Shumai is a powerful tensor library designed for JavaScript and TypeScript, created by Facebook Research (FAIR). The library stands out for its high performance, network connectivity, and differentiable capabilities. Built using Bun and Flashlight, it enables developers to seamlessly integrate deep learning and machine learning functionalities into web applications. It supports features such as GPU computation, making it ideal for complex scientific computations and model training. Shumai is aimed at providing a robust environment for developing advanced machine learning models in a TypeScript ecosystem.

TypedAI delivers a developer-centric library that wraps large language models in strongly typed TypeScript abstractions. You define input and output schemas to validate data at compile time, create reusable prompt templates, and handle streaming or batch responses. It supports function calling patterns to connect AI outputs with backend logic, and integrates with popular LLM providers like OpenAI, Anthropic, and Azure. With built-in error handling and logging, TypedAI helps you ship robust AI features—chat interfaces, document summarization, code generation, and custom agents—without sacrificing type safety or developer productivity.

Xemantic AI Tool Schema is a set of JSON Schema and TypeScript type definitions designed to standardize the way AI agent tools are described, validated, and invoked. Developers can define tool metadata such as name, description, and parameters, then validate instances against the schema or use generated TypeScript interfaces during development. The schema supports parameter types, nested structures, default values, and version control, ensuring robust validation and compatibility. By following a consistent schema, AI Agents can discover and call tools reliably at runtime, improving maintainability and reducing integration errors. The package integrates seamlessly with Xemantic AI Agents and can be extended for custom use cases.

GRASP provides a structured pipeline for building AI agents in TypeScript or JavaScript environments. At its core, developers define agents by registering a set of tools—functions or external API connectors—and specifying prompt templates that guide agent behavior. Built-in memory modules allow agents to store and retrieve contextual information, enabling multi-turn conversations with persistent state. The planning component orchestrates tool selection and execution based on user input, while the execution layer handles API calls and result processing. GRASP’s plugin system supports custom extensions, enabling capabilities such as retrieval-augmented generation (RAG), scheduling tasks, and logging. Its modular design means teams can choose only the components they need, facilitating integration with existing systems and services for chatbots, virtual assistants, and automated workflows.