
montygovernance/montycat-mcp
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Shared, persistent memory for AI agents. Self-hosted MCP server with semantic search, vector RAG, and live updates. Works with Claude, Cursor, Codex, and any MCP client.
Обзор
Claude, Codex, Cursor, and any Model Context Protocol client write to one memory and read each other's. - Decisions, preferences, and project context carry into the next session. - Every MCP client you use works from the same facts. - 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. - Server, engine, and embeddings run on your machine. No hosted memory service, no cloud embedding API. — 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. /mcp confirms the montycat server is connected. — point your client's stdio config at uvx montycat-mcp (Python 3.10+). For Codex: Memory lives in a Montycat Semantic engine. Montycat MCP starts a local one for you, so most people can stop reading here.
README
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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.
- 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.
Установка
uvx montycat-mcp` (Python 3.10+). For Codex: