Solo CLI

📢 IMPORTANT: Package Renamed

This package has been renamed from solo-server to solo-cli!

If you're upgrading from solo-server, please see the Migration Guide for upgrade instructions.

• Old: pip install solo-server
• New: pip install solo-cli
• Config Migration: ~/.solo_server → ~/.solo
• CLI Command: Still solo (unchanged) ✅

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.

Docs | About

Features & MCP Module Catalog

Medical & Healthcare (5 modules)

Agricultural & Environment (5 modules)

Industrial & Manufacturing (5 modules)

Robotics & Automation (5 modules)

Educational & Research (5 modules)

Installation

Upgrading from solo-server? See the Migration Guide first.

First, install the uv package manager and setup a virtual environment as explained in prereq.md

#Choose one of the following for solo-cli installation
#1. Install solo cli from PyPI python manager
uv pip install solo-cli

#2. Install solo cli from source
git clone https://github.com/GetSoloTech/solo-cli.git
cd solo-cli
uv pip install -e .

# Solo commands
solo --help

💻 Solo Tech Installation Demo

For the full video, click here to watch on YouTube.

Quick Installation for Mac (Automated)

For Mac users, we provide an automated installation script that handles all the setup steps:

# Clone the repository
git clone https://github.com/GetSoloTech/solo-cli.git
cd solo-cli

# Make the installation script executable
chmod +x install_mac.sh

# Run the automated installation
./install_mac.sh

The script will automatically:

• Install uv package manager (version 0.9.3)
• Create a virtual environment with Python 3.12.12
• Set up environment variables for dependencies
• Install solo-cli in development mode with fallback handling for mujoco dependencies

After installation, activate the virtual environment:

source solo_venv/bin/activate

💻 Quick Installation Demo

For the full video, click here to watch on YouTube.

Solo Commands:

solo --help
                                                                                                           
╭─ Commands ───────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ setup      Set up Solo Server environment with interactive prompts and saves configuration to config.json.           │
│ 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 server is running correctly. Performs an inference test to verify server functionality.  │
│ stop       Stops Solo Server services. You can specify a server type with 'ollama', 'vllm', or 'llama.cpp'           │
│            Otherwise, all Solo services will be stopped.                                                             │
│ download   Downloads a Hugging Face model using the huggingface repo id.                                             │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Start server with SML models

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

Interactive Lerobot With Solo Server

Find more details here: Solo Robo Documentation

# Motors (both) → Calibrate (both) → Teleop
solo robo --motors all
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 a hub model id (with optional Teleop override)
solo robo --inference

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