---
id: collate-dbt-artifacts-parser
version: "0.1.4"
license: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record)
license_treatment: permissive
maintenance: aging
---

# collate-dbt-artifacts-parser — A dbt artifacts parser in python

License: permissive · Maintenance: aging · Downloads: 235.6K/mo

## What it is and what it does

This package provides Python classes to deserialize dbt artifact JSON files—catalog.json, manifest.json, run-results.json, and sources.json—into typed pydantic objects. It is designed for dbt-core and supports multiple versions of each artifact format, allowing you to programmatically inspect dbt metadata, lineage, and execution results without manual JSON parsing. The package depends only on pydantic and supports Python 3.8 and later.

The parser is organized by artifact type and version, with classes like ManifestV1 through ManifestV12, RunResultsV1 through RunResultsV6, and corresponding catalog and sources parsers. You load a JSON file, pass it to a parser function, and receive a typed object you can query and manipulate in Python code. The package is in beta and has not been updated in several months, so it may lag behind the latest dbt releases.

Use it for:

- Programmatically extract model lineage and dependencies from manifest.json for documentation or impact analysis
- Parse run-results.json to programmatically check test outcomes and model execution status after a dbt run
- Load catalog.json to query column-level metadata and table properties in a Python application
- Build custom dbt metadata dashboards or reporting tools by accessing structured artifact data

## Worth the install?

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

Parses dbt artifact JSON files (catalog, manifest, run-results, sources) into typed Python objects using pydantic for structured access to dbt metadata.

Yes, if you work with dbt and need to programmatically access artifact metadata in Python. The install is frictionless and the license is permissive. However, verify that version 0.1.4 matches your dbt and pydantic versions—the package documentation warns that pydantic v1 is not supported for dbt 1.9 or later. The aging maintenance status (no updates in 263 days) means you should test compatibility with your dbt version before relying on it in production.

## Install

pip install collate-dbt-artifacts-parser
uv add collate-dbt-artifacts-parser
poetry add collate-dbt-artifacts-parser

## Installing collate-dbt-artifacts-parser

Before you install: Low friction install with a single pydantic dependency. The package is in beta status and has not been updated in 263 days, so expect slower response to issues or feature requests.

License in practice: Apache License 2.0 permits commercial and private use with minimal restrictions; you must include a copy of the license and note any modifications you make to the code.

Quickstart:

pip install collate-dbt-artifacts-parser

import json
from collate_dbt_artifacts_parser.parser import parse_manifest

with open("path/to/manifest.json", "r") as fp:
    manifest_dict = json.load(fp)
    manifest_obj = parse_manifest(manifest=manifest_dict)

Requires pydantic; documentation warns that pydantic v1 is not supported for dbt 1.9 or later

Verify before relying:

- Whether version 0.1.4 supports dbt 1.9 or requires migration to pydantic v2 as documented
- Actual test coverage and stability of the parser across all supported manifest versions (V1–V12)

## Package facts

- License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 235.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

dbt artifacts parser, parse dbt manifest json, dbt catalog parser, dbt run results parser, dbt metadata extraction, dbt json to python objects, dbt artifact validation, dbt, metadata-extraction, data-engineering

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