agentai 0.1.5

AgentAI is a Rust library designed to simplify the creation of AI agents.
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[![](https://img.shields.io/crates/v/agentai.svg)](https://crates.io/crates/agentai)
[![](https://docs.rs/agentai/badge.svg)](https://docs.rs/agentai)

<!-- cargo-rdme start -->

# AgentAI

AgentAI is a Rust library designed to simplify the creation of AI agents. It leverages
the [GenAI](https://crates.io/crates/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 reliable
and maintainable code. Whether you're developing simple or complex AI agents, AgentAI provides
a streamlined and efficient development process.

## Warning

This library is under heavy development. The interface may change at any time without notice.

## Features

- **Connect to any major LLM provider**: Support for OpenAI, Anthropic, Gemini, Ollama, and other OpenAI-compatible APIs.
- **Choose the right model for the job**: Flexibly select the best-suited model for each step in your agent's workflow.
- **Build custom tools with ease**: A simple interface for creating and managing your own tools using the [`ToolBox`](https://docs.rs/agentai/latest/agentai/tool/trait.ToolBox.html).
- **MCP Server Support**: Leverage existing solutions based on the Model-Context-Protocol, eliminating the need to build agent tools from scratch.
- **Structured Output**: No need to parse raw text from model, just provide structure, and AI agent will provide response in defined format.

## What's New

#### `ToolBox` (version 0.1.5)

This release introduces the [`ToolBox`](https://docs.rs/agentai/latest/agentai/tool/trait.ToolBox.html), a new feature providing an easy-to-use interface for supplying tools to AI agents.

## Future Plans

We are continuously working to improve AgentAI. Here are some of the features planned for the near future:

- **Agent Memory**: Enhance the user experience by adding new functionality for AI agent memory. This will give users control over memory behavior, such as persistence, record limits, and context management for new requests.
- **User Input and Streaming Output**: Not every AI agent works silently in the background. Some require additional interaction from the user. This feature will introduce an interface to handle user interactions and provide responses in a streaming format for a better user experience.
- **Configurable Behavior**: Introduce a comprehensive way to manage every aspect of an agent's configuration, from model parameters to error-handling behavior for tools.

## Installation

To add the AgentAI crate to your project, run the following command in your project's root directory:

```bash
cargo add agentai
```

This command adds the crate and its dependencies to your project.

## Feature Flags

<!-- FEATURE FLAGS -->

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

```bash
cargo add agentai -F mcp-client
```

Features list:

- `mcp-client` _(enabled by default)_ — Enables experimental support for Agent Tools based on MCP Servers
- `macros` _(enabled by default)_ — Enables support for macro [`#[toolbox]`](https://docs.rs/agentai/latest/agentai/tool/attr.toolbox.html)
- `tools-buildin` _(enabled by default)_ — Enables support for [buildin tools](https://docs.rs/agentai/latest/agentai/tool/buildin/index.html)
- `tools-web` _(enabled by default)_ — Enables support for [web tools](https://docs.rs/agentai/latest/agentai/tool/web/index.html)

## Usage

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

```rust
use agentai::Agent;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let mut agent = Agent::new("You are a useful assistant");
    let answer: String = agent.run("gpt-4o", "Why is the sky blue?", None).await?;
    println!("Answer: {}", answer);
    Ok(())
}
```

## Examples

For more examples, check out the [examples](https://docs.rs/agentai/latest/agentai/examples/) directory. To run an example, use the following command, replacing `<example_name>` with the name of the example file (without the `.rs` extension):

```bash
cargo run --example <example_name>
```

For instance, to run the `simple` example:

```bash
cargo run --example simple
```

<!-- cargo-rdme end -->

## Documentation

Full documentation is available on [docs.rs](https://docs.rs/agentai).

## Contributing

Contributions are welcome! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) for more details.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Acknowledgements

Special thanks to the creators of the [GenAI library](https://crates.io/crates/genai) for providing a robust framework for interfacing with various LLMs.