Awesome GitHub RepositoriesData Validation

Zod is a TypeScript-first schema declaration and validation library designed to ensure end-to-end data integrity. It functions as a runtime type guard, allowing developers to define complex data structures through a declarative, chainable syntax. By using these schema definitions, the library automatically derives static TypeScript types, eliminating the need for manual type duplication and ensuring that runtime data matches expected application contracts. The library distinguishes itself through functional schema composition, which enables the creation of hierarchical structures by nesting a

Pydantic is a data validation and serialization library that enforces schema constraints and performs type conversion on complex data structures. It utilizes standard Python type annotations to define data models, allowing developers to establish structured schemas that automatically enforce business rules and constraints without the need for custom domain-specific languages. The library distinguishes itself by transforming high-level model definitions into optimized code during initialization to minimize runtime overhead. It supports recursive validation for nested data structures and employ

Yup is a JavaScript schema validation library used to define data shapes and validate runtime values. It functions as an object schema validator and a data coercion engine, allowing developers to transform raw input values into desired types before performing validation checks. The library is distinguished by its support for dynamic schema validation, where rules can be adjusted at runtime based on sibling field values or external context. It also enables recursive data structuring for polymorphic fields and provides a system for extracting static TypeScript interfaces from runtime schema def

Joi is a JavaScript data validation library used to define schemas that ensure the structure and data types of objects remain consistent. It functions as a schema-based validator and object schema definition tool, preventing invalid information from entering an application by checking data against predefined constraints and rules. The library employs a chainable fluent interface and a constraint-based validation engine to build complex validation pipelines. It utilizes recursive tree traversal to validate nested data structures and a type-coercion pipeline to transform input values into the t

Ajv is a high-performance data validation framework that compiles JSON schemas into optimized, standalone JavaScript functions. By transforming declarative schema definitions into executable code, it eliminates runtime interpretation overhead and provides a secure, efficient way to enforce data integrity across both browser and server environments. The library distinguishes itself through its focus on performance and type safety. It employs advanced compilation techniques, including abstract syntax tree optimization and function caching, to ensure rapid validation. Beyond standard checks, it

Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin

Superstruct is a JavaScript and TypeScript data validation library used to verify that data structures match defined shapes and types. It functions as a composable schema builder and a TypeScript schema validator, ensuring that runtime data checks remain synchronized with static type definitions. The library features a data coercion engine that transforms input values or injects default values before the validation process is executed. It enables the creation of complex validation rules by nesting, merging, or omitting properties from existing structures. Its capabilities cover the validatio

Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine learning models and large language models. It functions as a monitoring tool for detecting data drift and quality degradation in tabular datasets, while providing a specialized analyzer for the faithfulness and correctness of retrieval augmented generation systems. The project distinguishes itself through an evaluation framework that utilizes judge models and custom rubrics to score language model outputs. It includes tools for iterative prompt optimization and the generation of

本项目是 JSON Schema 规范的 Python 实现，提供了一个用于验证数据实例是否符合定义模式的库。它作为一个数据验证框架，能够验证数据本身以及模式是否符合官方元模式，以确保结构正确性。 该库具有一个模式引用解析器，可将 URI 映射到定义，从而实现用于模块化模式管理的内部和远程引用解析。它专为可扩展性而设计，允许定义自定义关键字、自定义类型检查逻辑，并为专门的字符串格式注册新的验证函数。 该系统提供全面的错误报告，可识别数据实例中的所有违规行为，并将它们组织成树状层次结构。这允许对验证失败进行程序化查询、诊断元数据提取以及基于数据层次结构识别主要错误。 该实现通过特定版本的验证器类和模式方言规范支持多种规范草案。

本项目是一个 JSON Schema 验证库和框架，用于验证数据实例是否符合声明性定义。它作为一个验证引擎，强制执行结构约束和数据类型，同时也作为一个元验证器，确保模式定义本身在语法上符合官方元模式。 该库专为可扩展性而设计，允许用户通过将模式关键字映射到专门的可调用函数来定义自定义验证逻辑。它包括一个基于注册表的引用解析器，用于管理内部和外部 URI 引用，以便在不同文档间重用子模式。 该系统涵盖了广泛的验证功能，包括数据类型验证、针对电子邮件和 UUID 等类型的字符串格式检查，以及全面的错误报告。诊断工具提供嵌套错误跟踪和启发式过滤，以识别数据结构中最相关的失败。

Pandera is a data pipeline validation framework and statistical type validation tool. It functions as a library for defining and enforcing schemas on datasets to ensure data quality and consistency, specifically providing validation capabilities for Pandas dataframes. The project includes a schema inference tool that automates setup by analyzing existing dataset samples to generate validation schemas. It also serves as a synthetic data generator, creating artificial datasets based on predefined schemas to verify data-producing functions. The framework covers data engineering quality assuranc

Deepchecks 是一个机器学习模型验证框架和 MLOps 测试库。它作为 AI 数据质量套件和性能评估器，旨在从研究到生产全流程验证模型和数据集的完整性与性能。 该项目作为模型监控工具，用于跟踪生产环境中的数据漂移和性能下降。它允许创建自定义验证套件，并利用可插拔的检查架构在持续集成流水线中自动化质量检查。 该框架涵盖了广泛的功能，包括数据完整性验证、基于分布的漂移检测和模型版本比较。它为计算机视觉和自然语言处理提供了专门的分析，以及将验证指标转换为交互式视觉报告的报告工具。 该系统支持本地部署，以保持数据隐私和基础设施控制。

Lightweight, extensible data validation library for Python

Schema validation just got Pythonic

Python Data Structures for Humans™.

Clojure(Script) library for declarative data description and validation

CONTRIBUTIONS ONLY: Voluptuous, despite the name, is a Python data validation library.

High-performance data-driven data specification library for Clojure/Script.

A JSONSchema validator that uses code generation to be extremely fast

Library for exploring and validating machine learning data