Feature Highlights

🚀 Complete OpenAI API Coverage

• ✅ Chat Completions API - Full support for GPT models with streaming
• ✅ Embeddings API - Text embedding generation with multiple models
• ✅ Files API - Upload, retrieve, and manage files
• ✅ Batch API - Process requests in batches for cost optimization
• ✅ Response API - Advanced response handling with streaming support
• ✅ Model Context Protocol (MCP) - Integration with MCP servers

🔧 Developer Experience

• Type Safety - Fully typed Rust API with comprehensive error handling
• Async/Await - Built on tokio for high-performance async operations
• Streaming Support - Real-time streaming for chat and response APIs
• Flexible Authentication - Support for multiple OpenAI-compatible APIs
• Event Handling - Customizable event handlers for streaming data

🎯 Agent Development

• Tool Integration - Function calling and tool execution
• Memory Management - Built-in context and conversation management
• Error Recovery - Robust error handling and retry mechanisms
• Multi-Modal Support - Text, images, audio, and file inputs

Usage

Set up your environment:

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

The library provides comprehensive examples for all supported APIs. Check out the examples/ directory for complete, runnable code:

• chat.rs - Chat completions with streaming support
• embedding.rs - Text embeddings generation
• file.rs - File upload and management
• batch.rs - Batch processing for cost optimization
• response.rs - Advanced response API with real-time streaming
• mcp.rs - Model Context Protocol integration

Run any example with:

cargo run --example chat
cargo run --example response
cargo run --example embedding
# ... etc

Development

Architecture

The library is organized into several key modules:

• api/ - Core API implementations for different OpenAI endpoints

  • chat.rs - Chat completions API with streaming support
  • embedding.rs - Text embeddings generation
  • file.rs - File upload and management
  • batch.rs - Batch processing operations
  • response.rs - Advanced response API with real-time streaming
  • type.rs - Common types and utilities

• http.rs - HTTP client abstraction with retry logic and streaming

• mcp.rs - Model Context Protocol integration

• tool.rs - Function calling and tool execution framework

• agent.rs - High-level agent abstraction (work in progress)

• error.rs - Comprehensive error handling

Key Design Principles

1. Type Safety - Leverages Rust's type system for compile-time correctness
2. Async-First - Built on tokio for high-performance async operations
3. Streaming Support - Real-time data processing with Server-Sent Events
4. Modularity - Clean separation of concerns with trait-based design
5. Compatibility - Works with OpenAI and OpenAI-compatible APIs

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Commit your changes (git commit -m 'Add amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

Support Me

If you find this project helpful and would like to support its development, you can buy me a coffee!

Your support is greatly appreciated and motivates me to keep improving this project.

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Thank you for your support!

Appreciation

We would like to extend our heartfelt gratitude to the following projects and contributors:

• The Rust community for their continuous support and development of the Rust ecosystem.

Additional Acknowledgements

Special thanks to the following projects and technologies that make OpenAgent possible:

• OpenAI - For providing the foundational APIs and documentation
• Model Context Protocol - For the innovative protocol enabling tool integration

This project follows the principles of open-source development and aims to contribute back to the Rust ecosystem by providing a reliable, type-safe, and performant library for AI application development.