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.

This starter project provides a ready-to-use FastAPI application preconfigured for AI agent development. It uses Pydantic for request/response validation, environment-based configuration for OpenAI API keys, and modular endpoint scaffolding. Built-in features include Swagger UI docs, CORS handling, and structured logging, enabling teams to rapidly prototype and deploy AI-driven endpoints without boilerplate overhead. Developers simply define Pydantic models and agent functions to get a production-ready API server.

Pydantic AI uses Pydantic models to encapsulate AI agent definitions, enforcing type-safe inputs and outputs. Developers declare prompt templates as model fields, automatically validating user data and agent responses. The framework offers built-in error handling, retry logic, and function‐calling support. It integrates with popular LLMs (OpenAI, Azure, Anthropic, etc.), supports asynchronous workflows, and enables modular agent composition. With clear schemas and validation layers, Pydantic AI reduces runtime errors, simplifies prompt management, and accelerates the creation of robust, maintainable AI agents.