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The Agentive Operating System for Physical Space

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[Docs](https://docs.dimensionalos.com) • [Hardware](#hardware) • [Installation](#installation) • [Agent CLI & MCP](#agent-cli-and-mcp) • [Blueprints](#blueprints) • [dimTELE: Remote Teleop](#dimtele-remote-teleop) • [Development](#development) ⚠️ **Pre-Release Beta** ⚠️

# About 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](AGENTS.md) and our [CLI and MCP](#agent-cli-and-mcp) interfaces to start building powerful Dimensional applications. # Installation ## Interactive Install ```sh skip curl -fsSL https://raw.githubusercontent.com/dimensionalOS/dimos/main/scripts/install.sh | bash ``` > See [`scripts/install.sh --help`](scripts/install.sh) 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](docs/installation/ubuntu.md) - 🟩 [NixOS / General Linux](docs/installation/nix.md) - 🟧 [macOS](docs/installation/osx.md) > Full system requirements, tested configs, and dependency tiers: [docs/requirements.md](docs/requirements.md) ## Python Install ### Quickstart ```bash 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 ``` ```bash # 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 ``` ```bash # Control a real robot (Unitree quadruped over WebRTC) export 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](https://ollama.com) + `ollama serve`) | > Full blueprint docs: [docs/usage/blueprints.md](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. ```bash 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](docs/usage/cli.md) # dimTELE: Remote Teleop **dimTELE** is hosted teleoperation for DimOS robots: operate them remotely from any browser or Quest headset over WebRTC. The robot dials out to a hosted broker, so you don't need to open any inbound ports on the robot's network. It works behind a home router, on Wi-Fi, wired LAN, or cellular. 1. Open [teleop.dimensionalos.com](https://teleop.dimensionalos.com), log in, and grab an API key (**API Keys → + New Key**). 2. Run a teleop blueprint on the robot, passing the key as `TRANSPORTS__BROKER__API_KEY`: ```bash # Robot dials out to the broker with your API key TRANSPORTS__BROKER__API_KEY= \ dimos run teleop-hosted-go2-transport ``` 3. Your robot appears under **Available Robots** — click **Connect** and drive the robot from the browser. | Blueprint | Notes | |-----------|-------| | `teleop-hosted-go2-transport` | Browser teleop — drive + camera + minimap + click-to-nav (recommended) | | `teleop-hosted-go2-multicam` | Adds a second RealSense, operator-selectable, mux'd into one video track | > Full guide: [dimTELE](docs/capabilities/teleoperation/hosted.md) • [WebRTC internals](dimos/teleop/hosted/README.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. ```py skip 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. ```py skip 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 - [Modules](docs/usage/modules.md) - [LCM](docs/usage/lcm.md) - [Blueprints](docs/usage/blueprints.md) - [Transports](docs/usage/transports/index.md) — LCM, SHM, DDS, Zenoh, ROS 2 - [Data Streams](docs/usage/data_streams/index.md) - [Configuration](docs/usage/configuration.md) - [Visualization](docs/usage/visualization.md) ## Demos DimOS Demo

DimOS Demo

# Development ## Develop on DimOS ```sh skip 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: - [C++](examples/language-interop/cpp/) - [Lua](examples/language-interop/lua/) - [TypeScript](examples/language-interop/ts/)