Comprehensive 확장 가능한 설계 Tools for Every Need

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확장 가능한 설계

  • An AI agent template showing automated task planning, memory management, and tool execution via OpenAI API.
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    What is AI Agent Example?
    AI Agent Example is a hands-on demonstration repository for developers and researchers interested in building intelligent agents powered by large language models. The project includes sample code for agent planning, memory storage, and tool invocation, showcasing how to integrate external APIs or custom functions. It features a simple conversational interface that interprets user intents, formulates action plans, and executes tasks by calling predefined tools. Developers can follow clear patterns to extend the agent with new capabilities, such as scheduling events, web scraping, or automated data processing. By providing a modular architecture, this template accelerates experimentation with AI-driven workflows and personalized digital assistants while offering insights into agent orchestration and state management.
    AI Agent Example Core Features
    • Agent planning engine
    • Memory management module
    • Tool invocation interface
    • OpenAI GPT integration
    • Modular architecture
  • HMAS is a Python framework for building hierarchical multi-agent systems with communication and policy training features.
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    What is HMAS?
    HMAS is an open-source Python framework that enables development of hierarchical multi-agent systems. It offers abstractions for defining agent hierarchies, inter-agent communication protocols, environment integration, and built-in training loops. Researchers and developers can use HMAS to prototype complex multi-agent interactions, train coordinated policies, and evaluate performance in simulated environments. Its modular design makes it easy to extend and customize agents, environments, and training strategies.
  • Open-source Python framework enabling multiple AI agents to collaborate and efficiently solve combinatorial and logic puzzles.
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    What is MultiAgentPuzzleSolver?
    MultiAgentPuzzleSolver provides a modular environment where independent AI agents work together to solve puzzles such as sliding tiles, Rubik’s Cube, and logic grids. Agents share state information, negotiate subtask assignments, and apply diverse heuristics to explore the solution space more effectively than single-agent approaches. Developers can plug in new agent behaviors, customize communication protocols, and add novel puzzle definitions. The framework includes tools for real-time visualization of agent interactions, performance metrics collection, and experiment scripting. It supports Python 3.8+, standard libraries, and popular ML toolkits for seamless integration into research projects.
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