LLM Kit xAI

xAI (Grok) provider for LLM Kit - Complete integration with xAI's Grok models featuring reasoning capabilities, integrated search, and image generation.

Note: This provider uses the standardized builder pattern. See the Quick Start section for the recommended usage.

Features

• Text Generation: Full support for Grok models with streaming and tool calling
• Streaming: Real-time response streaming with Server-Sent Events
• Tool Calling: Complete function calling support with tool execution
• Image Generation: Create images with grok-2-image model
• Reasoning Mode: Access model reasoning with configurable effort levels
• Integrated Search: Web, X (Twitter), news, and RSS search capabilities
• Citations: Automatic citation extraction from search results
• Response Format: JSON mode and structured outputs with JSON schema

Installation

Add this to your Cargo.toml:

[dependencies]
llm-kit-xai = "0.1"
llm-kit-core = "0.1"
llm-kit-provider = "0.1"
tokio = { version = "1", features = ["full"] }

Quick Start

Using the Client Builder (Recommended)

use llm_kit_xai::XaiClient;
use llm_kit_provider::{Provider, LanguageModel};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create provider using the client builder
    let provider = XaiClient::new()
        .api_key("your-api-key")  // Or use XAI_API_KEY env var
        .build();
    
    // Create a language model
    let model = provider.chat_model("grok-4");
    
    println!("Model: {}", model.model_id());
    println!("Provider: {}", model.provider());
    Ok(())
}

Using Settings Directly (Alternative)

use llm_kit_xai::{XaiProvider, XaiProviderSettings};
use llm_kit_provider::{Provider, LanguageModel};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create provider with settings
    let provider = XaiProvider::new(XaiProviderSettings::default());
    
    let model = provider.chat_model("grok-4");
    
    println!("Model: {}", model.model_id());
    Ok(())
}

Configuration

Environment Variables

Set your xAI API key as an environment variable:

export XAI_API_KEY=your-api-key

Using the Client Builder

use llm_kit_xai::XaiClient;

let provider = XaiClient::new()
    .api_key("your-api-key")
    .base_url("https://api.x.ai/v1")
    .header("X-Custom-Header", "value")
    .name("my-xai-provider")
    .build();

Builder Methods

The XaiClient builder supports:

• .api_key(key) - Set the API key
• .base_url(url) - Set custom base URL
• .name(name) - Set provider name
• .header(key, value) - Add a single custom header
• .headers(map) - Add multiple custom headers
• .build() - Build the provider

Supported Models

Chat Models

• grok-4 - Latest Grok-4 model with advanced capabilities
• grok-4-fast-reasoning - Fast model with reasoning capabilities
• grok-4-fast-non-reasoning - Fast model without reasoning
• grok-code-fast-1 - Optimized for code generation
• grok-3 - Grok-3 base model
• grok-3-fast - Faster Grok-3 variant
• grok-3-mini - Smaller, efficient model
• grok-2-vision-1212 - Vision-capable model
• grok-2-1212 - Grok-2 model with December 2024 updates
• grok-beta - Beta model with latest features

// Create a chat model
let model = provider.chat_model("grok-4");

Image Models

• grok-2-image - Image generation model

// Create an image model
let model = provider.image_model("grok-2-image");

Provider-Specific Options

xAI supports advanced features through provider options that can be passed using the llm-kit-core API.

Reasoning Mode

Control the model's reasoning effort level:

use llm_kit_core::GenerateText;
use serde_json::json;

let result = GenerateText::new(model, prompt)
    .provider_options(json!({
        "reasoningEffort": "high"  // "low", "medium", or "high"
    }))
    .execute()
    .await?;

Access reasoning content in the response:

// Reasoning content is automatically extracted to result.content
for content in result.content {
    if let llm_kit_core::output::Output::Reasoning(reasoning) = content {
        println!("Model reasoning: {}", reasoning.text);
    }
}

Integrated Search

Enable web, X (Twitter), news, or RSS search:

use llm_kit_core::GenerateText;
use serde_json::json;

let result = GenerateText::new(model, prompt)
    .provider_options(json!({
        "searchParameters": {
            "recencyFilter": "day",  // "hour", "day", "week", "month", "year"
            "sources": [
                {"type": "web"},
                {"type": "x"},       // X (Twitter) search
                {"type": "news"},
                {"type": "rss", "url": "https://example.com/feed.xml"}
            ]
        }
    }))
    .execute()
    .await?;

Citations

Citations are automatically extracted from search results:

let result = GenerateText::new(model, prompt).execute().await?;

// Citations available in result.content
for content in result.content {
    if let llm_kit_core::output::Output::Source(source) = content {
        println!("Source: {} - {}", source.title, source.url);
    }
}

Response Format (JSON Mode)

Force structured JSON outputs:

use llm_kit_core::GenerateText;
use llm_kit_provider::language_model::call_options::LanguageModelResponseFormat;
use serde_json::json;

// Simple JSON mode
let result = GenerateText::new(model, prompt)
    .with_response_format(LanguageModelResponseFormat::Json {
        schema: None,
        name: None,
        description: None,
    })
    .execute()
    .await?;

// Structured outputs with JSON schema
let schema = json!({
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    },
    "required": ["name", "age"]
});

let result = GenerateText::new(model, prompt)
    .with_response_format(LanguageModelResponseFormat::Json {
        schema: Some(schema),
        name: Some("UserProfile".to_string()),
        description: Some("A user profile".to_string()),
    })
    .execute()
    .await?;

Parallel Function Calling

Control parallel tool execution:

use llm_kit_core::GenerateText;
use serde_json::json;

let result = GenerateText::new(model, prompt)
    .tools(tools)
    .provider_options(json!({
        "parallelFunctionCalling": true
    }))
    .execute()
    .await?;

Available Provider Options

Examples

See the examples/ directory for complete examples:

• chat.rs - Basic chat completion using do_generate() directly
• stream.rs - Streaming responses using do_stream() directly
• chat_tool_calling.rs - Tool calling using do_generate() directly
• stream_tool_calling.rs - Streaming with tools using do_stream() directly
• image_generation.rs - Image generation using do_generate() directly

Run examples with:

export XAI_API_KEY="your-api-key"
cargo run --example chat
cargo run --example stream
cargo run --example image_generation

Documentation

• API Documentation
• LLM Kit Documentation
• xAI API Reference
• xAI Console

License

MIT

Contributing

Contributions are welcome! Please see the Contributing Guide for more details.