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오픈소스 에이전트

  • Open Agent Leaderboard evaluates and ranks open-source AI agents on tasks like reasoning, planning, Q&A, and tool utilization.
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    What is Open Agent Leaderboard?
    Open Agent Leaderboard offers a complete evaluation pipeline for open-source AI agents. It includes a curated task suite covering reasoning, planning, question answering, and tool usage, an automated harness to run agents in isolated environments, and scripts to collect performance metrics such as success rate, runtime, and resource consumption. Results are aggregated and displayed on a web-based leaderboard with filters, charts, and historical comparisons. The framework supports Docker for reproducible setups, integration templates for popular agent architectures, and extensible configurations to add new tasks or metrics easily.
  • CrewAI Quickstart provides a Node.js template to rapidly configure, run, and manage conversational AI agents via CrewAI API.
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    What is CrewAI Quickstart?
    CrewAI Quickstart is a developer toolkit designed to streamline the creation and deployment of AI-driven conversational agents using the CrewAI framework. It offers a preconfigured Node.js environment, example scripts for interacting with CrewAI APIs, and best-practice patterns for prompt design, agent orchestration, and error handling. With this quickstart, teams can prototype chatbots, automate workflows, and integrate AI assistants into existing applications in minutes, reducing boilerplate code and ensuring consistency across projects.
  • MASlite is a lightweight Python multi-agent system framework for defining agents, messaging, scheduling, and environment simulation.
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    What is MASlite?
    MASlite provides a clear API to create agent classes, register behaviors, and handle event-driven messaging between agents. It includes a scheduler to manage agent tasks, environment modeling to simulate interactions, and a plugin system to extend core capabilities. Developers can rapidly prototype multi-agent scenarios in Python by defining agent lifecycle methods, connecting agents via channels, and running simulations in a headless mode or integrating with visualization tools.
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