BAML
UnclaimedThe first language for building reliable AI agents with type safety and structured outputs.
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TL;DR - BAML
- A new language for building reliable, type-safe AI agents.
- Generates native code for Python, TypeScript, Ruby, Go, integrating with any LLM.
- Enables structured outputs, CI/CD testing, and automatic error handling for AI pipelines.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Significantly improves reliability of AI pipelines
- Provides type safety for AI interactions
- Generates native code for popular programming languages
- Enables structured and validated outputs from LLMs
- Facilitates testing of AI agents in CI/CD
Cons
- Requires learning a new language (BAML)
- Limited information on advanced features or enterprise support
Preview
Key Features
Define prompts and functions in BAMLGenerate native functions in Python, TypeScript, Ruby, GoType-safe AI interfacesStructured outputs (JSON, XML, YAML, etc.)Test agents in CI/CD with baml-cli testAutomatic retry and fallback for failed requestsVSCode extension for developmentWorks with every LLM provider
Pricing Plans
Free TrialFree
$0/month
- Playground
- CLI & Editor extension access
- Unlimited BAML schemas
- TypeScript type generation
- Basic schema validation
- Local development
- Single developer
- Open source
- Community support
Team
$25/month
- Everything in previous plans
- Advanced type generation
- Runtime validation
- Unlimited schemas
- Team collaboration
- Unlimited developers
- Private schemas
- Custom transformations
- Advanced validation
- Priority support
- Custom integrations
- Enterprise features
Enterprise
Custom
- Everything in previous plans
- On-premise deployment
- SSO & SAML integration
- Custom rate limits
- Audit logs & compliance
- 99.9% uptime SLA
- Dedicated account manager
- Priority support
- Custom training & onboarding
What is BAML?
BAML (Basically A Made-Up Language) is a new programming language designed to make AI pipeline development 10x more reliable. It addresses the current developer experience challenges in building AI agents by introducing type safety and structured outputs for interactions with Large Language Models (LLMs).
BAML is for developers who want to build robust AI applications, offering a complete development workflow from defining prompts and functions to testing and deployment. It integrates seamlessly with existing programming languages like Python, TypeScript, Ruby, and Go, generating native functions that can be called directly within your codebase. This approach aims to reduce errors, improve iteration speed, and ensure predictable results from LLMs, making AI development more efficient and less prone to unexpected behaviors.
The tool is designed to work with any LLM provider and supports multi-cloud deployment, allowing developers to deploy their AI agents without special configurations. Key benefits include type-safe AI interfaces, validated structured outputs in various formats (JSON, XML, YAML), and the ability to test agents in CI/CD pipelines, along with automatic retry and fallback mechanisms for enhanced reliability.
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BAML FAQ
How does BAML ensure type safety for AI interfaces and outputs?
BAML allows users to define AI interfaces with schemas that automatically generate TypeScript types. This ensures that responses from any LLM are type-safe and validated, supporting various output formats like JSON, XML, and YAML.
What is the process for integrating BAML functions into an existing codebase written in a different programming language?
The
baml-cli generate tool converts BAML functions into native functions in your chosen programming language, such as Python, TypeScript, Ruby, or Go. This allows you to call BAML-defined prompt functions directly from your existing application code.Can BAML agents be tested within a continuous integration/continuous deployment (CI/CD) pipeline?
Yes, BAML supports testing agents in CI/CD pipelines to verify their expected behavior. This can be done using the
baml-cli test command, ensuring that AI components function correctly throughout the development lifecycle.What mechanisms does BAML provide for handling failures and ensuring the reliability of AI requests?
BAML includes features for automatic retry and fallback responses. This means that if an AI request fails, BAML will automatically attempt to re-execute it or provide a predefined fallback, enhancing the overall reliability of your AI applications.
How does BAML facilitate the deployment of AI agents across different cloud environments?
BAML generates native code in your chosen programming language, which means there are no special deployment steps required for BAML itself. You can deploy your BAML-powered agents to any cloud platform that supports your language, such as AWS Lambda, Vercel, Google Cloud, Azure Functions, or Railway.
Source: boundaryml.com