Frequently asked questions

Mnemoverse is a persistent memory API for AI agents. You store preferences, decisions, lessons, and context once, and recall them from any tool (Claude Code, Cursor, VS Code, Windsurf, ChatGPT, Python, or any HTTP client) with a single API key or OAuth.

A vector database does similarity search over stored embeddings. Nothing learns or changes. Mnemoverse is a memory: it forms associations between concepts (Hebbian), tunes recall from outcome feedback (Rescorla-Wagner), ranks stale memory down on later reads, and expands queries automatically. It improves with use.

They are complementary. RAG answers "what do the documents say?" by retrieving chunks of a fixed corpus. Mnemoverse answers "what did we discuss, decide, or learn last time?" It accumulates user- and project-specific experience across sessions. Many teams use both.

The client libraries (Python SDK, MCP server) are open source under MIT. The memory engine itself is a hosted service: we run it for you. On Enterprise plans, self-hosting can be agreed when security or compliance requirements call for it.

Mnemoverse works across Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, ChatGPT, the Python SDK, and any HTTP client with one API key or OAuth. The same memories are available everywhere.

One command: claude mcp add mnemoverse -s user -e MNEMOVERSE_API_KEY=mk_live_YOUR_KEY -- npx -y @mnemoverse/mcp-memory-server@latest. The MCP server exposes the Mnemoverse memory tools to the agent; the current list is served live at mnemoverse.com/.well-known/mcp/server-card.json.

Yes, through the hosted MCP connector or the REST API directly. Custom GPTs you already built keep working; as of August 2026, OpenAI limits creating new ones to Business, Enterprise and Edu workspaces — their rules here change often, so check their help center for the current state. The connector and the REST API work on every plan.

Yes: pip install mnemoverse (live on PyPI). It wraps write, read, feedback, and stats with typed errors.

@mnemoverse/mcp-memory-server is an open-source Model Context Protocol server (listed on the MCP Registry) that gives any MCP-capable agent the Mnemoverse memory tools over a standard stdio transport, the same set as the hosted connector.

Yes. Free is $0: 1,000 queries/day, 10,000 atoms, 60 requests/minute, no credit card required.

Any read or write call to the memory API. Daily limits reset every 24 hours; rate limits are per minute. Paid plans raise all three limits.

Yes. Plans are month-to-month (Pro $29/mo, Team $149/mo) and you can change or cancel at any time from the console.

Custom limits, dedicated infrastructure on instances we provide and operate, SSO/SAML, audit logs, data-residency options, and a custom SLA. Self-hosting is available by agreement when security or compliance requirements call for it; by default we run it for you. Contact sales to scope it.

Mnemoverse keeps every tenant's memories under a separate org identifier; cross-tenant reads return no results. Requests are authenticated per API key.

No. Mnemoverse does not train models on your stored memories.

Keys are hashed with SHA-256 before storage and compared in constant time to prevent timing attacks. The raw key is shown once at creation.

On managed PostgreSQL 17 with pgvector (US region today). Enterprise plans can choose data residency on dedicated instances we provide and operate. Where security or compliance requirements rule out a managed deployment, self-hosting can be agreed on an Enterprise plan.

Mnemoverse strengthens Hebbian associations between concepts recalled together, so related memories surface together over time. Outcome feedback (a +1/-1 signal when a recalled memory was useful) tunes future recall via a Rescorla-Wagner update.

Not on the hosted service today. Consolidation is built (HDBSCAN clustering merges redundant memories, with Von Restorff protection so distinctive singleton items are not lost), but it is off by default and there is no age-based expiry. What does change with use is ranking: outcome feedback re-ranks recall, so memories that keep helping surface first and the rest ranks down.

Yes. The underlying SLoD approach is published (arXiv:2603.08965) and was peer-reviewed and accepted at the GRAAI workshop (IEEE WCCI 2026). Mnemoverse is benchmarked on public memory benchmarks including LoCoMo and LongMemEval.