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Boundary AI is a comprehensive toolkit aimed primarily at facilitating tasks for AI engineers. Through its special config language known as BAML (Basically, A Made-up Language), it enhances the performance of LLMs (Large Language Models).

With BAML, AI engineers can turn complex prompt templates into typed functions that are not only easier to execute but also to test, eliminating parsing boilerplate and type errors.

In a sense, employing an LLM with BAML resembles invoking a regular function. Boundary AI also supports instantaneous testing of new prompts in various IDEs, including BAML's VSCode Playground UI.

Furthermore, the toolkit includes Boundary Studio, a feature for monitoring and tracking the performance of each LLM function over time. Importantly, BAML is primarily coded in Rust and supports Openai, Anthropic, Gemini, Mistral, and self-brought models with plans to include non-generative models.

Deployment with BAML generates Python or Typescript code. Unlike other data modeling libraries, BAML is uniquely typesafe and never obscures prompts. It features an integrated playground and can support any model.

The BAML compiler, as well as the VSCode extension for BAML, are free and open source, with paid services starting for those using the monitoring and improving functions of Boundary Studio.

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Pros & Cons


  • Special config language BAML
    Enhances LLM performance
    Turns complex templates into functions
    Easier test execution
    Eliminates parsing boilerplate
    Reduces type errors
    Instantaneous testing of prompts
    Supports various IDEs
    Includes VSCode Playground UI
    Performance monitoring feature
    Supports multiple models
    Plans for non-generative models
    Generates Python or Typescript code
    Uniquely typesafe
    Never obscures prompts
    Integrated playground feature
    Supports any model
    Free BAML compiler
    Free VSCode extension
    Paid services for monitoring
    Improving functions available
    BAML coded in Rust
    Trusted by various developers
    Validated output schemas
    Rapid testing in IDE
    Boundary Studio for performance tracking
    Deployment does not install compiler
    BAML-generated code is secure
    Transparent pricing structure
    Can be easily evaluated
    Compared favorably to Pydantic
    Backed by Ycombinator
    Supported by former Amazon engineers
    Custom-built compiler


  • Requires familiarity with BAML
    Reliance on specific IDEs
    Paid services for monitoring
    Doesn't support non-generative models yet
    Deployment limited to Python, TypeScript
    Primary codebase in Rust
    Requires manual activation for trace publishing
    No direct server communication
    Possible compatibility issues with other frameworks

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