GPT-5 Rust Client Library

⚠️ IN ACTIVE IMPROVEMENT ⚠️
This library is actively being improved and may have breaking changes.
Perfect for experimentation, learning, and development projects!

Latest Release: v0.2.1 - Leaner networking stack with rustls-only TLS and refreshed deps!

A comprehensive Rust client library for OpenAI's GPT-5 API with full support for function calling, reasoning capabilities, and type-safe enums.

Features

🚀 Core Capabilities

• Type-safe API - All parameters use strongly-typed enums for compile-time safety
• Function calling - Full support for OpenAI's function calling system with custom tools
• Reasoning capabilities - Configurable reasoning effort levels (Low, Medium, High)
• Verbosity control - Fine-tune response detail levels for different use cases
• Multiple models - Support for GPT-5, GPT-5 Mini, GPT-5 Nano, and custom models
• Built-in web search - Enable OpenAI's web search assistance with custom queries and result limits

⚡ Performance & Developer Experience

• Async/await - Built on tokio for high performance and concurrency
• Error handling - Comprehensive error types and validation with helpful messages
• Response parsing - Easy access to text, function calls, and metadata
• Request builder - Fluent API for building complex requests
• Validation - Built-in request validation with helpful warnings

📚 Documentation & Examples

• Comprehensive examples - 6 practical examples from basic to advanced
• Interactive chat - Ready-to-run chat loop example
• Function calling demos - Calculator and weather tool examples
• Error handling patterns - Production-ready error handling examples
• Quick start guide - Get running in minutes with minimal code

🔮 Coming Soon

• Streaming responses - Real-time response streaming for better UX
• Retry mechanisms - Automatic retry with exponential backoff
• Rate limiting - Built-in rate limiting and quota management
• Response caching - Optional response caching for cost optimization
• WebSocket support - Real-time bidirectional communication
• More examples - Advanced use cases and integration patterns
• CLI tool - Command-line interface for quick testing
• Benchmarks - Performance benchmarks and optimization guides

Quick Start

Add this to your Cargo.toml:

[dependencies]
gpt5 = "0.2.1"
tokio = { version = "1.0", features = ["rt-multi-thread", "macros"] }
serde_json = "1.0"  # For function calling examples

🚀 Try the Examples

The fastest way to get started is with our examples:

# Clone and run examples
git clone <repository-url>
cd gpt5
cargo run --example quick_start
cargo run --example basic_usage
cargo run --example simple_chat
cargo run --example web_search

See the examples/ directory for more detailed examples including function calling, error handling, and interactive chat.

Basic Usage

use gpt5::{Gpt5Client, Gpt5Model};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Gpt5Client::new("your-api-key".to_string());
    
    let response = client
        .simple(Gpt5Model::Gpt5Nano, "Hello, world!")
        .await?;
    
    println!("Response: {}", response);
    Ok(())
}

Advanced Usage with Function Calling

use gpt5::{Gpt5Client, Gpt5Model, Gpt5RequestBuilder, Tool, VerbosityLevel, ReasoningEffort};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Gpt5Client::new("your-api-key".to_string());
    
    // Define a weather tool
    let weather_tool = Tool {
        tool_type: "function".to_string(),
        name: "get_current_weather".to_string(),
        description: "Get the current weather in a given location".to_string(),
        parameters: json!({
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"]
                }
            },
            "required": ["location", "unit"]
        }),
    };
    
    // Build a request with tools
    let request = Gpt5RequestBuilder::new(Gpt5Model::Gpt5)
        .input("What's the weather like in Boston today?")
        .instructions("Use the weather tool to get current conditions")
        .tools(vec![weather_tool])
        .tool_choice("auto")
        .verbosity(VerbosityLevel::Medium)
        .reasoning_effort(ReasoningEffort::Medium)
        .max_output_tokens(500)
        .build();
    
    // Send the request
    let response = client.request(request).await?;
    
    // Check for function calls
    let function_calls = response.function_calls();
    if !function_calls.is_empty() {
        println!("Function calls made: {}", function_calls.len());
        for call in function_calls {
            println!("Function: {}", call.name.as_deref().unwrap_or("unknown"));
            println!("Arguments: {}", call.arguments.as_deref().unwrap_or("{}"));
        }
    }
    
    // Get text response
    if let Some(text) = response.text() {
        println!("Response: {}", text);
    }
    
    Ok(())
}

Enable Web Search Assistance

use gpt5::{Gpt5Client, Gpt5Model, Gpt5RequestBuilder};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Gpt5Client::new("your-api-key".to_string());

    let request = Gpt5RequestBuilder::new(Gpt5Model::Gpt5)
        .input("What were the latest announcements from the Rust team?")
        .web_search_enabled(true)
        .web_search_query("Rust language roadmap 2025")
        .web_search_max_results(5)
        .build();

    let response = client.request(request).await?;
    if let Some(text) = response.text() {
        println!("{}", text);
    }

    Ok(())
}

API Reference

Models

The library supports all GPT-5 models:

use gpt5::Gpt5Model;

let model = Gpt5Model::Gpt5;        // Main model - most capable
let mini = Gpt5Model::Gpt5Mini;     // Balanced performance and cost
let nano = Gpt5Model::Gpt5Nano;     // Fastest and most cost-effective
let custom = Gpt5Model::Custom("gpt-5-custom".to_string());

Reasoning Effort

Control how much computational effort GPT-5 puts into reasoning:

use gpt5::ReasoningEffort;

let low = ReasoningEffort::Low;     // Fast, basic reasoning
let medium = ReasoningEffort::Medium; // Balanced performance
let high = ReasoningEffort::High;   // Thorough analysis

Verbosity Levels

Control the detail level of responses:

use gpt5::VerbosityLevel;

let low = VerbosityLevel::Low;      // Concise responses
let medium = VerbosityLevel::Medium; // Balanced detail
let high = VerbosityLevel::High;    // Detailed responses

Response Status

Check response completion and status:

let response = client.request(request).await?;

if response.is_completed() {
    println!("Response completed successfully");
    if let Some(text) = response.text() {
        println!("Text: {}", text);
    }
} else {
    println!("Response incomplete: {:?}", response.status);
}

// Get usage statistics
println!("Total tokens: {}", response.total_tokens());
if let Some(reasoning_tokens) = response.reasoning_tokens() {
    println!("Reasoning tokens: {}", reasoning_tokens);
}

Error Handling

The library provides comprehensive error handling:

use gpt5::{Gpt5Client, Gpt5Model};

match client.simple(Gpt5Model::Gpt5Nano, "Hello").await {
    Ok(response) => println!("Success: {}", response),
    Err(e) => {
        match e.downcast_ref::<reqwest::Error>() {
            Some(req_err) => println!("Network error: {}", req_err),
            None => println!("Other error: {}", e),
        }
    }
}

The client now detects HTTP status failures from the OpenAI API and surfaces detailed error messages, making it easier to debug authentication or quota issues. If you need full control over networking (custom proxies, retry middleware, etc.), pass your own configured reqwest::Client via with_http_client and keep using the same high-level interface.

Validation

The library includes built-in validation for requests:

let request = Gpt5RequestBuilder::new(Gpt5Model::Gpt5Nano)
    .input("")  // Empty input will trigger a warning
    .max_output_tokens(5)  // Very low token count will trigger a warning
    .build();  // Validation runs automatically

License

This project is licensed under the MIT License - see the LICENSE file for details.

Examples

We provide comprehensive examples to help you get started quickly:

Prerequisites for Examples

Set your OpenAI API key:

export OPENAI_API_KEY="your-api-key-here"

Contributing

🚀 We're actively looking for contributors! This is a fresh library with lots of room for improvement.

Areas where we'd love help:

• 🐛 Bug fixes and edge case handling
• 📚 Documentation improvements
• 🧪 More comprehensive tests
• ⚡ Performance optimizations
• 🔧 Additional features and examples
• 📖 Better error messages and validation

How to contribute:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a Pull Request

Questions or ideas? Open an issue and let's discuss! We're very responsive and would love to hear from you.

Contributions are welcome! Please feel free to submit a Pull Request.

Changelog

See CHANGELOG.md for detailed release notes and version history.