The Multi-Agent AI framework provides an end-to-end solution for orchestrating multiple autonomous agents in cloud environments. It leverages Neon’s Postgres-compatible serverless database to store conversation history and agent state, Azure Functions to run agent logic at scale, and OpenAI APIs to power natural language understanding and generation. Built-in message queues and role-based behaviors allow agents to collaborate on tasks such as research, scheduling, customer support, and data analysis. Developers can customize agent policies, memory rules, and workflows to fit diverse business requirements.

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.

Autogen Studio Research is a GitHub-hosted research prototype for building, visualizing, and iterating on multi-agent AI applications. It offers a web-based UI that lets you drag and drop agent components, define communication channels, and configure execution pipelines. Under the hood, it uses a Python SDK to connect to various LLM backends (OpenAI, Azure, local models) and provides real-time logging, metrics, and debugging tools. The platform is designed for rapid prototyping of collaborative agent systems, decision-making workflows, and automated task orchestration.