This repository is a Rust library designed for building and managing generative AI agents, leveraging the capabilities of large language models (LLMs), such as ChatGPT. The aim of this project is to provide a robust and scalable framework that is adaptable to a wide range of scenarios.

ai-agents is at a very early stage of development.

• Structured Data Flow: Leverage PipelineNet for organized and efficient data flow between processing units, enabling complex data transformation and decision-making capabilities.
• Flexible Architectures: Utilize dynamic flow control within PipelineNet to adapt AI agent behaviors.
• Extendibility: Easily extend core functionalities with custom unit implementations.
• Contextual Grouping: Organize units into coherent groups for focused execution, simplifying task management and enhancing processing clarity.
• Asynchronous Support

• ai-agent-macro
• sllm-rs: A crate dedicated to interfacing with Large Language Models (LLMs), including utilities for sending requests and processing responses.

The following examples are simulations of limited situations, demonstrating the application of ai-agents to specific scenarios:

To run the examples, you need to set an environment variable OPEN_API_KEY with your API key. This can be done by creating a .env file in the root of the project.

OPEN_API_KEY=your_api_key_here

• Find Treasure: A game simulation where the player's goal is to find treasure in a dynamically generated scenario by interacting with NPCs.

• Ecommerce Chat Assistant: A limited simulation agent that, based on customer inputs (such as name and order ID), explains the current state of an order.