BAML is a prompting language designed to bring structure and reliability to AI workflows, targeting developers who build applications leveraging Large Language Models (LLMs). It transforms prompt engineering into a schema engineering process, enabling type-safe, maintainable, and robust AI integrations across multiple programming languages.

How It Works

BAML treats prompts as functions, defining inputs, outputs, and associated models within a dedicated syntax. This approach allows for explicit schema definition, enabling static analysis and type safety. It utilizes a proprietary Schema-Aligned Parsing (SAP) algorithm to reliably extract structured data from LLM responses, even when models lack native tool-calling capabilities or return unstructured formats like markdown or chain-of-thought.

Quick Start & Requirements

• Install via pip: pip install baml-py
• Requires Python 3.7+.
• Supports numerous LLM providers (OpenAI, Anthropic, Gemini, Ollama, etc.).
• Official Docs: https://docs.boundaryml.com
• Homepage: https://www.boundaryml.com

Highlighted Details

• Enables type-safe LLM function calls and streaming interfaces in Python, TypeScript, Ruby, Go, Java, C#, and Rust.
• Features IDE tooling (VSCode initially) for prompt visualization and faster iteration.
• Supports retry policies, fallbacks, and model rotations for resilient AI integrations.
• Fully open-source (Apache 2.0) and designed for offline, private use.

Maintenance & Community

• Actively developed by Boundary HQ.
• Weekly updates and production usage cited.
• Discord community available: https://discord.gg/BTNBeXGuaS

Licensing & Compatibility

• Apache 2.0 License.
• Permissive for commercial use and integration with closed-source applications.

Limitations & Caveats

• JetBrains and Neovim IDE support is listed as "coming soon."
• While many companies use it in production, the "basically made-up language" aspect implies a learning curve for the new syntax.