OpenClaw · Skill
BAML
Generate type-safe LLM extraction code. Use when creating structured outputs, classification, RAG, or agent workflows.
Web & Frontend Development
v2.0.0
VirusTotal: Suspicious
Install
Start with the primary install command. Alternate entrypoints are included below for ClawHub and OpenClaw CLI users.
Primary command
clawhub install killerapp/baml-codegenClawHub installer
npx clawhub@latest install killerapp/baml-codegenOpenClaw CLI
openclaw skills install killerapp/baml-codegenDirect OpenClaw install
openclaw install killerapp/baml-codegenWhat this skill does
Generate type-safe LLM extraction code. Use when creating structured outputs, classification, RAG, or agent workflows.
Why it matters
BAML acts as a transpiler rather than a runtime dependency, so generated code has no BAML overhead in production and works natively with existing frameworks.
Typical use cases
- Extracting structured invoice data from unstructured text
- Building a classification pipeline for support ticket routing
- Creating a RAG function that returns answers with source citations
- Wiring a multi-step LangGraph agent to typed LLM outputs
- Generating a vision extraction function from image inputs
Source instructions
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 everybaml-cli generate; checkbaml_src/generators.bamlforoutput_type(python, typescript, ruby, go) - ALWAYS edit
baml_src/- Source of truth for all BAML code - Run
baml-cli generateafter 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 testto 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
.bamlfiles (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