| 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. |
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<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, audio
- Containers:
Type[] (array), Type? (optional), map<string, Type> (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
- BAML Code - Complete
.baml files (types, functions, clients, retry_policy)
- Tests - pytest/Jest with 100% function coverage
- Integration - Framework-specific client code (LangGraph nodes, FastAPI endpoints, Next.js API routes)
- 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
