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Comprehensive colaboração multi-agente Tools for Every Need

Get access to colaboração multi-agente solutions that address multiple requirements. One-stop resources for streamlined workflows.

colaboração multi-agente

  • multi_agent_celar

    An open-source multi-agent framework enabling emergent language-based communication for scalable collaborative decision-making and environment exploration tasks.
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    What is multi_agent_celar?
    multi_agent_celar is designed as a modular AI platform enabling emergent-language communication among multiple intelligent agents in simulated environments. Users can define agent behaviors via policy files, configure environment parameters, and launch coordinated training sessions where agents evolve their own communication protocols to solve cooperative tasks. The framework includes evaluation scripts, visualization tools, and support for scalable experiments, making it ideal for research on multi-agent collaboration, emergent language, and decision-making processes.
    multi_agent_celar Core Features
    • Emergent language communication protocols
    • Multi-agent environment simulation
    • Configurable agent policies
    • Training and evaluation scripts
    • Visualization and logging tools

  • AI Agent Debate Autogen Tutorial

    A hands-on tutorial demonstrating how to orchestrate debate-style AI agents using LangChain AutoGen in Python.
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    What is AI Agent Debate Autogen Tutorial?
    The AI Agent Debate Autogen Tutorial provides a step-by-step framework for orchestrating multiple AI agents engaged in structured debates. It leverages LangChain’s AutoGen module to coordinate messaging, tool execution, and debate resolution. Users can customize templates, configure debate parameters, and view detailed logs and summaries of each round. Ideal for researchers evaluating model opinions or educators demonstrating AI collaboration, this tutorial delivers reusable code components for end-to-end debate orchestration in Python.
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