codeitlikemiley/typesafe-sdk-rust

codeitlikemiley/typesafe-sdk-rust on GitHub

Stars: 3
Forks: 0
Language: Rust
License: MIT
Last push: 17 Sep 2026

Read from GitHub on 24 Sep 2026. Counts change daily.

How it uses Jev

use typesafe_sdk::Question;

#[tokio::main]
async fn main() -> Result<(), typesafe_sdk::Error> {
    let rt = harness::ExampleRuntime::start(fixtures::mixed_primitives()).await?;
    println!("mode={}", harness::ExampleRuntime::mode_label());

    let response = rt
        .client
        .system_one(
            serde_json::json!({"document": "I was charged twice. Please fix this ASAP."}),
            [
                ("billing", Question::noul("Is this ticket about billing?")),
                (
                    "tone",

View in examples/system_one.rs

Open on GitHub

PrimitivesChoice Score Noul

Use casesStructured extraction

Introducing System One Models and Jev (Hacker News launch thread)

The 1,978-point, 513-comment launch thread. The top comment calls the speed comparison misleading, since Jev only returns structured output, and disputes the cannot-hallucinate claim because a valid type can still hold a wrong value. Others note ordinary LLMs can already be forced into structured output.

albelfioOpen

DiscussionIntent and model routing+3

DSPy: programming, not prompting, language models

Declares typed input/output Signatures for LLM modules and optimizes the underlying prompts and weights against a metric. Its Signature abstraction is the closest widely-used open equivalent of Jev's typed-question interface. Star count is GitHub's rounded display figure.

stanfordnlp38k starsOpen

AlternativeStructured extraction+3

Outlines: structured outputs for LLMs

Guarantees valid structured output during generation by constraining decoding to a grammar or schema. The mechanism HN commenters repeatedly cited as already covering Jev's cannot-produce-a-type-error guarantee on ordinary LLMs. Star count is GitHub's rounded display figure.

dottxt-ai16k starsOpen

AlternativeStructured extraction+2

Instructor: reliable JSON from any LLM

Pydantic-based library that extracts typed, validated structured data from LLMs across providers with automatic retries. The established way teams get Jev-style typed answers today, at LLM latency and cost. Star count is GitHub's rounded display figure.

567-labs14k starsOpen

AlternativeStructured extraction+1

GLiNER: generalist lightweight NER

Compact encoder model that takes the label set as input at inference time and extracts arbitrary entity types zero-shot on CPU. Same classifier-that-takes-its-categories-as-input shape, for extraction rather than decisions. Star count is GitHub's rounded display figure.

urchade3.9k starsOpen

AlternativeStructured extraction+2

vercel-labs/ai-cli

Vercel Labs CLI for generating from the terminal. Its evaluate command takes a state plus typed questions and defaults its evaluation model to typesafe-ai/jev on the AI Gateway, with -m jev as the shorthand and an --input flag for text or JSON state.

vercel-labs813 starsOpen

ToolStructured extraction

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