BAML Code Generation

Generate type-safe LLM extraction code. Use when creating structured outputs, classification, RAG, or agent workflows.

Golden Rules

• - NEVER edit baml_client/ - 100% generated, overwritten on every baml-cli generate; check baml_src/generators.baml for output_type (python, typescript, ruby, go)
• ALWAYS edit baml_src/ - Source of truth for all BAML code
• Run baml-cli generate after changes - Regenerates typed client code for target language

Philosophy (TL;DR)

• - Schema Is The Prompt - Define data models first, compiler injects types
• Types Over Strings - Use enums/classes/unions, not string parsing
• Fuzzy Parsing Is BAML's Job - BAML extracts valid JSON from messy LLM output
• Transpiler Not Library - Write .baml → generate native code (Python/TypeScript/Ruby/Go), no runtime dependency
• Test-Driven Prompting - Use VS Code playground or baml-cli test to iterate

Workflow

CODEBLOCK0

BAML Syntax

Element	Example
Class	INLINECODE8
Enum

enum Category { Tech @alias("technology") @description("Tech sector"), Finance, Other } | | Function | function Extract(text: string, img: image?) -> Invoice { client GPT5 prompt #"{{ text }} {{ img }} {{ ctx.output_format }}"# } | | Client | client<llm> GPT5 { provider openai options { model gpt-5 } retry_policy Exponential } | | Fallback | client<llm> Resilient { provider fallback options { strategy [FastModel, SlowModel] } } |

Types

• - Primitives: string, int, float, bool | Multimodal: image, INLINECODE18
• Containers: Type[] (array), Type? (optional), map<string, Type> (key-value)
• Composite: Type1 | Type2 (union), nested classes
• Annotations: @description("..."), @assert(condition), @alias("json_name"), INLINECODE26

Providers

INLINECODE27, anthropic, gemini, vertex, bedrock, ollama + any OpenAI-compatible via INLINECODE33

Pattern Categories

Pattern	Use Case	Model	Framework Markers
Extraction	Unstructured → structured	GPT-5	fastapi, next.js
Classification

Categorization | GPT-5-mini | any | | RAG | Answers with citations | GPT-5 | langgraph | | Agents | Multi-step reasoning | GPT-5 | langgraph | | Vision | Image/audio data extraction | GPT-5-Vision | multimodal |

Resilience

• - retry_policy: INLINECODE34
• fallback client: Chain models [FastCheap, SlowReliable] for cost/reliability tradeoff

MCP Indicators

• - Found patterns from baml-examples | Validated against BoundaryML/baml | Fixed errors using docs | MCP unavailable, using fallback

Output Artifacts

1. 1. BAML Code - Complete .baml files (types, functions, clients, retry_policy)
2. Tests - pytest/Jest with 100% function coverage
3. Integration - Framework-specific client code (LangGraph nodes, FastAPI endpoints, Next.js API routes)
4. Metadata - Pattern used, token count, cost estimate

References

• - providers.md - OpenAI, Anthropic, Google, Ollama, Azure, Bedrock, openai-generic
• types-and-schemas.md - Full type system, classes, enums, unions, map, image, audio
• validation.md - @assert, @check, @alias, block-level @@assert
• patterns.md - Pattern library with code examples
• philosophy.md - BAML principles, golden rules
• mcp-interface.md - Query workflow, caching
• languages-python.md - Python/Pydantic, async
• languages-typescript.md - TypeScript, React/Next.js
• frameworks-langgraph.md - LangGraph integration