Mnemoverse Memory

Mnemoverse Memory

Persistent memory for AI agents over MCP. Tell it a recalled memory helped or misled, and it re-ranks what comes back next. Shared rooms let several agents work from one memory. One key or OAuth across Claude Code, Cursor, VS Code and ChatGPT.

@mnemoverse/mcp-memory-server is the MIT-licensed MCP server for the hosted Mnemoverse memory engine.

What is Mnemoverse Memory?

Mnemoverse is a hosted memory engine for AI agents, reached over the Model Context Protocol. Mnemoverse stores what your agents learn — decisions, preferences, lessons — and returns it in any connected tool, so one memory follows you across Claude Code, Cursor, VS Code and ChatGPT with one account: an API key in a local config, or an OAuth sign-in on the hosted endpoint. Mnemoverse re-ranks recall from outcomes: report that a recalled memory helped and a Rescorla-Wagner update on the prediction error raises it, report that it misled and it sinks — a different mechanism from similarity scoring, usable alongside it.

What is open source here, and what is not. This repository, the MCP server, is MIT, and so is the Python SDK. The memory engine they talk to is a hosted service with a free tier. Self-hosting the engine is available on Enterprise plans by agreement, when security or compliance requirements call for it; by default we run it for you.

How it compares

Most agent memory today lives in one of three places. Per-tool instruction files — CLAUDE.md, .cursorrules, AGENTS.md — are versioned and readable, but each copy belongs to one repo and one tool, and nothing follows you to the next window. A vector store behind RAG retrieves by similarity, and similarity never changes because advice helped or misled. Local-first memory servers win on privacy and latency, and ask you to run and update the infrastructure yourself. Mnemoverse is the managed, cross-tool option in that landscape: nothing to deploy, one account everywhere, and ranking that moves with reported outcomes. If you need memory inside your own perimeter, a local-first server is the better choice; this one is hosted by default, with Enterprise self-hosting by agreement.

The consolidation stage of the engine — HDBSCAN clustering with Von Restorff protection, so distinctive memories are not absorbed into the average — is designed in and currently switched off on the hosted service; our docs say so rather than hide it.

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

Quick Start

No key: the hosted endpoint

If your client signs in over OAuth, you do not need a key at all. Create a free account at console.mnemoverse.com (no credit card), then connect the hosted endpoint.

Claude Code:

claude mcp add -s user --transport http mnemoverse https://mcp.mnemoverse.com/mcp

Then run /mcp in a session, select mnemoverse and choose Authenticate.

Cursor, in .cursor/mcp.json:

{ "mcpServers": { "mnemoverse": { "url": "https://mcp.mnemoverse.com/mcp" } } }

Claude Desktop, Devin Desktop (formerly Windsurf), VS Code and ChatGPT: Remote MCP setup. The local server below is the other path: it runs on your machine and reads an API key.

1. Get a free API key

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

Check the key before you put it in a config. Both forms ask for the key at a masked prompt and never pass it as a command argument, so it lands neither in your shell history nor in the process list.

macOS, Linux, Git Bash:

printf 'Mnemoverse API key: '; read -rs KEY; echo
printf 'X-Api-Key: %s\n' "$KEY" | curl -s -H @- https://core.mnemoverse.com/api/v1/memory/stats; unset KEY

Windows PowerShell 5.1 and PowerShell 7:

$k = [Net.NetworkCredential]::new('', (Read-Host 'Mnemoverse API key' -AsSecureString)).Password
try { (Invoke-WebRequest https://core.mnemoverse.com/api/v1/memory/stats -Headers @{ 'X-Api-Key' = $k } -UseBasicParsing).Content }
catch { if ($_.ErrorDetails.Message) { $_.ErrorDetails.Message } else { (New-Object IO.StreamReader($_.Exception.Response.GetResponseStream())).ReadToEnd() } }; Remove-Variable k

In the JSON, reason sits inside the details object (details.reason), next to details.keys_url, the console page where keys are created.

2. Connect to your AI tool

The two canonical setups, Claude Code and Cursor. Cursor's button adds the hosted server with a browser sign-in and no key; every setup that takes a key writes it once, at user scope, covering every project. Avoid a per-project config file for a key: it lives inside the repository and can be committed with it, and a key belongs outside:

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

On Windows (PowerShell), paste the same command as one line — PowerShell does not read the \ line continuations:

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 add the hosted server. Cursor signs you in through the browser, so there is no key to paste:

The button writes {"url": "https://mcp.mnemoverse.com/mcp"} under mnemoverse in ~/.cursor/mcp.json, the global config that covers every project. Then click Connect next to mnemoverse in Cursor's MCP settings (Customize in the sidebar; Cursor Settings → Tools & MCPs in Cursor 3.21.16) and approve in the browser. Cursor runs the sign-in and keeps the token itself.

To run the local server with an API key instead, add the JSON below to ~/.cursor/mcp.json, merging it with any servers already there, and put your own key in place of mk_live_YOUR_KEY. Get one at console.mnemoverse.com. Do not put it in a project-level .cursor/mcp.json: that file lives inside the repository and is committed with it unless you exclude it, and this config holds your key. Use one entry or the other, not both: they share the name mnemoverse.

{
  "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"
      }
    }
  }
}

{
  "inputs": [
    {
      "type": "promptString",
      "id": "mnemoverse-api-key",
      "description": "Mnemoverse API key (starts with mk_live_). Optional to install and inspect — the server starts and lists its tools without a key; every actual tool call requires one. Get one free in ~30s at https://console.mnemoverse.com",
      "password": true
    }
  ],
  "servers": {
    "mnemoverse": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@mnemoverse/mcp-memory-server@latest"
      ],
      "env": {
        "MNEMOVERSE_API_KEY": "${input:mnemoverse-api-key}",
        "MNEMOVERSE_API_URL": "https://core.mnemoverse.com/api/v1"
      }
    }
  }
}

{
  "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"
      }
    }
  }
}

{
  "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"
      }
    }
  }
}

{
  "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"
      }
    }
  }
}

{
  "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"
      }
    }
  }
}

⚠️ 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. On the hosted server there is no key to check; check that the browser sign-in finished (in Cursor, Connect next to mnemoverse).

⭐ If the second session remembered, star the repo. It helps other builders find it.

4. Make it a habit

The test above works because you asked the agent to call the tool. In normal work it will not, unless it has a standing instruction that says when to read memory and when to save. The setup_memory prompt writes those rules for Claude Code, Claude, Cursor or Codex, and Make Your Agent Use Memory explains where they go and how to confirm the tools work first.

Tools

Prompts

Four named prompts for clients that show MCP prompts as commands (Claude Code as /mcp__mnemoverse__<name>). None of them calls the API itself: three ask the model to use the tools above, and setup_memory hands you rules for your own agent.

Resources

memory://item/{memory_id} opens one saved memory by its id (the id: line of a memory_read result) for clients that attach MCP resources. It returns the memory's memory_id, content and domain as JSON. It reads your own store only: a memory read from a shared room cannot be opened by id.

Tool surface stability

tools/list is frozen per released version, so a client can save the list it saw and diff it against what the server serves today, by version.

• Within a PATCH (x.y.Z): tool names, argument schemas and the annotations object of every tool (title, readOnlyHint, destructiveHint, idempotentHint, openWorldHint) do not change. Only text may: descriptions and what a tool returns, as the CHANGELOG rules state.
• Within a MINOR (x.Y.0): tools and annotation fields may be added, never removed or renamed, and no declared annotation field disappears or flips silently. Every addition has a line in the CHANGELOG under that version. A MINOR may also add an output schema (outputSchema, with structuredContent returned alongside the same text) to a tool that did not have one; once declared, that schema's fields are add-only under this same rule. Where this server's output schema deliberately differs from the hosted connector's, the CHANGELOG entry says so; today that is memory_id in every output schema, a plain string here and a GUID-validated string there (this package's ids are opaque); memory_list_recent's next_cursor, optional here (absent when the service sent a continuation token this client will not pass on) and required there; memory_stats, which carries five optional fields (episodes, prototypes, hebbian_edges, avg_valence, avg_importance) the connector's schema does not declare; the room tools (memory_create_room, memory_invite_to_room, memory_join_room, memory_list_rooms), where several fields the connector marks required are optional here (name, scope, already_member, and the invite's code, scope, room_address and expires_at), because a value core did not send is an honest outcome here rather than a placeholder; and memory_list_rooms and vault_list, which drop a row with no usable identity (room_id, address, role; alias, context) from the data, reported once on stderr, instead of emitting empty strings into required fields.
• Removing or renaming a tool or a tool's input parameter, or dropping or renaming a declared annotation field, is announced one MINOR ahead: the tool (or parameter) stays, its description says deprecated since x.y, removed in x.z, and the change lands only in the announced version, with its CHANGELOG line. A renamed parameter is accepted under both names until then (0.11 renamed memory_feedback's atom_ids to memory_ids; the old name was removed in 0.13, as announced after 0.12 shipped sooner than the first announcement assumed). A rename is announced by naming both the old and the new name; the version pair alone does not say what a client should look for. Because a MINOR may add a field but not remove one, a renamed annotation field is declared under both names until the announced version.
• Any difference between two servers of the same version is a bug. Report it with both tools/list outputs. One exception, by configuration: a server built on the /shared entry point may ask for its own noun in the three descriptions that name the server (wording.serverNoun, below), which changes those three description strings and nothing else; tool names, input and output schemas and annotations never vary by configuration.

Every inputSchema and outputSchema is JSON Schema 2020-12, the dialect MCP assumes when a schema names none, and names none (#189).

The list above is the current surface: eleven tools, each declaring all four hints. The hosted connector at mcp.mnemoverse.com/mcp serves the same surface, registered from this package at the version it pins. The list itself is published as tools.json, generated from the built server (name, title, description and annotations per tool); the test suite and the release workflow fail when it does not list what the server registers, and the .mcpb manifest's tool list is derived from it.

If the hosted connector stops answering in a session. A client can keep showing the connector as connected while every call in that session fails with "not connected". Reconnecting it on claude.ai does not revive a session that is already stuck; reconnect from inside the session instead (in Claude Code, /mcp, then sign in again). Meanwhile this local server, set up with an API key from the same account as in the Quick Start, reaches the same memory and does not depend on that session's sign-in.

Building a second MCP server on this package

@mnemoverse/mcp-memory-server/shared is the entry point another server registers these same tools from, instead of keeping its own copy (ADR-025, mnemoverse-core). It exports registerMemoryTools/registerMemoryPrompts/registerMemoryResources, the three typed error classes (ApiError, NetworkError, UnreadableBodyError), MAX_RESULT_CHARS/capResult, and two optional dependencies a hosted deployment injects to speak in its own voice: wording (its own server noun and error vocabulary, including an OAuth mode under which no 401 or 403 explanation names an API key) and writeAuthor (vouching for the end user behind a write). See docs/shared.md for the full contract.

Use cases

The pattern that pays off first is cross-tool continuity: a decision made while pairing in Claude Code is there when you open Cursor an hour later, and the preference you stated in VS Code holds in a ChatGPT session that evening. Teams use shared rooms the same way — one place where an agent's lessons about a codebase accumulate instead of being re-taught per seat. And because recall re-ranks from feedback, the memories that keep proving useful surface first, which matters once a store grows past what anyone curates by hand.

Concrete things worth writing:

• 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

Research behind it

The retrieval model is published: arXiv:2603.08965, accepted at the GRAAI workshop at IEEE WCCI 2026 — it establishes the abstraction-discovery method the memory model builds on. No benchmark figures appear in this README, ours or anyone's: numbers will come with a reproducible run to stand behind, not before.

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

Other ways to install it

The same memory, packaged for hosts that prefer a plugin or an extension over an MCP config block. How each one connects and authenticates differs, so the line below says which is which rather than claiming one flow for all of them.

• Claude Code plugin — remote endpoint over MCP with an OAuth sign-in, no key to paste. Bundles the agent-memory-discipline skill

  claude plugin marketplace add mnemoverse/claude-plugin
  claude plugin install mnemoverse@mnemoverse
  

• Cursor plugin — same remote endpoint, same sign-in
• Gemini CLI extension — same remote endpoint. gemini extensions install https://github.com/mnemoverse/gemini-extension
• VS Code extension (VS Code Marketplace, Open VSX, source) — signs in through the browser, with pasting a key kept as a fallback command
• Desktop extension: manifest.json in this repository is an MCPB manifest. This one is different from the four above: it runs the server as a local node process and reads MNEMOVERSE_API_KEY from the extension settings rather than calling the hosted endpoint. The packaged .mcpb ships with each release

Standing rules, separate from this server

• agent-memory-discipline — when an agent should recall before acting and save afterward. CC0, backend-neutral, works against any memory store rather than this one. It carries its own marketplace manifest under .claude-plugin/.
• awesome-agent-memory — a curated index of the category, CC0, including the servers this one competes with

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