dbt-autofix
CLI to autofix deprecations in dbt projects
Decision gist · record as of 2026-08-14
Yes. The tool is actively maintained, has low install friction, carries no known security vulnerabilities, and directly solves a real problem for dbt users managing deprecation warnings and Fusion migration. The dry-run mode and JSON output make it safe to evaluate before applying changes. License status is unclear, so verify licensing terms before use in commercial projects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (and below 3.14).
- dbt project must be present at the specified path.
- Low install friction with a pure Python wheel and straightforward dependencies.
License · maintenance · safety
(unclear)
last release 2026-07-31 (14 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 181,587 downloads/mo, #10,119 on PyPI
Alternatives
Verify before relying
# Install with uv (recommended)
uvx dbt-autofix
# Or with pip in a virtual environment
pip install dbt-autofix
dbt-autofix deprecations --path . --dry-run- Whether all deprecations listed in the coverage tables are actually implemented in version 0.21.6 or if coverage has evolved since the description was written.
- Whether the package dependency upgrade feature (mentioned as new in 0.17.0) remains functional and well-maintained in current releases.
- Performance characteristics when running against large dbt projects with many files.
What it is and what it does
dbt-autofix is a CLI tool that automatically detects and fixes deprecated configurations in dbt projects. It handles a wide range of deprecation types across YAML, SQL, and Python files—including property migrations, custom key handling, duplicate key removal, and Jinja block cleanup—making it easier to resolve deprecation warnings introduced in dbt v1.10 and prepare projects for dbt Fusion. The tool runs in dry-run mode by default and outputs results in human-readable or JSON format, allowing you to review changes before applying them.
The package depends on common utilities like click, typer, httpx, jinja2, pyyaml, and yamllint, plus dbt-specific tools (dbt-extractor, dbt-fusion-package-tools). It can also check and upgrade package dependencies for Fusion compatibility. For deprecations that cannot be automatically resolved, the documentation references an AGENTS.md guide for manual intervention strategies.
Use it for
- Automatically fix deprecation warnings when upgrading a dbt project to dbt v1.10 or later.
- Prepare a dbt project for migration to the dbt Fusion engine by scanning and updating incompatible configurations.
- Batch-update YAML files across a large dbt project to move custom configs under the correct meta or config blocks.
- Check installed dbt packages for Fusion compatibility and automatically upgrade them to compatible versions.
- Run in dry-run mode to audit deprecations in a project before committing any changes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The tool is actively maintained, has low install friction, carries no known security vulnerabilities, and directly solves a real problem for dbt users managing deprecation warnings and Fusion migration. The dry-run mode and JSON output make it safe to evaluate before applying changes. License status is unclear, so verify licensing terms before use in commercial projects.
Install
dbt-autofix on PyPI
Before you install
Low install friction with a pure Python wheel and straightforward dependencies. Active maintenance with a recent release within the last two weeks.
Requires Python 3.10 or later (and below 3.14). dbt project must be present at the specified path.
Quickstart
# Install with uv (recommended)
uvx dbt-autofix
# Or with pip in a virtual environment
pip install dbt-autofix
dbt-autofix deprecations --path . --dry-runVerify before relying
- Whether all deprecations listed in the coverage tables are actually implemented in version 0.21.6 or if coverage has evolved since the description was written.
- Whether the package dependency upgrade feature (mentioned as new in 0.17.0) remains functional and well-maintained in current releases.
- Performance characteristics when running against large dbt projects with many files.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesclickdbt-extractordbt-fusion-package-toolshttpxjinja2pyyamlrichruamel-yamltyperyamllint |
| Maintenance | Actively maintained 14 days since the last release |
| First released | |
| Downloads | 181,587 / month, #10,119 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dbt_autofix-0.21.6-py3-none-any.whl
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See also dbt-fusion-package-tools · dbt-osmosis · dbt-coverage · dbt-metabase · dbt-core-experimental-parser · dbt-sqlserver · sqlfluff-templater-dbt · deprecation · dbt-loom · dbt-core