MCP Memory Server README
The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the MCP Memory Server listing page.
Mnemoverse Memory
@mnemoverse/mcp-memory-server — the MCP server for the 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 a single API key. 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.
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 key 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 design.
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
1. Get a free API key
Sign up at console.mnemoverse.com — takes 30 seconds, no credit card.
2. Connect to your AI tool
The two canonical setups, Claude Code and Cursor. Each writes the key once, at user scope, covering every project. Avoid a per-project config file for this: it lives inside the repository and can be committed with it, and a key belongs outside:
Claude Code — add via CLI:
On Windows (PowerShell), paste the same command as one line — PowerShell does not read the \ line continuations:
Cursor — click to install, or add the JSON below to ~/.cursor/mcp.json, the global config that covers every project. 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.
The install button carries the placeholder key mk_live_YOUR_KEY, not yours, so the shortest path is to skip the button: add the JSON below to ~/.cursor/mcp.json, merging it with any servers already there, and put your own key in place. Get one at console.mnemoverse.com. If you did click the button, edit the same key in the mcp.json it wrote; Cursor keeps MCP environment values in that file, not in a settings form. Until the key is real the server starts and lists its tools, but every tool call is refused.
All other clients — VS Code, Windsurf, Zed, JetBrains, Cline, Continue
VS Code — add to .vscode/mcp.json (note: VS Code uses servers, not mcpServers). That file is meant to be committed and shared with your team, so the key in it is too — if you'd rather keep it out of the repo, run MCP: Open User Configuration from the Command Palette and add the same JSON to your user profile's mcp.json instead:
Windsurf — add to ~/.codeium/windsurf/mcp_config.json:
More MCP clients — same server, different config file:
Zed — add to ~/.config/zed/settings.json (Zed uses context_servers, and "source": "custom" is required):
JetBrains (AI Assistant) — Settings → Tools → AI Assistant → Model Context Protocol (MCP), then paste:
Cline — MCP Servers → Configure (or edit cline_mcp_settings.json). Cline reads env values literally, so paste your real key — not a ${VAR} reference:
Continue — add ~/.continue/mcpServers/mnemoverse.yaml (Continue uses YAML):
Why
@latest? Barenpx @mnemoverse/mcp-memory-serveris cached indefinitely by npm and stops re-checking the registry. The@latestsuffix 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_writeto 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
| Tool | What it does |
|---|---|
memory_write | Store a memory — insight, preference, lesson learned |
memory_read | Search memories by natural language query (optional recency ordering, time bounds, author exclusion) |
memory_list_recent | List newest memories first — no query; since/until bounds (inclusive) + cursor paging |
memory_feedback | Rate memories as helpful or not (improves future recall) |
memory_stats | Check how many memories stored, which domains exist |
memory_create_room | Create a shared memory room; its address works as a domain on write/read |
memory_invite_to_room | Mint a one-time invite (code + link) for a room you own |
memory_join_room | Join a shared room with an invite code (mnvr_...) |
memory_list_rooms | List rooms you own or joined, with each room's address to use as domain |
vault_list | List Vault secrets by alias and purpose — the secret value is never returned |
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.
Configuration
| Env Variable | Required | Default |
|---|---|---|
MNEMOVERSE_API_KEY | For every tool call — the server starts and lists its tools without one | — |
MNEMOVERSE_API_URL | No | https://core.mnemoverse.com/api/v1 |
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-disciplineskill - 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 — signs in through the browser, with pasting a key kept as a fallback command
- Desktop extension:
manifest.jsonin this repository is an MCPB manifest. This one is different from the four above: it runs the server as a localnodeprocess and readsMNEMOVERSE_API_KEYfrom the extension settings rather than calling the hosted endpoint. The packaged.mcpbships 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
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:
| Tool | Data sent |
|---|---|
memory_write | the content, concepts, and domain you pass |
memory_read | the query, plus any filters: domain, since/until, exclude_author, top_k, order_by |
memory_list_recent | the feed filters: domain, since/until, exclude_author, limit, cursor |
memory_feedback | the atom_ids being rated and the outcome score |
memory_create_room | the room name and description |
memory_invite_to_room | the room_id, invite scope, and expiry |
memory_join_room | the invite code |
memory_stats / memory_list_rooms / vault_list | no request body — authenticated GETs |
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.
| Privacy Policy | https://mnemoverse.com/privacy |
| Retention & deletion | correct a wrong or stale memory by writing a fresh one; deletion is an administrative operation on the REST API, not exposed through this MCP server |
| Contact | hello@mnemoverse.com |
License
MIT © Mnemoverse