https://github.com/BoundaryML/baml
BAML is a language that helps you get structured data from LLMs, with the best DX possible. Works with all languages. Check out the promptfiddle.com playground
https://github.com/BoundaryML/baml
baml boundaryml guardrails llm llm-playground playground prompt prompt-config prompt-templates structured-data structured-generation structured-output vscode
Last synced: 3 months ago
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BAML is a language that helps you get structured data from LLMs, with the best DX possible. Works with all languages. Check out the promptfiddle.com playground
- Host: GitHub
- URL: https://github.com/BoundaryML/baml
- Owner: BoundaryML
- License: apache-2.0
- Created: 2023-10-06T18:57:41.000Z (over 1 year ago)
- Default Branch: canary
- Last Pushed: 2024-10-13T16:57:43.000Z (7 months ago)
- Last Synced: 2024-10-13T23:04:33.912Z (7 months ago)
- Topics: baml, boundaryml, guardrails, llm, llm-playground, playground, prompt, prompt-config, prompt-templates, structured-data, structured-generation, structured-output, vscode
- Language: Rust
- Homepage: https://docs.boundaryml.com
- Size: 67.7 MB
- Stars: 1,148
- Watchers: 11
- Forks: 38
- Open Issues: 65
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - BoundaryML/baml
README
## BAML: Basically a Made-up Language
*or "Bad-Ass Machine Learning" if your boss isn't around*
[Homepage](https://www.boundaryml.com/) | [Docs](https://docs.boundaryml.com) | [BAML Chat](https://www.boundaryml.com/chat) | [Discord](https://discord.gg/BTNBeXGuaS)
[](https://github.com/boundaryml/baml)
[](https://opensource.org/licenses/Apache-2)
[](https://pypi.org/project/baml-py/)
**Try BAML**: [Prompt Fiddle](https://www.promptfiddle.com) • [Examples](https://baml-examples.vercel.app/) • [Example Source Code](https://github.com/BoundaryML/baml-examples)
**5 minute quickstarts**
[Python](https://docs.boundaryml.com/guide/installation-language/python) • [Typescript](https://docs.boundaryml.com/guide/installation-language/typescript) • [NextJS](https://docs.boundaryml.com/guide/framework-integration/react-next-js/quick-start) • [Ruby](https://docs.boundaryml.com/guide/installation-language/ruby) • [Others](https://docs.boundaryml.com/guide/installation-language/rest-api-other-languages) (Go, Java, C++, Rust, PHP, etc)
| | |
| - | - |
| What is BAML? | BAML is a new programming language for builing AI applications. |
| Do I need to write my whole app in BAML? | Nope, only the AI parts, you can then use BAML with any existing language of your choice! [python](/python), [typescript](/ts), and [more](/more). |
| Is BAML stable? | Yes, many companies use it in production! We ship updates weekly and rarely have breaking changes |
| Why a new language? | [Jump to section](#why-a-new-programming-language) |
| Why a lamb? | Baaaaa-ml. LAMB == BAML |
## The core BAML principle: LLM Prompts are functions
The fundamental building block in BAML is a function. Every prompt is a function that takes in parameters and returns a type.
```baml
function ChatAgent(message: Message[], tone: "happy" | "sad") -> string
```
Every function additionally defines which models it uses and what its prompt is.
```baml
function ChatAgent(message: Message[], tone: "happy" | "sad") -> StopTool | ReplyTool {
client "openai/gpt-4o-mini"
prompt #"
Be a {{ tone }} bot.
{{ ctx.output_format }}
{% for m in message %}
{{ _.role(m.role) }}
{{ m.content }}
{% endfor %}
"#
}
class Message {
role string
content string
}
class ReplyTool {
response string
}
class StopTool {
action "stop" @description(#"
when it might be a good time to end the conversation
"#)
}
```
Then in any language of your choice you can do the following:
```python
from baml_client import b
from baml_client.types import Message, StopTool
messages = [Message(role="assistant", content="How can I help?")]
while True:
print(messages[-1].content)
user_reply = input()
messages.append(Message(role="user", content=user_reply))
tool = b.ChatAgent(messages, "happy")
if isinstance(tool, StopTool):
print("Goodbye!")
break
else:
messages.append(Message(role="assistant", content=tool.reply))
```
### Making prompts easy to find and read
Since every prompt is a function, we can build tools to find every prompt you've written. But we've taken BAML one step further and built native tooling for VSCode (jetbrains + neovim coming soon).
1. You can see the full prompt (including any multi-modal assets)
2. You can see the exact network request we are making
3. You can see every function you've ever written
### Swapping models: 1-line change
It's just 1 line (ok, maybe 2). [Docs](https://docs.boundaryml.com/guide/baml-basics/switching-llms)
[Retry policies](https://docs.boundaryml.com/ref/llm-client-strategies/retry-policy) • [fallbacks](https://docs.boundaryml.com/ref/llm-client-strategies/fallback) • [model rotations](https://docs.boundaryml.com/ref/llm-client-strategies/round-robin). All statically defined.
> Want to do pick models at runtime? Check out [Client Registry](https://docs.boundaryml.com/guide/baml-advanced/llm-client-registry).
We currently support: [OpenAI](https://docs.boundaryml.com/ref/llm-client-providers/open-ai) • [Anthropic](https://docs.boundaryml.com/ref/llm-client-providers/anthropic) • [Gemini](https://docs.boundaryml.com/ref/llm-client-providers/google-ai-gemini) • [Vertex](https://docs.boundaryml.com/ref/llm-client-providers/google-vertex) • [Bedrock](https://docs.boundaryml.com/ref/llm-client-providers/aws-bedrock) • [Azure OpenAI](https://docs.boundaryml.com/ref/llm-client-providers/open-ai-from-azure) • [Anything OpenAI Compatible](https://docs.boundaryml.com/ref/llm-client-providers/openai-generic) ([Ollama](https://docs.boundaryml.com/ref/llm-client-providers/openai-generic-ollama), [OpenRouter](https://docs.boundaryml.com/ref/llm-client-providers/openai-generic-open-router), [VLLM](https://docs.boundaryml.com/ref/llm-client-providers/openai-generic-v-llm), [LMStudio](https://docs.boundaryml.com/ref/llm-client-providers/openai-generic-lm-studio), [TogetherAI](https://docs.boundaryml.com/ref/llm-client-providers/openai-generic-together-ai), and more)
### Hot-reloading for prompts
Using AI is all about iteration speed.
If testing your pipeline takes 2 minutes, in 20 minutes, you can only test 10 ideas.
If testing your pipeline took 5 seconds, in 20 minutes, you can test 240 ideas.
Introducing testing, for prompts.
### Structured outputs with any LLM
JSON is amazing for REST APIs, but way too strict and verbose for LLMs. LLMs need something flexible. We created the SAP (schema-aligned parsing) algorithm to support the flexible outputs LLMs can provide, like markdown within a JSON blob or chain-of-thought prior to answering.
SAP works with any model on day-1, without depending on tool-use or function-calling APIs.
To learn more about SAP you can read this post: [Schema-Aligned Parsing](https://www.boundaryml.com/blog/schema-aligned-parsing).
See it in action with: [Deepseek-R1](https://www.boundaryml.com/blog/deepseek-r1-function-calling) and [OpenAI O1](https://www.boundaryml.com/blog/openai-o1).
### Streaming (when it's a first class citizen)
Streaming is way harder than it should be. With our [Python/Typescript/Ruby] generated code, streaming becomes natural and type-safe.
### No strings attached
- 100% open-source (Apache 2)
- 100% private. AGI will not require an internet connection, neither will BAML
- No network requests beyond model calls you explicitly set
- Not stored or used for any training data
- BAML files can be saved locally on your machine and checked into Github for easy diffs.
- Built in Rust. So fast, you can't even tell it's there.
## BAML's Design Philosophy
Everything is fair game when making new syntax. If you can code it, it can be yours. This is our design philosophy to help restrict ideas:
- **1:** Avoid invention when possible
- Yes, prompts need versioning — we have a great versioning tool: git
- Yes, you need to save prompts — we have a great storage tool: filesystems
- **2:** Any file editor and any terminal should be enough to use it
- **3:** Be fast
- **4:** A first year university student should be able to understand it
## Why a new programming language
We used to write websites like this:
```python
def home():
return " alert(\\\"hello!\\\")\">Click"
```
And now we do this:
```jsx
function Home() {
return setCount(prev => prev + 1)}>
{count} clicks!
}
```
New syntax can be incredible at expressing new ideas. Plus the idea of maintaining hundreds of f-strings for prompts kind of disgusts us 🤮. Strings are bad for maintainable codebases. We prefer structured strings.
The goal of BAML is to give you the expressiveness of English, but the structure of code.
Full [blog post](https://www.boundaryml.com/blog/ai-agents-need-new-syntax) by us.
## Conclusion
As models get better, we'll continue expecting even more out of them. But what will never change is that we'll want a way to write maintainable code that uses those models. The current way we all just assemble strings is very reminiscent of the early days PHP/HTML soup in web development. We hope some of the ideas we shared today can make a tiny dent in helping us all shape the way we all code tomorrow.
## Contributing
Checkout our [guide on getting started](/CONTRIBUTING.md)
---
Made with ❤️ by Boundary
HQ in Seattle, WA
P.S. We're hiring for software engineers that love rust. [Email us]([email protected]) or reach out on [discord](https://discord.gg/ENtBB6kkXH)!
