he Complex Agent Orchestration project is a Python script that utilizes OpenAI's LM Studio API to develop a conversational AI system. This script allows users to define their objectives and provide necessary file content or previous results. The opus_orchestrator function then orchestrates the tasks, delegates sub-tasks to sub-agents, refines outputs, and creates folder structures based on user objectives and interactions with AI models.
The parameters used in this script are:
Objective: The main task or goal that the user wants to achieve.
File Content: Used to pass the content of a file to the opus_orchestrator function.
Previous Results: A list containing the results of previous sub-tasks, which are used to pass the results of previous sub-tasks to the Orchestrator function.
Use Search (optional): A boolean flag that determines whether to include a search query generation step in the process. When set to True, the system will prompt the Orchestrator to generate a JSON object containing a search query.
The functionality of this script includes:
Orchestration: The AI system breaks down the objective into sub-tasks and orchestrates their completion.
Task delegation: Sub-agents are delegated tasks based on user objectives and interactions with AI models.
Output refinement: The system refines outputs to ensure they meet user requirements.
Folder structure creation: The system creates folder structures based on user objectives and interactions with AI models.
To use this script, you can start by defining your objective and providing any necessary file content or previous results. You can then call the opus_orchestrator function with these parameters to initiate the AI system's process. The system will then orchestrate the tasks, delegate sub-tasks to sub-agents, refine outputs, and create folder structures based on your objectives and interactions with AI models.
- Python 3.8 or later
- OpenAI's LM Studio API
To install the required dependencies, you can use pip:
pip install -r requirements.txt