Twin Me Up runs on your own machine — the client starts it, so there is no endpoint to ping. 906 installs a week from pypi. Last commit 16 Oct 2026.
A personal work twin for every AI agent: your confirmed guidance and explicit grants, enforced.
We read the source, 9 h ago · rules ccca72095570
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
raise SystemExit(subprocess.call(argv, env=env))
execFile(command, [...base, ...args], { env, maxBuffer: 16 << 20, timeout: 120_000 }, (error, stdout, stderr) => {
for (const [key, value] of Object.entries(process.env)) {
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add twin-me-up -- uvx twin-me-up
{
"mcpServers": {
"twin-me-up": {
"args": [
"twin-me-up"
],
"command": "uvx"
}
}
}
[mcp_servers.twin-me-up]
command = "uvx"
args = ["twin-me-up"]
{
"mcpServers": {
"twin-me-up": {
"args": [
"twin-me-up"
],
"command": "uvx"
}
}
}
{
"mcpServers": {
"twin-me-up": {
"args": [
"twin-me-up"
],
"command": "uvx"
}
}
}
This one needs environment variables set before it will start:
TWINMEUP_HOME (Directory holding your twin. Defaults to ~/.twinmeup. Run `twinmeup init` there first.).
The author declared them in the registry entry; get the values from the project itself.
Governed personal world model and memory for your AI agent. Pair once, connect over MCP.
Your personal knowledge base and memory for AI agents: search, explore and feed your myBrain.
Persistent graph-backed conversational memory for AI agents.
Your personal world model for any agent.
Persistent personal memory for AI assistants — save, search, and recall across every MCP client.
Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools.
Personal context for every AI: search, read, and write back to your private Markdown library.
shared AI-context layer for teams — persistent memory your agents search and update over MCP
Answers built from our own checks of this server.