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.

Sharp-GPT empowers .NET developers to create robust AI agents by leveraging custom attributes on interfaces to define prompt templates, configure models, and manage conversational memory. It offers streaming output for real-time interaction, automatic JSON deserialization for structured responses, and built-in support for fallback strategies and logging. With pluggable HTTP clients and provider abstraction, you can switch between OpenAI, Azure, or other LLM services effortlessly. Ideal for chatbots, content generation, summarization, classification, and more, Sharp-GPT reduces boilerplate and accelerates AI agent development on Windows, Linux, or macOS.

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.