MemoCat — Shared Memory for AI Agents and Systems

MemoCat gives Claude, OpenAI/Codex, Cursor, and other MCP-compatible AI systems one shared, persistent memory. Agents connected to the same memory can carry knowledge across conversations, hand context to one another, and react to what another agent learns in real time.

MemoCat is not tied to one model, app, or agent framework. It is a Model Context Protocol memory layer backed by Montycat. Run it locally for private memory across sessions and local clients, or connect multiple machines and systems to the same trusted Montycat engine for shared memory across agents.

Memories are embedded on-device and recalled by meaning, metadata, timestamp, or exact key. Montycat keeps the database, vector search, embeddings, persistent and in-memory storage, live subscriptions, and governance together, so agents do not need separate vector, embedding, messaging, and policy services. No cloud embedding API or per-query bill.

Features

• One persistent memory across conversations, agents, and MCP-compatible systems.
• Shared scopes let multiple agents work from the same facts and project context.
• Semantic vector search with metadata and time-range filtering for RAG.
• Persistent memory, in-memory working spaces, bulk writes, updates, and deletion.
• Real-time memory-change subscriptions without database polling.
• Private scopes, delegated-owner governance, and policy explanations.
• Keyspace lifecycle, semantic-model controls, snapshots, and revocation-safe watch buffers.
• One-command uvx memocat-mcp entry point with native/Docker engine bootstrap.

Why

AI systems forget between conversations, and separate agents cannot naturally share what they learn. MemoCat gives them a common memory: one agent can store a decision, another can recall it by meaning, and a third can receive its update live. Its 23 MCP tools also support exact retrieval, lifecycle management, and governed deployments, but the core product is the shared memory layer—not a model-specific plugin or another standalone vector database.

Install MemoCat MCP

The fastest option is uvx, which runs the latest published package in an isolated environment:

uvx memocat-mcp

If uvx is not installed yet:

curl -LsSf https://astral.sh/uv/install.sh | sh
uvx memocat-mcp

For a persistent command-line installation, use pipx:

pipx install memocat-mcp
memocat-mcp

You can also install it into an existing Python environment:

python -m pip install memocat-mcp
memocat-mcp

MemoCat requires Python 3.10 or newer. The package is published as memocat-mcp on PyPI.

Quick start with a Montycat engine

MemoCat reuses a configured Montycat Semantic engine or attempts the supported native/Docker bootstrap path. For an existing engine:

export MONTYCAT_URI="montycat://memory-agent:password@localhost:21210/memories"
uvx memocat-mcp

Tools

Tool	What it does
memocat_semantic_search	Recall by meaning (vector kNN), with text or a supplied query vector.
memocat_remember	Store a fact/record; embedded automatically or indexed with a supplied vector.
memocat_remember_bulk	Store many memories at once.
memocat_recall	Fetch by exact key or by field filter.
memocat_list_memories	Browse / list stored memories (optionally most-recent first).
memocat_update	Revise a memory in place — memory is mutable.
memocat_forget	Delete a stored record.
memocat_list_keyspaces	Discover available memory namespaces.
memocat_create_keyspace	Provision a namespace; superowners also create a missing configured store in the same engine request.
memocat_remove_keyspace	Permanently remove an authorized memory namespace with safe watch cleanup.
memocat_enable_semantic	Enable semantic search and backfill one authorized keyspace.
memocat_enable_external_vectors	Enroll one keyspace for caller-supplied vectors and a named embedding space.
memocat_semantic_status	Inspect semantic configuration and backfill state.
memocat_reembed_semantic	Replace an enrolled text embedding model and backfill the keyspace.
memocat_disable_semantic	Disable semantic search for one authorized keyspace.
memocat_start_snapshots	Start scheduled snapshots for one authorized in-memory keyspace.
memocat_stop_snapshots	Stop scheduled snapshots for one authorized in-memory keyspace.
memocat_clean_snapshots	Delete snapshot files for one authorized in-memory keyspace.
memocat_policy_view	View the configured owner's effective governance policy and constraints.
memocat_policy_explain	Explain whether a proposed governed action is allowed and why.
memocat_policy_history	View governance history visible to the configured owner.
memocat_await_memory_change	Wait for memory to change — returns the moment another agent or session writes. Live subscription, not polling.
memocat_install_engine	Install the Montycat engine on this computer and start it. Opens your OS installer and asks for an administrator password, so it only ever runs when you ask for it.

Real-time memory watch

Other memory servers can only be polled: ask again, and again, in case something changed. Montycat has native live subscriptions, so this one pushes.

agent B: memocat_await_memory_change(scope="shared", timeout_sec=60)
                    ⏳ sleeps — no polling, no wasted tokens
agent A: memocat_remember({"text": "the deploy key rotated"}, scope="shared")
agent B: ← returns in milliseconds with the key, the value, and the event

Two agents, one shared scope, one notices what the other just learned. Pass the returned next_seq back as since_seq to resume exactly where you left off — changes that happen between calls are buffered, not lost.

Memory namespaces are also exposed as MCP resources (memocat://memory/<keyspace>) with resources.subscribe support, so clients that implement resource subscriptions get notifications/resources/updated pushed to them as well. Both surfaces share one engine subscription.

Subscriptions open on demand and close when idle (MONTYCAT_WATCH_IDLE_TIMEOUT), so users who never watch pay nothing.

Montycat Semantic engine requirements

• Python 3.10+ when installing MemoCat through uv, pipx, or pip.

• Access to a Montycat Semantic engine. uvx memocat-mcp first reuses an existing engine, then attempts the supported native/platform installation path, and finally falls back to Docker. Semantic search is enabled by default in the Semantic edition.

  To start the engine manually with Docker, pick the tag for your CPU—the tag carries the architecture:

  Apple Silicon (M1/M2/M3/M4) — use arm64-semantic:

  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:arm64-semantic
  

  Intel / AMD (x86_64) — use semantic:

  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 the plain semantic tag is the amd64 image and runs under emulation, where the embedding runtime's warm-up crashes. Use arm64-semantic — a native build, not a workaround. Unsure which you have? uname -m prints arm64 on Apple Silicon and x86_64 on Intel.

  Port 21211 is the subscription server and is required for memocat_await_memory_change (real-time watch); without it the other tools still work.

Docker Compose deployment

Use Compose when you want a reproducible local deployment with a persistent Semantic engine and an MCP container on the same private Docker network. Docker is optional when you already manage a reachable Montycat server.

Create a .env file beside compose.yaml:

MONTYCAT_USERNAME=admin
MONTYCAT_PASSWORD=replace-with-a-strong-password
MONTYCAT_STORE=memories
MEMOCAT_VERSION=0.4.3
# Apple Silicon: arm64-semantic. Intel/AMD64: semantic.
MONTYCAT_IMAGE_TAG=semantic

Start the engine and build the MCP image:

docker compose up -d montycat
docker compose build mcp

After the public Docker image is released, use it instead of building from source:

docker pull montygovernance/memocat-mcp:0.4.3

The Compose service uses montygovernance/memocat-mcp:${MEMOCAT_VERSION} and waits for the Semantic engine health check before launching MCP. The image runs as an unprivileged memocat user and supports both AMD64 and ARM64.

The image installs the released montycat>=1.2.2,<2 Python client declared in the package metadata.

The engine data is stored in the named montycat_data volume. Ports 21210 and 21211 are published for debugging and external clients; the MCP container uses the private montycat:21210 network address. Credentials are passed as separate environment variables, so passwords with URL-special characters need no URL encoding. Port 21211 carries live subscription traffic for memocat_await_memory_change.

MCP uses stdio, so do not run it as a web service. Configure a desktop MCP client to invoke the Compose service on demand:

{
  "mcpServers": {
    "memocat": {
      "command": "docker",
      "args": [
        "compose",
        "-f", "/absolute/path/to/memocat-mcp/compose.yaml",
        "run", "--rm", "-T", "mcp"
      ]
    }
  }
}

For Apple Silicon set MONTYCAT_IMAGE_TAG=arm64-semantic in .env; the plain semantic image is AMD64. Stop the stack with docker compose down; include -v only when you intentionally want to erase persisted memories.

Engine auto-start

MemoCat starts serving immediately and acquires an engine in the background, so the MCP handshake is never held up by a container pull or an embedding-model download. While that is still in progress, memory tools say so and ask you to try again in a moment rather than hanging.

The engine may be local or remote. MemoCat first reuses one already reachable through MONTYCAT_URI or the host/port settings — including on another machine over TCP. If that address is not on this computer and nothing answers, MemoCat reports it and stops: starting a local engine for a remote address would create a second database and write memories where you are not looking.

For a local engine that is not running, it tries, in order:

Step	Route	If it cannot complete
1	Launch an already-installed montycat_bin	Docker
2	Start the montygovernance/montycat container	Report how to install

Before launching a local engine MemoCat asks the montycat CLI that ships beside it (montycat version, a compile-time constant that answers while the engine is down). An installation that cannot run — wrong architecture, missing ONNX libraries, no execute bit — is skipped immediately rather than launched and waited on, and the reported edition distinguishes a base-edition install from the Semantic one the memory tools need. Set MEMOCAT_ENGINE_CLI to point at a CLI in a non-standard location, or MEMOCAT_ENGINE_BINARY for the engine itself.

Installation is never automatic. Acquiring the engine opens your operating system's installer and asks for an administrator password (or, on Linux, runs the APT setup with sudo), which should not happen as a side effect of opening a chat client. Ask for memocat_install_engine instead, and it runs with your consent:

Platform	Route
macOS Apple Silicon	Discover and download the latest verified montycat-semantic_<version>_arm64.pkg, open Installer, and wait for installation (prompts for admin approval)
macOS Intel	No Semantic package currently published — use Docker
Windows x86_64	Download verified .msi and invoke Windows Installer (prompts for UAC)
Linux AMD64	Run the official one-command APT setup for montycat-semantic (prompts for sudo)
Other platforms	Use Docker

MemoCat asks the shared Montycat release catalog for the current Semantic artifact for macOS or Windows. Artifact URLs are treated as opaque, and the package's adjacent .sha256 is required and verified before Installer opens; verified packages are cached by filename. If catalog discovery is unavailable, installation stops instead of silently installing an older package. Override the URL with MEMOCAT_INSTALLER_URL, pin a release with MEMOCAT_ENGINE_VERSION, or adjust the Installer completion budget with MEMOCAT_INSTALLER_TIMEOUT. On Linux, set MEMOCAT_APT_INSTALL_COMMAND to use an organization-managed mirror or package command. ARM64 Linux goes directly to Docker because the official APT repository is AMD64-only. Set MEMOCAT_AUTOSTART=off to disable all start attempts, and MEMOCAT_READY_TIMEOUT (default 20s) to change how long a tool waits for a starting engine before reporting progress.

Connect multiple AI systems to one memory

Use the same MONTYCAT_URI in each client to give Claude Desktop, Cursor, OpenAI Codex, and other MCP-compatible systems access to the same memory. A default local engine shares memory across sessions and local clients on one machine; cross-machine sharing requires a trusted network-reachable Montycat engine.

Claude Desktop

Add MemoCat to claude_desktop_config.json, then restart Claude Desktop:

{
  "mcpServers": {
    "memocat": {
      "command": "uvx",
      "args": ["memocat-mcp"],
      "env": {
        "MONTYCAT_URI": "montycat://memory-agent:agent-password@localhost:21210/mystore"
      }
    }
  }
}

Cursor

Add the same server definition to your Cursor MCP configuration:

{
  "mcpServers": {
    "memocat": {
      "command": "uvx",
      "args": ["memocat-mcp"],
      "env": {
        "MONTYCAT_URI": "montycat://memory-agent:agent-password@localhost:21210/mystore"
      }
    }
  }
}

OpenAI Codex

Codex can register the local stdio server directly from a terminal:

codex mcp add memocat \
  --env MONTYCAT_URI="montycat://memory-agent:agent-password@localhost:21210/mystore" \
  -- uvx memocat-mcp

Confirm the registration with codex mcp list, then start a new Codex session.

ChatGPT integration

MemoCat currently runs as a local stdio MCP server. It works directly with clients that can launch local MCP commands, including Claude Desktop, Cursor, and Codex. A ChatGPT connector requires a remotely reachable MCP transport and cannot connect directly to this stdio command. Remote HTTP transport is not included in the current package; do not expose the engine's database port as an MCP endpoint.

Connect to a remote TLS engine

Keep the normal montycat:// connection URI and enable TLS separately:

export MONTYCAT_URI="montycat://memory-agent:agent-password@db.example.com:21210/mystore"
export MONTYCAT_TLS=true
uvx memocat-mcp

For a desktop client, add "MONTYCAT_TLS": "true" beside MONTYCAT_URI in the server's env object. The remote engine must present a certificate trusted by the machine running MemoCat. Setting MONTYCAT_URI disables local engine auto-install and auto-start because it explicitly selects a managed engine.

Security and delegated-owner setup

Use a delegated Montycat owner such as memory-agent for the MCP process. Grant that owner only the keyspace read/write and provisioning capabilities its agent needs. Keep the superowner credential in a separate bootstrap or governance-administration workflow.

The read-only memocat_policy_view, memocat_policy_explain, and memocat_policy_history tools expose policy information for the authenticated owner. They do not accept an owner override and cannot grant, revoke, deny, or otherwise mutate policy. When automatic keyspace provisioning fails, MemoCat also requests a read-only policy explanation and appends it to the original engine error when available.

memocat_remove_keyspace is destructive and remains engine-authorized. A delegated owner may remove a keyspace through creator authority or an explicit remove-keyspace grant unless policy contains an overriding denial. MemoCat closes active watches and releases resource subscriptions before requesting removal.

Semantic management is always keyspace-scoped. The MCP server does not expose database-wide semantic controls; Montycat checks manage-semantic, creator authority, denials, and model allow-lists for every enable or disable request.

Snapshot tools are likewise keyspace-scoped and work only with in-memory keyspaces. MemoCat does not expose the global snapshot-rate setting. A Snapshot rate is not set response means scheduling has not been configured on the engine; it is distinct from a governance denial.

Active watches use short authorization leases because the current engine checks read authority when a subscription opens but does not terminate that connection after a later revocation. MemoCat revalidates against the engine's filtered structure view, closes the subscription on access loss, removes MCP resource ownership, wakes pending callers with an error, and permanently purges buffered changes so they cannot be replayed after access is restored.

Configuration

Variable	Default	Purpose
MONTYCAT_URI	—	montycat://user:pass@host:port/store (preferred; overrides the parts below)
MONTYCAT_HOST	127.0.0.1	Engine host
MONTYCAT_PORT	21210	Engine port
MONTYCAT_USERNAME / MONTYCAT_PASSWORD	—	Credentials
MONTYCAT_STORE	—	Store name
MONTYCAT_TLS	false	Connect over TLS
MONTYCAT_DEFAULT_KEYSPACE	memory	Keyspace used when a tool omits scope/keyspace
MONTYCAT_PERSISTENT	true	Storage type for newly created keyspaces (durable vs in-memory). Existing keyspaces are auto-detected — the server binds the correct type regardless of this setting.
MONTYCAT_SCOPE	—	Default owner/scope, applied when a tool omits scope
MONTYCAT_SCOPE_PREFIX	mem_	Prefix for per-owner keyspaces (mem_<scope>)
MONTYCAT_SHARED_KEYSPACE	mem_shared	The common/shared keyspace name
MONTYCAT_AUTO_PROVISION	true	Auto-create a scope's keyspace on first use. Requires provision-keyspace authority for the configured owner and requested storage/model constraints.
MONTYCAT_AUTO_TIMESTAMP	true	Stamp each memory with an indexed _created_at, enabling time-range recall (since/until). Costs a server-side timestamp parse per write — turn off if memories are never recalled by time.
MONTYCAT_SUBSCRIPTION_PORT	main + 1	Engine subscription server port (21211 by default; enabled by default)
MONTYCAT_WATCH_BUFFER	500	Changes retained per watched keyspace, so changes between calls aren't lost
MONTYCAT_WATCH_IDLE_TIMEOUT	300	Seconds before an unused subscription is closed
MONTYCAT_WATCH_AUTH_LEASE_SEC	5	Seconds between read-authority checks for active watches. Access loss closes the subscription and purges buffered changes.
MONTYCAT_WATCH_AUTH_TIMEOUT_SEC	10	Maximum seconds allowed for one watch authorization check. A failed check closes the watch safely.

memocat_create_keyspace works with delegated-owner credentials when policy grants provision-keyspace for the requested store, storage type, and semantic model. The store must already exist for delegated owners. With superowner credentials, creating the first keyspace also creates a missing configured store in the same engine request. memocat_forget deletes one record and requires write authority for its keyspace. The engine makes every final authorization decision.

Memory scoping (multi-tenant)

Pass scope (an owner/user id) to any memory tool to isolate that owner's memory. Because Montycat's semantic search runs per keyspace, each scope gets its own keyspace mem_<scope> — so semantic recall for one owner never sees another owner's memories:

remember(value={"fact": "..."}, scope="alice")      # -> keyspace mem_alice
semantic_search(query="...", scope="alice")          # searches only mem_alice
remember(value={"fact": "..."}, scope="shared")      # -> the shared keyspace

• Per-owner private memory — scope="<owner>" → mem_<owner>, auto-created on first use when the configured owner has provisioning authority.
• Shared/common memory — scope="shared" → the MONTYCAT_SHARED_KEYSPACE.
• Group memory — use a group id as the scope (e.g. scope="team_eng").
• Single-tenant — set MONTYCAT_SCOPE once and omit scope per call.

This maps onto Montycat's keyspace governance. In production, run one server instance per agent or service with delegated-owner credentials and grant only the provisioning and data authority it needs.

Isolation note: scope is routing convenience, not authenticated identity. With one server instance sharing one connection, scopes provide logical keyspace organization. For credential-enforced isolation, run one server instance per owner with that owner's delegated credentials; the engine then denies cross-owner access. Reserve superowner credentials for bootstrap and governance administration.

Claude Desktop extension (MCPB)

MemoCat is also packaged as a local Claude Desktop extension. The extension runs this same stdio MCP server on the user's computer; it is not a hosted MCP service and does not expose the Montycat database ports as MCP endpoints.

Install

Install the released .mcpb using one of these Claude Desktop methods:

1. Double-click the .mcpb file.
2. Drag the file into the Claude Desktop window.
3. Open Settings > Extensions > Advanced settings > Install Extension and select the file.

Review the requested tools and configure the extension when prompted. Claude Desktop manages the UV-based Python runtime and installs the dependencies declared in pyproject.toml; a separate Python installation is not required by the MCPB runtime.

Extension settings

Setting	Purpose
Existing Montycat URI	Optional sensitive montycat://user:password@host:port/store connection. Leave blank for automatic local-engine discovery/setup.
Use TLS for existing engine	Enables TLS certificate verification for a configured remote engine.
Default memory keyspace	Namespace used when Claude does not specify a scope or keyspace. Defaults to memory.
Local engine startup mode	auto discovers or starts the supported native/Docker engine; off requires an already-running engine.

For a remote engine, use a least-privilege delegated owner and enable TLS. Do not put a superowner credential into a shared desktop configuration.

Update and uninstall

Install a newer signed/released MCPB with the same extension name and a higher version to update it. Removing the extension stops and removes its MCP process, but deliberately does not erase memory data.

To remove data as well:

• delete individual records or keyspaces before uninstalling when selective deletion is desired;
• remove the local MemoCat state directory at ~/.montycat only when all locally managed MemoCat configuration and cached installer state should be removed;
• if the engine was started through Docker, remove the memocat_data volume separately (for example, inspect it first with docker volume ls and remove that exact volume only when its stored memories are no longer needed);
• for a user-configured remote engine, remove its data using that engine operator's process—the desktop extension cannot delete an external deployment merely by being uninstalled.

Data deletion is irreversible. Back up required memories before removing a keyspace, engine data directory, snapshot set, or Docker volume.

MCPB troubleshooting

• Open the extension details in Claude Desktop Settings and inspect its logs.
• If startup reports that no engine is reachable, start the configured engine, correct the URI, or change startup mode from off to auto.
• If automatic setup cannot install a native engine, install Docker and retry, or install Montycat manually and configure its URI.
• On Apple Silicon, use the native arm64-semantic engine image; the plain semantic tag is AMD64.
• For remote TLS failures, verify the hostname and that the certificate is trusted by the user's machine. Do not disable TLS merely to bypass a certificate error.
• Report reproducible problems at https://github.com/MontyGovernance/memocat-mcp/issues or use https://montygovernance.com/contact-us.

Maintainers and directory reviewers can use MCPB_SUBMISSION.md for the complete listing copy, capability declarations, setup procedure, and every-tool verification sequence.

Privacy Policy

MemoCat processes memory values, search queries, vectors, and configuration only as needed to perform MCP calls. By default, the MCP process and Montycat engine run locally, embeddings are generated on-device, and MemoCat includes no product analytics or telemetry that sends memory contents to Monty Governance. A user-configured remote engine receives the MCP data sent to that engine and is governed by its operator's retention and privacy practices.

Persistent memories, snapshots, native engine data, and Docker volumes remain until the user deletes them; uninstalling the MCPB alone does not erase them. The complete policy—including collection, storage, sharing, retention, deletion, third-party distribution services, and contact information—is in PRIVACY.md and is published at https://github.com/MontyGovernance/memocat-mcp/blob/master/PRIVACY.md.

Links

• MemoCat MCP on PyPI
• MemoCat MCP source
• Report an issue
• Changelog
• Montycat documentation
• Montycat Semantic engine on Docker Hub
• Montycat Python client on PyPI

License

MIT.