Install and Run

Install the package

Choose the runtime you need.

" "pip"]}>

npm install pmll-memory-mcp

pnpm add pmll-memory-mcp

yarn add pmll-memory-mcp

bun add pmll-memory-mcp

pip install pmll-memory-mcp

Start the MCP server

For the Node implementation documented in mcp/src/index.ts, run:

npx pmll-memory-mcp

For the Python implementation documented in mcp/pmll_memory_mcp/server.py, run:

python -m pmll_memory_mcp.server

Both commands use stdio transport.

Register it with an MCP client

Use the npm server as the canonical config because it matches the 15-tool TypeScript surface:

{
  "mcpServers": {
    "pmll-memory-mcp": {
      "command": "npx",
      "args": ["pmll-memory-mcp"]
    }
  }
}

Verify the reusable Python API

This script exercises the same logic without an MCP transport:

from pmll_memory_mcp import (
    PMMemoryStore,
    QPromiseRegistry,
    peek_context,
    upsert_node,
    resolve_context,
)

session_id = "verify-install"
store = PMMemoryStore()
registry = QPromiseRegistry()

print(peek_context("search:docs", session_id, store, registry))
store.set("search:docs", "cached docs result")
upsert_node(session_id, "note", "docs", "cached docs result")
print(resolve_context(session_id, "docs", store))

Expected output:

{'hit': False}
{'source': 'long_term', 'value': 'cached docs result', 'score': 1.0}