Memory Server MCP Server

@mnemoverse/mcp-memory-server

Hosted memory for AI agents that learns which facts matter. Feedback re-ranks recall — a Rescorla-Wagner update on the prediction error, not a similarity score — so what helped rises and what misled sinks, with a bounded recency tie-breaker for fresh memories. The engine also ships consolidation (HDBSCAN clustering, with Von Restorff protection so distinctive memories survive compression). One API key works across Claude, Cursor, VS Code, ChatGPT, and any MCP client.

Memory that persists across sessions, projects, and tools — and improves with use. Hosted, so there's no infrastructure to run, and not locked to a single cloud.

⭐ If Mnemoverse saves you from re-explaining context to your agents, star the repo. It helps other builders find it.

Quick Start

1. Get a free API key

Sign up at console.mnemoverse.com — takes 30 seconds, no credit card.

2. Connect to your AI tool

Claude Code — add via CLI:

claude mcp add mnemoverse -s user \
  -e MNEMOVERSE_API_KEY=mk_live_YOUR_KEY \
  -e MNEMOVERSE_API_URL=https://core.mnemoverse.com/api/v1 \
  -- npx -y @mnemoverse/mcp-memory-server@latest

Cursor — click to install, or add to .cursor/mcp.json:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY",
        "MNEMOVERSE_API_URL": "https://core.mnemoverse.com/api/v1"
      }
    }
  }
}

VS Code — add to .vscode/mcp.json (note: VS Code uses servers, not mcpServers):

{
  "servers": {
    "mnemoverse": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY",
        "MNEMOVERSE_API_URL": "https://core.mnemoverse.com/api/v1"
      }
    }
  }
}

Windsurf — add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY",
        "MNEMOVERSE_API_URL": "https://core.mnemoverse.com/api/v1"
      }
    }
  }
}

More MCP clients — same server, different config file:

Zed — add to ~/.config/zed/settings.json (Zed uses context_servers, and "source": "custom" is required):

{
  "context_servers": {
    "mnemoverse": {
      "source": "custom",
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY",
        "MNEMOVERSE_API_URL": "https://core.mnemoverse.com/api/v1"
      }
    }
  }
}

JetBrains (AI Assistant) — Settings → Tools → AI Assistant → Model Context Protocol (MCP), then paste:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY",
        "MNEMOVERSE_API_URL": "https://core.mnemoverse.com/api/v1"
      }
    }
  }
}

Cline — MCP Servers → Configure (or edit cline_mcp_settings.json). Cline reads env values literally, so paste your real key — not a ${VAR} reference:

{
  "mcpServers": {
    "mnemoverse": {
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "mk_live_YOUR_KEY",
        "MNEMOVERSE_API_URL": "https://core.mnemoverse.com/api/v1"
      }
    }
  }
}

Continue — add ~/.continue/mcpServers/mnemoverse.yaml (Continue uses YAML):

mcpServers:
  - name: mnemoverse
    command: npx
    args:
      - "-y"
      - "@mnemoverse/mcp-memory-server@latest"
    env:
      MNEMOVERSE_API_KEY: "mk_live_YOUR_KEY"
      MNEMOVERSE_API_URL: "https://core.mnemoverse.com/api/v1"

Why @latest? Bare npx @mnemoverse/mcp-memory-server is cached indefinitely by npm and stops re-checking the registry. The @latest suffix forces a metadata lookup on every Claude Code / Cursor / VS Code session start (~100-300ms), so you always pick up new releases.

⚠️ Restart your AI client after editing the config. MCP servers are only picked up on client startup.

3. Try it — 30 seconds to verify it works

Paste this in your AI chat:

"Remember that my favourite TypeScript framework is Hono, and please call memory_write to save it."

Your agent should call memory_write and confirm the memory was stored.

Then open a new chat / new session (this is the whole point — memory survives restarts), and ask:

"What's my favourite TypeScript framework?"

Your agent should call memory_read, find the entry, and answer "Hono". If it does — you're wired up. Write whatever you want next.

If it doesn't remember: check that the client was fully restarted and the config has your real mk_live_... key, not the placeholder.

Tools

Ideas: What to Remember

• User preferences: "I use dark mode", "I prefer Tailwind over CSS modules"
• Project context: "This project uses PostgreSQL + Prisma", "Deploy to Railway"
• Lessons learned: "Always run tests before push on this repo"
• Decisions made: "We chose REST over GraphQL because of caching simplicity"
• People & roles: "Alice is the designer, Bob owns the API"
• Past mistakes: "Don't deploy on Fridays — learned this the hard way"

Universal Memory

The same API key works across all tools. Write a memory in Claude Code — read it in Cursor. Learn something in VS Code — your GPT Custom Action knows it too.

                    ┌── Claude Code (this MCP server)
                    ├── Cursor (this MCP server)
   Mnemoverse API ──├── VS Code (this MCP server)
   (one memory)     ├── GPT (Custom Actions)
                    ├── Python SDK (pip install mnemoverse)
                    └── REST API (curl)

Configuration

Links

Setup and reference

• Documentation
• Cursor · VS Code · Claude Code · ChatGPT
• Python SDK
• API Reference
• Console (get API key)

Background reading

• Memory MCP servers compared — thirteen shipping options, with pricing and registry presence
• How to choose a memory MCP server — the five questions that narrow the field
• What AI agent memory is — the category explained
• Is this a vector database? — what makes a memory layer different
• Shared memory for multi-agent systems — how Rooms work and when to use them

Project

• GitHub
• Releases
• MCP Registry entry
• Contributing

Privacy Policy

This server sends to the Mnemoverse API (core.mnemoverse.com), authenticated with your API key, what a tool call carries — and nothing else it can see. It does not read your AI client's conversation history, your local files, or anything you don't pass to a memory_* / vault_* tool. Stored memories live under your account; Mnemoverse never sells them and never shares them on its own. The one sharing path is the one you create yourself: inviting someone to a shared room grants their assistant access to that room's memories, bounded by the invite's scope.

What each tool sends:

One thing goes out that you did not explicitly request: since 0.8.1, when a search or feed comes back empty, the server sends one or two authenticated read-only GET probes (/memory/rooms and/or /memory/stats) so the empty answer can say what it did not cover. The probes carry your API key and nothing else, change no stored state, and are disclosed in the CHANGELOG.

License

MIT © Mnemoverse