BAML: Schema-Aligned Parsing Enables Typed Prompts Across Any LLM Model

BAML (BoundaryML) is a typed prompt DSL that compiles to Python/TS/Java/Go/Rust client libraries, with its Schema-Aligned Parsing algorithm enabling reliable structured output even from models without native tool-calling APIs.

1 min read|agenticonsult Intelligence

BAML: Schema-Aligned Parsing Enables Typed Prompts Across Any LLM Model

BoundaryML's BAML ("Basically a Made-up Language") is a typed prompting DSL that compiles to client libraries in Python, TypeScript, Ruby, Java, C#, Rust, and Go. Its core innovation is Schema-Aligned Parsing (SAP) — an algorithm that extracts structured outputs from any model response regardless of format, including markdown-wrapped JSON, chain-of-thought prefixes, or prose. This makes typed structured outputs work on Deepseek-R1, OpenAI O1, and any OpenAI-compatible endpoint on day one of release, without waiting for native tool-calling API support. Apache 2.0, fully offline-capable, Rust-built.

Why It Matters

BAML treats prompt engineering as schema engineering — functions with typed parameters and return values — which enables diff-based prompt review, streaming typed partials, and cross-language codegen from a single source of truth. SAP is the differentiator that makes this practical for production rather than constrained to models with perfect tool-calling compliance.

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BAML: Schema-Aligned Parsing Enables Typed Prompts Across Any LLM Model

BAML: Schema-Aligned Parsing Enables Typed Prompts Across Any LLM Model

Why It Matters

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