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Boundary AI

Boundary-AI

Description

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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Functionality
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Ease of Use
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Features
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Support
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Pros
Backed by Ycombinator
BAML coded in Rust
BAML-generated code is secure
Boundary Studio for performance tracking
Can be easily evaluated
Compared favorably to Pydantic
Custom-built compiler
Deployment does not install compiler
Easier test execution
Eliminates parsing boilerplate
Enhances LLM performance
Free BAML compiler
Free VSCode extension
Generates Python or Typescript code
Improving functions available
Includes VSCode Playground UI
Instantaneous testing of prompts
Integrated playground feature
Never obscures prompts
Paid services for monitoring
Performance monitoring feature
Plans for non-generative models
Rapid testing in IDE
Reduces type errors
Special config language BAML
Supported by former Amazon engineers
Supports any model
Supports multiple models
Supports various IDEs
Transparent pricing structure
Trusted by various developers
Turns complex templates into functions
Uniquely typesafe
Validated output schemas
Cons
Deployment limited to Python
Doesn’t support non-generative models yet
No direct server communication
Paid services for monitoring
Possible compatibility issues with other frameworks
Primary codebase in Rust
Reliance on specific IDEs
Requires familiarity with BAML
Requires manual activation for trace publishing
TypeScript

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