The Agentive Operating System for Physical Space

Nix NixOS CUDA

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

⚠️ Pre-Release Beta ⚠️

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.


Navigation and Mapping SLAM, dynamic obstacle avoidance, route planning, and autonomous exploration — via both DimOS native and ROS Watch video	Perception Detectors, 3d projections, VLMs, Audio processing

Agentive Control, MCP "hey Robot, go find the kitchen" Watch video	Spatial Memory Spatio-temporal RAG, Dynamic memory, Object localization and permanence Watch video

Hardware

Quadruped	Humanoid	Arm	Drone	Misc
🟩 Unitree Go2 pro/air 🟥 Unitree B1	🟨 Unitree G1	🟨 Xarm 🟨 AgileX Piper	🟧 MAVLink 🟧 DJI Mavic	🟥 Force Torque Sensor

🟩 stable 🟨 beta 🟧 alpha 🟥 experimental

[!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:

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-db go2_bigoffice run unitree-go2-memory`	Quadruped temporal memory replay
`dimos --simulation run unitree-go2-agentic`	Quadruped agentic + MCP server in simulation
`dimos --simulation run unitree-g1-sim`	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 --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.coordination.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_image.subscribe(lambda img: print(f"image {img.width}x{img.height}"))

if __name__ == "__main__":
    autoconnect(
        RobotConnection.blueprint(),
        Listener.blueprint(),
    ).build().loop()

Blueprints

Blueprints are instructions for how to construct and wire modules. We compose them with autoconnect(...), which connects streams by (name, type) and returns a Blueprint.

Blueprints can be composed, remapped, and have transports overridden if autoconnect() fails due to conflicting variable names or In[] and Out[] message types.

A blueprint example that connects the image stream from a robot to an MCP-backed LLM agent for reasoning and action execution.

from dimos.core.coordination.blueprints import autoconnect
from dimos.core.transport import LCMTransport
from dimos.msgs.sensor_msgs import Image
from dimos.robot.unitree.go2.connection import go2_connection
from dimos.agents.mcp.mcp_client import McpClient
from dimos.agents.mcp.mcp_server import McpServer

blueprint = autoconnect(
    go2_connection(),
    McpServer.blueprint(),
    McpClient.blueprint(),
).transports({("color_image", Image): LCMTransport("/color_image", Image)})

# Run the blueprint
if __name__ == "__main__":
    blueprint.build().loop()

Library API

Demos

Development

Develop on DimOS

export GIT_LFS_SKIP_SMUDGE=1
git clone https://github.com/dimensionalOS/dimos.git
cd dimos

# Run the default test suite (uv run syncs deps on demand; --all-groups
# only needed for self-hosted tests / mypy — see docs/development/testing.md)
uv run pytest --numprocesses=auto dimos

Multi Language Support

Python is our glue and prototyping language, but we support many languages via LCM interop.

Check our language interop examples:

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Release history Release notifications | RSS feed

0.0.14b1 pre-release

Jul 21, 2026

0.0.13.post1

Jul 1, 2026

This release

0.0.13

Jun 30, 2026

0.0.12.post2

May 16, 2026

0.0.12.post1

May 16, 2026

0.0.12

May 9, 2026

0.0.11

Mar 12, 2026

0.0.10.post2

Feb 27, 2026

0.0.10.post1

Feb 21, 2026

0.0.10

Feb 20, 2026

0.0.9

Jan 23, 2026

0.0.8

Jan 23, 2026

0.0.0

Oct 15, 2024

dimos 0.0.13

The Agentive Operating System for Physical Space

Intro

Navigation and Mapping

Perception

Agentive Control, MCP

Spatial Memory

Hardware

Quadruped

Humanoid

Arm

Drone

Misc

Installation

Interactive Install

Manual System Install

Python Install

Quickstart

Featured Runfiles

Agent CLI and MCP

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Use DimOS as a Library

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Develop on DimOS

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1 maintainer

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