> ## Documentation Index
> Fetch the complete documentation index at: https://dimensionalos.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Install DimOS, replay a robot session with no hardware, then run a real Unitree Go2.
In fifteen minutes you will: install DimOS, replay a recorded Unitree Go2 session in the Rerun viewer with no hardware, and then run the same stack on a real Go2.
This page is geared toward the Unitree Go2. If you were given a Galaxea A1Z arm instead, start with the [A1Z guide](/platforms/arms/a1z).
If you use coding agents (Claude Code, OpenClaw, or similar), point them at [`AGENTS.md`](https://github.com/dimensionalOS/dimos/blob/main/AGENTS.md) in the repo.
## System requirements
| Component | Minimum | Recommended |
| -------------- | ---------------------------- | ----------------------- |
| OS | Ubuntu 22.04, macOS 12.6+ | Ubuntu 24.04 |
| Python | 3.10 | 3.12 |
| RAM | 16 GB | 32 GB+ |
| Disk | 10 GB SSD | 25 GB+ SSD |
| CPU | 8-core Intel / AMD | 12+ cores |
| GPU (optional) | NVIDIA RTX 3000+ (8 GB VRAM) | RTX 4070+ (12 GB+ VRAM) |
> GPU is required only for perception, VLMs, and AI features. Optional for basic robot control and everything on this page.
## Step 1: Install
DimOS supports Python 3.10 to 3.12 and targets 3.12. The examples use [`uv`](https://docs.astral.sh/uv/); plain `python -m venv` and `pip` work too.
```bash theme={null}
# fetch large files on-demand instead of pulling all of them immediately
export GIT_LFS_SKIP_SMUDGE=1
git clone https://github.com/dimensionalOS/dimos.git
cd dimos
uv venv --python 3.12
source .venv/bin/activate
uv pip install --pre -e '.[base,unitree]'
```
This is an editable install from the repo: you get the `dimos` CLI for everything below, and you can open the code and write your own skills and blueprints right away. Extras keep installs lean: `base` is the runtime, modules, transports, and CLI; `unitree` adds WebRTC and skills for Go2 / G1 (real or replayed).
One-command interactive install (system deps + Python env):
```bash theme={null}
curl -fsSL https://raw.githubusercontent.com/dimensionalOS/dimos/main/scripts/install.sh | bash
```
Or install system dependencies yourself with the guide for your OS: [Ubuntu](/installation/ubuntu) (primary tested path), [macOS](/installation/osx) (Homebrew-based, less mature than Linux).
## Step 2: Replay a recorded session (no hardware)
```bash theme={null}
dimos --replay run unitree-go2
```
A Rerun viewer window opens showing the robot's camera, the live-built map, and the planned paths from a recorded office navigation session, with SLAM, costmap, and A-star planning running for real against the recorded sensors. On first run the window may stay black briefly while roughly **75 MB** of recorded data downloads.
While it runs, try the ops commands from a second terminal:
```bash theme={null}
dimos status # what is running
dimos log -f # follow logs
dimos stop # shut the stack down
```
## Step 3: Real robot
For a physical Unitree Go2 over WebRTC, set the robot's IP:
```bash theme={null}
export ROBOT_IP=
```
Newer Go2 firmware encrypts the connection. If you were given an AES key, set it the same way (robots on older firmware only need `ROBOT_IP`):
```bash theme={null}
export UNITREE_AES_128_KEY=
```
Then run the Go2 blueprint:
```bash theme={null}
dimos run unitree-go2
```
Do not skip the platform guide - network setup, latency, and safety habits matter: [Unitree Go2](/platforms/quadruped/go2/index). For other hardware, see [Platforms](/platforms/quadruped/go2/index) in the sidebar, including the [Galaxea A1Z arm](/platforms/arms/a1z).
## More things to run
| Command | What it does |
| ----------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
| `dimos --replay run unitree-go2` | Quadruped navigation replay: SLAM, costmap, A-star planning |
| `dimos --replay --replay-db go2_bigoffice run unitree-go2-memory` | Replay a long session while recording it into a queryable SQLite store ([memory2](/capabilities/memory)) |
| `dimos --simulation run unitree-go2-agentic` | Quadruped agent + 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 robot needed |
| `dimos run keyboard-teleop-xarm7` | Keyboard teleop with a mock xArm7 (`dimos[manipulation]` extra) |
`dimos list` prints every available blueprint. Blueprint reference: [Blueprints](/usage/blueprints).
## What next?
The mental model: modules, streams, and blueprints behind every workflow.
Natural language control: skills, MCP, and how to add your own.
Bring-up guides: Unitree Go2, G1, and manipulator arms.
The three memory systems and what each is for.