Apps 2024-05-13
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Boundary AI

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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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Boundary AI was manually vetted by our editorial team and was first featured on May 13th 2024.
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Pros and 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


What is Boundary AI?
What is BAML in Boundary AI?
How does BAML enhance the performance of Large Language Models?
How does BAML simplify complex programming tasks?
What IDEs does Boundary AI support?
What is the purpose of the VSCode Playground UI in BAML?
What features does Boundary Studio offer in the Boundary AI toolkit?
In what languages is BAML coded?
What kind of models does BAML support?
How does BAML assist in the deployment process?
What are the benefits of BAML being typesafe?
What is the role of an integrated playground in BAML?
How can BAML support any model?
What is the cost predicate for the VSCode extension for BAML?
What additional benefits are offered in the paid services of Boundary Studio?
Does the BAML compiler generate Python or Typescript code?
What are the security features of BAML?
How does BAML compares it to other data modeling libraries?
Why does BAML never obscure prompts?
Why was BAML language created?

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