feat: Multi-Agent System Based On The Role-Playing Module

[Appointat]

Description

We're excited to unveil our newly conceptualized framework design for a sophisticated multi-agent system (MAS), which promises to enhance task automation and process control through collaborative agent interaction.

Our framework's core is constructed around several primary features, ensuring a comprehensive and detailed approach to task management:

• Task Split Feature: This functionality dissects complex tasks into manageable subtasks, utilizing a variety of parameters such as task prompts, roles with descriptions, and a subtasks-oriented graph. It lays the groundwork for parallel processing and execution pipelines, optimizing the system's overall performance.

• Role Generation Feature: Key to the framework, this feature dynamically creates and assigns roles, encompassing the number, names, and descriptions, hence tailoring the system to handle specific tasks efficiently.

• Process Control (SOP): Central to orchestrating the MAS, the standard operating procedure (SOP) guides agents through their assigned tasks. It integrates subtasks with dependencies, ensuring that each action by the role-playing agents aligns with the overarching task's objectives.

• Action Agent: Acting as the executor within the framework, the Action Agent interfaces with APIs, tools, and commands to produce tangible execution results, informed by the selected roles and insights derived from data.

• Task Assignment Feature: This assigns subtasks to the appropriate agents based on their roles and an evaluation of their performance, guaranteeing an optimal distribution of tasks.

• Insight Generation Feature: By analyzing context and reference content, this feature provides agents with the insights necessary for informed decision-making and effective task execution.

Additionally, the framework includes specialized components like:

• Subtask JSON: An organized data structure providing details of each subtask, including descriptions, dependencies, inputs, and outputs, enabling agents to understand and process their specific roles.

• Env Agent: A crucial component that interacts with databases and utilizes tags to maintain and update the environment in which the agents operate.

• Deductive Reasoner: This agent processes states to deduce logical outcomes, providing a result of reasoning that aids in decision-making processes.

Our design is not just a blueprint; it's a commitment to elevating the intelligence and efficiency of multi-agent systems. By fostering an environment where agents can learn, adapt, and cooperate, our framework is poised to transform task execution in complex operational landscapes.

As we move forward with the development, we are keen to collaborate with experts and enthusiasts in the field to refine and realize the full potential of this design. Stay tuned for updates as we progress in turning this framework into a functioning model that redefines collaborative task execution.

• I have raised an issue to propose this change: [Feature Request] Multi-agent System based on the Role-playing module

Types of changes

What types of changes does your code introduce? Put an x in all the boxes that apply:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds core functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation (update in the documentation)
• Example (update in the folder of example)

Implemented Tasks

• Subtask 1
• Subtask 2
• Subtask 3

Checklist

Go over all the following points, and put an x in all the boxes that apply.
If you are unsure about any of these, don't hesitate to ask. We are here to help!

• I have read the CONTRIBUTION guide. (required)
• My change requires a change to the documentation.
• I have updated the tests accordingly. (required for a bug fix or a new feature)
• I have updated the documentation accordingly.