This framework allows users to design, configure, and deploy multiple AI agents that communicate via JSON messages through a central orchestrator. Each agent can have distinct roles, prompts, and memory modules, and you can plug in any LLM provider by implementing a provider interface. The system supports persistent conversation history, dynamic routing, and modular extensions. Ideal for simulating debates, automating customer support flows, or coordinating multi-step document generation, it runs on Python, with Docker support for containerized deployments.

multiagent-golang provides a structured approach to building multi-agent systems in Go. It introduces an Agent abstraction where each agent can be equipped with various sensors to perceive its environment and actuators to take actions. Agents run concurrently using Go routines and communicate through dedicated messaging channels. The framework also includes an environment simulation layer to handle events, manage the agent lifecycle, and track state changes. Developers can easily extend or customize agent behaviors, configure simulation parameters, and integrate additional modules for logging or analytics. It streamlines the creation of scalable, concurrent simulations for research and prototyping.

Agent-Squad is a modular Python framework that empowers teams to design, deploy, and run multi-agent systems for complex task execution. At its core, Agent-Squad lets users configure diverse agent profiles—such as data retrievers, summarizers, coders, and validators—that communicate through defined channels and share memory contexts. By decomposing high-level objectives into subtasks, the framework orchestrates parallel processing and leverages LLMs alongside external APIs, databases, or custom tools. Developers can specify workflows in JSON or code, monitor agent interactions, and adapt strategies dynamically using built-in logging and evaluation utilities. Common applications include automated research assistants, content generation pipelines, intelligent QA bots, and iterative code review processes. The open-source design integrates seamlessly with AWS services, enabling scalable deployments.

AutoGPT Node provides a JavaScript-based implementation of autonomous GPT-powered agents, bringing the features of Auto-GPT to the Node.js ecosystem. With this framework, you define goals or objectives, and the agent autonomously plans a sequence of tasks, executes commands, interacts with the file system, and leverages plugins or APIs as needed. Key capabilities include memory storage for context retention, dynamic tool invocation, iterative self-evaluation, error handling, and configurable logging. You can run multiple agents, configure custom commands, manage agent state, and integrate third-party tools to automate content generation, data analysis, code writing, DevOps scripts, and more through a simple JavaScript interface.