Memanto

Memanto MCP Server

mcp-name: io.github.moorcheh-ai/memanto

Persistent semantic memory for any MCP-compatible agent.

This package exposes Memanto's memory primitives — remember, recall, answer, and friends — as Model Context Protocol (MCP) tools so any MCP client (Claude Desktop, Cursor, Windsurf, Cline, Continue, Goose, custom agents, …) can plug into long-term memory in a single config line.

One Moorcheh API key → typed semantic memory across every agent that

shares the namespace, with sub-90 ms retrieval, conflict detection, and

zero ingestion latency.

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Install

pip install memanto-mcp

Requires Python 3.10+, memanto>=0.2.13, mcp>=1.2,<2, and a Moorcheh API key (free tier: 100K ops/month).

Quick start (Claude Desktop)

1. Get a Moorcheh API key from the console.
2. Edit claude_desktop_config.json (Settings → Developer → Edit Config):

{

"mcpServers": { "memanto": { "command": "memanto-mcp", "env": { "MOORCHEH_API_KEY": "mch_xxxxxxxxxxxxxxxxxx", "MEMANTO_DEFAULT_AGENT_ID": "my-assistant" } } } }

1. Restart Claude Desktop. Ask it to *"remember that I prefer concise answers"* — then in a brand-new chat tomorrow ask *"what do I prefer?"*.

The first call auto-creates the my-assistant agent and namespace; every subsequent call reuses the same persistent memory.

Quick start (Cursor / Windsurf / Cline / Continue / Goose)

Most clients consume a config file in the standard MCP shape. The same JSON snippet works almost verbatim:

{

"mcpServers": { "memanto": { "command": "memanto-mcp", "env": { "MOORCHEH_API_KEY": "mch_xxxxxxxxxxxxxxxxxx", "MEMANTO_DEFAULT_AGENT_ID": "cursor-workspace" } } } }

Available tools

The server registers 7 memory tools by default. Set MEMANTO_EXPOSE_ADMIN=true to also expose 4 agent-management tools.

Memory tools (always on)

Agent admin tools (opt-in)

Enabled when MEMANTO_EXPOSE_ADMIN=true:

Memory types accepted by remember / batch_remember: fact, preference, goal, decision, artifact, learning, event, instruction, relationship, context, observation, commitment, error.

Provenance values: explicit_statement, inferred, corrected, validated, observed, imported.

Source attribution

source names *who wrote* a memory, so recall can be attributed and filtered per writer. It is open: user, agent, tool, system, or a specific writer such as cursor, codex, claude_code, mem0. Labels are limited to 64 letters, digits, ., _, or - so that #source: stays a usable filter.

When a tool call omits source, the server attributes the write to the connected MCP client from the initialize handshake (cursor, codex, claude-ai, …), falling back to mcp-agent when the client sends no name. Two editors sharing one agent therefore stay distinguishable in recall without any extra configuration.

Configuration

All config is via environment variables (load order: process env → .env file in the working directory).

CLI flags (memanto-mcp --transport sse --port 9000) override env vars.

Running over HTTP / SSE

For remote clients or multi-process setups, run the server over a network transport:

# Streamable HTTP (recommended modern transport)

memanto-mcp --transport streamable-http --host 0.0.0.0 --port 8765

Server-Sent Events (older, still widely supported)

memanto-mcp --transport sse --host 0.0.0.0 --port 8765

Then point your client at http://your-host:8765/mcp (or whatever path the chosen transport advertises). Pair with a reverse proxy + auth for production deployments — the server itself authenticates upstream to Moorcheh using your API key but does not authenticate inbound MCP clients.

How it works

┌──────────────┐    MCP/stdio    ┌──────────────────┐    Moorcheh API    ┌─────────────┐

│ Claude / IDE │ ──────────────► │ memanto-mcp │ ────────────────► │ Moorcheh │ │ (client) │ ◄────────────── │ (this package) │ ◄──────────────── │ Service │ └──────────────┘ tool calls └──────────────────┘ HTTPS+API key └─────────────┘ │ └─ uses memanto.cli.client.SdkClient (same client the Memanto CLI uses)

• On startup, settings are validated; the API key is verified lazily on first tool call.
• On the first memory tool invocation for a given agent, the server ensures the agent exists (auto-creates if needed) and activates a JWT session. Sessions auto-renew before expiry, so long-running MCP connections never hit a session-expired error mid-conversation.
• The server intentionally keeps the session alive on shutdown: JWT sessions are TTL-bound and other Memanto clients (CLI, REST) may want to share them.

Programmatic embedding

If you're building a custom MCP host or wiring this server into a larger process, you can construct the FastMCP instance yourself:

from memanto_mcp import MCPServerSettings, build_server

settings = MCPServerSettings() # reads env / .env mcp = build_server(settings)

Add your own tools alongside Memanto's, then run.

mcp.run(transport="stdio")

Troubleshooting

License

MIT — same as the Memanto project. See LICENSE.

Links

• Memanto — the memory agent itself
• Moorcheh — the no-indexing semantic DB underneath
• Model Context Protocol spec
• Anthropic MCP Python SDK