swarms-rs

The Enterprise-Grade, Production-Ready Multi-Agent Orchestration Framework in Rust

Overview

swarms-rs is an enterprise-grade, production-ready multi-agent orchestration framework built in Rust, designed to handle the most demanding tasks with unparalleled speed and efficiency. Leveraging Rust's bleeding-edge performance and safety features, swarms-rs provides a powerful and scalable solution for orchestrating complex multi-agent systems across various industries.

Key Benefits

⚡ Extreme Performance

• Multi-Threaded Architecture: Utilize the full potential of modern multi-core processors with Rust's zero-cost abstractions and fearless concurrency. Swarms-rs ensures that your agents run with minimal overhead, achieving maximum throughput and efficiency.
• Bleeding-Edge Speed: Written in Rust, swarms-rs delivers near-zero latency and lightning-fast execution, making it the ideal choice for high-frequency and real-time applications.

🛡 Enterprise-Grade Reliability

• Memory Safety: Rust's ownership model guarantees memory safety without the need for a garbage collector, ensuring that your multi-agent systems are free from data races and memory leaks.
• Production-Ready: Designed for real-world deployment, swarms-rs is ready to handle mission-critical tasks with robustness and reliability that you can depend on.

🧠 Powerful Orchestration

• Advanced Agent Coordination: Seamlessly manage and coordinate thousands of agents, allowing them to communicate and collaborate efficiently to achieve complex goals.
• Extensible and Modular: Swarms-rs is highly modular, allowing developers to easily extend and customize the framework to suit specific use cases.

🚀 Scalable and Efficient

• Optimized for Scale: Whether you're orchestrating a handful of agents or scaling up to millions, swarms-rs is designed to grow with your needs, maintaining top-tier performance at every level.
• Resource Efficiency: Maximize the use of system resources with Rust's fine-grained control over memory and processing power, ensuring that your agents run optimally even under heavy loads.

Getting Started

Prerequisites

• Rust (latest stable version recommended)
• Cargo package manager
• An API key for your LLM provider (OpenAI, DeepSeek, etc.)

Installation

Add swarms-rs to your Cargo.toml:

[dependencies]
swarms-rs = "0.1"
# Or use the latest version from GitHub
swarms-rs = { git = "https://github.com/The-Swarm-Corporation/swarms-rs", branch = "main" }

Environment Setup

Create a .env file in your project root with your API credentials:

OPENAI_API_KEY=your_openai_key_here
OPENAI_BASE_URL=https://api.openai.com/v1

# Or for DeepSeek
DEEPSEEK_API_KEY=your_deepseek_key_here
DEEPSEEK_BASE_URL=https://api.deepseek.com/v1

Run Examples

In swarms-rs/examples there is our sample code, which can provide a considerable degree of reference:

To run the graph workflow example:

cargo run --example graph_workflow

DEEPSEEK_API_KEY and DEEPSEEK_BASE_URL environment variables are read by default.

MCP Tool Support

swarms-rs supports the Model Context Protocol (MCP), enabling agents to interact with external tools through standardized interfaces. This powerful feature allows your agents to access real-world data and perform actions beyond their language capabilities.

Supported MCP Server Types

• STDIO MCP Servers: Connect to command-line tools that implement the MCP protocol
• SSE MCP Servers: Connect to web-based MCP servers using Server-Sent Events

Example Usage

// Add a STDIO MCP server
.add_stdio_mcp_server("uvx", ["mcp-hn"])
.await

// Add an SSE MCP server
.add_sse_mcp_server("example-sse-mcp-server", "http://127.0.0.1:8000/sse")
.await

See the mcp_tool.rs example for a complete implementation.

Architecture

swarms-rs is built with a modular architecture that allows for easy extension and customization:

• Agent Layer: Core agent implementation with memory management and tool integration
• LLM Provider Layer: Abstraction for different LLM providers (OpenAI, DeepSeek, etc.)
• Tool System: Extensible tool framework for adding capabilities to agents
• MCP Integration: Support for Model Context Protocol tools via STDIO and SSE interfaces
• Swarm Orchestration: Coordination of multiple agents for complex workflows
• Persistence Layer: State management and recovery mechanisms

Development Setup

1. Clone the repository:

   git clone https://github.com/The-Swarm-Corporation/swarms-rs
   cd swarms-rs
   

2. Install development dependencies:

   cargo install cargo-nextest
   

3. Run tests:

   cargo nextest run
   

4. Run benchmarks:

   cargo bench
   

License

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

Contact

For questions, suggestions, or feedback, please open an issue or contact us at kye@swarms.world.