baml-codegen

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name: baml-codegen description: "Use when generating BAML code for type-safe LLM extraction, classification, RAG, or agent workflows - creates complete .baml files with types, functions, clients, tests, and framework integrations from natural language requirements. Queries official BoundaryML repositories via MCP for real-time patterns. Supports multimodal inputs (images, audio), Python/TypeScript/Ruby/Go, 10+ frameworks, 50-70% token optimization, 95%+ compilation success." license: "Apache-2.0" compatibility: "Requires MCP servers: baml_Docs (required), baml_Examples (optional). Works offline with 80% functionality using cached patterns."

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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

Analyze → Pattern Match (MCP) → Validate → Generate → Test → Deliver
         ↓ [IF ERRORS] Error Recovery (MCP) → Retry

BAML Syntax

| Element | Example | |---------|---------| | Class | class Invoice { total float @description("Amount") @assert(this > 0) @alias("amt") } | | 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 GPT5 { provider openai options { model gpt-5 } retry_policy Exponential } | | Fallback | client Resilient { provider fallback options { strategy [FastModel, SlowModel] } } |

Types

Primitives: string, int, float, bool | Multimodal: image, audio

Containers: Type[] (array), Type? (optional), map (key-value)

Composite: Type1 | Type2 (union), nested classes

Annotations: @description("..."), @assert(condition), @alias("json_name"), @check(name, condition)

Providers

openai, anthropic, gemini, vertex, bedrock, ollama + any OpenAI-compatible via openai-generic

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: retry_policy Exp { max_retries 3 strategy { type exponential_backoff } }

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. 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