OpenAPI Tools for Python: Parse, Validate, Transform, and Analyze OpenAPI Descriptions
Working with OpenAPI descriptions programmatically in Python shouldn’t require stitching together incomplete libraries or building your own object model from scratch. In this video, Vladimír Gorej of @JenticAI walks through OpenAPI Tools, an open-source Python toolkit designed to standardize how OpenAPI descriptions are parsed, validated, transformed, and represented in code.
What you’ll learn:
Why the Python ecosystem has historically lacked a solid “default” OpenAPI library, and what motivated Jentic to build one.
Practical use cases: building your own validators/linters, analyzing specs, or building higher-level tooling (including “AI readiness” style scoring).
What’s included in the Jentic OpenAPI Tools monorepo:
** Parser (standards-compliant YAML parsing for OpenAPI)
** Validator (structure validation + linting integrations)
** Transformer (common transformations like de-referencing and bundling)
** Data Model (a semantic AST object model for deep analysis)
** Plus traversal utilities for working with the model.
When this is useful? Use Jentic OpenAPI Tools when you need to:
Parse OpenAPI specs reliably (YAML + OpenAPI-specific parsing rules)
Programmatically inspect and analyze OpenAPI structures
Validate and lint OpenAPI documents using common engines
Transform specs (dereference, bundle, normalize) as part of a pipeline
Build tooling on top of OpenAPI (e.g., quality gates, scorecards, automation, generators, AI-related readiness checks).
Links:
GitHub (Open Source Repo): https://github.com/jentic/jentic-open...
API Scorecard (built on this tooling): https://jentic.com/scorecard
#OpenAPI #Python #APIs #OpenSource #APIEngineering #OpenAPITools #YAML #APIValidation #APILinting #DeveloperTools #Jentic
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