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Comprehensive experimentos em IA Tools for Every Need

Get access to experimentos em IA solutions that address multiple requirements. One-stop resources for streamlined workflows.

experimentos em IA

  • Flocking Multi-Agent

    A Python-based framework implementing flocking algorithms for multi-agent simulation, enabling AI agents to coordinate and navigate dynamically.
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    What is Flocking Multi-Agent?
    Flocking Multi-Agent offers a modular library for simulating autonomous agents exhibiting swarm intelligence. It encodes core steering behaviors—cohesion, separation and alignment—alongside obstacle avoidance and dynamic target pursuit. Using Python and Pygame for visualization, the framework allows adjustable parameters such as neighbor radius, maximum speed, and turning force. It supports extensibility through custom behavior functions and integration hooks for robotics or game engines. Ideal for experimentation in AI, robotics, game development, and academic research, it demonstrates how simple local rules lead to complex global formations.
    Flocking Multi-Agent Core Features
    • Implementation of alignment, cohesion, and separation behaviors
    • Obstacle avoidance and dynamic target pursuit
    • Real-time visualization with Pygame
    • Configurable agent parameters (speed, radius, force)
    • Extensibility through custom behavior hooks

  • MARFT

    MARFT is an open-source multi-agent RL fine-tuning toolkit for collaborative AI workflows and language model optimization.
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    What is MARFT?
    MARFT is a Python-based LLMs, enabling reproducible experiments and rapid prototyping of collaborative AI systems.

  • CAMEL-AI

    CAMEL-AI is an open-source LLM multi-agent framework enabling autonomous agents to collaborate using retrieval-augmented generation and tool integration.
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    What is CAMEL-AI?
    CAMEL-AI is a Python-based framework that allows developers and researchers to build, configure, and run multiple autonomous AI agents powered by LLMs. It offers built-in support for retrieval-augmented generation (RAG), external tool usage, agent communication, memory and state management, and scheduling. With modular components and easy integration, teams can prototype complex multi-agent systems, automate workflows, and scale experiments across different LLM backends.
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