Fast JSON schema validator for Python. Contribute to horejsek/python-fastjsonschema development by creating an account on GitHub.

>>> validate(instance={"name" : "Eggs", "price" : 34.99}, schema=schema) >>> validate( ... instance={"name" : "Eggs", "price" : "Invalid"}, schema=schema, ... ) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValidationError: 'Invalid' is not of type 'number' It can also be used from the command line by installing check-jsonschema.

Otherwise validr will fallback to pure python mode. ... --------------------------timeits--------------------------- voluptuous:default 10000 loops cost 0.368s schema:default 1000 loops cost 0.318s json:loads-dumps 100000 loops cost 1.380s validr:default 100000 loops cost 0.719s validr:model 100000 loops cost 1.676s jsonschema:draft3 10000 loops cost 0.822s jsonschema:draft4 10000 loops cost 0.785s schematics:default 1000 loops cost 0.792s ---------------------------scores--------------------------- voluptuous:default 375 schema:default 43 json:loads-dumps 1000 validr:default 1918 validr:model 823 jsonschema:draft3 168 jsonschema:draft4 176 schematics:default 17

voluptuous - Voluptuous, despite the name, is a Python data validation library. It is primarily intended for validating data coming into Python as JSON, YAML, etc.

Python JSON validator · Raw · validate_json.py · This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters ·

Support only for Python 3.3 and higher. fastjsonschema implements validation of JSON documents by JSON schema. The library implements JSON schema drafts 04, 06, and 07. The main purpose is to have a really fast implementation.

Currently the most complete and compliant JSON Schema validator available for python is Jsonschema.

The cycle is repeated until all the files are correct or the program is interrupted (default: False) --report REPORTFILENAME Store validation report (in JSON format) in a file (default: None) --annotation ANNOTREPORT JSON Path (accepted by jsonpath-ng) to extract an annotation to include from validated JSON in the report (for instance, '$._id') (default: None) --verbose-report When this flag is enabled, the report also embeds the json contents which were validated (default: True) --error-report When this flag is enabled, the report only includes the entries with errors (default: False) --dot-r

Returns clean data when successful, detailed feedback for retries when not. python json ai openai json-parser gpt json-validation claude structured-output ai-tools llm anthropic llm-ops json-repair ...

This project provides a Python tool for JSON data validation against user-defined schemas, ensuring data conformity to specific rules and structures. It offers features such as checking required fields, enforcing field value constraints, and validating data integrity.

Validates and makes JSON documents easy to read.

Repair malformed JSON online, validate with optional JSON Schema, and format output with json_repair. Fix syntax errors and enforce schema-guided types/defaults.

A CLI and set of pre-commit hooks for jsonschema validation with built-in support for GitHub Workflows, Renovate, Azure Pipelines, and more! - python-jsonschema/check-jsonschema

ejsonschema is a python json validater based on the jsonschema python package that supports validation of instance documents against both a core schema and one or more "extended" schemas.

The CLI exposes the same behavior with json_repair --strict input.json (or piping data via stdin). In strict mode the parser raises ValueError as soon as it encounters structural issues such as duplicate keys, missing : separators, empty keys/values introduced by stray commas, multiple top-level elements, or other ambiguous constructs. This is useful when you just need validation with friendlier error messages while still benefiting from json_repair’s resilience elsewhere in your stack.

Instead of writing complex Python scripts for each new validation rule, you can define them declaratively in a simple, powerful JSON format. This allows teachers and curriculum developers to easily create and adapt validation scenarios without deep programming knowledge. The framework analyzes the code's Abstract Syntax Tree (AST), providing a deep and reliable way to enforce best ...

A CLI and set of pre-commit hooks for jsonschema validation with built-in support for GitHub Workflows, Renovate, Azure Pipelines, and more! ... There was an error while loading. Please reload this page. python-jsonschema/sphinx-json-schema-spec’s past year of commit activity

JSON Formatter and JSON Validator help to auto format JSON and validate your JSON text.

Essentially, if you have just newline characters (\n) in your JSON and paste it into JSONLint from a Windows computer, it may validate it as valid erroneously since Windows may need a carriage return (\r) as well to detect newlines properly. As a solution, either use direct URL input, or make sure your content's newlines match the architecture your system expects! Maintained by CircleCell. Thanks to Douglas Crockford of JSON and JS Lint, and Zach Carter, who built a pure JavaScript implementation. You can download the JSONLint source code on GitHub.

Python · Star 53 · OASIS TC Open Repository: Validator for STIX 2.0 JSON normative requirements and best practices · python json validation validator python-script stix cti oasis cyber-threat-intelligence stix2 stix-validator stix2-validator · Updated · Mar 16, 2025 ·