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MemoCat — Shared AI Memory MCP server

by MontyGovernance·io.github.MontyGovernance/memocat-mcp·v0.4.3
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One shared, persistent memory across AI agents and systems, with semantic recall and live updates.

A85/100grade A
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A85/100

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55 stars

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If you have run it, two minutes of your experience saves the next person an afternoon.

MemoCat — Shared AI Memory tools (23, 5 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • memocat_await_memory_change

    Wait until memory CHANGES — returns the moment another agent or session writes, updates, or deletes something in this memory.

  • memocat_clean_snapshotswrite action

    Delete snapshot files for one existing in-memory keyspace.

  • memocat_create_keyspacewrite action

    Create a new memory namespace using the configured owner's authority.

  • memocat_disable_semantic

    Disable semantic search for one explicit keyspace.

  • memocat_enable_external_vectors

    Enroll a keyspace for caller-supplied embeddings instead of text embedding.

  • memocat_enable_semantic

    Enable semantic search for one explicit keyspace.

  • memocat_forgetwrite action

    Delete a stored record from memory by key or custom key.

  • memocat_install_engine

    Install the Montycat engine on THIS computer, then start it.

  • memocat_list_keyspaces

    List the available memory stores and keyspaces on this Montycat engine.

  • memocat_list_memories

    Browse stored memories — enumerate what is remembered, not search by meaning.

  • memocat_policy_explain

    Explain whether the configured owner may perform a proposed action.

  • memocat_policy_history

    View governance history visible to the configured owner.

  • memocat_policy_view

    View the configured owner's effective Montycat governance policy.

  • memocat_recall

    Recall memory by exact key or by field filter (not by meaning).

  • memocat_reembed_semantic

    Replace an enrolled keyspace's text embedding model and backfill it.

  • memocat_remember

    Store a fact or record in memory; it is embedded and indexed automatically.

  • memocat_remember_bulk

    Store many memories at once; all are embedded and indexed automatically.

  • memocat_remove_keyspacewrite action

    Permanently remove a memory namespace using the owner's authority.

  • memocat_semantic_search

    Search stored memory by MEANING (vector / semantic search), not keywords.

  • memocat_semantic_status

    Read the engine's actual semantic configuration and backfill state.

  • memocat_start_snapshots

    Start scheduled snapshots for one existing in-memory keyspace.

  • memocat_stop_snapshots

    Stop scheduled snapshots for one existing in-memory keyspace.

  • memocat_updatewrite action

    Revise an existing memory in place (memory is mutable).

Public scan report

scanner v0.1.9 · 2026-09-20 · same rubric, same numbers if you re-run it

no findings
  • Code scan9 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 1 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year9/10
Overall 85/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

What the publisher says

From the MemoCat — Shared AI Memory repository's README, as published. We do not edit it. Read it on GitHub

Montycat MCP - Shared Memory for AI Agents

A self-hosted MCP server that gives AI agents persistent, searchable memory. Claude, Codex, Cursor, and any Model Context Protocol client write to one memory and read each other's.

<!-- mcp-name: io.github.MontyGovernance/montycat-mcp -->

  • Memory that survives the chat. Decisions, preferences, and project context carry into the next session.
  • One memory, many agents. Every MCP client you use works from the same facts.
  • Recall by meaning, keyword, or both. Vector search finds a memory when the wording differs, BM25 nails exact identifiers, and hybrid mode fuses the two. Exact-key and metadata lookup too.
  • Yours. Server, engine, and embeddings run on your machine. No hosted memory service, no cloud embedding API.

Install

Claude Desktop — download montycat-mcp.mcpb and drag it into Claude Desktop. No Python needed. That link always serves the current release; every release also carries a version-named copy and a .sha256 to check it against.

Claude Code — install uv, then:

/plugin marketplace add MontyGovernance/montycat-mcp
/plugin install montycat-mcp@montygovernance

/mcp confirms the montycat server is connected.

Codex, Cursor, other MCP clients — point your client's stdio config at uvx montycat-mcp (Python 3.10+). For Codex:

codex mcp add montycat -- uvx montycat-mcp

The engine

Memory lives in a Montycat Semantic engine. Montycat MCP starts a local one for you, so most people can stop reading here.

Point it at an engine you already run:

export MONTYCAT_URI="montycat://memory-agent:password@localhost:21210/memories"
export MONTYCAT_TLS=true   # remote engines only

Or start one yourself with Docker:

docker run -d --name montycat -p 21210:21210 -p 21211:21211 \
  -e MONTYCAT_SUPEROWNER=admin -e MONTYCAT_PASSWORD=change-me \
  -v montycat_data:/var/lib/.montycat \
  montygovernance/montycat:semantic

On Apple Silicon use the arm64-semantic tag instead — semantic is the amd64 image, and it crashes under emulation. Port 21211 carries live memory watches.

Use it

Talk to your agent normally; it picks the tool.

Remember that the team chose PostgreSQL for the billing service.

What did we decide about the billing database?

Save this to the shared engineering scope.

scope decides where a memory lives — alice for private, engineering for a team, shared for common. It is a namespace, not a security boundary: for real isolation, give each MCP server its own least-privilege Montycat credential.

Tools

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Install directly

claude mcp add memocat-mcp -- uvx memocat-mcp
Add to Cursor

MemoCat — Shared AI Memory: common questions

Is MemoCat — Shared AI Memory MCP server safe?
Yes, by our scan: it is graded A (85/100). Read the MemoCat — Shared AI Memory safety report
How do I install MemoCat — Shared AI Memory?
It runs on your machine. Copy the Claude Code, Claude Desktop or Cursor config from the install section.
Does MemoCat — Shared AI Memory need an API key?
Yes. The registry entry asks for MONTYCAT_URI.
Is MemoCat — Shared AI Memory maintained?
The last commit was 2 days ago (2026-09-19). The latest release is v0.4.3.
What can I use instead of MemoCat — Shared AI Memory?
Servers from other publishers that do the same job: Claudexor MCP server, State Memory MCP server and Knowl MCP server. Compare all MemoCat — Shared AI Memory alternatives.

Alternatives to MemoCat — Shared AI Memory

Same job from other publishers: the closest match first, then the best rated.

All MemoCat — Shared AI Memory alternatives →
  • Claudexor
    Route coding work across Claude Code, Codex, Cursor, and OpenCode with shared context.
    A
  • State Memory MCP
    Deterministic, persistent graph server for tracking workflow state, decisions, and blockers.
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  • Knowl
    Persistent memory for Claude Code, Cursor and Codex. Facts retire when they change.
    A
  • Plur
    Open, local-first engram memory for AI agents: read, correct, delete; shared over MCP.
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  • Prism Coder
    Session memory for coding agents: local-first recall, drift detection, on-device inference.
    C

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