solo-cli 0.6.1

CLI for Physical AI

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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📢 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) ✅

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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.

Docs | About

Features & MCP Module Catalog

Medical & Healthcare (5 modules)

Module	Description	Input	Output	Use Case	Availability
🫀 VitalSignsMCP	Real-time patient monitoring	Sensor streams, video	Heart rate, SpO2, alerts	ICU monitoring, telemedicine	Free
🩻 MedicalImagingMCP	CT/MRI/X-ray analysis	Medical scans	Diagnosis, annotations	Radiology, emergency medicine	Free
💪🏾 RehabTrackingMCP	Physical therapy progress	Motion capture	Exercise tracking, recovery metrics	Physical therapy, sports medicine	Free
💉 SurgicalGuidanceMCP	OR instrument tracking	Video feeds, RFID	Tool identification, workflow	Operating room management	Pro
💊 DrugInteractionMCP	Medication safety analysis	Prescription data	Interaction warnings, dosing	Pharmacy, clinical decision support	Pro

Agricultural & Environment (5 modules)

Module	Description	Input	Output	Use Case	Availability
🫛 CropHealthMCP	Precision agriculture analysis	Drone imagery, sensors	Disease detection, yield prediction	Farm management, crop insurance	Free
🪴 SoilAnalysisMCP	Soil condition monitoring	Sensor networks	pH, nutrients, moisture levels	Precision farming, sustainability	Free
🌦️ WeatherPredictionMCP	Localized weather forecasting	Meteorological data	Micro-climate predictions	Irrigation planning, harvest timing	Free
🐄 LivestockManagementMCP	Animal health and tracking	RFID, cameras, sensors	Health status, location, behavior	Ranch management, veterinary care	Pro
📦 SupplyChainMCP	Agricultural logistics	Market data, inventory	Pricing, routing, demand forecasting	Food distribution, commodity trading	Pro

Industrial & Manufacturing (5 modules)

Module	Description	Input	Output	Use Case	Availability
🔩 PredictiveMaintenanceMCP	Equipment failure prediction	Vibration, thermal, acoustic	Failure alerts, maintenance schedules	Manufacturing, oil & gas	Free
✅ QualityControlMCP	Automated defect detection	Product images, measurements	Pass/fail, defect classification	Assembly lines, quality assurance	Free
⚡️ EnergyOptimizationMCP	Smart power management	Smart meters, usage patterns	Cost reduction, efficiency gains	Factory automation, green manufacturing	Free
🦾 RoboticsControlMCP	Multi-robot coordination	Robot states, task queues	Work allocation, path planning	Automated warehouses, assembly	Pro
🤖 DigitalTwinMCP	Real-time process mirroring	Production telemetry	Performance insights, optimization	Process industries, smart factories	Pro

Robotics & Automation (5 modules)

Module	Description	Input	Output	Use Case	Availability
🛣️ NavigationMCP	SLAM and path planning	LiDAR, cameras, IMU	Maps, waypoints, obstacle avoidance	Autonomous vehicles, service robots	Free
🦿 ManipulationMCP	Object detection and grasping	RGB-D cameras	Grasp poses, object properties	Pick-and-place, warehouse automation	Free
👤 HumanRobotMCP	Social interaction and safety	Cameras, microphones	Emotion recognition, voice commands	Service robots, eldercare	Free
🐝 SwarmControlMCP	Multi-agent coordination	Network communications	Formation control, task allocation	Drone swarms, distributed robotics	Pro
🚘 AutonomousVehicleMCP	Self-driving capabilities	Vehicle sensors	Steering, braking, route planning	Autonomous cars, delivery robots	Pro

Educational & Research (5 modules)

Module	Description	Input	Output	Use Case	Availability
📝 LearningAnalyticsMCP	Student performance tracking	Interaction data, assessments	Progress insights, recommendations	Online education, skill assessment	Free
🥼 LabAssistantMCP	Scientific experiment guidance	Protocols, sensor data	Step-by-step instructions, safety alerts	Research labs, STEM education	Free
🧏🏼‍♀️ AccessibilityMCP	Inclusive learning support	Text, audio, video	Translations, adaptations	Special needs education, language learning	Free
👨🏻‍🔬 ResearchAutomationMCP	Data analysis and hypothesis generation	Research datasets	Statistical insights, literature reviews	Academic research, R&D	Pro
👩🏻‍🏫 VirtualTutorMCP	Personalized instruction	Learning patterns, preferences	Adaptive curricula, feedback	Personalized education, corporate training	Pro

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

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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