BAML

BoundaryML's BAML is a domain-specific language designed to streamline the process of building reliable applications with Large Language Models (LLMs). Unlike traditional methods that rely on complex prompt engineering within Python environments, BAML provides an abstraction for prompt engineering, similar to how TSX/JSX simplified web development. This allows developers to write prompts as functions with logic, maintainable and testable, with the added benefit of viewing and running them directly within the editor, eliminating the need for extra setups or Python microservices. BAML handles the common pitfalls of LLM parsing, such as error handling, broken JSON, and output variations, auto-generating boilerplate code with type safety, retry logic and robust parsing, reducing development time and complexity.

BAML empowers developers to focus on the core logic of their AI applications, rather than getting bogged down in boilerplate. It's a versatile solution that works alongside existing LLM frameworks and supports many data types, and allows for immediate testing, code generation, and easy adaptation to type changes, all while improving output reliability and reducing token usage. BAML is ideal for AI engineers looking for a robust and efficient way to build reliable agents, chatbots, and other AI powered applications and extract data, offering a significant improvement over standard Pydantic and other frameworks like Marvin.