Embodify

Name: Embodify
Author: YidaYang

by YidaYangversion 0.1.0a2i★ –iRuns on your computer

Registry nameio.github.YidaYang/embodify

Install

Overview

Description from the maker (original English)

Give your agent a body: observe and control simulated robots (LIBERO, RoboDojo) over MCP.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "embodify": {
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add embodify -- uvx embodify-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "embodify": {
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "embodify": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "embodify-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account YidaYang, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32564 servers in this category
Published to the registry more recently than 53% of the other 32564 servers in this category
Basic checks equal to or better than 100% of the other 32564 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Embodify safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0a2) reached the registry 35 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Tools MCP

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README from the maker

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Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Embodify

Give your agent a body.

Your best embodied agent is your favorite agent.

Embodify lets the agent you use every day — Claude Code, Codex or any other MCP-capable agent — see and control robots directly, and keeps everything else that makes it yours. Bring in frontier models with embodied manipulation skills, such as GPT-6 Astra and Claude Opus 5.5, and let your AI companion step into the physical world.

中文 · One-line setup · Backends · For research · Skills · Roadmap

First release (0.1.0a1). Supports LIBERO and RoboDojo today; support for

RoboTwin and the LeRobot SO-101 arm is coming soon.

Why Embodify

Most embodied agents are built from scratch: a dedicated harness wraps a model, hands it a fixed set of robot actions, and nothing else. The agent you use every day already has what those harnesses lack:

Context and memory. It manages long sessions and remembers across them.
It knows you. Your preferences, your projects, your lab setup.
It talks with you in the terminal, IDE, desktop app or chat you already use. You can ask how it is going, step in, correct it, or teach it mid-task.
It has a computer. It writes and runs code, uses its tools, searches the web and reads papers.

Embodify keeps all of that and adds a body. Install the plugin and your agent gets robot observation and control tools over MCP — a protocol it already uses for everything else — plus skills that teach it how to operate robots and how to achieve recursive self-improvement (RSI) from its own experience.

Why "best"

A general agent with a body is stronger than a harness that can only move a robot:

It can think with tools, not only act. When a task needs geometry it can write a script; when it needs a fact it can search; when it needs perception it can run a model.
It keeps track. Long-horizon manipulation fails when the agent forgets what it already tried. Mature agents manage context and memory well.
It works with you. It asks you when a task is ambiguous, takes your guidance when it gets stuck, and remembers your corrections next time.
It controls robots in its native language. Robot control arrives as ordinary MCP tool calls, the same shape as every other tool the agent uses.
It builds on frontier embodied models. Your agent runs on models with frontier embodied manipulation skills, such as GPT-6 Astra and Claude Opus 5.5, and every model upgrade makes your robot better, with no retraining.
It improves itself recursively. It turns every episode into lessons, lessons into rules and rules into new skills, and gets better the more it works.
Zero-shot, few-shot and in-context learning come easily. A general agent takes on new tasks without training: describe a task in plain language (zero-shot), show it a few examples (few-shot), or put instructions, demonstrations and past experience in its context (in-context learning, ICL).

What's inside

Embodify is an agent plugin with two parts, and installing the plugin gives your agent both:

Part	What it gives your agent
MCP server (`embodify-mcp`, registered as `embodify`)	Tools to list tasks, start an episode, observe cameras and robot state, move end effectors, open and close grippers, and coordinate two arms.
Skills	Know-how: running a careful observe–act loop, keeping a profile of the robot body and cameras, and learning from past episodes. More perception skills are coming soon.

flowchart LR

A["Your agent Claude Code · Codex · …"] -- "MCP (stdio)" --> B["embodify-mcp episodes · budgets · logs"] K["Embodify skills"] -. "loaded by" .-> A B --> I["Backend interface"] I --> L["LIBERO"] I --> R["RoboDojo"] I --> T["RoboTwin (WIP)"] I --> H["SO-101 and other real robots (WIP)"] I -. "SSH / TCP" .-> G["Remote server"]

The MCP server is the front end. Each simulator, benchmark or robot is a backend behind one small interface, so adding a new one does not change what the agent sees. Highlights:

Works with any MCP host. No LLM calls inside; your agent keeps its own model, memory, skills and tools.
One call, one motion. The control loop runs next to the simulator. Each action returns the actual displacement, remaining error, a stop reason and fresh camera images.
Remote simulation. Run the simulator on your lab's GPU server over SSH while the agent stays on your laptop. Heartbeats keep high-latency links alive; if the link drops, the episode aborts cleanly and reset_task reconnects.
Fair evaluation. When you use Embodify to evaluate an agent's manipulation skills, task success is recorded for humans and never exposed to the agent.
Live monitor and replay. Watch running episodes live in a browser, or replay past ones frame by frame.

Backends

Backend	Robot	Status
`fake`, `fake-two-arm`	Kinematic diagnostic, one or two arms	✅ Included, no simulator needed
`libero`	Franka Panda, 130 tasks in 5 suites	✅ Included
`robodojo`	Dual ARX X5, all 54 simulation tasks	✅ Included
RoboTwin	Dual-arm manipulation benchmark	🚧 Coming soon
LeRobot SO-101	Real robot: 5-DoF arm with gripper	🚧 Coming soon
`remote`	Any backend above, on another machine	✅ SSH or trusted TCP

Coming soon: robot arms such as xArm, mobile manipulators, humanoids and dexterous hands. If there is a simulator or robot you would like us to support, open a GitHub issue with details about it. We welcome contributions of new backends.

For research

Embodify doubles as a testbed for the embodied manipulation abilities of language models and agent frameworks. Run GPT, Claude, Gemini, Qwen and other models, through Claude Code, Codex or your own agent framework, on the same simulated benchmarks with the same tools, and compare them directly.

Standard benchmarks: LIBERO (130 tasks in 5 suites) and RoboDojo (54 tasks), with RoboTwin coming soon.
Fair by design: task success is recorded for evaluators and never shown to the agent, and --lock-task fixes the scene.
Controlled budgets: episode budgets and per-call step limits are configurable and logged.
Complete records: tool calls, robot states and camera frames are saved for replay and analysis.

Typical studies include zero-shot robot manipulation by frontier models, comparisons of agent frameworks on the same model, the effect of skills and memory, and recursive self-improvement across episodes. See citation for how to cite Embodify and what to report.

Quick start

One-line setup

Paste this into Claude Code, Codex or any other agent you use:

Install Embodify (https://github.com/YidaYang/embodify) for yourself, then set up a LIBERO environment or connect to my existing simulator.

That's it. Your agent installs the Embodify plugin, sets up the simulator and tells you when to restart the session. (Agents: follow the agent setup guide.)

Manual install

Embodify starts its MCP server with uv, straight from PyPI. If you do not have uv yet, install it with python -m pip install uv or the official installer.

Claude Code

claude plugin marketplace add YidaYang/embodify

claude plugin install embodify@embodify

Codex

codex plugin marketplace add YidaYang/embodify

codex plugin add embodify@embodify

The plugin brings both the MCP server, registered as embodify, and the skills. The server starts on the Fake backend, so you can try the tools right away; connect a real simulator next. It reads its settings from ~/.embodify/config.json (see backend setup). To check the package on its own, without an agent:

uvx --from embodify-mcp embodify-mcp-smoke   # end-to-end check: no simulator, GPU or model needed

Other agents

Register the server in your agent's MCP configuration using examples/mcp.json.
If your agent supports Agent Skills (SKILL.md folders), copy or link the folders in plugin/skills/ into its skills directory.

Restart the session, then ask your agent something like *"Reset the task, describe what the cameras show, then raise the gripper 5 cm."*

Connect a real simulator

Let your agent do this step. Paste:

Connect Embodify to a real simulator for me: set up LIBERO on this machine, or connect to my existing simulator. Follow https://github.com/YidaYang/embodify/blob/main/docs/agent-setup.md.

Your agent installs the simulator or connects to yours over SSH, writes the server settings to ~/.embodify/config.json and asks you to restart the session. To do it by hand, see backend setup.

Watch robots live and replay episodes

Add "monitor-port": 8765 to ~/.embodify/config.json and open http://127.0.0.1:8765 to watch the robot work live, with every camera view, the robot state and each tool call your agent makes, or to replay any past episode frame by frame. To browse saved runs without a running server:

uvx --from embodify-mcp embodify-mcp-monitor   # reads ~/.embodify/runs

The monitor listens on localhost only.

MCP tools

Tool	Purpose
`get_session_info`	Current run, task, step budget and robot state; no images
`list_tasks`	Browse the backend's task catalogue
`reset_task`	Start an episode and return the first camera images
`observe`	Camera images and robot state, without moving
`move_relative`	Move one end effector by a translation and optional rotation
`set_gripper`	Open or close a gripper
`control_arms`	Move several arms together with common progress (two-arm backends)
`stop_episode`	End the episode and write its record

Translations are in meters, rotations in radians, quaternions in xyzw order. Frames and stop reasons are defined in the action contract.

Skills

The plugin contains these skills:

Skill	Status	What it does
embodied-control	v0	The observe–act loop: small moves, reading stop reasons, verifying grasps, releasing in a separate call
robot-profile	v0	Keep a profile of the robot: arms, cameras, frames, calibration and measured behavior
task-experience	v0	Write a lesson after every episode, read relevant lessons before the next, promote repeated lessons to rules
object-segmentation	Coming soon	Open-vocabulary segmentation of camera images
depth-ranging	Coming soon	Pixel to 3D position from depth and calibration

Skills are grouped into three families that will keep growing:

Embodiment knowledge: how this particular robot is built, where its cameras are and how it actually moves.
Manipulation tools: perception, measurement and planning tools, including ideas from work such as Code as Policies.
Recursive self-improvement: turning experience into lessons, lessons into rules and eventually into new skills, improving recursively.

Roadmap

Backends: RoboTwin; LeRobot SO-101 as the first real robot, with workspace limits, human-judged success and an emergency stop;

Only the beginning is shown; read the rest in the repo

Embodify

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Tools MCP

README from the maker

Why Embodify

Why "best"

What's inside

Backends

For research

Quick start

One-line setup

Manual install

Claude Code

Codex

Other agents

Connect a real simulator

Watch robots live and replay episodes

MCP tools

Skills

Roadmap