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Overview

Introduction

AIRS Protocols is a comprehensive Rust workspace that provides production-ready implementations of common AI communication protocols and LLM (Large Language Model) provider APIs. The project is designed to enable seamless integration between AI systems, tools, and external services while maintaining type safety, performance, and interoperability.

Vision

As AI agents become more sophisticated, the need for standardized communication protocols and unified API interfaces becomes critical. AIRS Protocols aims to be the go-to Rust library for:

  1. Protocol Implementation - Providing robust, spec-compliant implementations of agent communication protocols
  2. LLM Integration - Offering unified interfaces for interacting with various LLM providers
  3. Interoperability - Ensuring compatibility with official SDKs and other language implementations
  4. Production Readiness - Delivering battle-tested code with comprehensive error handling and testing

Project Goals

🎯 Core Objectives

  • Standards Compliance: Faithfully implement protocol specifications without deviation
  • Type Safety: Leverage Rust's type system to prevent runtime errors at compile time
  • Performance: Optimize for low latency and high throughput in production environments
  • Extensibility: Provide clear extension points for custom implementations
  • Documentation: Maintain comprehensive, up-to-date documentation with examples

🌟 Design Principles

1. Type Safety First

  • Compile-time verification of protocol messages
  • Strong typing for all API interactions
  • Zero-cost abstractions where possible
  • Explicit error handling with Result types

2. Protocol Agnostic Architecture

  • Clear separation between protocol logic and transport layer
  • Pluggable transport implementations (stdio, HTTP, WebSocket)
  • Support for multiple protocol bindings
  • Easy to add custom transports

3. Production Ready

  • Comprehensive error handling with context
  • Built-in authentication and authorization
  • Extensive test coverage (unit, integration, property-based)
  • Performance benchmarks and profiling
  • Proper logging and observability

4. Async Native

  • Built on the tokio async runtime
  • Non-blocking I/O throughout
  • Efficient concurrent request handling
  • Streaming support for long-running operations

5. Interoperability

  • Compatible with official protocol specifications
  • Works with existing SDKs (Python, TypeScript, etc.)
  • Bridge adapters for protocol translation
  • JSON-RPC compatibility layer

Workspace Organization

AIRS Protocols is organized as a Cargo workspace with two main categories:

Protocols (protocols/)

Communication protocols that enable AI agents to interact with tools, resources, and other agents.

Model Context Protocol (MCP)

  • Status: ✅ Available (v1.0.0-rc.1)
  • Purpose: Connect AI models to external capabilities
  • Features:
  • JSON-RPC 2.0 foundation
  • Tool, Resource, and Prompt providers
  • Multiple transport implementations
  • Authentication and authorization
  • Comprehensive documentation

Agent-to-Agent Protocol (A2A)

  • Status: 🚧 Planned
  • Purpose: Enable communication between independent AI agents
  • Features (Planned):
  • Multiple protocol bindings (JSON-RPC, gRPC, REST)
  • Task lifecycle management
  • Streaming support
  • Agent discovery and routing

APIs (apis/)

Client implementations for LLM provider APIs with unified interfaces.

Coming Soon

API client implementations are planned for future releases. The initial focus is on protocol implementations.

Planned providers: - Anthropic Claude API - OpenAI GPT API - Google Gemini API - Ollama (local models)

Technology Stack

Core Dependencies

  • Runtime: tokio - Async runtime for Rust
  • Serialization: serde - Serialization/deserialization framework
  • HTTP: axum - Ergonomic web framework
  • Error Handling: thiserror - Derive macros for error types

Development Tools

Version Policy

AIRS Protocols follows Semantic Versioning 2.0.0:

  • Major versions for breaking API changes
  • Minor versions for new features (backward compatible)
  • Patch versions for bug fixes and improvements

Each package in the workspace maintains its own version number, allowing independent evolution while maintaining compatibility.

Current Versions

Package Version Status
airsprotocols-mcp 1.0.0-rc.1 Release Candidate
airsprotocols-a2a - Planned
airsprotocols-anthropic - Planned
airsprotocols-openai - Planned

Development Status

✅ Phase 1: Foundation (Complete)

  • Project structure setup
  • Workspace configuration
  • MCP protocol implementation
  • Documentation infrastructure

🚧 Phase 2: Core Protocols (In Progress)

  • Complete MCP documentation
  • CI/CD pipeline
  • A2A protocol implementation
  • MCP ↔ A2A bridge adapters

🔮 Phase 3: LLM APIs (Planned)

  • Anthropic API client
  • OpenAI API client
  • Unified LLM interface

🔮 Phase 4: Ecosystem (Future)

  • Additional LLM providers
  • Performance optimizations
  • Advanced features and extensions

Use Cases

1. Building AI Agent Applications

Use AIRS Protocols to build sophisticated AI agents that can: - Access external tools and APIs - Retrieve and process data from various sources - Collaborate with other agents - Maintain context across conversations

2. Exposing Capabilities to LLMs

Create MCP servers that expose your application's capabilities: - File system access (with appropriate security) - Database queries - API integrations - Custom business logic

3. Multi-Agent Systems

Build systems where multiple AI agents collaborate: - Agent-to-agent communication - Task delegation and coordination - Shared resource management - Distributed problem solving

4. LLM Provider Abstraction

Write code once and work with multiple LLM providers: - Unified interface across providers - Easy provider switching - Fallback and retry logic - Cost optimization

Community and Governance

Open Source Philosophy

AIRS Protocols is committed to open-source principles: - Transparency: Open development process - Collaboration: Community-driven development - Standards: Following established protocol specifications - Quality: High standards for code and documentation

Protocol Governance

  • MCP: Anthropic's Model Context Protocol specification
  • A2A/ACP: Linux Foundation governance (merged protocols)
  • Community-driven: Open to contributions and feedback

Support and Resources

Getting Help

  • Documentation: Comprehensive guides and API references
  • Issues: GitHub Issues for bugs and feature requests
  • Discussions: GitHub Discussions for questions and ideas

External Resources

Next Steps

Ready to get started? Check out these resources:

Questions or feedback? Open an issue on GitHub or start a discussion.