BAML

Prompts become typed functions in BAML, a domain-specific language from BoundaryML in which you declare input parameters, an output schema, and a prompt template, then call the function from application code as if it were native. A Rust-based compiler generates clients for Python, TypeScript, and other languages, and schema-aligned parsing coerces model responses into the declared types even when the raw output is imperfect JSON, which makes structured extraction dependable across models. Because the model is just a configurable client, the same function runs against OpenAI, Anthropic, Google, or any local endpoint including Ollama, and switching providers is a small config change; a VS Code playground renders the exact prompt and lets you test functions without leaving the editor. The core toolchain is Apache-2.0 with no usage fees, while BoundaryML, a Y Combinator company, sells hosted observability on top. Around 8,700 GitHub stars and production adoption for extraction, classification, and agent pipelines have made it the reference implementation of schema-first prompting.