AIlice
AIlice is a fully autonomous, general-purpose AI agent.
Last updated
AIlice is a fully autonomous, general-purpose AI agent.
Last updated
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Ailice is a fully autonomous, general-purpose AI agent. This project aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. Ailice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, Ailice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities.
Ailice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models.
We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.
Key technical features of Ailice include:
In-depth research capabilities on specialized subjects.
The ability to read and analyze articles and scholarly works.
Advanced automation in programming and script execution, functioning as a comprehensive coder and an efficient system management tool, similar to an AI-powered operating system.
Voice interaction support.
Compatibility with open-source models and seamless integration with commercial models like GPT-4.
A more intuitive and flexible approach to user interaction, allowing for seamless conversation participation as an agent or the ability to intervene during task execution.
Support for multi-modal models.
A natural and highly fault-tolerant Interactive Agents Call Tree architecture.
Flexible parsing of LLM outputs, enabling a broader range of function call mechanisms.
The capability to self-construct and dynamically load modules for interacting with the environment, providing endless possibilities for expanding features.
Ailice's fundamental tasks are twofold: one is to fully unleash the capabilities of LLM based on text into the real world; the other is to explore better mechanisms for long-term memory and forming a coherent understanding of vast amounts of text. Our development efforts revolve around these two focal points.
If you are interested in the development of Ailice itself, you may consider the following directions:
Explore improved long-term memory mechanisms to enhance the capabilities of each Agent. We need a long-term memory mechanism that enables consistent understanding of large amounts of content and facilitates association. The most feasible option at the moment is to replace vector database with knowledge graph, which will greatly benefit the comprehension of long texts/codes and enable us to build genuine personal AI assistants.
Multimodal support. The support for the multimodal model has been completed, and the current development focus is shifting towards the multimodal support of peripheral modules. We need a module that operates computers based on screenshots and simulates mouse/keyboard actions.
Self-expanding support. Our goal is to enable language models to autonomously code and implement new peripheral modules/agent types and dynamically load them for immediate use. This capability will enable self-expansion, empowering the system to seamlessly integrate new functionalities. We've completed most of the functionality, but we still need to develop the capability to construct new types of agents.
Richer UI interface. We need to organize the agents output into a tree structure in dialog window and dynamically update the output of all agents. And accept user input on the web interface and transfer it to scripter's standard input, which is especially needed when using sudo.
Develop Agents with various functionalities based on the current framework.
Explore the application of IACT architecture on complex tasks. By utilizing an interactive agents call tree, we can break down large documents for improved reading comprehension, as well as decompose complex software engineering tasks into smaller modules, completing the entire project build and testing through iterations. This requires a series of intricate prompt designs and testing efforts, but it holds an exciting promise for the future. The IACT architecture significantly alleviates the resource constraints imposed by the context window, allowing us to dynamically adapt to more intricate tasks.
In addition to the tasks mentioned above, we should also start actively contemplating the possibility of creating a smaller LLM that possesses lower knowledge content but higher reasoning abilities.
Build rich external interaction modules using self-expansion mechanisms! This will be accomplished in .
