---
id: gto
version: "1.10.1"
license: Apache License 2.0
license_treatment: permissive
maintenance: active
---

# gto — Version and deploy your models following GitOps principles

License: permissive · Maintenance: active · Downloads: 2.1M/mo

## What it is and what it does

GTO is a command-line and Python tool that transforms a Git repository into an artifact registry by managing annotated Git tags in a standard format. It lets you track artifact versions, define lifecycle stages (like 'staging' or 'production'), and emit signals that trigger CI/CD automation when versions change or stages are updated. The tool works by creating and reading Git tags rather than maintaining a separate database, making it lightweight and Git-native.

The package is designed to work alongside DVC and serves as a backbone for Git-based model registries in systems like Iterative Studio. It has 10 runtime dependencies including click for CLI handling, pydantic for data validation, and ruamel.yaml for configuration. The codebase is actively maintained, supports Python 3.10 through 3.14, and carries no known security vulnerabilities.

Use it for:

- Track model versions and their deployment status across environments using Git tags as the source of truth.
- Automate CI/CD pipelines to deploy new artifact versions when lifecycle stages are updated (e.g., promoting from staging to production).
- Manage release readiness by marking artifacts with stages that indicate their consumption status for specific environments.
- Integrate with DVC-based ML workflows to maintain a Git-native model registry without external infrastructure.
- Signal downstream systems about artifact changes without polling, using Git tags as events.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

GTO turns a Git repository into an artifact registry by creating annotated Git tags to track artifact versions, manage lifecycle stages, and signal CI/CD systems about deployments and readiness changes.

Yes, if you need Git-native artifact versioning and lifecycle management for CI/CD workflows. The package is actively maintained, has low install friction, carries no vulnerabilities, and integrates well with DVC-based ML pipelines. Pre-Alpha status means the API may change, but the core functionality is stable enough for production use in teams already using Git as their source of truth.

## Install

pip install gto
uv add gto
poetry add gto

## Installing gto

Before you install: Low friction: pure Python wheel with 10 runtime dependencies including click, pydantic, and ruamel.yaml. Active maintenance with a release 36 days ago and recent commits; requires Python 3.10 or later.

License in practice: Apache License 2.0 is permissive; you can use, modify, and distribute this package freely in commercial and private projects provided you include the license notice.

Quickstart:

pip install gto

from gto import api
# Create a Git tag for artifact versioning
api.assign("path/to/repo", "artifact-name", "v1.0.0", "stage-name")

Requires Python 3.10 or later and a Git repository to operate on.

Verify before relying:

- Whether the Python API is fully documented or primarily a CLI tool.
- Integration requirements or configuration needed to connect with DVC or other CI/CD systems.
- Performance characteristics when managing large numbers of tags or artifacts.

## Package facts

- License: Apache License 2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 2.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

git tag artifact registry, version control model deployment, gitops artifact tracking, git-based model registry, release lifecycle management git, ci/cd deployment signaling, git version tagging automation, gitops, artifact-registry, ci-cd-automation

[View on SkillFed](https://skillfed.io/packages/gto) · [View on PyPI](https://pypi.org/project/gto/)