[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72318":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":27,"readmeContent":28,"aiSummary":29,"trendingCount":16,"starSnapshotCount":16,"syncStatus":30,"lastSyncTime":31,"discoverSource":32},72318,"dimos","dimensionalOS\u002Fdimos","dimensionalOS","Dimensional is the agentic operating system for physical space. Vibecode humanoids, quadrupeds, drones, and other hardware platforms in natural language and build multi-agent systems that work seamlessly with physical input (cameras, lidar, actuators).","https:\u002F\u002Fdimensionalos.com\u002F",null,"Python",3459,687,32,335,0,40,80,232,120,110.51,"Other",false,"main",true,[],"2026-06-12 04:01:04","\u003Cdiv align=\"center\">\n\n\u003Cimg width=\"1000\" alt=\"banner_bordered_trimmed\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F64f13b39-da06-4f58-add0-cfc44f04db4e\" \u002F>\n\n\u003Ch2>The Agentive Operating System for Physical Space\u003C\u002Fh2>\n\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1341146487186391173?style=flat-square&logo=discord&logoColor=white&label=Discord&color=5865F2)](https:\u002F\u002Fdiscord.gg\u002Fdimos)\n[![Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FdimensionalOS\u002Fdimos?style=flat-square)](https:\u002F\u002Fgithub.com\u002FdimensionalOS\u002Fdimos\u002Fstargazers)\n[![Forks](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002FdimensionalOS\u002Fdimos?style=flat-square)](https:\u002F\u002Fgithub.com\u002FdimensionalOS\u002Fdimos\u002Ffork)\n[![Contributors](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors\u002FdimensionalOS\u002Fdimos?style=flat-square)](https:\u002F\u002Fgithub.com\u002FdimensionalOS\u002Fdimos\u002Fgraphs\u002Fcontributors)\n![Nix](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNix-flakes-5277C3?style=flat-square&logo=NixOS&logoColor=white)\n![NixOS](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNixOS-supported-5277C3?style=flat-square&logo=NixOS&logoColor=white)\n![CUDA](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCUDA-supported-76B900?style=flat-square&logo=nvidia&logoColor=white)\n[![Docker](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDocker-ready-2496ED?style=flat-square&logo=docker&logoColor=white)](https:\u002F\u002Fwww.docker.com\u002F)\n\n\u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F23169\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F23169\" alt=\"dimensionalOS%2Fdimos | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\n\u003Cbig>\u003Cbig>\n\n[Hardware](#hardware) •\n[Installation](#installation) •\n[Agent CLI & MCP](#agent-cli-and-mcp) •\n[Blueprints](#blueprints) •\n[Development](#development)\n\n⚠️ **Pre-Release Beta** ⚠️\n\n\u003C\u002Fbig>\u003C\u002Fbig>\n\n\u003C\u002Fdiv>\n\n# Intro\n\nDimensional is the modern operating system for generalist robotics. We are setting the next-generation SDK standard, integrating with the majority of robot manufacturers.\n\nWith a simple install and no ROS required, build physical applications entirely in python that run on any humanoid, quadruped, or drone.\n\nDimensional 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.\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Ca href=\"docs\u002Fcapabilities\u002Fnavigation\u002Fnative\u002Findex.md\">\u003Cimg src=\"assets\u002Freadme\u002Fnavigation.gif\" alt=\"Navigation\" width=\"100%\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Cimg src=\"assets\u002Freadme\u002Fperception.png\" alt=\"Perception\" width=\"100%\">\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Ch3>\u003Ca href=\"docs\u002Fcapabilities\u002Fnavigation\u002Fnative\u002Findex.md\">Navigation and Mapping\u003C\u002Fa>\u003C\u002Fh3>\n      SLAM, dynamic obstacle avoidance, route planning, and autonomous exploration — via both DimOS native and ROS\u003Cbr>\u003Ca href=\"https:\u002F\u002Fx.com\u002Fstash_pomichter\u002Fstatus\u002F2010471593806545367\">Watch video\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Ch3>Perception\u003C\u002Fh3>\n      Detectors, 3d projections, VLMs, Audio processing\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Ca href=\"docs\u002Fcapabilities\u002Fagents\u002Freadme.md\">\u003Cimg src=\"assets\u002Freadme\u002Fagentic_control.gif\" alt=\"Agents\" width=\"100%\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Cimg src=\"assets\u002Freadme\u002Fspatial_memory.gif\" alt=\"Spatial Memory\" width=\"100%\">\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Ch3>\u003Ca href=\"docs\u002Fcapabilities\u002Fagents\u002Freadme.md\">Agentive Control, MCP\u003C\u002Fa>\u003C\u002Fh3>\n      \"hey Robot, go find the kitchen\"\u003Cbr>\u003Ca href=\"https:\u002F\u002Fx.com\u002Fstash_pomichter\u002Fstatus\u002F2015912688854200322\">Watch video\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Ch3>Spatial Memory\u003C\u002Fa>\u003C\u002Fh3>\n      Spatio-temporal RAG, Dynamic memory, Object localization and permanence\u003Cbr>\u003Ca href=\"https:\u002F\u002Fx.com\u002Fstash_pomichter\u002Fstatus\u002F1980741077205414328\">Watch video\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\n# Hardware\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"20%\">\n      \u003Ch3>Quadruped\u003C\u002Fh3>\n      \u003Cimg width=\"245\" height=\"1\" src=\"assets\u002Freadme\u002Fspacer.png\">\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      \u003Ch3>Humanoid\u003C\u002Fh3>\n      \u003Cimg width=\"245\" height=\"1\" src=\"assets\u002Freadme\u002Fspacer.png\">\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      \u003Ch3>Arm\u003C\u002Fh3>\n      \u003Cimg width=\"245\" height=\"1\" src=\"assets\u002Freadme\u002Fspacer.png\">\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      \u003Ch3>Drone\u003C\u002Fh3>\n      \u003Cimg width=\"245\" height=\"1\" src=\"assets\u002Freadme\u002Fspacer.png\">\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      \u003Ch3>Misc\u003C\u002Fh3>\n      \u003Cimg width=\"245\" height=\"1\" src=\"assets\u002Freadme\u002Fspacer.png\">\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"20%\">\n      🟩 \u003Ca href=\"docs\u002Fplatforms\u002Fquadruped\u002Fgo2\u002Findex.md\">Unitree Go2 pro\u002Fair\u003C\u002Fa>\u003Cbr>\n      🟥 \u003Ca href=\"dimos\u002Frobot\u002Funitree\u002Fb1\">Unitree B1\u003C\u002Fa>\u003Cbr>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      🟨 \u003Ca href=\"docs\u002Fplatforms\u002Fhumanoid\u002Fg1\u002Findex.md\">Unitree G1\u003C\u002Fa>\u003Cbr>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      🟨 \u003Ca href=\"docs\u002Fcapabilities\u002Fmanipulation\u002Freadme.md\">Xarm\u003C\u002Fa>\u003Cbr>\n      🟨 \u003Ca href=\"docs\u002Fcapabilities\u002Fmanipulation\u002Freadme.md\">AgileX Piper\u003C\u002Fa>\u003Cbr>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      🟧 \u003Ca href=\"dimos\u002Frobot\u002Fdrone\u002FREADME.md\">MAVLink\u003C\u002Fa>\u003Cbr>\n      🟧 \u003Ca href=\"dimos\u002Frobot\u002Fdrone\u002FREADME.md\">DJI Mavic\u003C\u002Fa>\u003Cbr>\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"20%\">\n      🟥 \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FdimensionalOS\u002FopenFT-sensor\">Force Torque Sensor\u003C\u002Fa>\u003Cbr>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\u003Cbr>\n\u003Cdiv align=\"right\">\n🟩 stable 🟨 beta 🟧 alpha 🟥 experimental\n\n\u003C\u002Fdiv>\n\n> [!IMPORTANT]\n> 🤖 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.\n\n# Installation\n\n## Interactive Install\n\n```sh skip\ncurl -fsSL https:\u002F\u002Fraw.githubusercontent.com\u002FdimensionalOS\u002Fdimos\u002Fmain\u002Fscripts\u002Finstall.sh | bash\n```\n\n> See [`scripts\u002Finstall.sh --help`](scripts\u002Finstall.sh) for non-interactive and advanced options.\n\n## Manual System Install\n\nTo set up your system dependencies, follow one of these guides:\n\n- 🟩 [Ubuntu 22.04 \u002F 24.04](docs\u002Finstallation\u002Fubuntu.md)\n- 🟩 [NixOS \u002F General Linux](docs\u002Finstallation\u002Fnix.md)\n- 🟧 [macOS](docs\u002Finstallation\u002Fosx.md)\n\n> Full system requirements, tested configs, and dependency tiers: [docs\u002Frequirements.md](docs\u002Frequirements.md)\n\n## Python Install\n\n### Quickstart\n\n```bash\nuv venv --python \"3.12\"\nsource .venv\u002Fbin\u002Factivate\nuv pip install 'dimos[base,unitree]'\n\n# Replay a recorded quadruped session (no hardware needed)\n# NOTE: First run will show a black rerun window while ~75 MB downloads from LFS\ndimos --replay run unitree-go2\n```\n\n```bash\n# Install with simulation support\nuv pip install 'dimos[base,unitree,sim]'\n\n# Run quadruped in MuJoCo simulation\ndimos --simulation run unitree-go2\n\n# Run humanoid in simulation\ndimos --simulation run unitree-g1-sim\n```\n\n```bash\n# Control a real robot (Unitree quadruped over WebRTC)\nexport ROBOT_IP=\u003CYOUR_ROBOT_IP>\ndimos run unitree-go2\n```\n\n# Featured Runfiles\n\n| Run command | What it does |\n|-------------|-------------|\n| `dimos --replay run unitree-go2` | Quadruped navigation replay — SLAM, costmap, A* planning |\n| `dimos --replay --replay-db go2_bigoffice run unitree-go2-memory` | Quadruped temporal memory replay |\n| `dimos --simulation run unitree-go2-agentic` | Quadruped agentic + MCP server in simulation |\n| `dimos --simulation run unitree-g1` | Humanoid in MuJoCo simulation |\n| `dimos --replay run drone-basic` | Drone video + telemetry replay |\n| `dimos --replay run drone-agentic` | Drone + LLM agent with flight skills (replay) |\n| `dimos run demo-camera` | Webcam demo — no hardware needed |\n| `dimos run keyboard-teleop-xarm7` | Keyboard teleop with mock xArm7 (requires `dimos[manipulation]` extra) |\n| `dimos --simulation run unitree-go2-agentic-ollama` | Quadruped agentic with local LLM (requires [Ollama](https:\u002F\u002Follama.com) + `ollama serve`) |\n\n> Full blueprint docs: [docs\u002Fusage\u002Fblueprints.md](docs\u002Fusage\u002Fblueprints.md)\n\n# Agent CLI and MCP\n\nThe `dimos` CLI manages the full lifecycle — run blueprints, inspect state, interact with agents, and call skills via MCP.\n\n```bash\ndimos run unitree-go2-agentic --daemon   # Start in background\ndimos status                              # Check what's running\ndimos log -f                              # Follow logs\ndimos agent-send \"explore the room\"       # Send agent a command\ndimos mcp list-tools                      # List available MCP skills\ndimos mcp call relative_move --arg forward=0.5  # Call a skill directly\ndimos stop                                # Shut down\n```\n\n> Full CLI reference: [docs\u002Fusage\u002Fcli.md](docs\u002Fusage\u002Fcli.md)\n\n\n# Usage\n\n## Use DimOS as a Library\n\nSee 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.\n\n```py skip\nimport threading, time, numpy as np\nfrom dimos.core.coordination.blueprints import autoconnect\nfrom dimos.core.core import rpc\nfrom dimos.core.module import Module\nfrom dimos.core.stream import In, Out\nfrom dimos.msgs.geometry_msgs import Twist\nfrom dimos.msgs.sensor_msgs import Image, ImageFormat\n\nclass RobotConnection(Module):\n    cmd_vel: In[Twist]\n    color_image: Out[Image]\n\n    @rpc\n    def start(self):\n        threading.Thread(target=self._image_loop, daemon=True).start()\n\n    def _image_loop(self):\n        while True:\n            img = Image.from_numpy(\n                np.zeros((120, 160, 3), np.uint8),\n                format=ImageFormat.RGB,\n                frame_id=\"camera_optical\",\n            )\n            self.color_image.publish(img)\n            time.sleep(0.2)\n\nclass Listener(Module):\n    color_image: In[Image]\n\n    @rpc\n    def start(self):\n        self.color_image.subscribe(lambda img: print(f\"image {img.width}x{img.height}\"))\n\nif __name__ == \"__main__\":\n    autoconnect(\n        RobotConnection.blueprint(),\n        Listener.blueprint(),\n    ).build().loop()\n```\n\n## Blueprints\n\nBlueprints are instructions for how to construct and wire modules. We compose them with\n`autoconnect(...)`, which connects streams by `(name, type)` and returns a `Blueprint`.\n\nBlueprints can be composed, remapped, and have transports overridden if `autoconnect()` fails due to conflicting variable names or `In[]` and `Out[]` message types.\n\nA blueprint example that connects the image stream from a robot to an MCP-backed LLM agent for reasoning and action execution.\n```py skip\nfrom dimos.core.coordination.blueprints import autoconnect\nfrom dimos.core.transport import LCMTransport\nfrom dimos.msgs.sensor_msgs import Image\nfrom dimos.robot.unitree.go2.connection import go2_connection\nfrom dimos.agents.mcp.mcp_client import McpClient\nfrom dimos.agents.mcp.mcp_server import McpServer\n\nblueprint = autoconnect(\n    go2_connection(),\n    McpServer.blueprint(),\n    McpClient.blueprint(),\n).transports({(\"color_image\", Image): LCMTransport(\"\u002Fcolor_image\", Image)})\n\n# Run the blueprint\nif __name__ == \"__main__\":\n    blueprint.build().loop()\n```\n\n## Library API\n\n- [Modules](docs\u002Fusage\u002Fmodules.md)\n- [LCM](docs\u002Fusage\u002Flcm.md)\n- [Blueprints](docs\u002Fusage\u002Fblueprints.md)\n- [Transports](docs\u002Fusage\u002Ftransports\u002Findex.md) — LCM, SHM, DDS, ROS 2\n- [Data Streams](docs\u002Fusage\u002Fdata_streams\u002FREADME.md)\n- [Configuration](docs\u002Fusage\u002Fconfiguration.md)\n- [Visualization](docs\u002Fusage\u002Fvisualization.md)\n\n## Demos\n\n\u003Cimg src=\"assets\u002Freadme\u002Fdimos_demo.gif\" alt=\"DimOS Demo\" width=\"100%\">\n\n# Development\n\n## Develop on DimOS\n\n```sh skip\nexport GIT_LFS_SKIP_SMUDGE=1\ngit clone https:\u002F\u002Fgithub.com\u002FdimensionalOS\u002Fdimos.git\ncd dimos\n\n# Run the default test suite (uv run syncs deps on demand; --all-groups\n# only needed for self-hosted tests \u002F mypy — see docs\u002Fdevelopment\u002Ftesting.md)\nuv run pytest --numprocesses=auto dimos\n```\n\n\n## Multi Language Support\n\nPython is our glue and prototyping language, but we support many languages via LCM interop.\n\nCheck our language interop examples:\n- [C++](examples\u002Flanguage-interop\u002Fcpp\u002F)\n- [Lua](examples\u002Flanguage-interop\u002Flua\u002F)\n- [TypeScript](examples\u002Flanguage-interop\u002Fts\u002F)\n","Dimensional 是一款专为物理空间设计的智能操作系统，支持通过自然语言对人形机器人、四足机器人、无人机等硬件平台进行编程，并构建与物理输入（如摄像头、激光雷达、执行器）无缝协作的多代理系统。其核心功能包括导航与制图、感知处理以及基于自然语言的控制逻辑编写能力，技术上采用了 Python 语言开发，并兼容 NixOS 和 CUDA 等环境。适用于需要灵活集成多种类型机器人并实现复杂交互场景的应用场合，如自动化仓库管理、服务型机器人开发等领域。",2,"2026-06-11 03:41:20","high_star"]