<div align="center"> <img width="1000" alt="banner_bordered_trimmed" src="https://github.com/user-attachments/assets/64f13b39-da06-4f58-add0-cfc44f04db4e" /> <h2>The Agentive Operating System for Physical Space</h2>

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Hardware • Installation • Agent CLI & MCP • Blueprints • Development

⚠️ Pre-Release Beta ⚠️

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Intro

Dimensional is the modern operating system for generalist robotics. We are setting the next-generation SDK standard, integrating with the majority of robot manufacturers.

With a simple install and no ROS required, build physical applications entirely in python that run on any humanoid, quadruped, or drone.

Dimensional is agent native -- "vibecode" your robots in natural language and build (local & hosted) multi-agent systems that work seamlessly with your hardware. Agents run as native modules — subscribing to any embedded stream, from perception (lidar, camera) and spatial memory down to control loops and motor drivers.

<table> <tr> <td align="center" width="50%"> <a href="docs/capabilities/navigation/native/index.md"><img src="assets/readme/navigation.gif" alt="Navigation" width="100%"></a> </td> <td align="center" width="50%"> <img src="assets/readme/perception.png" alt="Perception" width="100%"> </td> </tr> <tr> <td align="center" width="50%"> <h3><a href="docs/capabilities/navigation/native/index.md">Navigation and Mapping</a></h3> SLAM, dynamic obstacle avoidance, route planning, and autonomous exploration — via both DimOS native and ROS<br><a href="https://x.com/stash_pomichter/status/2010471593806545367">Watch video</a> </td> <td align="center" width="50%"> <h3>Perception</h3> Detectors, 3d projections, VLMs, Audio processing </td> </tr> <tr> <td align="center" width="50%"> <a href="docs/capabilities/agents/readme.md"><img src="assets/readme/agentic_control.gif" alt="Agents" width="100%"></a> </td> <td align="center" width="50%"> <img src="assets/readme/spatial_memory.gif" alt="Spatial Memory" width="100%"> </td> </tr> <tr> <td align="center" width="50%"> <h3><a href="docs/capabilities/agents/readme.md">Agentive Control, MCP</a></h3> "hey Robot, go find the kitchen"<br><a href="https://x.com/stash_pomichter/status/2015912688854200322">Watch video</a> </td> <td align="center" width="50%"> <h3>Spatial Memory</a></h3> Spatio-temporal RAG, Dynamic memory, Object localization and permanence<br><a href="https://x.com/stash_pomichter/status/1980741077205414328">Watch video</a> </td> </tr> </table>

Hardware

<table> <tr> <td align="center" width="20%"> <h3>Quadruped</h3> <img width="245" height="1" src="assets/readme/spacer.png"> </td> <td align="center" width="20%"> <h3>Humanoid</h3> <img width="245" height="1" src="assets/readme/spacer.png"> </td> <td align="center" width="20%"> <h3>Arm</h3> <img width="245" height="1" src="assets/readme/spacer.png"> </td> <td align="center" width="20%"> <h3>Drone</h3> <img width="245" height="1" src="assets/readme/spacer.png"> </td> <td align="center" width="20%"> <h3>Misc</h3> <img width="245" height="1" src="assets/readme/spacer.png"> </td> </tr> <tr> <td align="center" width="20%"> 🟩 <a href="docs/platforms/quadruped/go2/index.md">Unitree Go2 pro/air</a><br> 🟥 <a href="dimos/robot/unitree/b1">Unitree B1</a><br> </td> <td align="center" width="20%"> 🟨 <a href="docs/platforms/humanoid/g1/index.md">Unitree G1</a><br> </td> <td align="center" width="20%"> 🟨 <a href="docs/capabilities/manipulation/readme.md">Xarm</a><br> 🟨 <a href="docs/capabilities/manipulation/readme.md">AgileX Piper</a><br> </td> <td align="center" width="20%"> 🟧 <a href="dimos/robot/drone/README.md">MAVLink</a><br> 🟧 <a href="dimos/robot/drone/README.md">DJI Mavic</a><br> </td> <td align="center" width="20%"> 🟥 <a href="https://github.com/dimensionalOS/openFT-sensor">Force Torque Sensor</a><br> </td> </tr> </table> <br> <div align="right"> 🟩 stable 🟨 beta 🟧 alpha 🟥 experimental </div>

[!IMPORTANT] 🤖 Direct your favorite Agent (OpenClaw, Claude Code, etc.) to AGENTS.md and our CLI and MCP interfaces to start building powerful Dimensional applications.

Installation

Interactive Install

curl -fsSL https://raw.githubusercontent.com/dimensionalOS/dimos/main/scripts/install.sh | bash

See scripts/install.sh --help for non-interactive and advanced options.

Manual System Install

To set up your system dependencies, follow one of these guides:

• 🟩 Ubuntu 22.04 / 24.04
• 🟩 NixOS / General Linux
• 🟧 macOS

Full system requirements, tested configs, and dependency tiers: docs/requirements.md

Python Install

Quickstart

uv venv --python "3.12"
source .venv/bin/activate
uv pip install 'dimos[base,unitree]'

# Replay a recorded quadruped session (no hardware needed)
# NOTE: First run will show a black rerun window while ~75 MB downloads from LFS
dimos --replay run unitree-go2

# Install with simulation support
uv pip install 'dimos[base,unitree,sim]'

# Run quadruped in MuJoCo simulation
dimos --simulation run unitree-go2

# Run humanoid in simulation
dimos --simulation run unitree-g1-sim

# Control a real robot (Unitree quadruped over WebRTC)
export ROBOT_IP=<YOUR_ROBOT_IP>
dimos run unitree-go2

Featured Runfiles

| Run command | What it does | |-------------|-------------| | dimos --replay run unitree-go2 | Quadruped navigation replay — SLAM, costmap, A* planning | | dimos --replay --replay-dir unitree_go2_office_walk2 run unitree-go2-temporal-memory | Quadruped temporal memory replay | | dimos --simulation run unitree-go2-agentic-mcp | Quadruped agentic + MCP server in simulation | | dimos --simulation run unitree-g1 | Humanoid in MuJoCo simulation | | dimos --replay run drone-basic | Drone video + telemetry replay | | dimos --replay run drone-agentic | Drone + LLM agent with flight skills (replay) | | dimos run demo-camera | Webcam demo — no hardware needed | | dimos run keyboard-teleop-xarm7 | Keyboard teleop with mock xArm7 (requires dimos[manipulation] extra) | | dimos --simulation run unitree-go2-agentic-ollama | Quadruped agentic with local LLM (requires Ollama + ollama serve) |

Full blueprint docs: docs/usage/blueprints.md

Agent CLI and MCP

The dimos CLI manages the full lifecycle — run blueprints, inspect state, interact with agents, and call skills via MCP.

dimos run unitree-go2-agentic-mcp --daemon   # Start in background
dimos status                              # Check what's running
dimos log -f                              # Follow logs
dimos agent-send "explore the room"       # Send agent a command
dimos mcp list-tools                      # List available MCP skills
dimos mcp call relative_move --arg forward=0.5  # Call a skill directly
dimos stop                                # Shut down

Full CLI reference: docs/usage/cli.md

Usage

Use DimOS as a Library

See below a simple robot connection module that sends streams of continuous cmd_vel to the robot and receives color_image to a simple Listener module. DimOS Modules are subsystems on a robot that communicate with other modules using standardized messages.

import threading, time, numpy as np
from dimos.core.blueprints import autoconnect
from dimos.core.core import rpc
from dimos.core.module import Module
from dimos.core.stream import In, Out
from dimos.msgs.geometry_msgs import Twist
from dimos.msgs.sensor_msgs import Image, ImageFormat

class RobotConnection(Module):
    cmd_vel: In[Twist]
    color_image: Out[Image]

    @rpc
    def start(self):
        threading.Thread(target=self._image_loop, daemon=True).start()

    def _image_loop(self):
        while True:
            img = Image.from_numpy(
                np.zeros((120, 160, 3), np.uint8),
                format=ImageFormat.RGB,
                frame_id="camera_optical",
            )
            self.color_image.publish(img)
            time.sleep(0.2)

class Listener(Module):
    color_image: In[Image]

    @rpc
    def start(self):
        self.color_imag