solo-cli

Solo CLI

Fastest way to deploy Physical AI on your hardware

Simple CLI for Physical AI: Fine-tune and serve models in the physical world; optimized for edge & on-device operations

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Solo-CLI powers users of Physical AI Inference by providing access to efficiency tuned AI models in the real world. From language to vision to action models, Solo-CLI allows you to interact with cutting-edge, on-device AI directly within the terminal. It is tailored for context aware intelligence, specialized for mission-critical tasks, and tuned for the edge.

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[!TIP] Skip the terminal entirely. Rather than running setup commands yourself, tell an AI agent what you want and it handles everything — calibration, teleop, recording, training. Install the OpenClaw skills and use them in plain conversation:

Skill	What it does	Install
solo-cli-guide	Step-by-step tutor — walks you through each command, waits for your confirmation	clawhub install solo-cli-guide
solo-impl	Autonomous executor — runs every command for you, opens terminal windows automatically	clawhub install solo-impl

Requires OpenClaw. For a straightforward manual setup, follow the guide below.

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Installation

Prerequisites

Solo-CLI depends on solo-bot which uses Git LFS. If you haven't installed it before:

# Mac
brew install git-lfs

# Ubuntu / Debian
sudo apt-get install git-lfs

# Windows (pick one)
winget install GitHub.GitLFS

Then run once:

git lfs install

# Mac & Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows Powershell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Setup

# Create and activate a uv virtual environment (Python 3.12 recommended)
uv venv --python 3.12
source .venv/bin/activate  # Windows: .venv\Scripts\activate

Install

git clone https://github.com/GetSoloTech/solo-cli.git
cd solo-cli
uv pip install -e .

Solo Commands:

solo --help

╭─ Commands ────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ setup      Set up Solo CLI environment with interactive prompts and saves configuration to config.json.           │
│ login      Authenticate with Solo Hub using device-code login flow.                                               │
│ whoami     Display current user profile, organization, and subscription info.                                     │
│ download   Download a model from Solo Hub (format: org/model_name or solo:org/model_name).                        │
│ robo       Robotics operations: motor setup, calibration, teleoperation, data recording, training, and inference  │
│ serve      Start a model server with the specified model.                                                         │
│ status     Check running models, system status, and configuration.                                                │
│ list       List all downloaded models available in HuggingFace cache and Ollama.                                  │
│ test       Test if the Solo CLI is running correctly. Performs an inference test to verify server functionality.  │
│ stop       Stops Solo CLI services. You can specify a server type with 'ollama', 'vllm', or 'llama.cpp'           │
│            Otherwise, all Solo services will be stopped.                                                          │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Solo Hub - Authentication and Model Downloads

# Authenticate with Solo Hub
solo login

# Force re-authentication
solo login --force

# Check your profile and subscription
solo whoami

# Download a model from Solo Hub
solo download org/model_name

# Download to a specific local directory
solo download org/model_name --local-dir ./my-models

Interactive CLI for Robots

Find more details here: Solo Robo Docs

# Calibrate -> Teleop
solo robo --calibrate all
solo robo --teleop

# Record a new local dataset with prompts
solo robo --record

# Train ACT or SmolVLA Policy on a recorded dataset and push to Hub
solo robo --train

# Inference with VLA
solo robo --inference

# Replay a recorded action/episode
solo robo --replay

# Use -y or --yes to auto-use saved settings (skip prompts)
solo robo --teleop -y
solo robo --record --yes

Interactive CLI for Local AI Deployment

# Note that you will need Docker for solo serve
solo setup
solo serve --server ollama --model llama3.2:1b

API Reference

Find more details here: OpenAI -> OpenAI API Docs Ollama -> Ollama API Docs

vLLM & llama.cpp (OpenAI Compatible)

# Chat request endpoint
curl http://localhost:5070/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.2",
    "messages": [{"role": "user", "content": "Analyze sensor data"}],
    "tools": [{"type": "mcp", "name": "VitalSignsMCP"}]
  }'

Ollama

# Chat request endpoint
curl http://localhost:5070/api/chat -d '{
  "model": "llama3.2",
  "messages": [
    {
      "role": "user",
      "content": "why is the sky blue?"
    }
  ]
}'

Configuration

Navigate to config file .solo/config.json

{
    "hardware": {
        "use_gpu": false,
        "cpu_model": "Apple M3",
        "cpu_cores": 8,
        "memory_gb": 16.0,
        "gpu_vendor": "None",
        "gpu_model": "None",
        "gpu_memory": 0,
        "compute_backend": "CPU",
        "os": "Darwin"
    },
    "user": {
        "domain": "Software",
        "role": "Full-Stack Developer"
    },
    "server": {
        "type": "ollama",
        "ollama": {
            "default_port": 5070
        }
    },
    "active_model": {
        "server": "ollama",
        "name": "llama3.2:1b",
        "full_model_name": "llama3.2:1b",
        "port": 5070,
        "last_used": "2025-10-09 11:30:06"
    }
}

Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/name)
3. Commit changes (git commit -m 'Add feature')
4. Push to branch (git push origin feature/name)
5. Open Pull Request