# baml-codegen

> 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.

- Author: Vaskin Kissoyan
- Repository: Agentic-Insights/foundry
- Version: 20260109122925
- Stars: 5
- Forks: 0
- Last Updated: 2026-02-06
- Source: https://github.com/Agentic-Insights/foundry
- Web: https://mule.run/skillshub/@@Agentic-Insights/foundry~baml-codegen:20260109122925

---

---
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

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