Search Results for "schema"
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Showing 147 open source projects for "schema"
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Schemathesis
Guarantee flawless API functionality with test scenarios
...Schemathesis uses your API's schema to generate both valid and invalid test scenarios, helping you verify API compliance and catch potential issues. It also verifies examples from the schema itself. -
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Ariadne
Python library for implementing GraphQL servers
...If you are missing or unhappy with something, extend or easily swap with your own. Asynchronous resolvers and query execution. Subscriptions. Custom scalars, enums, and schema directives. Unions and interfaces. File uploads. Defining schema using SDL strings. Loading schema from .graphql files. WSGI middleware for implementing GraphQL in existing sites. -
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Flask-Migrate
SQLAlchemy database migrations for Flask applications using Alembic
Flask-Migrate is a Flask extension that integrates SQLAlchemy database migrations into Flask applications by leveraging Alembic, making schema changes easier and safer across development and deployment environments. It provides a set of command-line routines tied into Flask’s CLI such that developers can generate migration scripts, apply upgrades, and roll back changes with simple commands, keeping the database structure in sync with evolving application models. The extension handles versioning of the database schema so teams can track and share structural changes alongside application code, reducing manual migration errors and enabling collaborative development. ... -
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EdgeDB
A next-generation graph-relational database
...It aims to solve some hard design problems that make existing databases unnecessarily onerous to use. Powered by the Postgres query engine under the hood, EdgeDB thinks about schema the same way you do: as objects with properties connected by links. It's like a relational database with an object-oriented data model, or a graph database with strict schema. We call it a graph-relational database. The core unit of schema in the graph-relational model is the object type, analogous to a table in SQL. Object types contain properties and can be linked to other object types to form a schema graph. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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DataProfiler
Extract schema, statistics and entities from datasets
DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and more. ... -
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jsonschema
An implementation of the JSON Schema specification for Python
jsonschema is an implementation of the JSON Schema specification for Python. Full support for Draft 2020-12, Draft 2019-09, Draft 7, Draft 6, Draft 4 and Draft 3. Lazy validation that can iteratively report all validation errors. Programmatic querying of which properties or items failed validation. -
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Nexa SDK
Nexa SDK is a comprehensive toolkit for supporting ONNX and GGML
...It supports text generation, image generation, vision-language models (VLM), and speech-to-text (ASR), and text-to-speech (TTS) capabilities. Additionally, it offers an OpenAI-compatible API server with JSON schema mode for function calling and streaming support, and a user-friendly Streamlit UI. Users can run Nexa SDK in any device with Python environment, and GPU acceleration is supported, including CUDA, Metal, and ROCm. An executable version is also available. -
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Barfi
A Python visual Flow Based Programming library
A Python visual Flow-Based Programming library that integrates into your existing workflow. Barfi is a Flow-Based Programming environment that provides a graphical programming interface. It is integratable into your existing Python workflows. A schema is built using barfi.Blocks. Then the schema is executed with barfi.ComputeEngine. Each barfi.Block has some properties that enable the FBP and schema building. Firstly, each Block has Input and Output interfaces that link to other Blocks. Each Block can carry an executable function, that is specified by the user. This function can access/get data from the Input interface, perform computations or calculations, and set the Output interface. ... -
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Recap
Recap tracks and transform schemas across your whole application
Recap is a schema language and multi-language toolkit to track and transform schemas across your whole application. Your data passes through web services, databases, message brokers, and object stores. Recap describes these schemas in a single language, regardless of which system your data passes through. Recap schemas can be defined in YAML, TOML, JSON, XML, or any other compatible language. -
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Aerich
A database migrations tool for TortoiseORM, ready to production
Aerich is a lightweight database migration tool built for Tortoise ORM, an async ORM for Python. It handles schema evolution through versioned migrations, enabling developers to update and rollback database changes with ease. Designed for asyncio-based applications, Aerich is ideal for fast-moving Python projects that need simple migration control without heavy dependencies. -
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TypeChat
Library for building type-safe natural language interfaces with LLMs
...With the rise of large language models, developers can interpret user requests more easily, but they still face challenges related to output reliability, safety, and structured responses. TypeChat addresses these challenges by replacing traditional prompt engineering with a concept called schema engineering. Instead of writing complex prompts, developers define types that represent the intents supported by their applications. It then uses those type definitions to construct prompts for language models and translate user input into structured data that follows the defined schema. -
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Mashumaro
Fast and well tested serialization library on top of dataclasses
When using data classes, you often need to dump and load objects based on the schema you have. Mashumaro not only lets you save and load things in different ways, but it also does it super quickly. -
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SGR Agent Core
Schema-Guided Reasoning (SGR) has agentic system design
SGR Agent Core is an open-source framework for building intelligent AI research agents based on a methodology known as Schema-Guided Reasoning (SGR). The framework provides a core library that allows developers to design autonomous agents capable of structured reasoning and complex task execution. Instead of relying solely on free-form prompts, the system organizes reasoning processes around schemas that guide how agents analyze problems, gather information, and generate outputs. ... -
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MCP Neo4j
Model Context Protocol with Neo4j
An implementation of the Model Context Protocol with Neo4j, enabling natural language interactions with Neo4j databases and facilitating operations such as schema retrieval and Cypher query execution. -
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MCP Server DuckDB
A Model Context Protocol (MCP) server implementation for DuckDB
An MCP server implementation for DuckDB, providing database interaction capabilities through MCP tools, allowing operations like querying, table creation, and schema inspection. -
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ODMantic
Sync and Async ODM (Object Document Mapper) for MongoDB
Odmantic is an Object-Document Mapper (ODM) for MongoDB, designed for Python applications using Pydantic models, providing a seamless integration with type safety and validation. -
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Strawberry GraphQL
A GraphQL library for Python that leverages type annotations
...Django and ASGI support allow having your API deployed in production in a matter of minutes. The quick start method provides a server and CLI to get going quickly. Strawberry comes with a mypy plugin that enables statically type-checking your GraphQL schema. A Django view is provided for adding a GraphQL endpoint to your application. To support graphql Subscriptions over WebSockets you need to provide a WebSocket enabled server. Create a GraphQL schema defining a User type and a single query field user that will return a hardcoded user. -
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Supabase MCP Server
Query MCP enables end-to-end management of Supabase via chat interface
An open-source MCP server that enables comprehensive management of Supabase projects through natural language interactions, providing capabilities such as SQL execution, schema management, and API integration. -
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Claude SEO
Universal SEO skill for Claude Code
...Recommendations include first-principle observations, dependency relationships, falsifiability checks, and leading indicators. It can run full site audits, single-page reviews, schema validation, AI search readiness checks, sitemap workflows, local SEO analysis, and SEO reporting. Claude SEO is useful for agencies, in-house teams, and consultants who want repeatable SEO audits inside Claude Code. -
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Data Contract CLI
Enforce Data Contracts
...It uses YAML-based contract files to define the structure, meaning, quality rules, service levels, and connection details for a data product. The tool can connect to real data sources and check whether the actual dataset matches the schema, constraints, and quality expectations described in the contract. It supports both the Data Contract Specification and the Open Data Contract Standard, making it useful for teams standardizing data governance across different platforms. It can run locally, inside CI/CD pipelines, through Docker, or directly from Python code. Overall, it helps data producers and consumers treat data products more like APIs, with explicit expectations, automated checks, and clearer accountability. -
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Instructor
Structured outputs for llms
...Instructor is powered by Pydantic, which is powered by type hints. Schema validation and prompting are controlled by type annotations; less to learn, and less code to write, and it integrates with your IDE. -
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Prisma Client Python
Prisma Client Python is an auto-generated and fully type-safe database
prisma-client-py is an auto-generated Python ORM for Prisma schema files, bringing Prisma's developer experience to Python projects. It provides a type-safe, intuitive interface for interacting with databases like PostgreSQL and MySQL. prisma-client-py is ideal for Python developers who want static typing, code completion, and seamless integration with modern backend stacks. -
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Elementary
Open-source data observability for analytics engineers
...Gain immediate visibility, detect data issues, send actionable alerts, and understand the impact and root cause. Generate a data observability report, host it or share with your team. Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are configured and executed like native tests in dbt your project. Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. Inspect upstream and downstream dependencies to understand impact and root cause of data issues. -
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msgspec
A fast serialization and validation library, with builtin
msgspec is a fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML. -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live runtime context — to generate SQL queries that are both technically correct and semantically meaningful. The system then executes those queries against a database and interprets the results, returning human-friendly insights not just raw rows, while learning from errors and successes to reduce repeated mistakes.