AgentAI

AgentAI is a Rust library designed to simplify the creation of AI agents. It leverages the GenAI library to interface with a wide range of popular Large Language Models (LLMs), making it versatile and powerful. Written in Rust, AgentAI benefits from strong static typing and robust error handling, ensuring more reliable and maintainable code. Whether you're developing simple or complex AI agents, AgentAI provides a streamlined and efficient development process.

Features

• Multi LLM -- we support most of LLM API (OpenAI, Anthropic, Gemini, Ollama and all OpenAI API Compatible)
• Choose correct LLM for the task -- use many smaller specialized LLMs to save costs and choose best of all the worlds
• Support for MCP Server -- no need to write your own Agent Tools, you can leverage, already existing solutions, based on Model Context Protocol

Warning This library is under heavy development. The interface can change at any moment without any notice.

Installation

To start using AgentAI crate just enter in root directory for your project this command:

cargo add agentai genai

This will install this crate with all required dependencies.

Feature flags

Available features for agentai crate. To enable any of these features, you need to enter this command:

cargo add agentai -F mcp-client

Features list:

• mcp-client (enabled by default) — Enables experimental support for Agent Tools based on MCP Servers

Usage

Here is a basic example of how to create an AI agent using AgentAI:

use agentai::Agent;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let client = genai::Client::default();
    let mut agent = Agent::new(&client, "You are a useful assistant", &());
    let answer: String = agent.run("gpt-4o", "Why sky is blue?").await?;
    println!("Answer: {}", answer);
    Ok(())
}

Examples

For more examples, check out the examples directory. You can build and run them using Cargo with the following command:

cargo run --example <example_name>

The <example_name> should match the filename of the example you want to run (without the file extension). For example, to run the example that includes the essential parts required to implement an AI agent, use:

cargo run --example simple

Documentation

Full documentation is available on docs.rs.

Contributing

Contributions are welcome! Please see our CONTRIBUTING.md for more details.

License

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

Acknowledgements

Special thanks to the creators of the GenAI library for providing a robust framework for interfacing with various LLMs.