This open-source framework combines the AgentSimJs agent modeling library with Three.js's 3D graphics engine to deliver interactive, browser-based multi-agent simulations. Users can define agent types, behaviors, and environmental rules, configure collision detection and event handling, and visualize simulations in real time with customizable rendering options. The library supports dynamic controls, scene management, and performance tuning, making it ideal for research, education, and prototyping of complex agent-based scenarios.

Symbol-LLM offers a modular architecture for constructing AI agents powered by large language models augmented with symbolic memory stores. It features a planner module to break down complex tasks, an executor to invoke tools, and a memory system to maintain context across interactions. With built-in toolkits like web search, calculator and code runner, plus simple APIs for custom tool integration, Symbol-LLM enables developers and researchers to rapidly prototype and deploy sophisticated LLM-based assistants for various domains including research, customer support, and workflow automation.

Team Coordination is a lightweight Python library designed to simplify the orchestration of multiple AI agents working together on complex tasks. By defining specialized agent roles—such as planners, executors, evaluators, or communicators—users can decompose a high-level objective into manageable sub-tasks, delegate them to individual agents, and facilitate structured communication between them. The framework handles asynchronous execution, protocol routing, and result aggregation, allowing teams of AI agents to collaborate efficiently. Its plugin system supports integration with popular LLMs, APIs, and custom logic, making it ideal for applications in automated customer service, research, game AI, and data processing pipelines. With clear abstractions and extensible components, Team Coordination accelerates the development of scalable multi-agent workflows.

BAML Agents is designed for developers and AI practitioners seeking a modular, extensible platform to build autonomous agents. It provides a plugin-based architecture for seamless integration of custom tools, a memory subsystem for maintaining conversational context, and built-in support for multi-step reasoning workflows. With BAML Agents, users can quickly configure agent behaviors, connect to external APIs, and orchestrate complex tasks without reinventing common agent patterns. Its lightweight design and clear abstractions make it ideal for prototyping, research, and production-grade deployments in various automation scenarios.

Genai is a versatile platform built to transform how users interact through chatbots. By collecting and mixing data from diverse sources, Genai facilitates the creation and deployment of custom chatbots in minutes, streamlining workflows in education, research, and more. It offers a superior user experience and optimizes processes by leveraging advanced AI technologies.