jambo
Jambo - JSON Schema to Pydantic Converter
Stars: 56
Jambo is a Python package that automatically converts JSON Schema definitions into Pydantic models. It streamlines schema validation and enforces type safety using Pydantic's validation features. The tool supports various JSON Schema features like strings, integers, floats, booleans, arrays, nested objects, and more. It enforces constraints such as minLength, maxLength, pattern, minimum, maximum, uniqueItems, and provides a zero-config approach for generating models. Jambo is designed to simplify the process of dynamically generating Pydantic models for AI frameworks.
README:
Jambo is a Python package that automatically converts JSON Schema definitions into Pydantic models. It's designed to streamline schema validation and enforce type safety using Pydantic's powerful validation features.
Created to simplifying the process of dynamically generating Pydantic models for AI frameworks like LangChain, CrewAI, and others.
- ✅ Convert JSON Schema into Pydantic models dynamically;
- 🔒 Supports validation for:
- strings
- integers
- floats
- booleans
- arrays
- nested objects
- allOf
- anyOf
- oneOf
- ref
- enum
- const
- ⚙️ Enforces constraints like
minLength,maxLength,pattern,minimum,maximum,uniqueItems, and more; - 📦 Zero config — just pass your schema and get a model.
pip install jambofrom jambo import SchemaConverter
schema = {
"title": "Person",
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"},
},
"required": ["name"],
}
Person = SchemaConverter.build(schema)
obj = Person(name="Alice", age=30)
print(obj)Following are some examples of how to use Jambo to create Pydantic models with various JSON Schema features, but for more information, please refer to the documentation.
from jambo import SchemaConverter
schema = {
"title": "EmailExample",
"type": "object",
"properties": {
"email": {
"type": "string",
"minLength": 5,
"maxLength": 50,
"pattern": r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$",
},
},
"required": ["email"],
}
Model = SchemaConverter.build(schema)
obj = Model(email="[email protected]")
print(obj)from jambo import SchemaConverter
schema = {
"title": "AgeExample",
"type": "object",
"properties": {
"age": {"type": "integer", "minimum": 0, "maximum": 120}
},
"required": ["age"],
}
Model = SchemaConverter.build(schema)
obj = Model(age=25)
print(obj)from jambo import SchemaConverter
schema = {
"title": "NestedObjectExample",
"type": "object",
"properties": {
"address": {
"type": "object",
"properties": {
"street": {"type": "string"},
"city": {"type": "string"},
},
"required": ["street", "city"],
}
},
"required": ["address"],
}
Model = SchemaConverter.build(schema)
obj = Model(address={"street": "Main St", "city": "Gotham"})
print(obj)from jambo import SchemaConverter
schema = {
"title": "person",
"$ref": "#/$defs/person",
"$defs": {
"person": {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"},
"emergency_contact": {
"$ref": "#/$defs/person",
},
},
}
},
}
model = SchemaConverter.build(schema)
obj = model(
name="John",
age=30,
emergency_contact=model(
name="Jane",
age=28,
),
)To run the test suite:
poe testsOr manually:
python -m unittest discover -s tests -vTo set up the project locally:
- Clone the repository
- Install uv (if not already installed)
- Install dependencies:
uv sync- Set up git hooks:
poe create-hooks- [ ] Better error reporting for unsupported schema types
PRs are welcome! This project uses MIT for licensing, so feel free to fork and modify as you see fit.
MIT License.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for jambo
Similar Open Source Tools
jambo
Jambo is a Python package that automatically converts JSON Schema definitions into Pydantic models. It streamlines schema validation and enforces type safety using Pydantic's validation features. The tool supports various JSON Schema features like strings, integers, floats, booleans, arrays, nested objects, and more. It enforces constraints such as minLength, maxLength, pattern, minimum, maximum, uniqueItems, and provides a zero-config approach for generating models. Jambo is designed to simplify the process of dynamically generating Pydantic models for AI frameworks.
python-utcp
The Universal Tool Calling Protocol (UTCP) is a secure and scalable standard for defining and interacting with tools across various communication protocols. UTCP emphasizes scalability, extensibility, interoperability, and ease of use. It offers a modular core with a plugin-based architecture, making it extensible, testable, and easy to package. The repository contains the complete UTCP Python implementation with core components and protocol-specific plugins for HTTP, CLI, Model Context Protocol, file-based tools, and more.
manga-image-translator
Translate texts in manga/images. Some manga/images will never be translated, therefore this project is born. * Image/Manga Translator * Samples * Online Demo * Disclaimer * Installation * Pip/venv * Poetry * Additional instructions for **Windows** * Docker * Hosting the web server * Using as CLI * Setting Translation Secrets * Using with Nvidia GPU * Building locally * Usage * Batch mode (default) * Demo mode * Web Mode * Api Mode * Related Projects * Docs * Recommended Modules * Tips to improve translation quality * Options * Language Code Reference * Translators Reference * GPT Config Reference * Using Gimp for rendering * Api Documentation * Synchronous mode * Asynchronous mode * Manual translation * Next steps * Support Us * Thanks To All Our Contributors :
RagaAI-Catalyst
RagaAI Catalyst is a comprehensive platform designed to enhance the management and optimization of LLM projects. It offers features such as project management, dataset management, evaluation management, trace management, prompt management, synthetic data generation, and guardrail management. These functionalities enable efficient evaluation and safeguarding of LLM applications.
Bindu
Bindu is an operating layer for AI agents that provides identity, communication, and payment capabilities. It delivers a production-ready service with a convenient API to connect, authenticate, and orchestrate agents across distributed systems using open protocols: A2A, AP2, and X402. Built with a distributed architecture, Bindu makes it fast to develop and easy to integrate with any AI framework. Transform any agent framework into a fully interoperable service for communication, collaboration, and commerce in the Internet of Agents.
gp.nvim
Gp.nvim (GPT prompt) Neovim AI plugin provides a seamless integration of GPT models into Neovim, offering features like streaming responses, extensibility via hook functions, minimal dependencies, ChatGPT-like sessions, instructable text/code operations, speech-to-text support, and image generation directly within Neovim. The plugin aims to enhance the Neovim experience by leveraging the power of AI models in a user-friendly and native way.
crush
Crush is a versatile tool designed to enhance coding workflows in your terminal. It offers support for multiple LLMs, allows for flexible switching between models, and enables session-based work management. Crush is extensible through MCPs and works across various operating systems. It can be installed using package managers like Homebrew and NPM, or downloaded directly. Crush supports various APIs like Anthropic, OpenAI, Groq, and Google Gemini, and allows for customization through environment variables. The tool can be configured locally or globally, and supports LSPs for additional context. Crush also provides options for ignoring files, allowing tools, and configuring local models. It respects `.gitignore` files and offers logging capabilities for troubleshooting and debugging.
mlx-vlm
MLX-VLM is a package designed for running Vision LLMs on Mac systems using MLX. It provides a convenient way to install and utilize the package for processing large language models related to vision tasks. The tool simplifies the process of running LLMs on Mac computers, offering a seamless experience for users interested in leveraging MLX for vision-related projects.
mcp-hub
MCP Hub is a centralized manager for Model Context Protocol (MCP) servers, offering dynamic server management and monitoring, REST API for tool execution and resource access, MCP Server marketplace integration, real-time server status tracking, client connection management, and process lifecycle handling. It acts as a central management server connecting to and managing multiple MCP servers, providing unified API endpoints for client access, handling server lifecycle and health monitoring, and routing requests between clients and MCP servers.
mcp-server-odoo
The MCP Server for Odoo is a tool that enables AI assistants like Claude to interact with Odoo ERP systems. Users can access business data, search records, create new entries, update existing data, and manage their Odoo instance through natural language. The server works with any Odoo instance and offers features like search and retrieve, create new records, update existing data, delete records, browse multiple records, count records, inspect model fields, secure access, smart pagination, LLM-optimized output, and YOLO Mode for quick access. Installation and configuration instructions are provided for different environments, along with troubleshooting tips. The tool supports various tasks such as searching and retrieving records, creating and managing records, listing models, updating records, deleting records, and accessing Odoo data through resource URIs.
ogpt.nvim
OGPT.nvim is a Neovim plugin that enables users to interact with various language models (LLMs) such as Ollama, OpenAI, TextGenUI, and more. Users can engage in interactive question-and-answer sessions, have persona-based conversations, and execute customizable actions like grammar correction, translation, keyword generation, docstring creation, test addition, code optimization, summarization, bug fixing, code explanation, and code readability analysis. The plugin allows users to define custom actions using a JSON file or plugin configurations.
functionary
Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.
nexus
Nexus is a tool that acts as a unified gateway for multiple LLM providers and MCP servers. It allows users to aggregate, govern, and control their AI stack by connecting multiple servers and providers through a single endpoint. Nexus provides features like MCP Server Aggregation, LLM Provider Routing, Context-Aware Tool Search, Protocol Support, Flexible Configuration, Security features, Rate Limiting, and Docker readiness. It supports tool calling, tool discovery, and error handling for STDIO servers. Nexus also integrates with AI assistants, Cursor, Claude Code, and LangChain for seamless usage.
superagent
Superagent is an open-source AI assistant framework and API that allows developers to add powerful AI assistants to their applications. These assistants use large language models (LLMs), retrieval augmented generation (RAG), and generative AI to help users with a variety of tasks, including question answering, chatbot development, content generation, data aggregation, and workflow automation. Superagent is backed by Y Combinator and is part of YC W24.
Scrapegraph-ai
ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.
For similar tasks
jambo
Jambo is a Python package that automatically converts JSON Schema definitions into Pydantic models. It streamlines schema validation and enforces type safety using Pydantic's validation features. The tool supports various JSON Schema features like strings, integers, floats, booleans, arrays, nested objects, and more. It enforces constraints such as minLength, maxLength, pattern, minimum, maximum, uniqueItems, and provides a zero-config approach for generating models. Jambo is designed to simplify the process of dynamically generating Pydantic models for AI frameworks.
vscode-dbt-power-user
The vscode-dbt-power-user is an open-source extension that enhances the functionality of Visual Studio Code to seamlessly work with dbt™. It provides features such as auto-complete for dbt™ code, previewing query results, column lineage visualization, generating dbt™ models, documentation generation, deferring model builds, running parent/child models and tests with a click, compiled query preview and explanation, project health check, SQL validation, BigQuery cost estimation, and other features like dbt™ logs viewer. The extension is fully compatible with dev containers, code spaces, and remote extensions, supporting dbt™ versions above 1.0.
abliteration
Abliteration is a tool that allows users to create abliterated models using transformers quickly and easily. It is not a tool for uncensorship, but rather for making models that will not explicitly refuse users. Users can clone the repository, install dependencies, and make abliterations using the provided commands. The tool supports adjusting parameters for stubborn models and offers various options for customization. Abliteration can be used for creating modified models for specific tasks or topics.
opendataeditor
The Open Data Editor (ODE) is a no-code application to explore, validate and publish data in a simple way. It is an open source project powered by the Frictionless Framework. The ODE is currently available for download and testing in beta.
instructor-js
Instructor is a Typescript library for structured extraction in Typescript, powered by llms, designed for simplicity, transparency, and control. It stands out for its simplicity, transparency, and user-centric design. Whether you're a seasoned developer or just starting out, you'll find Instructor's approach intuitive and steerable.
aiohttp-pydantic
Aiohttp pydantic is an aiohttp view to easily parse and validate requests. You define using function annotations what your methods for handling HTTP verbs expect, and Aiohttp pydantic parses the HTTP request for you, validates the data, and injects the parameters you want. It provides features like query string, request body, URL path, and HTTP headers validation, as well as Open API Specification generation.
island-ai
island-ai is a TypeScript toolkit tailored for developers engaging with structured outputs from Large Language Models. It offers streamlined processes for handling, parsing, streaming, and leveraging AI-generated data across various applications. The toolkit includes packages like zod-stream for interfacing with LLM streams, stream-hooks for integrating streaming JSON data into React applications, and schema-stream for JSON streaming parsing based on Zod schemas. Additionally, related packages like @instructor-ai/instructor-js focus on data validation and retry mechanisms, enhancing the reliability of data processing workflows.
instructor_ex
Instructor is a tool designed to structure outputs from OpenAI and other OSS LLMs by coaxing them to return JSON that maps to a provided Ecto schema. It allows for defining validation logic to guide LLMs in making corrections, and supports automatic retries. Instructor is primarily used with the OpenAI API but can be extended to work with other platforms. The tool simplifies usage by creating an ecto schema, defining a validation function, and making calls to chat_completion with instructions for the LLM. It also offers features like max_retries to fix validation errors iteratively.
For similar jobs
sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.