Persistent memory API for AI agents
Persistent memory for AI agents
One memory. Every AI tool. Write once, recall anywhere: across Claude Code, Cursor, VS Code, ChatGPT, and Python, with one API key or OAuth.
Using ChatGPT? Install Mnemoverse Memory →
claude mcp add --scope user --transport http mnemoverse https://mcp.mnemoverse.com/mcpStart a fresh Claude Code session, run /mcp, select mnemoverse → Authenticate, and sign in in the browser. Ask Claude to verify the connection with memory_stats. No API key to copy.
Local server with an API key
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@latestThis alternative needs Node.js and your Mnemoverse API key. Replace the placeholder in your local configuration; keep the key out of chat and committed files. You can also ask Claude to help:
claude "Add the local @mnemoverse/mcp-memory-server MCP server with env var MNEMOVERSE_API_KEY, then guide me to enter my key directly in protected local settings, never in chat"Any MCP-capable agent can do this, not just Claude. Agent setup guide →
Mnemoverse is a persistent memory API for AI agents: write a preference, decision, or lesson once and recall it from any tool. It is not a vector database: memories learn from outcomes via Hebbian associations and Rescorla-Wagner, get re-ranked so stale memory ranks down, and improve with use. One API key or OAuth connects six client integrations (Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, ChatGPT) plus Python and REST, and an MCP server exposing the memory tools. Mnemoverse publishes that server on npm and the official MCP Registry, a Python SDK on PyPI, and a one-click VS Code extension that runs the same server, so one memory is reachable from Python code, from any MCP client, and from VS Code. The free tier is $0 with 1,000 queries/day. Mnemoverse is benchmarked on public memory benchmarks (LoCoMo and LongMemEval), with every published number tracing to a committed run artifact, and the underlying method is described in the SLoD paper (arXiv:2603.08965), peer-reviewed and accepted at the GRAAI workshop (IEEE WCCI 2026).
New to the category? What an AI agent memory layer is — the definitions, how the approaches differ, and where Mnemoverse fits.
Not a vector database
Memory that learns from outcomes and improves
Vector databases store and retrieve. In Mnemoverse, telling it a recalled memory helped or misled re-ranks what comes back next, and shared rooms let several agents work from one memory.
| Mnemoverse | Vector DB (Pinecone / Weaviate / Chroma) | |
|---|---|---|
| Core model | Hebbian associations + prediction-error feedback (Rescorla-Wagner-style) | Vector embeddings (cosine similarity) |
| Learns from outcomes | Yes: feedback updates valence | No outcome-feedback loop (reranking is similarity-based) |
| Concept associations | Three-factor Hebbian graph | None |
| Memory compression | Consolidation built (HDBSCAN + Von Restorff protection); switched off on the hosted service today | Accumulate forever |
| Query expansion | Automatic via learned associations | No learned-association expansion (hybrid/keyword only) |
| Starting price | Free (1,000 queries/day) | Free tier, then ~$20–50/mo |
How it works
Write → Learn → Recall
The same loop on every tool. Memories that lead to good outcomes rank higher next time. The system improves with use.
Write
Store a preference, decision, or lesson with a single call. A write too similar to a memory you already have can be refused, and the response tells you whether it was stored.
Learn
Report outcomes and the system updates valence. Memories that lead to good answers rank higher; ones that consistently fail rank lower. Nothing is erased.
Recall
Query in natural language. Hebbian expansion pulls in associated concepts and valence-boosted ranking surfaces what actually mattered last time, across every tool with one API key or OAuth.
See it work
Watch an agent's memory take shape
This is a real memory graph, not an illustration: atoms, associations, and recall as the engine stores and re-ranks. Drag to explore.
Atoms are memories
Each node is one stored memory, sized by importance and colored by type. The graph is what the agent actually knows.
Links are associations
When two concepts are recalled together, a Hebbian link forms and thickens, so related memories surface as a group, not one at a time.
Consolidation is built, not enabled
Look-alike memories merge (HDBSCAN), while distinctive ones are protected. That is built and not enabled on the hosted service today. Recall stays sharp by re-ranking on outcomes.
Retrieval lights the path
A query activates associated concepts and walks the links to the right atom, the same recall path measured in our public LoCoMo runs. The graph below is a real run artifact, not an illustration.
For agents (MCP)
Local MCP server with an API key
This alternative runs @mnemoverse/mcp-memory-server locally through Node.js and connects to Mnemoverse with your API key. Hosted connections use browser sign-in; available tools depend on the host and configured connection. The package is listed on the official MCP Registry.
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@latestBenchmarks
Benchmarked, honestly
Mnemoverse is evaluated on public memory benchmarks (LoCoMo and LongMemEval) rather than internal scores you can't reproduce. Every published number traces back to a committed run artifact: configuration, prompts, and code version frozen.
Live, verified numbers: see the benchmarks page.
Pricing
Start free. Scale when you need to.
Free tier ships with full API access and the MCP server, no credit card required. Reads, writes and feedback all count as queries, only successful requests count, and daily limits reset every 24 hours.
Enterprise
Custom limits · SSO/SAML · audit logs · SLA · data residency · BAA/SOC 2 on request.
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FAQ
Frequently asked
It learns from outcomes (Hebbian associations + Rescorla-Wagner), re-ranks recall from that feedback so stale memory ranks down, and improves over time. A vector database only does similarity search.
Yes. Install the published Mnemoverse Memory plugin in ChatGPT and sign in to Mnemoverse in the browser. Connect Claude and your other tools to the same Mnemoverse account to share its memories. Install in ChatGPT →
Yes. Free is $0: 1,000 queries/day, 10,000 atoms, 60 req/min. No credit card.
Per-tenant org isolation. API keys are SHA-256 hashed and compared in constant time, and we don't train on your data.
RAG answers “what do the docs say?” Mnemoverse remembers “what did we discuss, decide, or learn last time?” They're complementary.
Yes. @mnemoverse/mcp-memory-server exposes memory tools through a local stdio server, and hosted MCP connections are also available. Tool availability depends on the host and configured connection. Deletion isn't an agent-facing tool at all: it's an administrative REST operation, so no MCP client can wipe a memory. To fix something wrong, you write a corrected memory (explicit supersession is coming).
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