Quick Start
Ax gives Rust one typed contract for LLM programs: signatures for data shape, ai() for model access, ax() for structured generation, agent() for tool-using runtime loops, and AxGEPA for improving programs with examples.
Install
git clone https://github.com/ax-llm/ax && cd ax/packages/rust
cargo run --example signature_schemaFirst Program
Start with a small typed task. The signature declares the fields the model receives and the fields Ax must parse back out.
use axllm::{ai, ax};
use serde_json::json;
let llm = ai("openai", json!({"apiKey": std::env::var("OPENAI_API_KEY")?}))?;
let classify = ax("review:string -> sentiment:class \"positive, negative, neutral\"")?;That is the core loop:
- create a provider client
- declare the input and output contract
- run the program with typed inputs
- read typed outputs instead of scraping prose
flowchart LR A["ai() client"] --> C["forward() with typed inputs"] B["Signature"] --> C C --> D["Validate + retry"] D --> E["Typed output"]
The rest of the site keeps the same concepts but swaps install commands, imports, examples, and API names for Rust.
Where To Go Next
Use Examples when you want runnable files. Use Concepts when you want the mental model. Use Subsystems when you know which surface you are trying to use and want the practical call shape.