratary Memory MCP repository's README, as published. We do not edit it. Read it on GitHub
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 — 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?
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
Ratary Memory MCP: common questions
- Is Ratary Memory MCP server safe?
- Mostly: it is graded B (75/100). Read the Ratary Memory MCP safety report
- How do I install Ratary Memory MCP?
- It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
- Does Ratary Memory MCP need an API key?
- Yes. The registry entry asks for
RATARY_API_KEY. - Is Ratary Memory MCP maintained?
- The last commit was 86 days ago (2026-08-10). The latest release is v1.1.1.
- What can I use instead of Ratary Memory MCP?
- Servers from other publishers that do the same job: Reverie MCP server, Octobrain MCP server and Misakanet MCP server.
- How does Ratary Memory MCP compare with other Web search MCP servers?
- safety score 75/100 (higher than 45% of 3,470 Web search servers); tools 6 (the typical Web search server has 5); tools that change things 17% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.
Alternatives to Ratary Memory MCP
Same job from other publishers: the closest match first, then the best rated.
- ReverieGraph memory that dreams: Neo4j knowledge-graph memory for AI agents with hybrid searchnot reviewedEstablishedB
- OctobrainPersistent memory for AI assistants with semantic search and knowledge graph relationships.not reviewedGrowingA
- MisakanetFailure-memory layer for coding agents: search evidence-rated failure lessons by error text.not reviewedEstablishedA
- LinkLocal personal memory for agents as MCP tools — remember, recall, search, context, graph traversal.not reviewedEstablishedA
Shodh MemoryCognitive memory for AI agents — semantic search, Hebbian learning, knowledge graphs.not reviewedEstablishedC
