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.

mermaid

flowchart LR
  A["Your agent<br/>Claude Code · Codex · …"] -- "MCP (stdio)" --> B["embodify-mcp<br/>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:

text

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

Terminal

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:

text

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; more real robots and simulators.
Embodiments: mobile manipulators, humanoids, dexterous hands.
Observations: depth images, camera calibration and more through the tools.
Skills: segmentation, depth ranging and other perception tools; code-as-policy style tools; a stronger recursive self-improvement loop, and more.

Development

python -m pip install -e ".[dev]"
python -m pytest -q
python tools/check_release.py

See contributing, writing a backend, backend setup, action contract and security.

License and citation

Original code is licensed under Apache-2.0. Bundled third-party code keeps its original notices. See citation for how to cite Embodify.