Command line interface and SDKs for managing Prime Intellect GPU resources, sandboxes, and environments.
# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install prime
uv tool install prime
# Authenticate
prime login
# Browse verified environments
prime env list
# List available GPU resources
prime availability list
- Environments - Access hundreds of verified environments on our community hub
- GPU Resource Management - Query and filter available GPU resources
- Pod Management - Create, monitor, and terminate compute pods
- Sandboxes - Easily run AI-generated code in the cloud
- SSH Access - Direct SSH access to running pods
- Team Support - Manage resources across team environments
First, install uv if you haven't already:
curl -LsSf https://astral.sh/uv/install.sh | sh
Then install prime:
uv tool install prime
pip install prime
If you only need the sandboxes SDK (lightweight, ~50KB):
uv pip install prime-sandboxes
See prime-sandboxes documentation for SDK usage.
# Interactive mode (recommended - hides input)
prime config set-api-key
# Non-interactive mode (for automation)
prime config set-api-key YOUR_API_KEY
# Environment variable (most secure for scripts)
export PRIME_API_KEY="your-api-key-here"
# Configure SSH key for pod access
prime config set-ssh-key-path
# View current configuration
prime config view
Security Note: When using non-interactive mode, the API key may be visible in your shell history. For enhanced security, use interactive mode or environment variables.
Access hundreds of verified environments on our community hub with deep integrations with sandboxes, training, and evaluation stack.
# Browse available environments
prime env list
# View environment details
prime env info <environment-name>
# Install an environment locally
prime env install <environment-name>
# Create and push your own environment
prime env init my-environment
prime env push my-environment
# List all available GPUs
prime availability list
# Filter by GPU type
prime availability list --gpu-type H100_80GB
# Show available GPU types
prime availability gpu-types
# List your pods
prime pods list
# Create a pod
prime pods create
prime pods create --id <ID> # With specific GPU config
prime pods create --name my-pod # With custom name
# Monitor and manage pods
prime pods status <pod-id>
prime pods terminate <pod-id>
prime pods ssh <pod-id>
# List teams
prime teams list
# Set team context
prime config set-team-id
# Clone the repository
git clone https://github.com/PrimeIntellect-ai/prime-cli
cd prime-cli
# Set up workspace (installs all packages in editable mode)
uv sync
# Install CLI globally in editable mode
uv tool install -e packages/prime
# Now you can use the CLI directly
prime --help
# Run tests
uv run pytest packages/prime/tests
uv run pytest packages/prime-sandboxes/tests
All packages (prime-core, prime-sandboxes, prime) are installed in editable mode. Changes to code are immediately reflected.
This monorepo contains two independently versioned packages: prime
(CLI + full SDK) and prime-sandboxes
(lightweight SDK).
Versions are single-sourced from each package's __init__.py
file:
- prime:
packages/prime/src/prime_cli/__init__.py
- prime-sandboxes:
packages/prime-sandboxes/src/prime_sandboxes/__init__.py
- Update the
__version__
string in the appropriate__init__.py
file - Commit and push the change
Tagging and publishing to PyPI is handled automatically by CI.
When releasing prime
, consider whether prime-sandboxes
should also be bumped, as prime
depends on prime-sandboxes
. The packages can be released independently or together depending on what changed.
This project is licensed under the MIT License - see the LICENSE file for details.