Boundary AI
Boundary AI is a comprehensive toolkit aimed primarily at facilitating tasks for AI engineers. Through its special config language known as BAML (Basically, A M...
Last verified:
What is Boundary AI?
BAML is a domain-specific programming language designed specifically for generating reliable structured outputs from large language models (LLMs), transforming prompt engineering into a type-safe coding process. It enables developers to define schemas, functions, and prompts that produce validated, consistent data like JSON or custom objects, outperforming even native LLM structured output modes in reliability. With BAML, users build robust AI agents, RAG chatbots, PDF data extractors, and more, integrating seamlessly across Python, TypeScript, and other languages.
Key features include a VSCode playground for fast iteration and testing without setups, full type-safety with autocomplete even during streaming, support for any LLM provider like OpenAI, Anthropic, or open-source models, and state-of-the-art parsing that handles common LLM errors such as unescaped quotes or trailing commas. It offers automatic retries, fallbacks, CI/CD testing for agents, multimodal inputs, dynamic prompts, and classifiers. The baml-cli generates client code for easy calls from any language.
BAML targets developers and AI engineers building production AI applications who need deterministic outputs, reduced token usage, and engineering rigor in LLM workflows, avoiding the pitfalls of raw prompting like inconsistency and parsing failures. It's ideal for those tired of verbose JSON instructions in prompts or unreliable function calling.
Boundary AI pricing
Pricing model: Free
BAML core is completely free and open-source under Apache 2.0 license with no paid tiers mentioned; enterprise solutions offer SLA guarantees and architectural reviews but pricing details are not listed on the site.
Boundary AI pros
- Fully type-safe structured outputs
- VSCode playground for instant testing
- Works with any LLM provider
- Compatible with every programming language
- Outperforms OpenAI on their own models
- Handles LLM parsing errors automatically
- Streaming support with autocomplete
- Automatic retries and fallbacks
- CI/CD agent testing integration
- Multimodal input support
- Dynamic prompt capabilities
- Reduces token usage significantly
- Generates TypeScript types automatically
- Fast time-to-first-token
- Open-source Apache 2.0 license
- Static analysis for prompts
- No setup for prompt iteration
Boundary AI cons
- Requires learning BAML syntax
- CLI setup needed for code generation
- Limited to structured output use cases
- Enterprise features may cost extra
- VSCode extension dependency
- Less flexible for unstructured text
- Community-driven support mainly
- Open-source lacks SLAs
- Integration overhead for legacy code
Frequently asked questions about Boundary AI
What is BAML?
BAML is a domain-specific language for generating structured outputs from LLMs with type safety, enabling reliable agents, RAG chatbots, and data extraction from PDFs through schema-defined functions.
Which languages does BAML support?
BAML generates client code compatible with Python, TypeScript, Ruby, Java, C#, Rust, Go, and more via baml-cli, allowing calls from any preferred programming language.
How does the VSCode playground work?
The VSCode extension provides an amazingly fast developer experience for writing, testing, and hot-reloading BAML prompts directly in the editor without Python setups or microservices.
Does BAML work with open-source LLMs?
Yes, BAML supports any LLM including open-source models, achieving state-of-the-art structured outputs that outperform proprietary models in function-calling tasks.
What output formats does BAML support?
BAML supports JSON, XML, YAML, and custom schemas for type-safe validated responses, with a specialized parser fixing common LLM errors like unescaped characters.
Can I test BAML functions in CI/CD?
Yes, BAML allows testing agents and prompts in CI/CD pipelines to ensure they work as expected, with automated validation of structured outputs.
How does BAML improve reliability?
BAML provides type-safe interfaces, automatic retries, fallbacks for failed requests, and schema enforcement, making AI pipelines 10x more reliable than raw prompting.
Is BAML free to use?
BAML is completely free under Apache 2.0 open-source license; enterprise options with SLAs are available separately without specified pricing.
What are BAML functions?
BAML functions define prompts with schemas like class Resume { name string; title string; } function ExtractResume(resume: string) -> Resume, generating callable code.
How to get started with BAML?
Install via guide, use the VSCode playground or online playground, define prompts/functions, run baml-cli generate, then call from your language like from baml_client import b.