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BAML in brief
BAML elegantly solves the problem of unstructured LLM outputs. For teams building serious LLM applications in Python or TypeScript, it's a solid approach.
- Price30 €/month
- CategoryCode
- RecommendedYes
The essentials in 20 seconds
- Open source DSL for defining LLM functions with strict output types
- Generates type-safe Python and TypeScript code from BAML definitions
- Resilience to malformed LLM outputs with robust parsers
- Pricing: free open source + $30/month for cloud features
Verdict: BAML is the most elegant approach for type-safe LLM functions. A must-have if you're building LLM pipelines in production.
What is BAML
BAML (Boundary AI Markup Language) is a DSL designed by Boundary ML to define functions that call LLMs with guaranteed return types. You define a function, its typed inputs, its typed outputs, and the associated prompt. BAML generates the corresponding Python or TypeScript code with robust parsers.
The problem it solves: LLMs return free text. BAML forces structure and handles cases where the model doesn't exactly follow the requested format.
Strengths
Type-safety for LLM outputs
The heart of BAML: you declare output class AnalysisResult { sentiment: "positive" | "negative" | "neutral"; confidence: float; reasoning: string; } and BAML guarantees you get exactly that type in output, even if the LLM makes a formatting mistake.
Resilient parser
BAML doesn't do simple JSON parsing. Its parser understands partially malformed outputs and corrects them. Production robustness far superior to a naive json.loads().
Multi-LLM and multi-provider
OpenAI, Anthropic, Google, Ollama, Azure OpenAI. You switch providers in one line in the BAML config.
Limits
A new language to learn
BAML adds a DSL to your stack. The .baml file is clear, but it's still an extra layer with its own syntax.
Still a small community
Compared to LangChain or Instructor, the BAML community is more limited. Online resources are fewer.
Pricing
- Core (OSS): free, Python and TypeScript codegen
- BAML Cloud: $30/month, observability, logs, shared tests
Alternatives
- Instructor for structured output in Python with Pydantic
- Outlines for low-level structured generation
- LangChain for a more generalist approach
Verdict
BAML is the tool you reach for when you're fed up with try: json.loads(response) except: ... in production. The DSL approach can be intimidating at first, but the type safety it delivers is absolutely worth it for serious applications.
FAQ
Does BAML replace LangChain?
No. BAML is focused on defining type-safe LLM functions. LangChain covers a much broader scope (agents, RAG, memory). The two can coexist.
Does BAML generate React or Vue code?
No. BAML targets backend Python and TypeScript code.
Can you use BAML with local models?
Yes. Ollama is supported as a provider. You can run BAML entirely locally.
BAML vs Pydantic + Instructor: what's the difference?
Instructor is a pure Python library. BAML is a DSL with code generation. BAML gives more clarity on prompts and types by separating them from Python code. Instructor is more familiar for anyone who already knows Pydantic.
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Screenshots BAML
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BAML : 0/10.
BAML elegantly solves the problem of unstructured LLM outputs. For teams building serious LLM applications in Python or TypeScript, it's a solid approach..
Test BAML yourself
A free trial is available. Plan thirty minutes to form your own opinion.
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BAML
30 €/month