LLM-Agent provides a structured architecture for building intelligent agents using LLMs. It includes a toolkit for defining custom tools, memory modules for context preservation, and executors that orchestrate complex chains of actions. Agents can call APIs, run local processes, query databases, and manage conversational state. Prompt templates and plugin hooks allow fine-tuning of agent behavior. Designed for extensibility, LLM-Agent supports adding new tool interfaces, custom evaluators, and dynamic routing of tasks, enabling automated research, data analysis, code generation, and more.

Super-Agent-Party allows developers to define a Party object where individual AI agents perform distinct roles such as planning, researching, drafting, and reviewing. Each agent can be configured with custom prompts, tools, and model parameters. The framework manages message routing and shared context, enabling agents to collaborate in real time on subtasks. It supports plugin integration for third-party services, flexible agent orchestration strategies, and error handling routines. With an intuitive API, users can dynamically add or remove agents, chain workflows, and visualize agent interactions. Built on Node.js and compatible with major cloud providers, Super-Agent-Party streamlines the development of scalable, maintainable AI multi-agent systems for automation, content generation, data analysis, and more.