Open-source domain-specific language from BoundaryML for writing typed LLM functions with structured inputs and outputs, a VSCode playground, and generated clients you can call from Python, TypeScript, Go, and more.
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## Editorial notes
BAML is useful when Claude-adjacent teams want LLM calls to behave like typed functions rather than loose prompt strings. You define a function's inputs, output schema, and prompt in BAML, then generate clients that your application code calls in Python, TypeScript, Go, and more, so structured outputs and types are part of the toolchain instead of hand-written parsing.
This is distinct from existing entries: rather than an agent framework or a runtime library, BAML is a domain-specific language and toolchain (with a VSCode playground and generated clients) for authoring and testing typed LLM functions that other frameworks and application code can call.
## Source notes
- The official repository describes BAML ("Basically A Made-up Language") as the programming language for agents, designed so agents make fewer mistakes.
- BAML looks like TypeScript, is statically typed, and provides typed, statically analyzed errors for LLM function definitions.
- BAML can run standalone or be called from other languages, including Python, TypeScript, and Go.
- The toolchain includes a CLI (installable via Homebrew), project scaffolding (`baml init`), and a VSCode extension / playground for editing and testing functions.
- Language SDKs are published as `@boundaryml/baml` on npm and `baml-py` on PyPI, with documentation at docs.boundaryml.com.
- The GitHub repository is `BoundaryML/baml`, is Apache-2.0 licensed per its LICENSE file, and is maintained by BoundaryML.
## Duplicate check
Checked current `content/tools/`, `content/mcp/`, agents, skills, hooks, rules, commands, guides, open pull requests, and repository-wide content for `BAML`, `baml`, `boundaryml`, `boundaryml.com`, `github.com/BoundaryML/baml`, `@boundaryml/baml`, `baml-py`, and `typed llm functions`. Existing entries such as Instructor-style and DSPy-style tools cover adjacent structured-output needs, but no dedicated BAML tools entry, BAML source URL duplicate, or open duplicate PR was found.
## Disclosure
Editorial listing. No paid placement or affiliate link is used.BAML is useful when Claude-adjacent teams want LLM calls to behave like typed functions rather than loose prompt strings. You define a function's inputs, output schema, and prompt in BAML, then generate clients that your application code calls in Python, TypeScript, Go, and more, so structured outputs and types are part of the toolchain instead of hand-written parsing.
This is distinct from existing entries: rather than an agent framework or a runtime library, BAML is a domain-specific language and toolchain (with a VSCode playground and generated clients) for authoring and testing typed LLM functions that other frameworks and application code can call.
baml init), and a VSCode extension / playground for editing and testing functions.@boundaryml/baml on npm and baml-py on PyPI, with documentation at docs.boundaryml.com.BoundaryML/baml, is Apache-2.0 licensed per its LICENSE file, and is maintained by BoundaryML.Checked current content/tools/, content/mcp/, agents, skills, hooks, rules, commands, guides, open pull requests, and repository-wide content for BAML, baml, boundaryml, boundaryml.com, github.com/BoundaryML/baml, @boundaryml/baml, baml-py, and typed llm functions. Existing entries such as Instructor-style and DSPy-style tools cover adjacent structured-output needs, but no dedicated BAML tools entry, BAML source URL duplicate, or open duplicate PR was found.
Editorial listing. No paid placement or affiliate link is used.
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[](https://heyclau.de/entry/tools/baml)BAML side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
| Field | Open-source domain-specific language from BoundaryML for writing typed LLM functions with structured inputs and outputs, a VSCode playground, and generated clients you can call from Python, TypeScript, Go, and more. Open dossier | Open-source Python framework from Prefect for structured outputs and agentic AI workflows, with tasks, specialized agents, threads, and extract/cast/classify/generate utilities. Open dossier | Open-source Python library from dottxt for structured LLM generation, guaranteeing outputs that match a JSON schema, Pydantic model, regex, grammar, or multiple-choice set during generation across many model backends. Open dossier | Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications. Open dossier |
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| Next steps | ||||
| Trust | ||||
| Review status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | davion-knight | davion-knight | davion-knight | oktofeesh1 |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | ||||
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | BoundaryML | PrefectHQ | dottxt-ai | Agenta |
| Added | 2026-07-09 | 2026-07-09 | 2026-07-09 | 2026-06-03 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | ✓BAML functions call LLM providers using the credentials you configure, so scope those provider keys to the minimum needed and keep them out of source control. BAML generates client code that runs inside your application; review generated clients before shipping, and treat typed outputs as untrusted input for account, billing, data, or infrastructure actions. Static typing and schema validation reduce parsing errors, but they do not guarantee that a model's answer is correct, complete, or safe. The BAML CLI and VSCode extension run locally and write generated code into your project; run them in an environment where writing those files is expected. Keep production usage and permissions narrower than playground or example projects. | ✓Marvin agents can be given tools that run code, call external APIs, query databases, or take other actions; review each tool's side effects before assigning it to a task. Structured outputs (extract, cast, classify, generate) reduce parsing errors but do not prove that a model response is correct, complete, or safe for a downstream decision. Tool names, descriptions, task instructions, and thread history become model-facing context, so treat them as untrusted input that can steer agent behavior. Add human-in-the-loop approval, timeouts, and rollback policies before agents perform account, billing, data, or infrastructure actions. Keep production permissions narrower than notebook or demo examples, and scope model-provider and tool credentials to least privilege. | ✓Outlines constrains the structure of a model's output, but it does not verify that the content is correct, complete, or safe; a schema-valid response can still be wrong for a downstream action. Generation runs through the model backend you configure, so it uses that backend's credentials and compute; local backends run models on your machine and hosted backends send prompts to the provider. Complex grammars or schemas can affect latency and cost, so test constraints before relying on them in production. Treat prompt template inputs and generated outputs as untrusted, and validate any values used to take account, billing, data, or infrastructure actions. Keep production usage and permissions narrower than notebook or example code. | ✓Agenta can manage and deploy prompt or configuration changes, so production updates should go through review and rollback controls. Webhooks and GitHub automations tied to prompt or deployment changes should be scoped to trusted repositories and guarded workflows. Evaluation and online monitoring results should support, not replace, domain review for high-risk application behavior. |
| Privacy notes | ✓BAML functions send prompts and inputs to the configured model providers, which process that data under their own data-handling terms. Prompt templates, test inputs, and example data can contain personal or proprietary information, so keep them and provider credentials out of version control. Generated outputs, and any logging or tracing you add around BAML calls, can retain prompts and results outside the library. Apply normal retention and access-control policies to BAML source files, generated clients, and test fixtures that include real data. | ✓Marvin sends prompts, task instructions, inputs, tool arguments, tool results, and thread history to configured model providers when running tasks and agents. Extract, cast, and classify calls pass whatever unstructured input you provide to the model, which can include personal, customer, or proprietary data. Threads, memory, tool outputs, and any observability or logging destinations can retain prompts, outputs, and metadata outside the application runtime. Tools that read files, databases, or APIs can surface local or workspace data into prompts, outputs, and stored thread state, so apply normal retention and access-control policies. | ✓Prompts and any data placed into prompt templates are sent to the configured model backend, which may be a local model or a hosted API. Hosted backends process prompts and outputs under their own data-handling terms, while local backends keep inference on your machine. Generated outputs, schemas, and templates can contain personal or proprietary data, so apply normal retention and access-control policies. Logs, traces, or caches produced by your backend or application can retain prompts and outputs outside the library itself. | ✓Prompt records, variants, test sets, traces, model inputs and outputs, feedback, annotations, and evaluation results may be stored in Agenta. Hosted Agenta use sends that data to Agenta Cloud; self-hosted deployments still require retention, access-control, and backup policies. Review Agenta's sensitive-data redaction and retention guidance before sending production, customer, or regulated data. |
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Python framework for structured outputs and agentic workflows with tasks, agents, and threads.
Python library for guaranteed structured LLM outputs (JSON schema, regex, grammar) during generation.
Source-backed guides for putting this to work.
Instrument LLM and agent apps with traces, metrics, logs, and redaction.
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