TypeChat

TypeChat is an open-source library by Microsoft that makes it easy to build natural language interfaces using TypeScript types. Instead of complex prompt engineering, TypeChat uses schema engineering, allowing developers to define types that represent intents and get structured, type-safe AI responses that conform to their defined schemas.

Key Capabilities

TypeChat replaces traditional prompt engineering with schema engineering, using TypeScript type definitions to construct prompts and validate LLM responses automatically. When a response does not conform to the expected schema, TypeChat repairs the output through further language model interaction. The library supports prompt prototyping, schema validation, and type-safe structured outputs. TypeChat.NET extends these capabilities to the .NET ecosystem for cross-platform development.

Who Should Use TypeChat

TypeChat is designed for developers building natural language interfaces who want type-safe, structured responses from language models. TypeScript and .NET developers who are frustrated with unpredictable LLM outputs will find TypeChat invaluable for ensuring response conformity. Teams building production applications that require reliable, structured AI outputs rather than free-form text will benefit most from this approach.

Getting Started

Visit microsoft.github.io/TypeChat or the GitHub repository at github.com/microsoft/TypeChat to get started. Install TypeChat via npm and define your TypeScript types that represent the intents your application supports. TypeChat handles prompt construction, response validation, and automatic repair of non-conforming outputs. The library works with Azure OpenAI, OpenAI APIs, and is designed to be model-neutral.

Pricing & Accessibility: TypeChat is completely free and open source under the MIT license. There are no subscription fees or usage limits beyond the costs of the underlying LLM API calls you make through your own provider accounts.

Why Consider TypeChat: TypeChat fundamentally simplifies building natural language interfaces by replacing error-prone prompt engineering with reliable schema engineering. Its approach of using TypeScript types to guarantee structured AI outputs makes it essential for developers building production-grade applications that depend on predictable LLM responses.