AgentAI

What is AgentAI about?

AgentAI provides a Rust‑native framework for building AI agents. It abstracts LLM provider details, supports the Model‑Context‑Protocol (MCP) for server‑based tools, and delivers structured responses so developers can focus on agent logic rather than low‑level API handling.

How to use AgentAI?

1. Add the crate to your project:

   cargo add agentai
   

2. Enable desired features (e.g., mcp-client, macros, tools-buildin, tools-web).
3. Create an Agent instance, select a model, and call run:

   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(())
   }
   

4. For advanced scenarios, implement custom tools via the ToolBox trait or enable the macros attribute to generate tool scaffolding automatically.

Key Features of AgentAI

• Connect to major LLM providers (OpenAI, Anthropic, Gemini, Ollama, etc.)
• Flexible model selection per workflow step
• ToolBox trait for easy creation and management of custom tools
• Built‑in support for MCP servers, eliminating the need to craft tool back‑ends from scratch
• Structured output handling to avoid raw‑text parsing
• Feature flags for macros, build‑in tools, and web tools

Use Cases of AgentAI

• Building conversational assistants that keep context across turns
• Creating task‑oriented agents that call external APIs via custom tools
• Prototyping AI‑driven workflows with deterministic, typed responses
• Integrating AI agents into Rust back‑ends or CLI utilities
• Leveraging MCP‑based tool servers for scalable, reusable tool implementations

FAQ from the AgentAI

Q: Is the API stable? A: The library is under heavy development; interfaces may change without notice.

Q: Do I need to enable mcp-client? A: It is enabled by default and provides experimental MCP tool support. Disable it only if you do not need MCP integration.

Q: How can I add my own tool? A: Implement the ToolBox trait for your tool or use the #[toolbox] macro to generate the boilerplate.

Q: What Rust version is required? A: The crate follows the latest stable Rust release; consult Cargo.toml for the minimum version.

Q: Where can I find more examples? A: The examples directory on docs.rs contains runnable demos; run them with cargo run --example <name>.

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 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.
• 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, 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:

cargo add agentai

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

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
• macros (enabled by default) — Enables support for macro #[toolbox]
• tools-buildin (enabled by default) — Enables support for buildin tools
• tools-web (enabled by default) — Enables support for web tools

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 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 directory. To run an example, use the following command, replacing <example_name> with the name of the example file (without the .rs extension):

cargo run --example <example_name>

For instance, to run the simple example:

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.