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
PipelineNetfor organized and efficient data flow between processing units, enabling complex data transformation and decision-making capabilities. - Flexible Architectures: Utilize dynamic flow control within
PipelineNetto 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-macrosllm-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.
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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.