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BAML

BAML is a special programming language that helps you build strong and reliable AI applications. It turns AI prompts into structured functions with clear inputs and outputs. This makes your AI workflows more organized, easier to test, and simpler to maintain, working with any AI model and programming language.

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About BAML

Who It's For

BAML is for AI engineers, MLOps engineers, data scientists, and developers. It helps anyone creating AI agents, chatbots, or structured AI workflows. If you need to make your AI applications more dependable and easier to manage, this tool is designed for you.

What You Get

With BAML, you get outputs from AI models that are structured and validated, like JSON or XML. It helps you break down complex AI tasks into small, testable functions. The tool also handles errors gracefully with automatic retries and provides streaming support for real-time, type-safe results.

How It Works

BAML treats every AI prompt as a function that has clear inputs and returns a specific type of output. It uses a special method to make sure the AI's response always matches the structure you define. You write your AI functions, test them, and BAML creates native code in your chosen language, making it easy to integrate and deploy your AI agents.

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Features & Capabilities

⚙️ Core AI Development

Type-Safe AI Interfaces

Define AI interfaces with confidence, generating TypeScript types automatically to ensure robust applications.

Structured Outputs

Get type-safe, validated responses from any LLM, supporting formats like JSON, XML, and YAML.

Automatic Error Handling

Provides automatic retry mechanisms for failed requests and fallback responses for robust agent reliability.

🛠️ Seamless Developer Workflow

Multi-LLM & Language Compatibility

Works with every LLM provider and generates native code for popular languages like Python, TypeScript, Ruby, and Go.

Integrated Testing & CI/CD

Test prompt functions within your IDE or integrate agent testing into your continuous integration/delivery pipelines.

Flexible Deployment

Deploy agents to any cloud platform, including AWS Lambda and Vercel, without special BAML-specific setup.

IDE Integration

Leverage dedicated VSCode extension support for defining and developing prompt functions efficiently.

Use Cases

Ensuring Reliable, Type-Safe LLM Outputs for AI Applications

Developers building AI applications often struggle with inconsistent and unstructured outputs from Large Language Models, leading to unpredictable application behavior and increased debugging time. BAML provides a domain-specific language and framework to define type-safe AI interfaces and structured output schemas (JSON, XML, YAML), guaranteeing validated responses from any LLM, thus enhancing reliability and reducing development friction.

Software DevelopmentFor: AI Engineers, Developers

Accelerating AI Agent Development with Robust Testing and MLOps Integration

Building and deploying complex AI agents often involves managing intricate prompts, ensuring consistent behavior across different LLMs, and integrating testing into CI/CD pipelines. BAML simplifies this by treating prompts as modular, testable functions, allowing developers and MLOps engineers to define, test, and deploy AI agents with confidence, significantly improving iteration speed and reducing operational overhead.

AI/ML Product Development, Enterprise ITFor: AI Engineers, MLOps Engineers

Building Resilient and Performant Multi-LLM AI Systems

Developers integrating LLMs into critical applications face challenges with API failures, latency, and managing multiple providers. BAML provides built-in automatic retry and fallback mechanisms, ensuring AI applications remain robust and deliver consistent performance even when underlying LLM APIs experience issues. This allows developers to seamlessly switch between or combine LLMs without extensive custom error handling.

Cloud Software, AI/ML Platform DevelopmentFor: AI Engineers, MLOps Engineers

Automating High-Precision Structured Data Extraction

Many businesses need to extract specific pieces of information from large volumes of unstructured text, such as resumes, customer feedback, or legal documents, but traditional methods or raw LLM outputs often lack accuracy and consistency. BAML enables the definition of strict output schemas, ensuring that AI agents reliably extract and format data into desired structures (e.g., JSON), significantly improving data processing automation and accuracy.

Data Processing, Document Management, HR TechFor: Data Scientists, AI Engineers, Developers

Frequently asked questions

BAML is a domain-specific programming language designed to build reliable AI workflows and agents. It transforms prompts into structured functions with defined inputs and outputs, making prompt engineering more modular, testable, and maintainable.

Every prompt in BAML is a function that takes parameters and returns a specific type. BAML uses a schema-aligned parsing (SAP) algorithm to ensure outputs conform to your defined schema, even if the underlying LLM (Large Language Model) returns messy or unstructured text. You can also chain BAML functions to create complex agents and workflows.

The key features of BAML include type-safe outputs, which ensures structured and validated responses from any LLM (like JSON, XML, or YAML). It is modular and composable, allowing you to build agents as small, testable functions. BAML is language agnostic, working with any LLM (such as OpenAI, Google Gemini, or Deepseek) and any programming language. It offers testing and debugging with built-in support for unit tests, CI/CD pipelines, and runtime assertions. Additionally, BAML provides automatic retry and fallback to handle API errors gracefully, and streaming support to get type-safe outputs even when streaming partial results.

BAML differs from regular prompt engineering by focusing on output schemas rather than just crafting prompt text. It provides programmatic control over LLM outputs, reducing the need for re-prompting and manual parsing. Furthermore, BAML functions are testable and maintainable, similar to regular software code.

Yes. BAML supports any LLM provider (OpenAI, Google Gemini, Deepseek, etc.) and can be configured with custom API keys and retry policies.

To get started with BAML, you should install the BAML SDK and VSCode extension, define your functions and schemas in BAML syntax, use the BAML CLI or playground to test and run your agents, and finally integrate BAML outputs into your application code.

Yes, BAML is open source and available on GitHub.

You can find documentation and examples through the official documentation, the GitHub repository, and video tutorials.

BAML is designed for AI engineers, MLOps engineers, data scientists, and developers who are building AI agents, chatbots, or structured workflows.

Yes. BAML supports streaming structured data with type-safe outputs for each chunk.

BAML handles errors and retries by automatically retrying failed LLM API calls, and it allows you to define fallback responses and custom retry policies.

Yes. BAML allows dynamic schema updates, which is useful for self-optimizing or autonomous agents.

Yes, there is a community and support for BAML available through GitHub Discussions, official documentation and tutorials, and a community presence on Y Combinator and developer forums.

Tags

prompt engineeringworkflow orchestrationai application developmenttype-safe aierror handling

Specifications

Deployment
Browser
API
Self-hosted
Cloud
Browser
Target Audience
Individual
Startup
Business
Complexity
Developer

Integrations

GitHub
VSCode
Python
TypeScript
AWS Lambda
Google Cloud

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