Crate aither_core 

aither

Write AI applications that work with any provider 🚀

aither-core hosts the no-std trait APIs that power the rest of the workspace. Use it directly (or through the top-level aither crate) to describe portable language models, embeddings, moderation, image/audio generators, and more. Every provider crate simply implements these traits.

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Your App      │───▶│    aither        │◀───│   Providers     │
│                 │    │   (this crate)   │    │                 │
│ - Chat bots     │    │                  │    │ - openai        │
│ - Search        │    │ - LanguageModel  │    │ - anthropic     │
│ - Content gen   │    │ - EmbeddingModel │    │ - llama.cpp     │
│ - Voice apps    │    │ - ImageGenerator │    │ - whisper       │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Supported AI Capabilities

Capability	Trait	Description
Language Models	LanguageModel	Streaming events (text, reasoning, tool calls)
Embeddings	EmbeddingModel	Convert text to vectors for semantic search
Image Generation	ImageGenerator	Create images with progressive quality improvement
Text-to-Speech	AudioGenerator	Generate speech audio from text
Speech-to-Text	AudioTranscriber	Transcribe audio to text
Content Moderation	Moderation	Detect policy violations with confidence scores

Examples

Streaming Responses with Events

use aither_core::llm::{LanguageModel, Event, Message, LLMRequest, model::Parameters};
use futures_lite::StreamExt;

async fn event_demo(model: impl LanguageModel) -> aither_core::Result {
    let request = LLMRequest::new([
        Message::user("Explain how rainbows form like I'm five."),
    ])
    .with_parameters(Parameters::default().include_reasoning(true));

    let mut stream = model.respond(request);
    let mut answer = String::new();

    while let Some(event) = stream.next().await {
        match event? {
            Event::Text(text) => answer.push_str(&text),
            Event::Reasoning(thought) => println!("thinking: {}", thought),
            Event::ToolCall(call) => println!("tool requested: {}", call.name),
            _ => {}
        }
    }
    Ok(answer)
}

Structured Output with Tools

use aither_core::llm::{LLMRequest, Message, Tool, ToolResult};
use schemars::JsonSchema;
use serde::Deserialize;
use std::borrow::Cow;

/// Get current weather for a location.
#[derive(JsonSchema, Deserialize)]
struct WeatherQuery {
    /// City to report on, e.g. "Tokyo".
    location: String,
}

struct WeatherTool;

impl Tool for WeatherTool {
    fn name(&self) -> Cow<'static, str> {
        Cow::Borrowed("get_weather")
    }

    type Arguments = WeatherQuery;
    type Res = ToolResult;

    async fn call(&self, args: Self::Arguments) -> aither_core::Result<Self::Res> {
        Ok(ToolResult::text(format!("Weather in {}: 22°C, sunny", args.location)))
    }
}

// Advertise the tool on a request. The model replies with a ToolCall event;
// executing it is up to the caller (see `aither-agent`).
let request = LLMRequest::new([Message::user("What is the weather in Tokyo?")])
    .with_tool(&WeatherTool);

See llm::tool for more details on using tools with language models.

Semantic Search with Embeddings

use aither_core::EmbeddingModel;

async fn embed_query(
    model: impl EmbeddingModel,
    query: &str,
) -> aither_core::Result<Vec<f32>> {
    // Compare this against your stored document embeddings with cosine
    // similarity, or hand it to `aither-rag`.
    model.embed(query).await
}

Progressive Image Generation

use aither_core::{ImageGenerator, image::{Prompt, Size}};
use futures_lite::StreamExt;

async fn generate_image(generator: impl ImageGenerator) -> Result<Vec<u8>, Box<dyn std::error::Error>> {
    let prompt = Prompt::new("A beautiful sunset over mountains");
    let size = Size::square(1024);
     
    let mut image_stream = generator.create(prompt, size);
    let mut final_image = Vec::new();
     
    // Each iteration gives us a complete image with progressively better quality
    while let Some(image_result) = image_stream.next().await {
        let current_image = image_result?;
        final_image = current_image; // Keep the latest (highest quality) version
         
        // Optional: Display preview of current quality level
        println!("Received image update, {} bytes", final_image.len());
    }
     
    Ok(final_image) // Return the final highest-quality image
}

Modules

• audio — text-to-speech and transcription traits.
• embedding — turn text into dense vectors.
• image — image generation + editing APIs.
• llm — request builders, messages, provider traits, reasoning streams.
• moderation — moderation scoring traits.

Modules

audio

Audio generation and transcription.

embedding

Text embeddings.

image

Text-to-image generation.

llm

Language Models and Conversation Management

moderation

Content moderation utilities.

Structs

Error

The Error type, a wrapper around a dynamic error type.

Traits

AudioGenerator

Generates audio from text prompts.

AudioTranscriber

Transcribes audio to text.

EmbeddingModel

Converts text to vector representations.

ImageGenerator

Trait for generating and editing images from prompts and masks.

LanguageModel

Language models for text generation and conversation.

Moderation

Trait for content moderation services.

Type Aliases

Result

Result type used throughout the crate.

Attribute Macros

tool

Converts an async function into an AI tool that can be called by language models.