Artificial ‒ typed, composable prompt engineering in Rust

Artificial is a batteries-included toy framework that demonstrates how to build strongly-typed, provider-agnostic prompt pipelines in Rust.

The code base is intentionally small—less than 3 k lines spread over multiple crates—yet it already shows how to:

• Compose reusable prompt fragments into high-level templates.
• Target multiple model providers via pluggable back-ends (currently OpenAI, adding others is trivial).
• Describe the LLM’s JSON response format in Rust and have it checked at compile time with schemars.
• Bubble up provider errors through a single, unified error type.
• Keep all heavy dependencies optional behind feature flags.

If you are curious how the new crop of “AI SDKs” work under the hood—or you need a lean starting point for your own experiments—this repo is for you.

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Table of contents

1. Crate layout
2. Installation
3. Quick start
4. Library tour
5. Design goals
6. FAQ
7. License

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Crate layout

Crate	Purpose
artificial-core	Provider-agnostic traits (ChatCompletionProvider, PromptTemplate), client, error types
artificial-prompt	String-building helpers (PromptBuilder, PromptChain)
artificial-types	Shared fragments (CurrentDateFragment, StaticFragment) and output helpers
artificial-openai	Thin wrapper around OpenAI /v1 with JSON-Schema function calling
artificial	Glue crate that re-exports everything above for convenience

Each crate lives under crates/* and can be used independently, but most people will depend on the umbrella crate:

[dependencies]
artificial = { version = "*", features = ["openai"] }

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Installation

Artificial is published as a workspace example, so you typically work against the Git repository directly:

git clone https://github.com/mrcrgl/artificial-rs.git
cd artificial-rs
cargo run -p artificial --example openai_hello_world

Requirements:

• Rust 1.77 or newer (edition 2024)
• An OpenAI API key exported as OPENAI_API_KEY
• Internet access for the example back-end

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Quick start

Below is a minimal “Hello, JSON” example taken from examples/openai_hello_world.rs:

use artificial::{
    ArtificialClient,
    generic::{GenericMessage, GenericRole},
    model::{Model, OpenAiModel},
    template::{IntoPrompt, PromptTemplate},
};
use artificial_openai::OpenAiAdapterBuilder;
use schemars::JsonSchema;
use serde::{Deserialize, Serialize};

#[derive(Debug, Serialize, Deserialize, JsonSchema)]
#[serde(deny_unknown_fields)]
struct HelloResponse { greeting: String }

struct HelloPrompt;

impl IntoPrompt for HelloPrompt {
    type Message = GenericMessage;
    fn into_prompt(self) -> Vec<Self::Message> {
        vec![
            GenericMessage::new("You are R2-D2.".into(), GenericRole::System),
            GenericMessage::new("Say hello!".into(),  GenericRole::User),
        ]
    }
}

impl PromptTemplate for HelloPrompt {
    type Output = HelloResponse;
    const MODEL: Model = Model::OpenAi(OpenAiModel::Gpt4oMini);
}

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let backend  = OpenAiAdapterBuilder::new_from_env().build()?;
    let client   = ArtificialClient::new(backend);
    let response = client.chat_complete(HelloPrompt).await?;
    println!("The droid says: {:?}", response);
    Ok(())
}

Run it:

cargo run -p artificial --example openai_hello_world

The program sends a request with an inline JSON-Schema and prints the deserialised reply.

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Library tour

Prompt fragments

artificial-types::fragments contains small building blocks that turn into GenericMessages—think “Current date”, “Static system instruction”, “Last user message”. You can combine them via PromptChain:

PromptChain::new()
    .with(CurrentDateFragment::new())
    .with(StaticFragment::new("You are a helpful bot.", GenericRole::System))
    .with(last_user_message)
    .build();

Strongly-typed outputs

Define a struct (must implement JsonSchema + Deserialize) and declare it as PromptTemplate::Output. The OpenAI back-end automatically injects the schema as response_format = json_schema.

Provider back-ends

A back-end only has to implement the single-method trait

trait ChatCompletionProvider {
    type Message;
    fn chat_complete<P>(&self, prompt: P) -> Pin<Box<dyn Future<Output = Result<P::Output>> + Send>>
}

Because the PromptTemplate carries the desired model, the back-end can map it to the provider’s naming scheme (gpt-4o-mini, gpt-4o, …).

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Design goals

• Minimal surface area – the framework should fit in one screenful of source code, making it easy to fork or copy-paste.
• Compile-time checks everywhere – wrong model name? missing OPENAI_API_KEY? Incompatible JSON schema? You find out early.
• Pluggable transports – swap out artificial-openai for an Ollama/Anthropic adapter without touching user code.
• No macros – generic traits and impls keep the magic transparent.

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FAQ

Why another AI SDK? Because many existing SDKs are either too heavyweight or hide the inner workings behind procedural macros. Artificial aims to be the smallest possible blueprint you can still use in production.

Does it support streaming completions? Not yet. The ChatCompletionProvider trait is purposely tiny; adding another method for streaming is straightforward.

Is the OpenAI back-end production ready? It handles basic JSON-Schema function calling, retries and streaming are missing. Treat it as a reference implementation.

How do I add Anthropic or Ollama?

1. Create artificial-anthropic (or similar) crate.
2. Wrap the provider’s HTTP API in a small client struct.
3. Implement ChatCompletionProvider for the adapter.
4. Done—ArtificialClient works instantly with the new provider.

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License

Licensed under MIT. See LICENSE for the full text.