ThreeAgents is built in Python, leveraging OpenAI's chat completions API to instantiate multiple AI agents with distinct roles (system, assistant, user). It provides abstractions for agent prompting, role-based message handling, and context memory management. Developers can define custom prompt templates, configure agent personalities, and chain interactions to simulate realistic dialogues or task-oriented workflows. The framework handles message passing, context window management, and logging, enabling experiments in collaborative decision-making or hierarchical task decomposition. With support for environment variables and modular agents, ThreeAgents allows seamless swapping between OpenAI and local LLM backends, facilitating rapid prototyping of multi-agent AI systems. It ships with example scripts and Docker support for quick setup.

Multi-Agent-AI-Dynamic-Interaction offers a flexible environment to design, configure, and run systems composed of multiple autonomous AI agents. Each agent can be assigned specific roles, objectives, and communication protocols. The framework manages message passing, conversation context, and sequential or parallel interactions. It supports integration with OpenAI GPT, other LLM APIs, and custom modules. Users define scenarios via YAML or Python scripts, specifying agent details, workflow steps, and stopping criteria. The system logs all interactions for debugging and analysis, allowing fine-grained control over agent behaviors for experiments in collaboration, negotiation, decision-making, and complex problem-solving.